Abstract:
This invention provides a multiscale platform for coordinating behavior using synthetic biology. The platform reduces the impact of underlying noise, making outputs more coherent and reliable at the macroscopic level. In one aspect, the invention provides a frequency-modulated biosensor, comprising a microfluidic array comprising two or more separate colonies or populations of sensing cells to grow and communicate by gas exchange, wherein the colonies or populations of sensing cells output synchronized oscillating signals.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a U.S. national phase under 35 U.S.C. §371 of Intl. Appl. No. PCT/US2012/069914, filed on Dec. 14, 2012, which claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application No. 61/576,976, filed on Dec. 16, 2011, which are hereby incorporated herein by reference in their entirety for all purposes. 
    
    
     STATEMENT OF GOVERNMENTAL SUPPORT 
     This invention was made with government support under Grant No. GM069811 awarded by the National Institutes of Health. The government has certain rights in the invention. 
    
    
     SEQUENCE LISTING 
     The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Dec. 10, 2014, is named UCSDP023_SL.txt and is 58,055 bytes in size. 
     FIELD OF THE INVENTION 
     The present invention relates to a multiscale platform for coordinating behavior using synthetic biology. The platform reduces the impact of underlying noise, making outputs more coherent and reliable at the macroscopic level. 
     BACKGROUND OF THE INVENTION 
     Synthetic biology can be broadly broken down into the ‘top-down’ synthesis of genomes (Gibson, et al.,  Science  (2010) 329:52-56) and the ‘bottom-up’ engineering of relatively small genetic circuits (Hasty, et al.,  Nature  (2001) 420:224-230 (2002); Sprinzak, et al.,  Nature  438:443-448 (2005); Endy,  Nature  (2005) 438:449-453; Ellis, et al.,  Nature Biotechnol . (2009) 27:465-471; Kobayashi, et al.  Proc. Natl Acad. Sci. USA  (2004) 101: 8414-8419; You, et al.,  Nature  (2004) 428:868-871; Basu, et al.,  Nature  (2005) 434:1130-1134; Mukherji, et al.,  Nature Rev. Genet.  10:859-871; Grilly, et al.,  Mol. Syst. Biol . (2007) 3:127). In the field of genetic circuits, toggle switches (Gardner, et al.,  Nature  (2000) 403:339-342) and oscillators (Elowitz, et al.,  Nature  (2000) 403, 335-338) have progressed into triggers (Lu, et al.,  Proc. Natl Acad. Sci. USA  104:11197-11202), counters (Friedland, et al.,  Science  (2009) 324:1199-1202) and synchronized clocks (Danino, et al.,  Nature  (2010) 463:326-330). Sensors have arisen as a major focus in the context of biotechnology (Kobayashi, et al.  Proc. Natl Acad. Sci. USA  (2004) 101: 8414-8419; Tamsir, et al.,  Nature  (2011) 469:212-215; Tabor, et al.,  Cell  (2009) 137:1272-1281), while oscillators have provided insights into the basic-science functionality of cyclic regulatory processes (Stricker, et al.,  Nature  (2008) 456:516-519; Mondragon-Palomino, et al.,  Science  (2011) 333:1315-1319; Tigges, et al.,  Nature  (2009) 457:309-312). A common theme is the concurrent development of mathematical modelling that can be used for experimental design and characterization, as in physics and the engineering disciplines. 
     The synchronization of genetic clocks provides a particularly attractive avenue for synthetic biology applications. Oscillations permeate science and technology in a number of disciplines, with familiar examples including alternating current (AC) power (U.S. Pat. No. 373,035), the global positioning system (GPS) (Lewandowski, et al.,  Proc. IEEE  (1999) 87:163-172) and lasers (Vladimirov, et al.,  Europhys. Lett . (2003) 61:613). These technologies have demonstrated that operating in the frequency domain can offer considerable advantages over steady-state designs in terms of information gathering and transmission. In particular, oscillatory sensors confer a number of advantages to traditional ones (Gast,  J. Phys. E: Sci. Instrum . (1985) 18:783), as frequency is easily digitized and can be quickly updated with repeated measurements. For sensors that use optical reporters, measurements of frequency are less sensitive to experimental factors such as beam power and exposure time than intensity measurements, which must be normalized and calibrated. 
     Although the bottom-up approach to synthetic biology is increasingly benefiting from DNA synthesis technologies, the general design principles are still evolving. In this context, a substantial challenge is the construction of robust circuits in a cellular environment that is governed by noisy processes such as random bursts of transcription and translation (Ozbudak, et al.,  Nature Genet . (2002) 31:69-73; Elowitz, et al.,  Science  (2002) 297:1183-1186; Golding, et al.,  Cell  (2005) 123:1025-1036; Blake, et al.,  Mol. Cell  (2006) 24:853-865; Austin, et al. Nature (2006) 439:608-611). Such an environment leads to considerable inter-cellular variability in circuit behavior, which can impede coherent functionality at the colony level. An ideal design strategy for reducing variability across a cellular population would involve both strong and long-range coupling that would instantaneously synchronize the response of millions of cells. Quorum sensing typically involves strong intercellular coupling over tens of micrometers (Basu, et al.,  Nature  (2005) 434:1130-1134; Danino, et al.,  Nature  (2010) 463:326-330; Waters, et al.,  Annu. Rev. Cell Dev. Biol . (2005) 21:319-346), yet the relatively slow diffusion time of molecular communication through cellular media leads to signalling delays over millimeter scales. Faster communication mechanisms, such as those mediated in the gas or vapor phase, may increase the length scale for instantaneous communication, but are comparatively weak and short lived because the vapor species more readily disperse. 
     SUMMARY OF THE INVENTION 
     In one aspect, the invention provides a frequency-modulated biosensor, comprising a microfluidic array comprising two or more separate colonies or populations of sensing cells to grow and communicate by gas exchange, wherein the colonies or populations of sensing cells output synchronized oscillating signals. In some embodiments, the colonies or populations of sensing cells are selected from the group consisting of microbial cells, bacterial cells, yeast cells, mammalian cells, insect cells, photosynthetic cells, and plant cells. In some embodiments, thousands of small oscillating cell colonies or populations are operatively coupled in a microfluidic array. In some embodiments, the degree to which neighboring colonies or populations are able to influence each other via fluid diffusion is negligible owing to the high media channel flow rates. In some embodiments, the colonies or populations of sensing cells output synchronized oscillations of approximately 2.5 million cells across a distance of about 5 mm. In some embodiments, the colonies or populations of sensing cells are in two or more devices that share no common fluid sources or channels. In some embodiments, the two or more devices comprise oxygen-permeable polydimethylsiloxane (PDMS) walls. In some embodiments, the colonies or populations of sensing cells are bacterial cells and the operative or intercellular coupling or communication of the cell colonies or populations involves redox signaling by hydrogen peroxide (H 2 O 2 ). In some embodiments, the biosensor comprises a detector that detects oscillating bursts of H 2 O 2  released from the colonies of cells. In some embodiments, the colonies or populations of sensing cells are bacterial cells and the synchronized oscillations are coordinated by hydrogen peroxide (H 2 O 2 ). In some embodiments, the bacterial cells are  E. coli  cells. In some embodiments, the cells comprise a gene coding for NADH dehydrogenase II (ndh) under the control of a second lux promoter. In some embodiments, the cells comprise a gene coding for green fluorescent protein (GFP) under the control of a second lux promoter. In some embodiments, the cells comprise an of acyl-homoserine lactone (AHL) synthase LuxI, under the control of a native arsenite-responsive promoter that is repressed by ArsR in the absence of arsenite. In some embodiments, the cells comprise a luxR gene or an acyl-homoserine lactone (AHL) synthase LuxI gene controlled by an response element selected from the group consisting of an arsenite response element (pArsR), a cadmium response element (yodA/cadA/cadR), a copper response element (copA/cueR), a mercury response element (merR), a cobalt response element, a lead response element, a zinc response element, a cyanide response element (CNO), a microcystin response element (mlrABCD), and an organophosphorus (OP) neurotoxin response element. 
     In varying embodiments of the biosensor, the colonies or populations of cells (e.g., each, all or substantially all cells in a colony or population) comprise the following expression cassettes: 
     i) a LuxR gene under the control of a response element promoter; 
     ii) an aiiA gene under the control of a luxI promoter; 
     iii) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of the luxI promoter; and 
     iv) a nucleic acid encoding a protein that produces free radicals or oxygen reactive species (e.g., H 2 O 2 ) under the control of the luxI promoter, wherein the colonies or populations of cells comprise a thresholding sensor that produces an oscillating signal in the presence of concentrations of an analyte above a threshold concentration, wherein the analyte binds to the response element promoter. In some embodiments, the colonies or populations of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 27-756 of SEQ ID NO:1, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 901-1744 of SEQ ID NO:1, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-913 of SEQ ID NO:2, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-1366 of SEQ ID NO:3. In some embodiments, the colonies or populations of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:1, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:2, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:3. 
     In varying embodiments of the biosensor, the colonies or populations of cells comprise the following expression cassettes: 
     i) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of a response element promoter; 
     ii) a LuxR gene under the control of a luxR promoter and an aiiA gene under the control of a luxI promoter; 
     iii) a LuxR gene under the control of a luxR promoter and a nucleic acid encoding a protein that produces free radicals or oxygen reactive species (e.g., H 2 O 2 ) under the control of a luxI promoter; and 
     iv) a LuxR gene under the control of a luxR promoter and a LuxI gene under the control of a luxI promoter, wherein the colonies or populations of cells comprise a period modulation sensor that produces a changed oscillating signal in the presence of concentrations of an analyte above a threshold concentration, wherein the analyte binds to the response element promoter. In some embodiments, the changed oscillating signal comprises increased oscillatory amplitude and period. In some embodiments, the colonies or populations of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-1795 of SEQ ID NO:4, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1895-3488 of SEQ ID NO:4, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-1771 of SEQ ID NO:5, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1-1203 of SEQ ID NO:6. In some embodiments, the colonies or populations of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:4, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:5, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:6. 
     In varying embodiments of the biosensor, the expression cassettes are on one or multiple plasmids. In some embodiments, the response element promoter is selected from the group consisting of an arsenite response element (pArsR), a cadmium response element (yodA/cadA/cadR), a copper response element (copA/cueR), a mercury response element (merR), a cobalt response element, a lead response element, a zinc response element, a cyanide response element (CNO), a microcystin response element (mlrABCD), and an organophosphorus (OP) neurotoxin response element. In some embodiments, the nucleic acid encoding a detectable protein encodes a fluorescent protein. In some embodiments, the fluorescent protein is selected from the group consisting of green fluorescent protein, a yellow fluorescent protein, a cyan fluorescent protein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG. In some embodiments, the measurable output is detected optically. In some embodiments, the optical detection is selected from Luminescence, Fluorescence, and/or Colorimetry. In some embodiments, the measurable output is electrochemical. In some embodiments, the electrochemical detection is selected from Amperometric, Potentiometric, and/or Conductimetric signals. In some embodiments, the cellular biosensor is directly linked to an electronic system to convert the output. In some embodiments, the biosensor is a continuous monitoring system. In some embodiments, the microfluidic array comprises trapping chambers outside the flow of fluid for growing and housing the colonies of cells. In some embodiments, the trapping chambers are of a size of about 100×(80-100) μm 2 . In some embodiments, the microfluidic array is 24 mm×12 mm and comprises over 12,000 communicating colonies of cells. In some embodiments, the microfluidic array comprises ports for infusing nutrient media and test compounds or solutions. In varying embodiments, the microfluidic array comprises a configuration as depicted in  FIG. 3 . In varying embodiments, the microfluidic array comprises multiple devices and comprises a configuration as depicted in  FIG. 7 . In some embodiments, the biosensor does not need to be calibrated. In some embodiments, the cells are in a fresh, frozen or dehydrated form. In some embodiments, the biosensor is implantable in a human. In some embodiments, cameras and/or microscopes and/or computers are used to monitor output. In some embodiments, wireless transmitters are used to monitor output. 
     In a related aspect, the invention comprises a kit comprising a biosensor as described herein. 
     In another aspect, the invention comprises methods of measuring and/or detecting the levels of an analyte, comprising contacting a test sample suspected of comprising the analyte with the biosensor as described herein, e.g., under conditions that allow the analyte to bind to a response element for the analyte, and measuring an oscillating signal output from the biosensor, thereby measuring the levels of the analyte. In some embodiments, the analyte is a small molecule. In some embodiments, the small molecule is selected from the group consisting of a small organic molecule, a small inorganic molecule, an element, a heavy metal, a peptide, a carbohydrate or a nucleic acid. In some embodiments, the analyte is selected from the group consisting of arsenic (arsenite), cadmium, copper, mercury, cobalt, lead, zinc, cyanide, a cyanobacterial microcystin, and an organophosphorus (OP) neurotoxin. In varying embodiments, the presence of the oscillating signal output indicates the detection of the analyte. In varying embodiments, the increased frequency and amplitude of the oscillating signal output indicates the detection of the analyte. In some embodiments, the test sample is blood, water or air. 
     In a related aspect, the invention is also directed to sets of expression cassettes, as described above and herein. The sets of expression cassettes, when expressed in a population of host cells (e.g.,  E. coli ) produce colonies of cells that can communicate via gas or vapor phase and output synchronized oscillating signals. Further aspects include, plasmid, cells, colonies of cell and biosensors comprising the sets of expression cassettes, as described above and herein. 
     In one aspect, the invention provides methods of reducing background signal noise in biosensors and/or improving or augmenting biosensor signal detection. In some embodiments, the methods comprise synchronizing signaling from colonies or populations of reporter cells. In a further aspect, the invention provides methods comprising using frequency modulation as a mode of detection in biosensors. In various embodiments, the methods employ a biosensor as described herein. In some embodiments of the methods and biosensors, the cells are synchronized by production of a diffusible signal. In some embodiments, the signal is a redox reactant. In some embodiments, the redox reactant is H 2 O 2 . In some embodiments, the biosensor comprises microbial, mammalian, and/or photosynthetic cells. In some embodiments, the measurable output or reporter is a fluorescent probe. In some embodiments, the fluorescent probe is GFP. In some embodiments, the reporter is detected optically. In some embodiments, the optical detection is selected from Luminescence, Fluorescence, and/or Colorimetry. In some embodiments, the measurable output is electrochemical. In some embodiments, the electrochemical detection is selected from Amperometric, Potentiometric, and/or Conductimetric signals. In some embodiments, the cellular biosensor is directly linked to an electronic system to convert the output. 
     In a related aspect, the invention provides methods of assaying water quality, comprising synchronizing signaling from colonies or populations of reporter cells or using frequency modulation as a mode of detection in biosensors. In various embodiments, the methods employ a biosensor as described herein. In some embodiments, the levels of a heavy metal or a toxic metal (e.g., iron, manganese, aluminum, mercury, cadmium, beryllium, arsenic (arsenite), plutonium, lead, cadmium, chromium, cobalt, copper, manganese, nickel, tin, thallium, zinc) in water are determined. In some embodiments, the levels of one or more of arsenic (arsenite), cadmium, mercury, lead, and iron in the water are determined. In some embodiments, the water is potable water. 
     In a related aspect, the invention provides methods of assaying biomolecules, comprising synchronizing signaling from colonies or populations of reporter cells or using frequency modulation as a mode of detection in biosensors. In various embodiments, the methods employ a biosensor as described herein. In various embodiments, the analyte or biomolecule is selected from proteins, peptides (e.g., cytokines, hormones, antigens), carbohydrates, nucleic acids (e.g., DNA, RNA, micro RNA) and small organic compounds (e.g., metabolites, vitamins, hormones). In various embodiments, the levels of the biomolecules in a blood sample are assayed. 
     In another aspect, the invention provides methods of assaying small organic and/or inorganic compounds, comprising synchronizing signaling from colonies or populations of reporter cells or using frequency modulation as a mode of detection in biosensors. In various embodiments, the methods employ a biosensor as described herein. In some embodiments, the small organic and/or inorganic compounds are assayed in samples of water, blood or air. 
     In another aspect, the invention provides methods for spatial and temporal coordination of cellular behavior across two or more populations of cells utilizing a diffusible signal. In some embodiments, the diffusible signal is a vapor or a gas. In some embodiments, the diffusible signal is vapor phase H 2 O 2 . In some embodiments, the diffusible signal is a small molecule. In some embodiments, the small molecule is in a gas phase or a liquid phase. In some embodiments, the small molecule is selected from the group consisting of redox reactants, quorum sensing molecules, and cytokines. In some embodiments, the diffusible signal is produced internally by individual cells. In some embodiments, the diffusible signal is generated from a photosensitizer, mediated by external energy source. In some embodiments, the diffusible signal is introduced systematically across a population of cells. In some embodiments, the coordinated cellular behavior comprises natural phenotypes, synthetic phenotypes, and combinations thereof. In some embodiments, the phenotype of the coordinated cellular behavior is modulated by light signals. In some embodiments, the lines of spatial communication are directed by light cues. In some embodiments, the methods comprise coordinating cellular behavior of a cell selected from the group consisting of a microbial cell, a bacterial cell, a yeast cell, a mammalian cell, an insect cell, a photosynthetic cell, and a plant cell. In some embodiments, the cellular behavior is coordinated between cells in one or multiple devices. In some embodiments, the cells are set in one or multiple devices within a biofilm, microfluidic (2D/3D), or bioreactor culture. In some embodiments, the two or more populations of cells are used to produce synthetic drugs, biologics, and/or advanced biofuels. In some embodiments, the two or more populations of cells integrate a signal from a set of input stimuli. In some embodiments, the integrated signal is used as a diagnostic. In some embodiments, the integrated signal is a diagnostic indicator of a clinical pathology or environmental safety. In some embodiments, the two or more populations of cells direct stem cell differentiation. 
     With respect to practicing the various methods, in some embodiments, the biosensor is a continuous monitoring system. In some embodiments, the cells are housed in microscopic chambers. In some embodiments, the cells are in a fresh, frozen or dehydrated form. In some embodiments, the microscopic chambers are implantable in a human. In some embodiments, the microscopic chambers have ports for infusing nutrient media and test compounds or solutions. In some embodiments, the cells are in a fresh, frozen or dehydrated form. In some embodiments, the microscopic chambers are implantable in a human. In some embodiments, cameras and/or microscopes and/or computers are used to monitor reporting. In some embodiments, wireless transmitters are used to monitor reporting. In some embodiments, the cells are selected from the group consisting of a microbial cell, a bacterial cell, a yeast cell, a mammalian cell, an insect cell, a photosynthetic cell, and a plant cell. 
     DEFINITIONS 
     The term “response element” refers to sequences of DNA that are able to bind specific transcription factors or analytes and regulate transcription of genes. 
     The term “analyte” refers to any compound of agent of interest for detection. As appropriate, the analyte can be an element, a nucleic acid, a protein, a carbohydrate, a lipid or a small organic compound. The analyte can be organic or inorganic. 
     The terms “identical” or percent “identity,” and variants thereof in the context of two or more polynucleotide sequences, refer to two or more sequences or subsequences that are the same. Sequences are “substantially identical” if they have a specified percentage of nucleic acid residues or nucleotides that are the same (e.g., at least 60% identity, optionally at least 65%, 70%, 75%, 80%, 85%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identity over a specified region (or the whole reference sequence when not specified)), when compared to a reference sequence (e.g., SEQ ID NOs: 1-6) and aligned for maximum correspondence over a comparison window, or designated region as measured using one of the following sequence comparison algorithms or by manual alignment and visual inspection. The present invention provides polynucleotides improved for expression in host cells that are substantially identical to the polynucleotides of described herein. Optionally, the identity exists over a region that is at least about 50 nucleic acid bases or residues in length, or more preferably over a region that is 100, 200, 300, 400, 500, 600, 800, 1000, 1500, 2000, 2500, 3000, or more, nucleic acids in length, or over the full-length of the sequence. 
     For sequence comparison, typically one sequence acts as a reference sequence, to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are entered into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. Default program parameters can be used, or alternative parameters can be designated. The sequence comparison algorithm then calculates the percent sequence identities for the test sequences relative to the reference sequence, based on the program parameters. 
     The term “comparison window”, and variants thereof, includes reference to a segment of any one of the number of contiguous positions selected from the group consisting of from 20 to 600, usually about 50 to about 200, more usually about 100 to about 150 in which a sequence may be compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned. Methods of alignment of sequences for comparison are well known in the art. Optimal alignment of sequences for comparison can also be conducted by the local homology algorithm of Smith and Waterman Add. APL. Math. 2:482 (1981), by the homology alignment algorithm of Needle man and Wunsch J. Mol. Biol. 48:443 (1970), by the search for similarity method of Pearson and Lipman Proc. Natl. Acad. Sci. (U.S.A.) 85: 2444 (1988), by computerized implementations of these algorithms (GAP, BESTFIT, BLAST, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group (GCG), 575 Science Dr., Madison, Wis.), Karlin and Altschul Proc. Natl. Acad. Sci. (U.S.A.) 87:2264-2268 (1990), or by manual alignment and visual inspection (see, e.g., Ausubel et al., Current Protocols in Molecular Biology (1995 supplement)). Examples of an algorithm that is suitable for determining percent sequence identity and sequence similarity include the BLAST suite using default parameters, available on the internet at blast.ncbi.nlm.nih.gov/, and known to those of skill in the art. 
     The term “substantial identity” of polynucleotide sequences means that a polynucleotide comprises a sequence that has at least 80% sequence identity, e.g., at least about 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher, compared to a reference sequence (e.g., SEQ ID NOs: 1-6), using sequence alignment/comparison algorithms set to standard parameters. One of skill will recognize that these values can be appropriately adjusted to determine corresponding identity of proteins encoded by two nucleotide sequences by taking into account codon degeneracy, amino acid similarity, reading frame positioning and the like. 
     “Substantial identity” of amino acid sequences for these purposes means sequence identity of at least 80% sequence identity, e.g., at least about 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or higher, using sequence alignment/comparison algorithms set to standard parameters. Polypeptides which are “substantially similar” share sequences as noted above except that residue positions which are not identical may differ by conservative amino acid changes. Conservative amino acid substitutions refer to the interchangeability of residues having similar side chains. For example, a group of amino acids having aliphatic side chains is glycine, alanine, valine, leucine, and isoleucine; a group of amino acids having aliphatic-hydroxyl side chains is serine and threonine; a group of amino acids having amide-containing side chains is asparagine and glutamine; a group of amino acids having aromatic side chains is phenylalanine, tyrosine, and tryptophan; a group of amino acids having basic side chains is lysine, arginine, and histidine; and a group of amino acids having sulfur-containing side chains is cysteine and methionine. Preferred conservative amino acids substitution groups are: valine-leucine-isoleucine, phenylalanine-tyrosine, lysine-arginine, alanine-valine, asp artic acid-glutamic acid, and asparagine-glutamine. Determination of “substantial identity” can be focused over defined subsequences, such as known structural domains. 
     Another indication that nucleotide sequences are substantially identical is if two molecules hybridize to each other, or a third nucleic acid, under stringent conditions. Stringent conditions are sequence dependent and will be different in different circumstances. Generally, stringent conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. The Tm is the temperature (under defined ionic strength and pH) at which 50% of the target sequence hybridizes to a perfectly matched probe. Typically, stringent conditions will be those in which the salt concentration is about 1 molar at pH 7 and the temperature is at least about 60° C. 
     An “expression cassette” refers to a nucleic acid construct, which when introduced into a host cell, results in transcription and/or translation of a RNA or polypeptide, respectively. 
     The term “promoter” or “regulatory element” refers to a region or sequence determinants located upstream or downstream from the start of transcription that direct transcription. As used herein, a promoter includes necessary nucleic acid sequences near the start site of transcription, such as, in the case of a polymerase II type promoter, a TATA element. A promoter also optionally includes distal elements, which can be located as much as several thousand base pairs from the start site of transcription. A “constitutive” promoter is a promoter that is active under most environmental and developmental conditions. An “inducible” promoter is a promoter that is active under environmental or developmental regulation. The term “operably linked” refers to a functional linkage between a nucleic acid expression control sequence (such as a promoter) and a second nucleic acid sequence, such as a nucleic acid encoding an antigen, wherein the expression control sequence directs transcription of the nucleic acid corresponding to the second sequence. The promoters used in the present expression cassettes are active in the host cells, but need not originate from that organism. It is understood that limited modifications can be made without destroying the biological function of a regulatory element and that such limited modifications can result in regulatory elements that have substantially equivalent or enhanced function as compared to a wild type regulatory element. These modifications can be deliberate, as through site-directed mutagenesis, or can be accidental such as through mutation in hosts harboring the regulatory element. All such modified nucleotide sequences are included in the definition of a regulatory element as long as the ability to confer expression in the host cell is substantially retained. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A-D  illustrate a sensing array of radically coupled genetic biopixels. a, Network diagram. The luxI promoter drives expression of luxI, aiiA, ndh and sfGFP (superfolder variant of GFP) in four identical transcription modules. The quorum-sensing genes luxI and aiiA generate synchronized oscillations within a colony via AHL. The ndh gene codes for NDH-2, an enzyme that generates H 2 O 2  vapor, which is an additional activator of the luxI promoter. H 2 O 2  is capable of migrating between colonies and synchronizing them. b, Conceptual design of the sensing array. AHL diffuses within colonies while H 2 O 2  migrates between adjacent colonies through the PDMS. Arsenite-containing media is passed in through the parallel feeding channels. c, Fluorescent image of an array of 500  E. coli  biopixels containing about 2.5 million cells. Inset, bright-field and fluorescent images display a biopixel of 5,000 cells. d, Heat map and trajectories depicting time-lapse output of 500 individual biopixels undergoing rapid synchronization. Sampling time is 2 min. 
         FIG. 2A-C  illustrate the plasmids used to create biosensor circuits with frequency modulated output. Top row is the thresholding sensor: 2 oscillator plasmids with luxR genes removed and a plasmid containing pArs::luxR. Middle row is the period modulator: 2 oscillator plasmids and a plasmid containing pArs::luxI-laa. Bottom row contains 2 plasmids used to study H 2 O 2  production and synchronization: pLux::ndh and pLux::sodA. NDH-2 synchronization strain is the oscillator plasmids with pZSm45 ndhII. 
         FIG. 3A-B  illustrate a microfluidic device used for this study. A. Media containing variable analyte (e.g., arsenite) concentration is fed through the cell port, flowing past the biopixel array into the cell and waste ports. During loading, pressure is increased at the cell port and decreased at the waste ports to reverse the flow, allowing cells to pass by the trapping regions. Other microfluidic devices used have the same layout with trap number, separation, and size varied. B. An illustrative trapping chamber populated with cell colonies and showing the fluid flow path. 
         FIG. 4  illustrates biopixels with NDH-2 engineered synchronization observed at ultra-low fluorescence (4×, 20 ms exposure, 3% power) using an EMCCD camera to ensure no fluorescence interaction. Synchronized oscillations are maintained across the array for the length of the experiment (14 hours). 
         FIG. 5  illustrates that catalase degrades external H 2 O 2  and prevents communication between colonies. When a synchronized population of biopixels was exposed to a step increase of 200 U/ml catalase, synchronization was broken and biopixels continued to oscillate individually. Since catalase cannot cross the cell membrane, this shows that synchronization between colonies depends on H 2 O 2  but oscillations with a colony do not. 
         FIG. 6  illustrates that SodA produces H 2 O 2  internal to the cell, permanently switching the cellular redox state (oxidizing) thereby activating lux-controlled genes. Biopixels rapidly fire and lock on in a spatial wave, far earlier than is typical for colonies of this size. The propagation of ON biopixels suggests that colonies are capable of activating those nearby via migrating H 2 O 2  species. 
         FIG. 7  illustrates that synchronized oscillations occur across 2 fluidically isolated devices held in close proximity. In this experiment, the devices were started at different times yet become synchronized. Since these devices share no common fluid sources or sinks, this confirms that synchronization is mediated by vapor species. 
         FIG. 8  illustrates a heatmap of trajectories extracted from low fluorescence intensity control when NDH-2 plasmid is not present. Biopixels oscillate individually but fail to synchronize. 
         FIG. 9  illustrates that the introduction of thiourea, a potent radical quencher, produces decaying synchronized oscillations across a population of biopixels. Because radical species are precursors for H 2 O 2 , eliminating them lowers the production of H 2 O 2  and therefore dampens the oscillations. Colonies are still able to synchronize because, while thiourea eliminates radicals within cells, it does not prevent H 2 O 2  from diffusing between cells. 
         FIG. 10  illustrates that synchronization is prevented when 100 μg/ml Ampicillin is used in the media. The constructs, strains, and experimental conditions are otherwise identical. 
         FIGS. 11A-D  illustrate a frequency-modulated genetic biosensor. a, Network diagrams depicting two constructed sensing modules. In thresholding (1), the luxR gene is removed from the oscillator network and supplemented by a new copy driven by an arsenite-responsive promoter. In period modulation (2), a supplementary luxI gene tagged for increased degradation is driven by the arsenic-responsive promoter, which affects the period of oscillation. b, A sample period modulation sensor output following a step increase of 0.8 mM arsenite. Oscillatory period increases from 69 min to 79 min. c, Top, period versus arsenite concentration for the sensor array. Error bars indicate 6 1 standard deviation averaged over 500 biopixel trajectories. Dotted line represents model-predicted curve. Bottom, sensor calibration curve generated from experimental data. Points indicate the maximum arsenite level with 95% certainty for a given measured period as determined statistically from experimental data. d, Thresholder output following a step increase of 0.25 mM arsenite. A marked shift from rest to oscillatory behavior is observed within 20 min after the addition of arsenite. 
         FIGS. 12A-D  illustrate computational modelling of radical synchronization and biosensing. a, Time series of a population of biopixels producing varying amounts of H 2 O 2  vapor. Synchronization occurs only for moderate levels whereas high levels lock ON and low levels oscillate asynchronously. b, A typical time series for our period modulation sensor undergoing a step increase of arsenite. Oscillations increase in both amplitude and period. c, A typical time series output for the thresholding sensor. Oscillations arise after the addition of arsenite. d, Experimental and computational output depicting complex dynamic behaviors between neighboring traps. Top, 1:2 resonance and anti-phase synchronization observed when trap size (left, black/blue 5.95 mm depth and red/magenta 5.85 mm depth) and separation distance (right, same colors) are modified experimentally. Middle, scaled-up array experimental data for increased trap separation experiments demonstrating anti-phase synchronization. Bottom, computational model trajectories depicting 1:2 resonance and anti-phase synchronization when trap size (same colors as experimental data) and separation distance are changed. 
         FIG. 13  illustrates oscillations of alternating large and small amplitude when LuxR is limited in experiments and simulations. The alternating oscillations vanish when LuxR is restored to its normal level in the model. Experimentally, we were unable to build a system in which LuxR is tunable between big/small and normal amplitude regimes. This is probably due to the small dynamic range of arsenite promoter-driven output of LuxR compared to the level produced by 3 constitutively expressed copies in the original circuit. 
         FIG. 14  illustrates antiphase behavior of 4 neighboring biopixels having equal trap sizes and spacing Δ=3. 
         FIGS. 15A-D  illustrate radical synchronization on a macroscopic scale. a, The scaled-up array is 24 mm×12 mm and houses over 12,000 biopixels that contain approximately 50 million total cells when filled. b, Global synchronization is maintained across the array. Heat map of individual trajectories of all 12,224 oscillating biopixels. c, Image series depicting global synchronization and oscillation for the macroscopic array. Each image is produced by stitching 72 fields of view imaged at 34 magnification. d, Schematic diagram illustrating our design for a handheld device using the sensing array. An LED (1) excites the array (2) and emitted light is collected by a photodetector (3), analysed by an onboard processor (4), and displayed graphically (5). 
         FIG. 16  illustrates computational results depicting biopixel synchronicity as a function of trap separation distance. As biopixels are moved farther apart, the entropy increases due to decreased effective migration of H 2 O 2  between colonies. 
         FIG. 17  illustrates a biosensor on a microscope slide connected to an output detection device. 
     
    
    
     DETAILED DESCRIPTION 
     1. Introduction 
     Although there has been considerable progress in the development of engineering principles for synthetic biology, a substantial challenge is the construction of robust circuits in a noisy cellular environment. Such an environment leads to considerable intercellular variability in circuit behavior, which can hinder functionality at the colony level. Here, we engineered the synchronization of thousands of oscillating colony ‘biopixels’ over centimeter-length scales through the use of synergistic intercellular coupling involving quorum sensing within a colony and gas-phase redox signalling between colonies. We used this platform to construct a liquid crystal display (LCD)-like macroscopic clock that can be used to sense an analyte of interest (e.g., biomolecules, small organic and/or inorganic compounds, heavy metals, e.g., arsenic (arsenite), cadmium, mercury, lead) via modulation of the oscillatory period. Given the repertoire of sensing capabilities of bacteria such as  Escherichia coli , the ability to coordinate their behavior over large length scales sets the stage for the construction of low cost genetic biosensors that are capable of detecting an analyte of interest (e.g., heavy metals and pathogens) in the field. 
     Our model of the frequency-modulated biosensor is based on a previously described model for the quorum-sensing synchronized oscillator (Danino, et al.,  Nature  (2010) 463:326-330). In addition to the reactions reflected in that model, we include the arsenite-induced production and degradation of LuxI and/or LuxR. From the biochemical reactions, we derived a set of delay differential equations to be used as our model. These delayed reactions mimic the complex cascade of processes (transcription, translation, maturation, etc.) leading to formation of functional proteins. As expected, our model predicts oscillations that change frequency when changes in arsenite occur ( FIGS. 11 c  and 12 b   ). The amplitude and period of the oscillations both depend on the concentrations of the toxin. We then modified the model to describe the LuxR-based detection system. Our model predicts a marked transition from rest to oscillations upon addition of arsenite, consistent with experimental observations ( FIG. 12 c   ). 
     The multi-scale nature of communication in our array allows us to treat colony and array-level dynamics separately; in the latter, arsenite affects the quorum-sensing machinery of a colony, producing changes to oscillatory period that propagate between biopixels in the array. To describe quantitatively the mechanisms driving synchronization at the array level, we treat each colony as a single oscillator that acts according to degrade-and-fire kinetics (Mather, et al.,  Phys. Rev. Lett.  (2009) 102:068105). We also include the production of H 2 O 2  and its interaction with neighboring colonies by two-dimensional diffusion. Using this model we identified three regimes that correlate well with experimental observations ( FIG. 12 a   ). When the effective production of H 2 O 2  is low, as with catalase, we observe unsynchronized oscillations owing to constant, mild repression of the lux promoter via ArcAB ( FIG. 12 a   , left). In contrast, when H 2 O 2  production is very high, neighboring colonies rapidly fire in succession and remain on because of the permanent activation of the lux promoter, consistent with the SOD experiment ( FIG. 12 a   , right). Finally, at intermediate H 2 O 2 , we observe globally synchronized oscillations ( FIG. 12 a   , middle). As colonies are moved further apart, synchronicity breaks owing to slowed migration of H 2 O 2  ( FIG. 16 ). 
     The present oscillating biosensors can coordinate more cells faster and over greater length scales than quorum sensing. Cell-cell communication solely by quorum sensing cannot coordinate isolated colonies. Quorum sensing is slower and operates on shorter distances than the present methods and biosensors. The present oscillating biosensors can coordinate between isolated colonies by gas-phase communication. Whereas quorum sensing coordinates cells locally, redox signaling, described herein, coordinates over long distances between colonies. These properties allow one to precisely and synergistically synchronize the behavior of millions of cells. 
     Advantages of biosensors include specicity, low cost, ease of use, and portability. However, currently available field kits for arsenic cannot accurately measure concentrations at low enough concentrations, and they are also prone to false negative results (Rahman, et al.,  Environ. Sci. Technol , (2002) 36(24), 5385-5394). Conventional methods for heavy metal detection, on the other hand, are precise but suffer from the disadvantages of high cost and lack of portability. 
     Most existing biosensors rely on direct induction; that is, a reporter is directly produced in response to a toxin of interest, in a graded fashion proportional to concentration. A significant limitation of this approach is that induction curves require precise calibration to ensure proper correlation between input and output. In order to avoid false positives and accurately measure absolute concentration, calibration is required, making it difficult to obtain consistent results from day-to-day or even sample-to-sample. Any sensor that will be ultimately used in a field-ready system must be robust to variations in experimental technique and environmental circumstances. A related problem involves the control of the microbial population size. The ability to regulate and maintain a particular size of cell colony is important not only for calibration purposed but also for safety. 
     Our approach benefits from the use of a periodic output, with frequency correlated with input concentration. The use of a blinking frequency avoids the calibration problem. Another advantage is the use of regulated killing to maintain a constant population size, which will allow for extended usage and reliability. Finally, the ability to expand our technology to two or more specific inputs is a major advantage over existing solutions. Furthermore, the frequency modulated biosensor design is adaptable, specific, and sensitive, and computational techniques allow for the precise deduction of the input concentrations base on the components of the output signal. For these reasons, the frequency modulated biosensors described herein offer significant improvements over existing technologies. 
     2. Cells 
     The biosensors comprise colonies of cells capable of expressing the proteins encoded in the expression cassettes described herein, non-fluid (e.g., vapor phase) communication and producing oscillating output signals. In varying embodiments of the biosensors, the colonies of cells are substantially genetically identical, e.g., are of the same species and/or of the same strain. In varying embodiments, the colonies of host cells are microbial cells, bacterial cells, yeast cells, mammalian cells, insect cells, photosynthetic cells, or plant cells. In some embodiments, the colonies of cells are bacterial cells, e.g.,  E. coli  cells. 
     3. Expression Cassettes 
     The synchronized oscillator design is based on elements of the quorum sensing machineries in  Vibrio fisheri  and  Bacillus Thurigensis . We placed the luxI gene (from  V. fischeri ), aiiA gene (from  B. Thurigensis ) and a fluorescent protein gene (e.g., yemGFP) under the control of three identical copies of the luxI promoter. The LuxI synthase enzymatically produces an acyl-homoserine lactone (AHL), which is a small molecule that can diffuse across the cell membrane and mediates intercellular coupling. It binds intracellularly to the constitutively produced LuxR, and the LuxR-AHL complex is a transcriptional activator for the luxI promoter (Waters and Bassler,  Annu Rev Cell Dev Biol . (2005) 21:319-46). AiiA negatively regulates the promoter by catalyzing the degradation of AHL (Liu et al.,  Biochemistry , (2008) 47(29):7706-7714). 
     Most quorum sensing systems require a critical cell density for generation of coordinated behavior (Reading and Sperandio,  FEMS microbiology letters , (2006) 254(1):1-11). We modified the local cell density of the synchronized oscillator cells (denoted TDQS1) through the use of microfluidic devices (Cookson et al.,  Mol. Syst. Biol ., (2006) 1, msb4100032-E1-6) of differing geometries. The device used for monitoring the bulk oscillations comprises a main nutrient-delivery channel that feeds a rectangular trapping chamber. Once seeded, a monolayer of  E. coli  cells grow in the chamber and are eventually pushed into the channel where they then flow to the waste port. The biosensor devices described herein allow for a constant supply of nutrients or inducers and the maintenance of an exponentially growing colony of cells for more than four days. We found that chamber sizes of 100×(80-100) μm 2  are useful for monitoring the intercellular oscillator, because such chamber sizes allowed for sufficient nutrient distribution and increased cell and AHL densities. In the context of the design parameters, the flow rate can be modulated in order to change the local concentration of AHL. 
     After an initial transient period, the synchronized oscillator cells exhibit stable synchronized oscillations which are easily discernible at the colony level. The dynamics of the oscillations can be understood as follows. Since AHL is swept away by the fluid flow and is degraded by AiiA internally, a small colony of individual cells cannot produce enough inducer to activate expression from the luxI promoter. However, once the population reaches a critical density, there is a “burst” of transcription of the luxI promoters, resulting in increased levels of LuxI, AiiA, and GFP. As AiiA accumulates, it begins to degrade AHL, and after a sufficient time, the promoters return to their inactivated state. The production of AiiA is then attenuated, which permits another round of AHL accumulation and another burst of the promoters. 
     The colonies of cells in the biosensors comprise multiple expression cassettes, e.g., contained on one or multiple plasmids, or incorporated into the host cells&#39;s genome that allow for the production of an oscillating output signal in the presence of an analyte of interest above above a threshold or detection level. The expression cassettes expressed by the colonies of host cells in the biosensors will vary depending on whether one or multiple analytes are being detected, and whether signal output is turned on or changes in the presence of detectable analyte or analyte concentrations above the threshold concentration. 
     In varying embodiments, one or more of the expression cassettes comprise one or more arcA binding sites positioned within about 150 bp, e.g., within about 140 bp, 130 bp, 120 bp, 115 bp, 110 bp, 100 bp, 95 bp, 90 bp, 85 bp, 80 bp, 75 bp, 70 bp, 65 bp, 60 bp, 55 bp, 50 bp, 45 bp, 40 bp, 35 bp, 30 bp, 25 bp, 20 bp, 15 bp, 10 bp, or 5 bp of a target gene or abutted to a target gene, e.g., a LuxR gene, a LuxI gene, and/or a nucleic acid encoding a protein that produces free radicals or oxygen reactive species (e.g., a fluorescent protein, e.g., GFP, YFP, CFP, rs-GFP, miniSOG). In varying embodiments, the one or more ArcA or ArcAB binding sites are positioned upstream or 5′ and within about 150 bp, e.g., within about 140 bp, 130 bp, 120 bp, 115 bp, 110 bp, 100 bp, 95 bp, 90 bp, 85 bp, 80 bp, 75 bp, 70 bp, 65 bp, 60 bp, 55 bp, 50 bp, 45 bp, 40 bp, 35 bp, 30 bp, 25 bp, 20 bp, 15 bp, 10 bp, or 5 bp of the target gene or abutted to the target gene An arcA site binds arcA, which is released by arcB in the presence of oxidative conditions (H 2 O 2 ). As used herein, an arcA binding site is about 15-20 bp in length, e.g., about 15 bp, 16 bp, 17 bp, 18 bp, 19 bp or 20 bp in length, and comprises a nucleic acid sequence having substantial sequence identity, e.g., at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to the nucleic acid sequence CAATTACTTAACATAAGC (SEQ ID NO:9). 
     Generally, with respect to different embodiments and designs of the biosensors, the expression cassettes are on one or multiple plasmids, e.g., 1, 2, 3, 4, or more plasmids. In varying embodiments of the threshold biosensors, the expression cassettes are incorporated into the genome of the host cells. 
     In varying embodiments of the biosensors, the response element promoter is selected from the group consisting of an arsenite response element (pArsR), a cadmium response element (yodA/cadA/cadR), a copper response element (copA/cueR), a mercury response element (merR), a cobalt response element, a lead response element, a zinc response element, a cyanide response element (CNO), a microcystin response element (mlrABCD), and an organophosphorus (OP) neurotoxin response element. For example, in varying embodiments, the arsenite response element (pArsR) or cadmium response element used in the illustrative examples can be replaced with another response element known in the art for detecting an analyte of interest, including the above listed response elements for detecting heavy metals and toxins. Response elements for detecting hormones and vitamins are also known in the art. 
     In varying embodiments, of the threshold sensor, the nucleic acid encoding a protein that produces free radicals or oxygen reactive species (e.g., H 2 O 2 ) encodes a fluorescent protein. Fluorescent proteins and their coding sequences are known in the art. Illustrative fluorescent proteins include, e.g., a green fluorescent protein, a yellow fluorescent protein, a cyan fluorescent protein, a red-shifted green fluorescent protein (rs-GFP), and miniSOG. 
     Threshold Biosensors 
     In one embodiment, the colonies of cells comprise expression cassettes designed to produce a threshold biosensor. Under this design, synchronized oscillating output signals are produced in the presence of analyte above a threshold concentration or above a concentration detected by the response element. In a particular embodiment, colonies of cells in a threshold biosensor comprise the following expression cassettes: 
     i) a LuxR gene under the control of a response element promoter; 
     ii) an aiiA gene under the control of a luxI promoter; 
     iii) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of the luxI promoter; and 
     iv) a nucleic acid encoding a protein that produces free radicals or oxygen reactive species (e.g., H 2 O 2 ) under the control of the luxI promoter, wherein the colonies of cells comprise a threshold sensor that produces an oscillating signal in the presence of concentrations of an analyte above a threshold concentration, wherein the analyte binds to the response element promoter. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a set of expression cassettes comprising: 
     i) a LuxR gene under the control of an arsenite response element (pArsR); 
     ii) an aiiA gene under the control of a luxI promoter; 
     iii) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of the luxI promoter; and 
     iv) a nucleic acid encoding a protein that produces free radicals or oxygen reactive species under the control of the luxI promoter, wherein the biosensor detects arsenic. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a set of expression cassettes comprising: 
     i) a LuxR gene under the control of a cadmium response element (yodA/cadA/cadR); 
     ii) an aiiA gene under the control of a luxI promoter; 
     iii) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of the luxI promoter; and 
     iv) a nucleic acid encoding a protein that produces free radicals or oxygen reactive species under the control of the luxI promoter, wherein the biosensor detects cadmium. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a set of expression cassettes comprising: 
     i) a LuxR gene under the control of an arsenite response element (pArsR); 
     ii) a LuxR gene under the control of a cadmium response element (yodA/cadA/cadR); 
     iii) an aiiA gene under the control of a luxI promoter; 
     iv) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of the luxI promoter; and 
     v) a nucleic acid encoding a protein that produces free radicals or oxygen reactive species under the control of the luxI promoter, wherein the biosensor detects cadmium and arsenic. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 27-756 of SEQ ID NO:1, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 901-1744 of SEQ ID NO:1, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-913 of SEQ ID NO:2, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-1366 of SEQ ID NO:3, wherein the biosensor detects arsenic. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:1, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:2, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:3, wherein the biosensor detect arsenic. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 27-756 of SEQ ID NO:1, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 901-1744 of SEQ ID NO:1, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-913 of SEQ ID NO:2, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-788 of SEQ ID NO:7, wherein the biosensor detects cadmium. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:1, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:2, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:7, wherein the biosensor detects cadmium. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 27-756 of SEQ ID NO:1, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 901-1744 of SEQ ID NO:1, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-913 of SEQ ID NO:2, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-790 of SEQ ID NO:8, wherein the biosensor detects cadmium, zinc and mercury. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:1, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:2, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:8, wherein the biosensor detects cadmium, zinc and mercury. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity to nucleic acid residues 27-756 of SEQ ID NO:1, a second expression cassette comprising at least 90% sequence identity to nucleic acid residues 901-1744 of SEQ ID NO:1, a third expression cassette comprising at least 90% sequence identity to nucleic acid residues 10 913 of SEQ ID NO:2, a fourth expression cassette comprising at least 90% sequence identity to nucleic acid residues 7-1366 of SEQ ID NO:3, and a fifth expression cassette comprising at least 90% sequence identity to nucleic acid residues 7-788 of SEQ ID NO:7, wherein the biosensor detects arsenic and cadmium. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity to SEQ ID NO:1, a second plasmid comprising at least 90% sequence identity to SEQ ID NO:2, a third plasmid comprising at least 90% sequence identity to SEQ ID NO:3, and a fourth plasmid comprising at least 90% sequence identity to SEQ ID NO:7, wherein the biosensor detects arsenic and cadmium. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 27-756 of SEQ ID NO:1, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 901-1744 of SEQ ID NO:1, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-913 of SEQ ID NO:2, a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-1366 of SEQ ID NO:3, and a fifth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-790 of SEQ ID NO:8, wherein the biosensor detects arsenic, cadmium, zinc and mercury. 
     In varying embodiments of the threshold biosensors, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:1, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:2, a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:3, and a fourth plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:8, wherein the biosensor detects arsenic, cadmium, zinc and mercury. 
     Period Modulation Biosensors 
     In one embodiment, the colonies of cells comprise expression cassettes designed to produce a period modulation biosensor. Under this design, changed synchronized oscillating output signals (e.g., increased amplitude and period) are produced in the presence of analyte above a threshold concentration or above a concentration detected by the response element. In a particular embodiment, colonies of cells in a period modulation biosensor comprise the following expression cassettes: 
     i) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of a response element promoter; 
     ii) a LuxR gene under the control of a luxR promoter and an aiiA gene under the control of a luxI promoter; 
     iii) a LuxR gene under the control of a luxR promoter and a nucleic acid encoding a protein that produces free radicals or oxygen reactive species (e.g., H 2 O 2 ) under the control of a luxI promoter; and 
     iv) a LuxR gene under the control of a luxR promoter and a LuxI gene under the control of a luxI promoter, wherein the colonies of cells comprise a period modulation sensor that produces a changed oscillating signal in the presence of concentrations of an analyte above a threshold concentration, wherein the analyte binds to the response element promoter. In varying embodiments, the changed oscillating signal comprises increased oscillatory amplitude and period. 
     In varying embodiments of the period modulation biosensor, the cells comprise a set of expression cassettes comprising: 
     i) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of an arsenite response element (pArsR); 
     ii) a LuxR gene under the control of a luxR promoter and an aiiA gene under the control of a luxI promoter; 
     iii) a LuxR gene under the control of a luxR promoter and a nucleic acid encoding a protein that produces free radicals or oxygen reactive species under the control of a luxI promoter; and 
     iv) a LuxR gene under the control of a luxR promoter and a LuxI gene under the control of a luxI promoter, wherein the biosensor detects arsenic. 
     In varying embodiments of the period modulation biosensor, the cells comprise a set of expression cassettes comprising: 
     i) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of a cadmium response element (yodA/cadA/cadR); 
     ii) a LuxR gene under the control of a luxR promoter and an aiiA gene under the control of a luxI promoter; 
     iii) a LuxR gene under the control of a luxR promoter and a nucleic acid encoding a protein that produces free radicals or oxygen reactive species under the control of a luxI promoter; and 
     iv) a LuxR gene under the control of a luxR promoter and a LuxI gene under the control of a luxI promoter, wherein the biosensor detects cadmium. 
     In varying embodiments of the period modulation biosensor, the cells comprise a set of expression cassettes comprising: 
     i) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of an arsenite response element (pArsR); 
     ii) an acyl-homoserine lactone (AHL) synthase LuxI gene under the control of a cadmium response element (yodA/cadA/cadR); 
     iii) a LuxR gene under the control of a luxR promoter and an aiiA gene under the control of a luxI promoter; 
     iv) a LuxR gene under the control of a luxR promoter and a nucleic acid encoding a protein that produces free radicals or oxygen reactive species under the control of a luxI promoter; and 
     v) a LuxR gene under the control of a luxR promoter and a LuxI gene under the control of a luxI promoter, wherein the biosensor detects arsenic and cadmium. 
     In some embodiments of the period modulation biosensor, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-1795 of SEQ ID NO:4, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1895-3488 of SEQ ID NO:4, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-1771 of SEQ ID NO:5, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1-1203 of SEQ ID NO:6. 
     In some embodiments of the period modulation biosensor, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:4, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:5, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:6. 
     In some embodiments of the period modulation biosensor, the colonies of cells further comprise an expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1-641 of SEQ ID NO:10, wherein the biosensor further detects arsenic and cadmium. 
     In some embodiments of the period modulation biosensor, the colonies of cells further comprise comprising an expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1-643 of SEQ ID NO:11, wherein the biosensor further detects arsenic, cadmium, zinc and mercury. 
     In some embodiments of the period modulation biosensor, the colonies of cells further comprise a plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:10, wherein the biosensor further detects arsenic and cadmium. 
     In some embodiments of the period modulation biosensor, the colonies of cells further comprise a plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:11, wherein the biosensor further detects arsenic, cadmium, zinc and mercury. 
     In some embodiments of the period modulation biosensor, the colonies of cells comprise a first expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 7-1795 of SEQ ID NO:4, a second expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1895-3488 of SEQ ID NO:4, a third expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 10-1771 of SEQ ID NO:5, and a fourth expression cassette comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1-641 of SEQ ID NO:10 or at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to nucleic acid residues 1-643 of SEQ ID NO:11, wherein the biosensor detects cadmium. 
     In some embodiments of the period modulation biosensor, the colonies of cells comprise a first plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:4, a second plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:5, and a third plasmid comprising at least 90% sequence identity, e.g., at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:10 or at least 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity, to SEQ ID NO:11. 
     The expression cassettes can also include termination sequences appropriate to the host cells. The expression cassettes can be inserted into a vector (e.g., a plasmid, a viral vector) for expression in an appropriate host cell. Alternatively, the expression cassettes can be incorporated into the genome of the host cells. When incorporated into plasmids vectors, sequences for replication, copy number and selection (e.g., antiobiotic resistance genes) can be included. Components of plasmid expression vectors are known in the art and plasmid expression vectors for a variety of host cells, including bacterial cells, yeast cells, algae cells, plant cells, mammalian cells, and insect cells, are commercially available. In varying embodiments the expression cassettes are incorporated into a plasmid vector for expression in bacterial cells, e.g.,  E. coli  cells. The biosensor circuits created by different assemblies and configurations of the expression cassettes can be readily configured according to the analyte intended to be detected. One or multiple plasmids comprising a set of expression cassettes that create a biological circuit with frequency modulated output can be readily designed and assemble to a desired purpose, e.g., using the modular plasmid system described in Lutz and Bujard (Nucleic Acids Res., (1997) 25(6), 1203-10). 
     4. Sensors and Response Elements 
     As appropriate, known response elements and response element promoters can be used in the biosensors to detect an analyte of interest. As described herein, the arsR promoter of  E. coli  fused to a reporter gene can be used to detect arsenic (Stocker, et al., Environmental Science &amp; Technology, (2003) 37(20):4743-4750). For detection of mercury, the  E. coli  merR gene can be employed and a truncated version of the merT gene fused to a reporter (Lyngberg, et al.,  J Ind Microbiol Biotechnol , (1999) 23(1):668-676). For detection of cadmium, the pSLzntR/pDNPzntA—reporter system can be used (Ivask, et al.,  BMC Biotech  (2009) 9:41). 
     Another class of potentially dangerous chemicals are organophosphorus (OP) neurotoxins, a group which includes both common pesticides and nerve agents such as VX and sarin (Mulchandani and Rajesh.,  Appl Biochem Biotechnol , (2011) 165(2):687-699; Rainina, et al.,  Biosens Bioelectron , (1996) 11(10):991-1000). Due to their wide distribution throughout the world as industrial and agricultural chemicals, they have long been viewed as potential weapons by terrorist groups [Gleick.  Water Policy , (2006) 8(6):481-503; Hickman, Technical report, Air University Press Maxwell AFB, AL 36112-6615, 1999]. An  E. coli  strain expressing the organophosphorus hydrolase (OPH) gene of  Flavobacterium  sp. ATCC 27551, e.g., as described in [Rainina, et al.,  Biosens Bioelectron , (1996) 11(10):991-1000], can be used. This enzyme hydrolyzes a large group of OP compounds including sarin, VX and pesticides such as paraoxon [Rainina, et al., supra]. RNA-seq can be employed to find genes induced upon OP hydrolysis to generate signaling inputs for the classifier. 
     A number of toxins arise from cyanobacteria blooms which are becoming more common across the globe. Cyanobacteria of various genera, especially Microcystis, can produce cyclic heptapeptide toxins known as microcystins [States, et al.,  Journal American Water Works Association , (2003) 95(4):103-115]. These toxins promote tumors and can cause death by liver haemorrhage and respiratory arrest [Codd,  Ecological Engineering , (2000) 16(1):51-60, 2000; Pouria, et al.,  Lancet , (1998) 352(9121):21-26]. To detect microcystins, the mlrABCD genes of  Sphingomonas  sp. can be expressed, which metabolize microcystin [Bourne, et al.,  Environ Toxicol , (2001) 16(6):523-534]. The recombinant cells can be exposed to microcystin and output genes identified using the RNA-seq methodology described herein. Similarly, in order to detect the presence of cyanide, the cyanide oxygenase gene (CNO) of the bacterium  Pseudomonas fluorescens  NCIMB 11764 can be expressed (e.g., in  E. coli ), and genes differentially expressed in response to cyanide exposure can be detected. 
     Novel genetic outputs responding to toxin exposure can be readily discovered, e.g., using RNA-seq technology. In varying embodiments,  E. coli  cells can be exposed to a toxin of interest and the expression profiles of induced genes to control cultures can be compared. Total RNA is extracted from the cell cultures and enrich for mRNA (van Vliet,  FEMS Microbiol Lett , (2010) 302(1):1-7). Random hexamer primers can be used to produce cDNA. cDNA can be sequenced using any method known in the art, e.g., an Illumina MySeq machine and the data set can be analyzed using any appropriate available analytical software, e.g., the EDENA program [Croucher and Thomson.,  Curr Opin Microbiol , (2010) 13(5):619-624). Genes differentially expressed in response to toxin exposure are determined and used as signals for the classifier. Since the whole transcriptome is being scanned with this technique multiple potential outputs for each toxin can be identified, facilitating the selectivity of the sensor. RNA-seq technology can be combined with known response promoters to detect heavy metals. 
     Any analyte which  E. coli  responds to can be detected using the presently designed biosensors (e.g., by switching the response element promoter). In more complicated cases where a single promoter may not exist, suites of promoters can be identified that make up a vector-based “promoter signature” using next-gen sequencing. Such analytes include without limitation heavy metals, bacterially metabolizable compounds, and other compounds, depending on the response. Pathogenic bacteria can also be readily detected since they use quorum-sensing as well, by switching the quorum-sensing response element in the presently described circuits. 
     Arsenic Biosensors 
     Biosensors offer a convenient, rapid, specific and sensitive means of monitoring analytes and of reporting the presence of specific toxins, generally producing a signal which proportional to concentration (Morris,  Cell Biochemistry and Biophysics , (2010) 56(1), 19-37). Several whole-cell bacterial biosensors for arsenic have been described in the literature (Tauriainen, et al.,  Applied and Environmental Microbiology , (1997) 63(11):4456; Scott, et al.,  Anal. Chem , (1997) 69(1), 16-20; Ramanathan, et al.,  Anal. Chem , (1997) 69(16), 3380-3384.), which typically employ nonpathogenic laboratory strains of  E. coli , the natural resistance mechanism of  E. coli  against arsenite and arsenate, and reporter proteins (Stocker, et al., Environ. Sci. Technol, (2003) 37(20):4743-4750). 
     These biosensors typically employ the natural resistance mechanism of bacteria against arsenic, encoded by the ars operon (Kaur and Rosen,  Plasmid , (1992) 27(1):29-40). In the absence of arsenite, the ArsR repressor binds to its operator/promotor site within the ars operon and prevents further expression of itself and the downstream genes (Rosen,  Journal Of Basic And Clinical Physiology And Pharmacology , (1995) 6(3-4):251). When arsenite enters the cell, it interacts with ArsR, causing a conformational change, dissociation of the ArsR protein from its operator, and expression of the ars genes. Therefore, one approach is to perform arsenite measurements using genetically engineered bacterial cells, which produce a fluorescent protein under control of the ArsR regulatable promoter. 
     The first large-scale environmental validation of a microbial reporter-based test to measure arsenic concentrations in natural water resources was developed in 2005 (Trang, et al.,  Environmental Science  &amp;  Technology , (2005) 39(19), 7625-7630). In this study, a bioluminescence-producing arsenic-inducible strain was shown to perform far better than most chemical field test kits in detecting arsenic at low concentrations. Realizing the importance of a closed, single-use incubation and detection system, another group designed a microfluidic arsenic biochip containing immobilized  E. coli  biosensor bacteria that express GFP when exposed to arsenite ions (Theytaz, et al.,  Procedia Chemistry , (2009) 1(1):1003-1006). 
     Taking a slightly different approach, a group in Edinburgh devised a biosensor that senses arsenic in drinking water and produces a pH change as the output (Aleksic, et al.,  Synthetic Biology , IET, (2007) 1(1.2):87-90). This system employs the same arsenate-responsive promoter of  E. coli , but uses urease to increase pH in the absence of arsenate and β-galactosidase (LacZ) to decrease pH in the presence of arsenate. However, while the group was able to generate pH changes for some levels of arsenate, they ran into problems with dynamic range, repeatability, and response time. 
     Heavy Metal Biosensors 
     While heavy metals are toxic in concentrations above a given threshold to microorganisms, some bacteria can adapt to their presence by using precisely regulated, genetically encoded resistance mechanisms. The resistance to certain toxic metals, including cadmium, works by employing an energy-dependent pump in the cell membrane to exclude the molecules (Nucifora, et al.,  Proc Natl Acad Sci USA , (1989) 86(10):3544; Zhang, et al.,  Current Microbiology , (2008) 56(3), 236-239). Some bacteria have natural metal resistance operons, such as the cadmium resistance system, CadA, of  Pseudomonas putida , which is regulated by cadR (Lee, et al.,  Applied and Environmental Microbiology , (2001) 67(4):1437). The precise regulation of these genes has been used to engineer sensor bacteria, in which the regulatory element controls the expression of a reporter gene. 
     In one study, an  E. coli  strain was engineered to express red-shifted green fluorescent protein (rs-GFP) under control of CadC, the regulatory protein of the cad operon in  Staphylococcus aureus  (Shetty, et al.,  Analytical And Bioanalytical Chemistry , (2003) 376(1):11-17). The reporter protein was produced proportionally to the amount of cadmium and lead used to induce the bacteria, and the bacterial sensing systems were found to respond to cadmium, lead, and zinc ions but had no significant response to other metals. A similar system was constructed by another group, which used the regulation unit from the CadA system to control the expression of firefly luciferase. The response to extracellular heavy metals was studied in several bacteria, and specificity was generally an issue (Tauriainen et al.,  Biosensors and Bioelectronics , (1998) 13(9):931-938). 
     Most recently, synthetic biology was employed to create a whole-cell cadmium sensor using a toggle gene circuit (Wu, et al.,  Biotechnology Progress , (2009) 25(3)). A cadmium-inducible promoter was used to produce GFP in response to addition of cadmium to the medium, and this approach presented improvements in sensitivity and specificity over other existing biosensor strains. However, the inherent limitation of providing an “on-off” reading greatly limited the range of detection. 
     The biosensors described herein can be used for the development of a genetic classifier integrated with a frequency modulated (FM) output circuit. A classifier is a system that uses multiple inputs to successfully discriminate between at least two possible outcomes; analyte levels that are above or below a predetermined threshold level (e.g. dangerous vs. acceptable levels of toxins). One feature of a classifier is the ability to train based on presented examples. In designs described herein, during training and operation, the levels of chemical inducers that shift the dynamic range and the output threshold of the classifier can be continuously computed and altered within the microfluidic device in an iterative feedback fashion. 
     The synthetic classifier can be tightly integrated with the synthetic gene oscillator as a frequency-modulated output element [Danino, et al.,  Nature , (2010) 463(7279):326-330; Prindle, et al.,  Nature , (2011) 481(7379):39-44, both of which are hereby incorporated herein by reference in their entirety for all purposes]. Using a threshold biosensor design, the system can be trained so that the biopixels will produce periodic pulses of H 2 O 2  for the levels of toxins above a pre-defined threshold and remain silent below it. To adjust the threshold level the concentrations of chemical inducers that directly affect the input oscillator sensitivity to toxin-reporting proteins can be shifted. Algorithms developed in machine learning [Bishop. Pattern Recognition and Machine Learning, Information Science and Statistics. Springer, 1st ed. 2006. con. 2nd printing edition, October 2007; Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, N.Y., USA, 1995; and Muller, et al., IEEE transactions on neural networks, (2001) 12(2):181-201] to perform training and optimization tasks in a continuous fashion during training and actual operation can be used. In a multi-trap microfluidic design, spatially varying concentrations of chemical inducers across a field of biopixels can be used. Collecting multi-channel readouts from biopixels subjected to different inducer concentrations allows improved learning and classification efficiency. 
     5. Devices 
     The devices generally comprise cell colonies capable of synchronized communication (e.g., via gas phase) within a microfluidic array. The colonies or populations of cells within the biosensors allow for synchronization of 2.5 million or more cells, e.g., 3, 3.5, 4, 4.5 or 5 million cells, across a distance of at least 5 mm, e.g., 5.5 mm, 6.0 mm, 6.5 mm, 7.0 mm, 7.5 mm, or longer. Provided herein are microfluidic-based biosensors which have the capability to detect analytes of interest, including, e.g., sub-lethal quantities of heavy metals (e.g., mercury, arsenic, cadmium, zinc, lead and others listed herein), organophosphate based nerve agents and pesticides, cyanobacterial toxins (e.g., microcystin and cyanide). The biosensor device can be readily designed and configured to detect other analytes of interest, e.g., using next generation sequencing technologies. In particular embodiments,  E. coli  can be used as the cellular platform. While  E. coli  is not generally found in unpolluted natural waters, the design of the present microfluidic devices ensures that the  E. coli  cell colonies are capable of robust sensing in these environments. Specifically, the mass transfer from the influent sample stream to the cell chambers is substantially completely or solely due to diffusion, preventing these cells from contacting viruses, bacteria and protozoa that are responsible for eliminating  E. coli  in natural waters. Furthermore the device described herein can be designed to be contained in a small, temperature controlled enclosure, preventing exposure to damaging UV radiation. 
     In varying embodiments, the sensor design can incorporate miniaturized redox electrodes patterned directly on-chip in the biopixel array. The resulting “bacto-electronic” sensor converts environmental stimuli to electrical current via a programmable biological intermediate. Electronic detection of H 2 O 2  has previously been performed in microfluidic devices [Yan, et al.,  Biomicrofluidics , (2011) 5(3):032008; Ikariyama, et al.,  Journal of Electroanalytical Chemistry and Interfacial Electrochemistry , (1988) 251(2):267-274; Ino, et al.,  Biosensors and Bioelectronics , (2010) 25(7):1723-1728], usually with an enzyme catalyst such as horseradish peroxidase (HRP), to detect concentrations as low as 1 μM H 2 O 2 . The composition of the working electrode can comprise designs including micropatterned gold [Yan, et al., supra], platinum black [Ikariyama, et al., supra], or indium-tin-oxide coated with conductive polymer [Lei, et al.,  Analytica Chimica Acta , (2006) 568(1):200-210]. Platinum [Jones. Applications of hydrogen peroxide and derivatives, Volume 2 of Rsc Clean Technology Monographs.  Royal Society of Chemistry,  1999] and palladium electrodes are electrocatalytic, meaning HRP is not needed; however, oxide formation decreases electrode performance over time [Gilroy and Conway,  Canadian Journal of Chemistry , (1968) 46(6):875-890, 1968]. 
     Evaporative deposition and etching can be employed to deposit platinum black electrodes on a silicon substrate. This process employs similar equipment to the photolithography method used in constructing a variety of microfluidic devices, and can be performed simultaneously. H 2 O 2  in bulk solution (over 1 mL sample volume) can be detected using an off-the-shelf oxidation-reduction potential (ORP) probe system. H 2 O 2  was detected at concentrations as low as 100 nM, which is improved over a previously described bacterial H 2 O 2  output in excess of 1 mM with the spxB gene [Pericone, et al.,  Journal of Bacteriology , (2003) 185(23):6815-6825]. In varying embodiments, the microfluidic arrays fit on a chip the size of a standard microscope slide or smaller. 
     EXAMPLES 
     The following examples are offered to illustrate, but not to limit the claimed invention. 
     Example 1 
     A Sensing Array of Radically Coupled Genetic ‘Biopixels’ 
     Synergistic Synchronization 
     To develop a frequency-modulated biosensor, we designed a gene network capable of synchronizing genetic oscillations across multiple scales ( FIG. 1 a    and  FIG. 2 ). We constructed an LCD-like microfluidic (Ferry, et al.,  Methods Enzymol . (2011) 497:295) array that allows many separate colonies of sensing bacteria to grow and communicate rapidly by gas exchange ( FIG. 1 b, c    and  FIG. 3 ). As previous work (Danino, et al.,  Nature  (2010) 463:326-330) has demonstrated that coupling through quorum sensing leads to incoherent oscillations at the millimeter scale, this mode of cellular communication is too slow for the generation of macroscopic synchronized oscillations. However, the slower quorum sensing can be used to synchronize small local colonies, provided there is a second level of design that involves faster communication for coordination between the colonies. Therefore, rather than attempting to engineer a sensor from a single large-colony oscillator, we wired together thousands of small oscillating colonies, or ‘biopixels’, in a microfluidic array. Coupling between biopixels involves redox signalling by hydrogen peroxide (H 2 O 2 ) and the native redox sensing machineries of  E. coli . The two coupling mechanisms act synergistically in the sense that the stronger, yet short-range, quorum sensing is necessary to coherently synchronize the weaker, yet long-range, redox signalling. Using this method we demonstrate synchronization of approximately 2.5 million cells across a distance of 5 mm, over 1,000 times the length of an individual cell ( FIG. 1 c, d   ). This degree of synchronization yields extremely consistent oscillations, with a temporal accuracy of about 2 min compared to 5-10 min for a single oscillator (Danino, et al.,  Nature  (2010) 463:326-330) ( FIG. 1 d   ). 
     The global synchronization mechanism is comprised of two modes of communication that work on different scales. The quorum-sensing machinery (LuxI, AiiA) uses an acyl-homoserine lactone (AHL) to mediate intracolony synchronization. In our device, the degree to which neighboring colonies are able to influence each other via AHL diffusion is negligible owing to the high media channel flow rates. Instead, we engineered the cells to communicate via gas exchange by placing a copy of the gene coding for NADH dehydrogenase II (ndh) under the control of an additional lux promoter. NDH-2 is a membrane-bound respiratory enzyme that produces low levels of H 2 O 2  and superoxide (O 2   2 ) (Messner, et al.,  J. Biol. Chem . (1999) 274:10119-10128). As H 2 O 2  vapor is able to pass through the 25-mm oxygen-permeable polydimethylsiloxane (PDMS) walls that separate adjacent colonies, periodic production of NDH-2 yields periodic exchange of H 2 O 2  between biopixels. When H 2 O 2  enters the cell, it transiently changes its redox state, interacting with our synthetic circuit through the native aerobic response control systems, including ArcAB, which has a binding site in the lux promoter region (Bose, et al.,  Mol. Microbiol . (2007) 65, 538-553; Georgellis, et al.,  Science  (2001) 292:2314-2316). Under normal conditions, ArcAB is partially active so lux is partially repressed. In contrast, oxidizing conditions triggered by H 2 O 2  inactivate ArcAB, relieving this repression. Each oscillatory burst promotes firing in neighboring colonies by relieving repression on the lux promoter. This constitutes an additional positive feedback that rapidly synchronizes the population ( FIG. 4 ). 
     We investigated the effects of catalase and superoxide dismutase (SOD) to probe the nature of H 2 O 2  communication. When a population of synchronized colonies was exposed to a step increase of 200 U/ml catalase, an enzyme that rapidly degrades extracellular H 2 O 2  (Seaver, et al.,  J. Bacteriol . (2001) 183:7182-7189), synchronization was broken and colonies continued to oscillate individually ( FIG. 5 ). As the cell membrane is impermeable to catalase asynchronous colony oscillations confirm that communication between colonies depends on external H 2 O 2  whereas oscillations within a colony do not. Conversely, when we enhanced the rate of superoxide conversion to H 2 O 2  by expressing soda (Fridovich, et al.,  Science  (1978) 201:875-880 (1978); McCord, et al.,  J. Biol. Chem . (1969) 244:6049-6055) from an additional lux promoter, colonies quickly fired in a spatial wave and failed to oscillate further despite no changes to growth rate or cell viability ( FIG. 6 ). Because H 2 O 2  is produced internal to the cell, this confirms that H 2 O 2  is capable of escaping the cell and activating lux-regulated genes in neighboring colonies via diffusion. The apparent higher output of H 2 O 2  by SOD as compared to NDH-2 is probably due to its very high catalytic efficiency (Berg, et al., Biochemistry (W.H. Freeman, 2006). Lastly, we observed synchronization between arrays of traps even when they were fluidically isolated but held in close proximity ( FIG. 7 ). These devices share no common fluid sources or channels, making communication by dissolved molecules like AHL impossible. Taken together, these results confirm that gaseous H 2 O 2  is the mode of communication between oscillating colonies. 
     On the basis of our understanding of the mechanism for global synchronization, we expected that we could simplify the circuitry by eliminating ndh and achieve the same effect with intermittent bursts of high-intensity blue light. In this design, the GFP molecule acts as a photosensitizer, releasing free radicals upon exposure that produce reactive oxygen species (ROS) including H 2 O 2  (Remington, et al.,  Curr. Opin. Struct. Biol . (2006) 16:714-721). At the peak of oscillation, considerable vapor-phase H 2 O 2  is produced by exposing GFP-containing cells to fluorescent light. Conversely, at the trough of oscillation, cells contain almost no GFP, and therefore produce very little H 2 O 2  upon fluorescing. Bursts of light thus generate bursts of H 2 O 2  vapor whose concentration depends on the oscillating GFP level, just as periodic production of NDH-2 did previously. Indeed, this strategy was similarly able to synchronize our sensor array ( FIG. 1 d   ). Numerous controls were performed to ensure that synchronized oscillations did not occur at low fluorescence intensities ( FIG. 8 ). 
     To probe this mode of synchronization, we investigated the effects of thiourea and the antibiotics ampicillin and kanamycin. When a synchronized population of colonies was exposed to 35 mM thiourea, a potent radical quencher (Kelner, et al.,  J. Biol. Chem . (1990) 265:1306-1311; Touati, et al.,  J. Bacteriol . (1995) 177:2305-2314), we observed sharply decaying synchronized oscillations whereas growth rate and cell viability were unaffected ( FIG. 9 ). This suggests that without radical species, oscillations cannot be produced. Next, we ran a series of experiments switching the antibiotic resistance genes on our plasmids. We noted that radical-producing antibiotics (Kohanski, et al.,  Mol. Cell  (2010) 37:311-320), particularly ampicillin, significantly reduced the degree of synchronization, showing that an excess of radical species also hinders communication ( FIG. 10 ). As our final constructs included a plasmid with kanamycin resistance, which was also found to produce some radicals, we used full (50 μg/ml) selection when growing up the cells but very low (5 μg/ml) selection during the experimental run. Persistence of oscillations, sequencing, and subsequent growth in full selection following the run confirmed the presence of all three plasmids despite this low experimental selection. Catalase and SOD results were identical to those with NDH-2 synchronization. These results show that fluorescence-mediated synchronization involves the production of radical species after fluorescence exposure and communication via H 2 O 2 . 
     Sensing Array of Biopixels 
     With a platform for generating consistent and readily detectable oscillations, we sought to use the circuit to engineer an arsenic-sensing macroscopic biosensor. We rewired the network to include an extra copy of the positive-feedback element, the AHL synthase LuxI, under the control of a native arsenite-responsive promoter that is repressed by ArsR in the absence of arsenite ( FIG. 11 a   , right). When arsenite is not present in the media, supplementary luxI is not transcribed and the circuit functions normally, generating baseline oscillations. However, the addition of trace amounts of arsenite relieves this repression and allows supplementary luxI to be transcribed, increasing the oscillatory amplitude and period. Tuning the level of LuxI by varying arsenite concentration results in clear changes to the oscillatory period ( FIG. 11 b   ). To determine the range of detection, we swept arsenite concentrations from 0-1 mM and measured the oscillatory period ( FIG. 11 c   , top). Using statistical methods, we generated a sensor calibration curve ( FIG. 11 c   , bottom) that depicts the maximum possible arsenite concentration present (about 5.95%) for a given measured period. This curve is an illustration of how data generated by our array can be used to measure arsenite concentrations in an unknown sample using our device. Our system was able to reliably quantify arsenite levels as low as 0.2 mM, below the 0.5 mM World Health Organization-recommended level for developing nations (Nordstrom,  Science  (2002) 296:2143). 
     As an alternative sensing strategy, we rewired the network to include a copy of the luxR gene controlled by an arsenite-responsive promoter while removing it from the rest of the circuit ( FIG. 11 a   , left). Because the LuxR-AHL complex must be present to activate the lux promoter (Waters, et al.,  Annu. Rev. Cell Dev. Biol . (2005) 21:319-346), cells produce no LuxR when the media is free of arsenite, generating no fluorescence or oscillations. The addition of arsenite stimulates the production of LuxR, restoring circuit function and producing clear, synchronized oscillations ( FIG. 11 d   ). This ON/OFF detection system has a threshold of 0.25 mM, a detection limit that can be adjusted by changing the copy number, ribosome binding site (RBS) strength, or promoter strength of the sensing plasmid. 
     The sensing array is also capable of producing complex behaviors arising from the dynamic interaction of cellular colonies. By making modifications to the size, number and arrangement of biopixels in the device, we are able to markedly alter the output waveforms. For example, when we constructed a device in which trap separation distance is increased (45 mm versus 25 mm), we observed local anti-phase synchronization between neighboring colonies ( FIG. 12 d   , top right). To explore this phenomenon on a larger scale, we constructed a device that contains an array of 416 traps constructed according to the specifications above. In these experiments, we observe initial global synchronization that gradually falls into local anti-phase synchronization across the array ( FIG. 12 d   , middle). Phase alignment is maintained over at least 48 h, with patches of synchronization typically 3-6 colonies in size. Alternatively, by changing dimensions such that the array contains traps of two slightly different sizes, we observe a 1:2 resonance synchronization where larger traps pulse at double the frequency of smaller traps while maintaining synchronization ( FIG. 12 d   , top left). Lastly, when LuxR is limited, as in the thresholding scheme, we observe synchronized oscillations of alternating large and small peaks in both experiment and model ( FIG. 13 ). Our computational model captures these effects ( FIG. 12 d   , bottom, and  FIGS. 14 and 13 ) and indicates that further array manipulation will yield new, richer dynamics that could not be produced directly by changing circuit structure. 
     Although our sensor array is capable of performing a variety of complex functions in the laboratory, adapting this technology to a real-world device eliminates the expensive and bulky microscopy equipment. However, measuring genetic oscillations in the absence of any magnification or powerful illumination requires an even further increased signal. Using this mechanism of global synchronization, we were able to scale up to a 24 mm by 12 mm array that houses over 12,000 communicating biopixels ( FIG. 15 a   ). Synchronization is maintained across the entire array, a distance over 5,000 times the length of an individual cell, using an inexpensive light emitting diode (LED;  FIG. 15 b, c   ). The signal strength generated by the large number of cells in the array (about 50 million) allows adapting the device to function as a handheld sensor. In our design ( FIG. 15 d   ), the sensor continuously reads the oscillatory frequency using off-the-shelf electronic components costing less than 50 dollars. 
     There have been many examples of bacteria-based biosensors (van der Meer, et al.,  Nature Rev. Microbiol . (2010) 8:511-522 (2010); Daunert, et al., Chem. Rev. (2000) 100:2705-2738; Leveau, et al.,  Curr. Opin. Microbiol . (2002) 5:259-265), usually involving an optical reporter driven by a toxin-responsive promoter. Because optical intensity readings are sensitive to imaging conditions like beam power and exposure time, measurements must typically be normalized and calibrated. Measuring the period of oscillation allows us to avoid these issues because peak-to-peak time does not depend on individual peak intensity. Also, oscillations produced at the colony level effectively decouple the signal from the growth state of individual cells, which can also affect fluorescence intensity. By using a dynamic readout that depends on communication between biopixels, we scan and tune potential output signals by changing device parameters rather than redesigning the underlying circuit. For example, a new sensing scheme could be designed in which oscillations synchronize with the addition of some toxin and shift to anti-phase or resonant synchronization when critical toxin levels are present. 
     Scaling up Synthetic Biology 
     By nesting two modes of communication we are able to expand the scale over which individual cells are coordinated and increase the complexity of their interaction. Indeed, there are many familiar examples of hierarchical systems. Airline routes are often designed such that small airports are connected locally to larger hubs that are connected internationally. It would neither be feasible nor desirable to connect every airport together. Similarly, individual cells communicate locally by one method, generating impulses large enough to enable colonies to communicate globally by another. Nesting communication mechanisms in this way allows us to better scale up synthetic circuits of different types, such as switches and logic gates. 
     Methods Summary 
     Strains and Plasmids. 
     The plasmids were constructed using a PCR-based cloning strategy (Quan, et al.,  PLoS ONE  (2009) 4:e6441) in which the origin of replication, antibiotic resistance, and circuit genes were assembled in different combinations. The ndh and sodA genes were amplified directly from the native  E. coli  genome by PCR. Various arsenite-responsive promoters were tested, including a recently reported synthetic version (Stocker, et al.,  Environ. Sci. Technol.  (2003) 37:4743-4750), but the final design uses the native  E. coli  version. Promoter output was tuned by changing the RBS sequence and quantified using flow cytometry. All circuit components except luxR were tagged by PCR with a carboxy-terminal ssrA tag (AANDENYALAA) (SEQ ID NO: 12) (Keiler, et al,  Science  (1996) 271:990-993) for fast degradation. 
     Microfluidics and Microscopy. 
     Image acquisition was performed on a Nikon Eclipse TI epifluorescent inverted microscope outfitted with fluorescence filter cubes optimized for GFP imaging and a phase-contrast-based autofocus algorithm. Images were acquired using an Andor Clara cooled CCD camera or Andor DU-897 EMCCD camera, both controlled by Nikon Elements software. Images were acquired every 2 min in phase contrast and fluorescence. The cells were imaged inside a microfluidic device with an upstream switch, with the ability to mix or switch between two different media sources. A custom application written in LabVIEW (National Instruments) controlled linear actuators, to which two reservoirs of arsenite-containing and pure medium were attached. Using this algorithm, arsenite concentration was dynamically varied to probe sensor output. 
     Plasmid Construction. 
     The oscillator plasmids were constructed by modifying the constructs used in a previous study (Danino, et al.,  Nature  (2010) 463, 326-330). The antibiotic resistance genes of pTD103AiiA was switched to chloramphenicol. The reporter protein on pTD103LuxI/GFP was switched to a recently reported superfolding green fluorescent protein, sfGFP (Pedelacq, et al.,  Nature Biotechnology  (2006) 24, 79-88). The ndh and sodA genes were amplified directly from the native  E. coli  genome by PCR. Promoter output was tuned by changing the RBS sequence and quantified using flow cytometry. We initially constructed the sensing plasmid with a published synthetic background-reduced version that contains additional ArsR operator sites (Stocker, J. et al.  Environ. Sci. Technol  (2003) 37, 4743-4750) but failed to produce enough LuxR. To increase LuxR output, we reverted to the native promoter sequence, switched the RBS to that of pZ plasmids, and increased the copy number by a factor of 5 by switching to a mutated SC101 origin of replication. All circuit components except LuxR were tagged by PCR with a carboxy-terminal ssrA tag (AANDENYALAA) (SEQ ID NO: 12) (Keiler, et al.,  Science  (1996) 271, 990) for fast degradation. Modular pieces (resistance genes, promoters, origins, and ORFs) were assembled using a PCR-based cloning scheme named CPEC (Quan, J. and Tian, J.,  PloS one  (2009) 4, e6441). 
     Data Analysis. 
     Fluorescence data was obtained by importing fluorescent images into ImageJ and subtracting cell signal from background signal. Oscillatory period was taken to be the average of peak-to-peak and trough-to-trough distance, calculated using a MATLAB script. The data represented in  FIGS. 1 d  and 11 b - d    were collected by stitching 4 images taken at 4× magnification. The mean trajectory in  FIG. 1 d    was found by averaging 373 individual biopixel trajectories, of which 20 are shown. Biopixel trajectories were extracted from image series using a MATLAB script, where a bright field image of the corresponding array was used to generate a mask. The data shown in  FIG. 11 c    was measured over 4 separate experiments using 10-30 oscillatory periods per data point. Sensor calibration curve ( FIG. 11 c   , bottom) was generated using a series of 2-population ttests comparing the experimental datasets to randomly generated new sample sets. The mean of generated sets was decremented until the ttest failed with α=95%, indicating the lowest period that could be associated with that arsenite concentration. We repeated this process for each arsenite level and fit the points with a quadratic since we expected it to take the inverse shape of the period vs. arsenite measurements. 
     Microscopy and Microfluidics. 
     We used a microscopy system similar to our recent studies (Danino, et al.,  Nature  (2010) 463, 326-330), with the addition of a highsensitivity Andor DU-897 EMCCD camera. Fluorescent images were taken at 4× every 30 seconds using the EMCCD camera (20 ms exposure, 97% attenuation) or 2 minutes (2 s exposure, 90% attenuation) using a standard CCD camera to prevent photobleaching or phototoxicity. 
     In each device,  E. coli  cells are loaded from the cell port while keeping the media port at sufficiently higher pressure than the waste port below to prevent contamination ( FIG. 10 ). Cells were loaded into the cell traps by manually applying pressure pulses to the lines to induce a momentary flow change. The flow was then reversed and allowed for cells to receive fresh media with 0.075% Tween which prevented cells from adhering to the main channels and waste ports. To measure fluid flow rate before each experiment, we measured the streak length of fluorescent beads (1.0 μm) upon 100 ms exposure to fluorescent light. We averaged at least 1,000 data points for each. 
     We constructed several microfluidic devices over the course of the study. The trap dimensions were 100 μm×85 μm×1.65 μm high, which we previously found to be useful for oscillator function, except when size was varied to study dynamic interactions. Spacing between traps was 25 μm, except in devices designed to study the effects of increasing separation distance between traps. For sensor array devices, we constructed 500 and 12,000 trap arrays as well as a tandem device which holds two 150 trap arrays in close proximity (25 μm) without sharing fluid sources or sinks. 
     Modeling. 
     To model the dynamics of the quorum-sensing oscillator, we used our previously described model for intracellular concentrations of LuxI (I), AiiA (A), internal AHL (H i ), and external AHL (H e ) (Danino, et al.,  Nature  (2010) 463, 326-330), 
     
       
         
           
             
               
                 
                   
                     
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     In the original model, the concentration of the constitutively produced LuxR protein R was assumed constant. In the ON/OFF threshold arsenic biosensor circuit, LuxR production is induced by arsenic, which we model by the equation 
                     R   .     =           α   c     ⁢   A       (       A   0     +   A     )       -       γ   R     ⁢   R               (   5   )               
in which the LuxR expression from the arsenic promoter follows a standard saturating function of the arsenic concentration A. Accordingly, we modified the Hill function for Lux promoter to include the explicit dependence on R:
 
     
       
         
           
             
               
                 
                   
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     For modeling the period-modulating sensor, we modified the equation for LuxI (2) to include additional production from the arsenic promoter, 
     
       
         
           
             
               
                 
                   
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     The following additional parameters were used for the biosensor simulations: α c =50, A 0 =2, γ R =0.1. 
     Arsenic levels were swept across the dynamic range of the arsenic promoter to produce the curve in  FIG. 11 c   . The period for each arsenic level was calculated from the peak-to-peak average of 15 oscillatory periods. 
     To model the spatial synchronization of oscillating colonies across a microfluidic array, we generalized a simplified “degrade-and-fire” model (Mather, et al., Physical Review Letters (2009) 102, 068105). The delay-differential equation 
                       X   .       i   ,   j       =         α   ⁡     (     1   +     v   ⁢           ⁢     P     i   ,   j   ,     τ   2             )           (     1   +       x     i   ,   j   ,     τ   1           C   0         )     2       -       γ   ⁢           ⁢     X     i   ,   j           k   +     X     i   ,   j                     (   8   )               
describes oscillations of individual biopixel (i,j) as a combined effect of production and delayed autorepression (first term in the r.h.s.) of the colon-averaged LuxI concentration X i,j  and its enzymatic degradation by ClpXP (second term). Unlike (6), the first (production) term is Eq. 8 describes both delayed auto-repression of LuxI and its delayed activation by H 2 O 2  proportional to its local concentration P i,j . Subscripts r 1  and r 2  indicate the delayed concentrations, X i,j,r     1   (t)=X i,j (t−r 1 ) and P i,j,r     2   (t)=P i,j (t−r 2 ). The dynamics of P i,j  is described by the equation
 
 P   i,j =μ+α p   X   i,j −γ p   P   i,j   +S ( P   i,j )  (9)
 
where the first three terms describe the basal and induced production and degradation of H 2 O 2 . The last term models the spatial coupling of neighboring biopixels via the H 2 O 2  exchange. For a square N×N array of traps, we used for the following discrete diffusion form of the spatial operator,
 
 Ŝ ( p   i,j )= DΔ   −2   [P   i−1,j   +P   i+1,j   +P   i,j−1   +P   i,j+ −4 P   i,j ]  (10)
 
Each colony is affected by the H 2 O 2  produced in for neighboring colonies, two in each dimension of the array, separated by the equal distance Δ. We used the boundary condition P i,j =0 for the edge of the array i,j=0, N+1. This represents the infinite external sink of H 2 O 2  diffusing out of the microfluidic chip. The diffusion operator above can be generalized if the row spacing differs from the column spacing, or for other spatial arrangements of colonies within the biosensor.
 
     We introduced variability among different traps by randomizing oscillator parameters for individual traps in each simulation. Specifically, LuxI (X) activation and degradation parameters (p={α,γ}) of each of the oscillators in the array were varied around their normal value (p 0 ) as p=p 0 +δ where δ is a random number uniformly distributed between −0.25 and 0.25. We used the following dimensionless parameters for most of our simulations α 0 =8.25, γ 0 =5.75, ν=1, r 2 =10, r 2 =20, C 0 =6, k=10, μ=20, α p 1, γ P =10, D=7, Δ=1. 
     For the characterization of various regimes of array synchronization, 16 colonies were modeled in the 4×4 array. Scaling up the simulation with larger numbers of colonies produced equivalent results. Overproduction of H 2 O 2  by expressing sodA was captured by increasing α p  20-fold. This is consistent with expression from a pSC101m plasmid with a copy number of 20-30. Depletion of external H 2 O 2  by catalase was modeled by increasing H 2 O 2  degradation (γ p ) and decreasing H 2 O 2  diffusion, D. In  FIG. 3  we show the variance of the concentrations X i,j  within the array averaged over time and parameter variations. This plot demonstrates that the synchronicity among the biopixels decreases with increase of spacing among them, and for Δ&gt;5 is completely lost. 
     Increasing the trap spacing Δ2-fold while simultaneously decreasing k 4-fold allowed us to reproduce the more complex waveforms observed experimentally in our arrays. Note that changing k models the change of the trap depth. As the size of the trap decreases, the flow of media is able to more rapidly sweep away AHL and increase the effective degradation for the colony. Simulating smaller and more sparse trap sizes recovered antiphase behavior for neighboring biopixels ( FIG. 16 ). We also simulated the arrays with traps of two different sizes in different rows and recovered the experimental 2:1 biopixel resonance or 2:1+ antiphase behavior depending on the trap spacing ( FIG. 12 d   , bottom). 
     The model was also able to capture the alternating large and small amplitude oscillations observed in the ON/OFF biosensor ( FIG. 14 ). This behavior was seen when C 0  was increased 2-fold, capturing the decreased level of LuxR in ON/OFF experiments where it was the limiting factor for oscillations. 
     It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.