# Presentation: 608320

## Stepping Up the Pace of Discovery: The Genomes to Life Program

**Marvin Frazier**
- Director, Life Sciences Division
- Biological and Environmental Research Program
- U.S. Department of Energy
- Germantown, Maryland
**August 13, 2003**

## GTL Speakers

**August 13, 2003**
**7:00 PM Marvin Frazier (Chair)***Introduction*
**7:05 PM Ed Uberbacher**
*	**Computing for the DOE Genomes to Life Program*
**7:35 PM Adam Arkin***Motifs and Modules in Cellular Signal Processing: Applications to Microbial Stress Response Pathways*
**8:05 PM Grant S. Heffelfinger***An Overview of the Sandia National Laboratories Genomes to Life Project, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling*"
**8:35 PM BREAK**
**8:45 PM Derek Lovley***Analysis of the Genetic Potential and Gene Expression of Microbial Communities Involved in the In Situ Bioremediation of Uranium and Harvesting Electrical Energy from Organic Matter*
**9:15 PM Mike Knotek**
- 	Revolutionizing Biology with Technology and Computing: Déjà vu All Over Again

## DOE and the Genome Revolution A Growing Legacy

**2004 ****	****(Projected) MGP funded sequencing of 100 microbial genomes**
**2003****	****MGP funded the sequencing of a total of 70 microbial genomes (finished or draft)**
**2002****	****Initiated the Genomes to Life systems biology program**
**1999****	****Formed the Joint Genome Institute for production DNA sequencing**
**1994****	****Started the Microbial Genome Program (MGP); First federal agency to fund the complete genomic sequencing of a microbe (*****M. genitalium*****)**
**Initiated the Human Genome Project; First federal agency to fund a genome program**
**1983****	****Established the National Laboratory Gene Library Project to generate single-chromosome human DNA clone libraries**
**Created the first DNA sequence database                      (later named GenBank)**

**Notes:**
DOE started the forerunner of GenBank for sequences.

DOE initiated HGP, MGP and now GTL

## DOE Genome Publications

**DOE Genome Publications**

***Ciona***

***Fugu***

**Human draft**

**Notes:**
DOE has been a significant player in genomics

## Growing Mandate to Move Biology Forward

- “Molecular Level Understanding of Life Processes” is a National Science Priority. — *OMB/OSTP FY 2005 Budget Guidance Memo*
- “Instrument development & new facilities are *critical* needs in microbial science.” — American Academy of Microbiology 2001 Colloquium, “*Microbial Ecology **and Genomics: A Crossroads of **Opportunity*”
- “DOE should now create unique, high throughput research facilities and resources to translate the new biology, embodied in the Genomes to Life (GTL) program, into a reality for the nation.”* *—*Biological and **Environmental Research Advisory **Committee*
- Agency Comments

- R. Aebersold and J. D. Watts, “The Need for National Centers for Proteomics,” *Nature Biotechnology, ***20**: 651 (July 2002).

**Notes:**
It’s recognized that understanding biology at the molecular level – with the genome as the foundation-- is critical.

White House Climate Change Study: The U.S. government has funded research to develop several technologies that mitigate climate change.  In general, these technologies are aimed at: increasing energy efficiency to reduce the amount of energy consumed for goods produced in the economy; creating opportunities to switch to fuels and products that emit relatively lower amounts of greenhouse gases; enhancing carbon removal and storage in terrestrial, ocean, and geological sinks; and exploring innovative concepts along unconventional paths to discover new ways to reduce greenhouse gas emissions, such as advanced biotechnology concepts. 

Yet these advanced concepts may have the greatest potential to reduce greenhouse gas emissions at very low cost.  For example, technological advances in areas such as biotechnology offer the potential for dramatic innovations in many areas.  An important technology is the development of bioreactors that can harness the potential of microbial communities, such as photosynthetic bacteria, to produce clean fuels such as hydrogen.  These bioreactors can exploit our increasing understanding of the biochemical pathways of microbial communities.  While these biotechnologies are currently producing higher value products, like pharmaceuticals, significant new scientific research will be required for the direct production of fuels.
Yet these advanced concepts may have the greatest potential to reduce greenhouse gas emissions at very low cost.  For example, technological advances in areas such as biotechnology offer the potential for dramatic innovations in many areas.  An important technology is the development of bioreactors that can harness the potential of microbial communities, such as photosynthetic bacteria, to produce clean fuels such as hydrogen.  These bioreactors can exploit our increasing understanding of the biochemical pathways of microbial communities.  While these biotechnologies are currently producing higher value products, like pharmaceuticals, significant new scientific research will be required for the direct production of fuels.
(for next 2-3 slides)
The revolution in biology has enormous national strategic importance – commerce, health, security, environment
American Academy of Microbiology 2001Colloquium: “Microbial Ecology and Genomics: A Crossroads of Opportunity” --  Instrument development & new facilities are critical needs in microbial science --Collaboration of device engineers, chemists, physicists & computational scientists
NIH has positively reviewed this approach
BERAC is enthusiastic and has helped involve the scientific community
Biology is now bigger than “biology” (too important for the biologists to manage?) – there will be a much broader disciplinary and institutional involvement in the next era.

## Genomes to Life: Research Roadmap and User Facilities for 21st Century Systems Biology

- Biotechnology Revolution offers DOE mission solutions
- The genome is the key to major advancements in understanding and applications
- Microbes are Us: Major Discoveries and Diverse Beneficial Capabilities
- The Genomic Approach: High-Tech, High-Throughput, and High-Quality Data to free the scientist to do science
- Pilot Projects and Technology Development:  We have already established the bases for facilities
- We have a plan: Science, Technologies, Facilities, and Management

**Notes:**
Talking points:
DOE has missions in energy, cleanup, security and climate that can benefit from the revolution that is occurring in biology.  Creating the infrastructure now can rapidly advance critical biotechnologies; DOE has an opportunity to do something truly revolutionary and important – the genome is a watershed event in science as few other things have been – the right actions now can have a telling effect for decades – high leverage point in time.
The genome is the basis for all life and life processes – genomes are now routinely available, and if we can learn their principles for function and develop methods for analyzing the details of individual species we can transform biology into a predictive and open a new era in biotechnology for many uses.
Microbes Enable the Planet to Sustain Life
Control Biogeochemical Cycles: Soil, Water, Climates
One of the most exciting frontiers in biology today – secrets of life and life sustaining processes
 Microbes are masters at harvesting energy 
degrade wastes and organic matter, cycle nutrients, convert sunlight into energy and fix CO2 
Microbes long ago “solved” many problems for which scientists are seeking solutions 
Applying their natural capabilities requires systems knowledge – The Genome
DOE has already sequenced 100 microbes
The genome projects showed the value on investments in hi-tech, and the necessity of scaling up to production levels in large facilities – this model is even more of a requirement in the coming era of biology.
We have funded numerous pilot activities which have successfully established a technical baseline to begin acquisition of the facilities we describe here.
We have held 15+ workshops, involving hundreds of scientists, the national laboratories, and a wide spectrum of academia, to define the science, the technical needs, technologies to meet those needs, and facilities that incorporate those technologies in an integrated and time-phased way.  We are going to show you those today.

## Tree of Life

**Bacteria ****   ****                        ****Archaea****                  ****Eukarya**

**Euryarchaeota**

-  Animals

-  Fungi

-  Plants

- Ciliates

-  Stramenophiles

-  Trichomonads

- Microsporidia

- Diplomonads

- Slime molds

*Entamoebae*

*Thermoplasma      *

*Methanosarcina*

-  *Methanobacterium*

-  *Methanococcus*

- Thermococcus

*        **Thermoproteus*
*  **Pyrodictium*

**Crenarchaeota**

- Green nonsulfur bacteria

*Deinococci*

-  *Aquifex*

-  Thermotogales

-  Spirochetes

*Flavobacteria*

- Cyanobacteria

-  Purple bacteria

-  Gram-positive

*Chlamydiae*

-  Halophiles

**Representative species completely sequenced ****or in the process of being sequenced by DOE**6 Feb 2003

**Tree of Life**

## Genomes to Life: Research Roadmap and User Facilities for 21st Century Systems Biology

- Biotechnology Revolution offers DOE mission solutions
- The genome is the key to major advancements in understanding and applications
- Microbes are Us: Major Discoveries and Diverse Beneficial Capabilities
- The Genomic Approach: High-Tech, High-Throughput, and High-Quality Data to free the scientist to do science
- Pilot Projects and Technology Development:  We have already established the bases for facilities
- We have a plan: Science, Technologies, Facilities, and Management

**Notes:**
Talking points:
DOE has missions in energy, cleanup, security and climate that can benefit from the revolution that is occurring in biology.  Creating the infrastructure now can rapidly advance critical biotechnologies; DOE has an opportunity to do something truly revolutionary and important – the genome is a watershed event in science as few other things have been – the right actions now can have a telling effect for decades – high leverage point in time.
The genome is the basis for all life and life processes – genomes are now routinely available, and if we can learn their principles for function and develop methods for analyzing the details of individual species we can transform biology into a predictive and open a new era in biotechnology for many uses.
Microbes Enable the Planet to Sustain Life
Control Biogeochemical Cycles: Soil, Water, Climates
One of the most exciting frontiers in biology today – secrets of life and life sustaining processes
 Microbes are masters at harvesting energy 
degrade wastes and organic matter, cycle nutrients, convert sunlight into energy and fix CO2 
Microbes long ago “solved” many problems for which scientists are seeking solutions 
Applying their natural capabilities requires systems knowledge – The Genome
DOE has already sequenced 100 microbes
The genome projects showed the value on investments in hi-tech, and the necessity of scaling up to production levels in large facilities – this model is even more of a requirement in the coming era of biology.
We have funded numerous pilot activities which have successfully established a technical baseline to begin acquisition of the facilities we describe here.
We have held 15+ workshops, involving hundreds of scientists, the national laboratories, and a wide spectrum of academia, to define the science, the technical needs, technologies to meet those needs, and facilities that incorporate those technologies in an integrated and time-phased way.  We are going to show you those today.

## GTL Program Goals

**Program Goals**

***Using DNA sequence and high-throughput technologies***

***goal 1***
**Identify and characterize the molecular machines of life**
***goal 2***
**Characterize gene regulatory networks**
***goal 3***
**Characterize the functional repertoire of complex microbial communities in their natural environments at the molecular level**
***goal 4***
**Develop the computational capabilities to advance understanding of complex biological systems and predict their behavior**

**Systems Biology**
**Gain a comprehensive and predictive understanding of the dynamic, interconnected processes underlying living systems**

**Notes:**
I’m not sure this contrast is any better than the old goals slide. We can continue to work on it.

## Genomes to Life Tutorial

**Apply knowledge of microbial functional capabilities**

**Genes and other DNA sequences contain instructions on how and when to build proteins**

**Many protein machines interact through complex, interconnected pathways.**
** ****Analyzing these dynamic processes will lead to models of life processes.**

**DNA Sequence Data from Genome Projects**

**Communities of Cells**

**Working Cell **

**Proteins perform many of life’s most essential functions. To carry out their specific roles, they often work together in the cell as protein machines.**

## Understanding Molecular Machines:Putting Them to Work

**Cellulase molecular machine**
**converting cellulose to glucose**

**Potential to make cellulose a viable energy feedstock and to create a new energy industry that improves energy security and helps displace greenhouse gases created by fossil fuel combustion.**

**Understanding Molecular Machines:****Putting Them to Work**

**Natural forms of cellulase machines are too inefficient for commercial ethanol production.**
**Fundamental knowledge of plant and microbial processes gained in GTL can be applied to develop more efficient methods.**

**Notes:**
Cellulases are enzyme-machines produced by a number of organisms including fungi. DOE has delivered the gene sequence of white rot fungus that decomposes dead trees. DOE is sequencing the Poplar genome now.

Improvements in key cellulytic enzymes are needed to increase efficiency in biomass conversion to alcohol

Potential to make cellulose a viable energy feedstock and to create a new energy industry.

Enhanced plant qualities and microbial bioprocesses can be used to generate clean, sustainable energy. Microbial protein “machines” can break down the cellulose in plant cell walls for fermentation to ethanol. Today, the process is too inefficient for commercial production. Fundamental knowledge of gene regulation and protein machines gained in GTL can be applied to develop highly efficient methods in support large-scale ethanol production and displace a significant amount of fossil fuel.

## Molecular Crowding The Cell is Full of Molecular Machines

- Published in *Trends in Biochemical Sciences*

**Molecular Crowding**** ****The Cell is Full of Molecular Machines**

**Notes:**
Large-scale facilities are required for identifying, characterizing, and modeling the activity of the tens of thousands of interacting components present at any given moment in a cell.

“Crowded Cell” by D. Goodsell © 1999

## Analysis of Microbes

**Subcellular location **
**and dynamics of **
**machines**

**Gene expression**
**in individual cells**

**Single cells**

**Populations**

**Communities**

**Who is expressing**
**what, when, where**
**& under what conditions?**

***Systems Biology from Molecules to Cells to Communities***

**Notes:**
Actual slide of microbes
Molecules microbes confer their capabilities
Populations of microbial cells working in the same environment

## Ultimate Goal is to Provide Predictive Models of Microbes

- Complete datasets
- Quantitative measurements
- Comprehensive physical characterization:
**Protein expression and interactions**
**Spatial distributions**
**Process kinetics**
**Computational:**
- Automated data analysis and validation
- Automated integration of diverse data sets
- Human and computer-accessible databases
- Molecular, Pathway and cell-level simulations

**The goals require a new synergy between computing and biology****.**

**Ultimate Goal is to Provide Predictive Models of Microbes**

***This goal drives data collection and computing strategy.***

**Notes:**
Just as super computers are used to model and simulate automobile accidents, biologists now need to figure out how proteins dock and interact. 
We can currently do earth and climate modeling, but cellular modeling is a frontier science.

## It’s all About the Complexity

**The Human genome has changed the way biologists approach  scientific challenges.**
**Biology is an information science**
**Biology applications are scaling at a rate that exceeds the computing capability**
**GTL presents the opportunity to expand throughput in 5-50 fold increases per year.**

## Billions of Bases in GenBank

**According to the GOLD database, there are 146 published genomes, 344 prokaryotic ongoing genomes projects, and 243 eukaryotic ongoing genome projects.**

**Notes:**
Lots of organisms are being sequenced; genomes provided huge growth in biological data. This slide plots the number of sequence bases over time. We’ve all seen this growth of GenBank plot.

## Biological Computing Outstrips Moore’s Law

**Slide provided by David States, dstates@umich.edu, **(Personal communication)

**Notes:**
Even back in 1997 the data search times in GenBank were taking increasingly long times and were out stripping the processor speeds seen in Moore’s Law. Computing isn’t scaling with the biological data sets.
Shown in the figure is a comparison of the computational requirements to perform a GenBank self comparison (green line, arbitrary units) and the processing power of computers as indicated by the Spec95 integer benchmark run the year the computer was introduced.  The vertical scale is in logarithmic units for both curves.  Moore’s law is empirically confirmed by the approximately linear increase of log (compute performance) with time.  The computing required to perform a GenBank self comparison is also increasing exponentially with time, but with about twice the rate of doubling.  Improvements in software and algorithms (Smith Waterman vs. Fasta vs. Blast) have also made important contributions to our ability to keep pace with the computational requirements for biological data analysis.

## Growth of Proteomic Data vs. Sequence Data

**Notes:**
And we thought we had too much sequence data! This slide only deals with the growth of proteomics data. An even larger amount of other types of data will be created (including metabolic data).

Already, in 2003 (in any single month beginning in the later half of 2003 after PNNL’s sample doubling breakthrough) DOE’s pilot proteomics facility will produce more raw data in a month than GenBank accumulated in more than 20 years.

PNNL: 24 samples/day X ~24 days X 200 GB/sample = 115 TB   vs. GenBank's ~107 TB.      

GenBank’s 107 TB data size was calculated as follows: total GenBank size of sequence data is 32.6 GB,  plus additional character data totals to 107 GB total in GenBank according to release notes #136, Jun 2003. Multiply 107 GB  X 1000 correction factor to more realistically represent the raw data that had to be created to produce the data sets represented in GenBank. So, the amount of raw data that would have been required to produce the most current release of GenBank = 107 TB  to make GenBank data more comparable to raw proteomic data. 

A kilobyte is 1024 bytes
Gigabyte is over one billion bytes (1,000,000,000)
Terabyte over one trillion bytes (1,000,000,000,000)
Petabyte is over 1,000,000,000,000,000 bytes

## GTL Experiment Template

**Notes:**
PNL’s goal is to analyze 310 samples per day to get 75,000 samples per year throughput (allows for downtime). They should reach this goal in 4-5 years.

Currently, there can only be 12 samples per day per machine (have 2 machines).
When they get to 96 samples per day, they can begin to use automation. 

On the horizon, a potential ten fold change, instead of doubling. 

## Users of Genomes to Life KnowledgebasePartnership for Success in Systems Biology

**Users of Genomes to Life Knowledgebase****Partnership for Success in Systems Biology**

**Office of Science**
**Office of Biological and Environmental Research**
**Office of Advanced Scientific Computing Research**

## Environmental Cleanup Understanding How Bacteria Reduce Metals

**Atomic Force Microscopy**
**Image of *****Shewanella***** on**
**An Iron Oxide Particle**

**Confocal Microscopy**
**Image of *****Shewanella***** Expressing**
**Green Fluorescent Protein on same**
** ****Iron Oxide Particle**

- (cell length ~ 1 micron)

**Notes:**
Some microbial communities in precipitate toxic metals and prevent them from entering the ground water.

## Genomes to Life: Potential Impact on CO2 Emissions

**Genomes to Life: Potential Impact on CO2 Emissions**

**Notes:**
The knowledge provided by GTL can be used in applications that simultaneously create less polluting energy and reduce global climate change.

Reducing net CO2 atmospheric emissions and dependence on imported oil.

Greater carbon storage ability to help reduce net atmospheric CO2 and global climate change.

Carbon management: Smokestack scrubbers, Biosequestration in photosynthetic terrestrial and aquatic organisms.

## Coccolithophores are Key to Ocean Carbon Cycling and Sequestration

**Coccolithophores are Key to Ocean Carbon Cycling and Sequestration**

- Satellite picture of blooms off Newfoundland in the western Atlantic, 21st July 1999. Provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and ORBIMAGE.

## CO2 Effects on Phytoplankton

- SEM images of coccolithophores for different CO2 values. **a**,**b**,**d**,**e**: Emiliania huxleyi; **c**,**f**: Gephyrocapsa oceanica from cultures grown at CO2 conc. of ~12 μmol/l (~300 ppmv) (**a-c**) & CO2 conc. of ~30-33 μmol/l (780-850 ppmv) (**d-f**), (780-850 ppmv). Scale bars = 1 μm.
*U. Riebesell et al. "Reduced calcification of marine phytoplankton in response to increased atmospheric CO**2**", Nature, ****407****, pg 364-7, 21 Sept., 2000.*

**300  ppm**

**800 ppm**

**Notes:**
Effects of high CO2 concentration on coccolithophores that are so critical to maintaining ocean carbon balance and life on earth.

## GTL User Facilities HallmarksFor the Research Community

**GTL User Facilities Hallmarks*****For the Research Community***

**Advanced Technologies**

**Microbial Genomics**

**High-Throughput Methods**

**Biosystems Training**

**Informatics and Databases**

**Computing and Simulation**

**Technology Integration for Tomorrow’s Biology**

**DOE Mission Applications**

**Notes:**
These facilities would serve as focal points for the life sciences research community, providing a national venue to pursue multidisciplinary systems biology and promote cross-disciplinary education.

## Human Genome Project Making the Impossible Routine

- DOE R&D Contributed to:
**Decreased Cost**
**Increased Throughput**

**Providing the Paradigm for Genomes to Life Facilities**

**Human Genome Project ****Making the Impossible Routine**

**Cost per base in dollars.   Millions of bases finished.**

**Notes:**
So how are we going to scale our research to produce the needed system biology data sets?
Henry Ford example first
The HGP demonstrated that technology infusion, innovation, scale-up and production philosophy – data for all – can be particularly powerful for biology.
THE HGP approach has changed the paradigm for biology.
Huge gains in efficacy, efficiency, and quality can be attained if the challenge is there.

Graph: Large-scale facilities spur cost, productivity improvements. The dramatically increased productivity and reduced costs achieved in the HGP via high-throughput production environments (e.g, the DOG Join Genome Institute) provide the paradigm for the dedicated industrial scale facilities envisioned for Genomes to Life.

## Slide 27

**Notes:**
DOE has many years experience in designing and running user facilities (Mike Knotek’s talk on Wednesday night.)

GTL brings the physical, life, and computing sciences together – facilities are the highest level manifestation of this fusion – GTL facilities will continue that critical marriage.
DOE’s role as the progenitor and operator of facilities is strategically important to US science and the GTL facilities will continue that legacy.
New facilities will be combined with enhanced usage of existing facilities.

## GTL Facilities Pilot Studies

**Prerequisite pilot studies initiated to demonstrate bottlenecks, areas for technology development, and scale of facilities needed **
**Specialized technologies needed for GTL science in high-resolution imaging, mass spectrometry, computing, and structural biology**
**Microbial ecology pilot to create research strategies for the difficult task of determining the microbial “players” **

**GTL Facilities Pilot Studies**

**Notes:**
BER has invested for several years in the critical technologies and concepts necessary for successful GTL facilities.
Successful pilots and technology development have established hardware, methods, and skilled manpower foundations – significantly reducing the risk for this investment.

## High-throughput Proteomics

***Deinococcus radiodurans *****R1**

**3.1 megabase; 3,116 predicted ORF’s**
**Large redundancy in DNA complement**
**Potential relevance to bioremediation and understanding DNA repair**
**~83% of all predicted proteins observed/ confirmed using AMT tags**

**Notes:**
ICR mass spectrometry allows proteomics on a true high-throughput comprehensive basis.

## GTL Systems Biology Pilot Studies 

**Eight systems biology pilot studies focus on microbes of DOE environmental and energy-security relevance:**
***Shewanella oneidensis***** **
***Geobacter sulfurreducens***** **
***Deinococcus radiodurans ***
***Caulobacter crescentus ***
***Rhodopseudomonas palustris***
***Rhodobacter sphaeroides***
***Methanococcus maripaludis***
***Synechocysits***** **
***Prochlorococcus***

**Soft X-Ray image showing cellular pore structures ****(****Advanced Light Source at Lawrence Berkeley National Laboratory)**

**Notes:**
Mary Helen Barcelos-Hoff (LBNL) gave a seminar at ORNL on Monday and she made a good point about dealing with image data and the value of computing in this realm: 

She said about "imaging bioinformatics" that there's a lot more information in a micrograph that must be extracted via statistical and bioinformatics techniques. She said that without these techniques, the image information is lost from most scientists except those few that are particularly expert in interpreting images of the specific type of cell  they are expert in.

## DOE GTL Shewanella Federation

**DOE GTL *****Shewanella***** Federation**

**Sequencing, **
**Annotation**
- TIGR
- (J. Heidelberg)

**Microarrays, **
**LIMS Database**
- Center for Microbial Ecology, MSU
- (J. Tiedje, J. Cole,
- J. Klappenbach)

**Physiology, **
**Genetics**
- USC, JPL
- (K. Nealson)

**Phage Display**
- ANL
- (C. Giometti)

**Pathway Construction and Modeling**
- UCSD (B. Palsson), LBL (A. Arkin), Woods Hole (M. Riley)

**Physiology, **
**MS, Proteomics**
- PNNL
- (J. Frederickson, D. Smith)

**Modeling**
- ISB
- (E. Kolker)

**Bioinformatics, MS**
- ORNL LSD, CASD
- (F. Larimer, D. Xu,
- Y. Xu, B. Hettich)

**DOE GTL *****Shewanella***** Federation**

**Metabolomics**
- UCB
- (J. Keasling)

**Microbial Functional **
**Genomics Group**
- ORNL, ESD
- (Zhou)

**Notes:**
The Shewanella Federation is a model for other microbial systems biology projects. 

It’s all about the complexity. Studying one microbe requires multiple partnerships each with a different scientific focus. Computing is central to the success of understanding one of these microbes. 

## Slide 32

**Notes:**

GTL Facilities will create: 
Research infrastructure for the new biology
Comprehensive informatics and computing infrastructure 
Comprehensive microbial knowledgebase:  From genomics to predictive biology
A new multidisciplinary community of scientists trained in the new biology
A new biotechnology base for DOE missions
Training and education

## Facility IProduction and Characterization of ProteinsEstimating Microbial Genome Capability

**Computational Analysis**
**Genome analysis of genes, proteins, and operons**
**Metabolic pathways analysis from reference data**
**Protein machines estimate from PM reference data**
**Knowledge Captured**
**Initial annotation of genome**
**Initial perceptions of pathways and processes**
**Recognized machines, function, and homology**
**Novel proteins/machines (including ****prioritization)**
**Production conditions and experience**

**Notes:**
Facility 1: Production and characterization of proteins
High-throughput production of proteins on genome-wide scale
Produce affinity reagents for each protein
Biophysical characterizations
Reagents, databases, and computational tools accessible to the broad scientific community
Overcomes a principal roadblock to whole-system analysis

## Analysis and Modeling
Mass spectrometry expression analysis
Metabolic and regulatory pathway/ network analysis and modeling
Knowledge Captured
Expression data and conditions
Novel pathways and processes
Functional inferences about novel proteins/machines
Genome super annotation: regulation, function, and processes (deep knowledge about cellular subsystems)

**Analysis and Modeling**
**Mass spectrometry expression analysis**
**Metabolic and regulatory pathway/ network analysis and modeling**
**Knowledge Captured**
**Expression data and conditions**
**Novel pathways and processes**
**Functional inferences about novel proteins/machines**
**Genome super annotation: regulation, function, and processes (deep knowledge about cellular subsystems)**

**Facility II ****Whole Proteome Analysis****Modeling Proteome Expression, Regulation, and Pathways**

**Notes:**
Facility II Whole Proteome Analysis
Measure proteome and metabolites for a cell or community systems under controlled conditions.
Gain functional insights by characterizing known and unknown dynamic processes to correlate proteins and machines that work together in a process.

## Facility III Characterization and Imaging of Molecular MachinesExploring Molecular Machine Geometry and Dynamics

**Facility III ****Characterization and Imaging of Molecular Machines****Exploring Molecular Machine Geometry and Dynamics**

**Computational Analysis, Modeling and Simulation**
**Image analysis/cryoelectron microscopy**
**Protein interaction analysis/mass spec**
**Machine geometry and docking modeling**
**Machine biophysical dynamic simulation**
**Knowledge Captured**
**Machine composition, organization, geometry, assembly and disassembly**
**Component docking and dynamic simulations of machines**

**Notes:**
Facility III Characterization and Imaging of Molecular Machines
Isolate the repertoire of molecular machines.
Characterize machines in terms of composition and molecular organization.

High-throughput isolation and identification of complexes from cells
Characterize the interactions of the components in the complex 
Simulations for molecular machine function, models for assembly/disassembly of complexes
Interpret, archive and disseminate data, models, and computational simulations to the greater biological community

## Facility IVAnalysis and Modeling of Cellular Systems Simulating Cell and Community Dynamics

**Facility IV****Analysis and Modeling of Cellular Systems**** ****Simulating Cell and Community Dynamics**

**Analysis, Modeling and Simulation**
**Couple knowledge of pathways, networks, and machines to generate an understanding of cellular and multi-cellular systems**
**Metabolism, regulation, and machine ****simulation**
**Cell and multicell modeling and flux ****visualization**
**Knowledge Captured**
**Cell and community measurement data sets**
**Protein machine assembly time-course data sets**
**Dynamic models and simulations of cell processes**

**Notes:**

Facility IV Modeling and Analysis of Cellular Systems
Couple knowledge of pathways, networks, and molecular machines to generate understanding of cellular and multicellular systems.
Measure structure and properties of a single cell in a population or community under controlled conditions.

Couple knowledge of pathways, networks, and molecular machines to generate an understanding of cellular and multi-cellular systems

Determine the timed choreography of events and  the state of molecular machines during dynamic cell processes

## Computing Issues for GTL Facilities and Projects

**Creating an Integrated Computational Biology Environment**

**2. Data Capture and Archiving**

**4. Interpretation / Modeling / Simulation**

**3. Data Analysis / Reduction**

**1. LIMS & Workflow Management**

**5. The Community Data Resource**

**6. Infrastructure**

**Computing Issues for** **GTL Facilities and Projects**

**Notes:**
Establish comprehensive hierarchical models for cells and communities

## Central Role of GTL Facilities in Compute Planning

- The GTL Facilities will represent
-  	the cornerstone of the GTL enterprise
-  	and major sites for development of
-  	computing systems.
- They will generate massive amounts
-  	of data for use by the community and
-  	for constructing models of biology
- The facilities will be the sites where experiment workflow must be facilitated, data must be analyzed, and systems biology data and models provided to the community
- They are likely to contain integrated high performance computing,  share suites of tools to analyze data and massive data archives.
- Their combined and integrated output will become the major portion of the GTL community resource (GTL knowledge base)

**Central Role of GTL Facilities in Compute Planning**

**Notes:**
Establish comprehensive hierarchical models for cells and communities

## GTL Computing Roadmap

**GTL Computing Roadmap**

**Biological Complexity**

**Comparative**
**Genomics**

**Constraint-Based**
**Flexible Docking**

****

****

**Computing and Information**
** ****Infrastructure Capabilities**

**Constrained**
** ****rigid**
** ****docking**

**Genome-scale**
** ****protein threading**

**Community metabolic**
** ****regulatory, signaling simulations**

**Molecular machine**
** ****classical simulation**

**Protein machine**
**Interactions**

**Cell, pathway, and network simulation**

**Molecule-based**
**cell simulation**

**Current U.S. Computing**

**Notes:**

Red arrow indicates current level of computing power. 

Need more computing power.
Need better ways to analyze algorithms.
Need better data handling.

Data Processing Will Be Remote
Databases will be so huge that current  paradigm of localized databases will be inadequate. Databases will have to be distributed across several networks and machines because there is not enough bandwidth to handle large volumes of data.

“The chess board is the world, the pieces are the phenomena of the universe, the rules of the game are what we call the laws of Nature. 
The player on the other side is hidden from us. We know that his play is always fair, just and patient. But we also know, to our cost, that he never overlooks a mistake, or makes the smallest allowance for ignorance.” --Thomas Henry Huxley 

In Biology we will soon have the chess board of entire genomes—the information that provide continuity of a species, including our own.  We will have the Chess pieces of genes and proteins, and —once we discover ONE rule by which the peieces are played, we will be able—by homologous conservation over evolutionary time—be able to efficiently make hypothesis about other rules with similar chess pieces.   

Bioinformatics tracks the moves made by biologists and integrates the observed data; 
Computational Biology anticipates the moves and finds patterns in the rules of the game with homology and other data analysis.

## Need New Data Handling and Computing Resources to Handle Data Tsunami 

**Current data infrastructure**

**DATA**

**Sequence**
**Proteomic**
**Metabolic**
**Image**
**    ****Modeling  Simulation**
**And more**

**Help!**