BACKGROUND
living cells continuously process information about their environment, and based on this information and their own internal state mount appropriate responses to these signals.
this information processing is carried out by various regulatory networks functioning in a highly crowded, viscous cellular interior, with characteristic times spanning several orders of magnitude.
the fastest among these are signal transduction networks: they range from simple two-component pathways in prokaryotes to the highly complex signal transduction networks of mammalian cells.
fast signaling, however, is frequently followed by slower transcriptional regulatory  events, during which regulatory gene products, such as transcription factors  and regulatory rnas, alter the rate of transcription of other genes, reorganizing gene expression to achieve new metabolic states, or initiate cellular programs, such as the cell cycle, sporulation, or differentiation  <cit> .
understanding the system-level properties of these networks requires both experimental and computational efforts that start with mapping out potential regulatory interactions that exist in a given cell type.
in the yeast saccharomyces cerevisiae and in the bacterium escherichia coli, the static 'wiring diagrams' of potential tf-mediated interactions have been mapped out to such a degree  <cit>  that their system-level characteristics and function can be investigated.
subsequent computational analyses have shown that in both tr networks the regulatory interactions between tfs and the regulated genes are often organized into basic information processing subgraphs, called motifs  <cit>  that can form even larger potential information processing units, such as motif clusters  <cit> , themes and thematic maps  <cit> , and transcriptional modules  <cit> .
it is also evident that the tr network is utilized in a condition-specific manner  <cit> , perhaps through the activation of distinct, signal-specific subnetworks  <cit> .
in spite of these advances the principles along which regulatory networks process signals, encode the relevant signals at different layers of the network, and integrate them with other signals remain poorly understood.
here we show that regulatory interactions among an intermediate layer of transcription factors is a key determinant of information transfer within the s. cerevisiae tr network, and that this layer naturally segregates into distinct, sparsely communicating subnets in which tfs are densely interlinked in a hierarchical manner.
we also show that tfs and the genes regulated by them respond to external signals by utilizing various fractions of these subnetworks.
the identified features suggest a model in which successive waves of transcriptional regulation of gene expression via multiple interferences at various levels of tf interaction hierarchy constitute a key feature of developing robust integrated responses to complex stimuli.
RESULTS
hierarchies and signal-specific subnets in the s. cerevisiae tr network
with the exception of a few mutually regulating pairs, the links of the s. cerevisiae tr network are unidirectional, and its nodes can be arranged into three main layers based on their position, regulation, and function.
the layers reflect the flow of information from the input nodes , through intermediate tfs to the output nodes  ; a path from an input to an output node contains usually  <dig> to  <dig> steps, and the maximum length is  <dig> steps.
in the s. cerevisiae tr network each tf regulates a limited number of target genes , with an average number of  <dig> .
as described recently for the tr network of e. coli  <cit> , the genes directly or indirectly regulated by a given input tf form a signal-specific subnet, or origon, and the nodes at the intermediate and output layers of the origons are often shared by two or more origons.
figure 1a illustrates two overlapping origons, originating from the input tfs yap <dig> and skn <dig>  since the network contains  <dig> input tfs, there is a total of  <dig> origons in the s. cerevisiae tr network, of which only two are isolated from the rest of the network  .
classification of the yeast tr network based on its global topological properties
to gain insight into the overall yeast tr network organization we first assessed the connectivity distribution of all nodes , and separately those of input tfs, intermediate tfs, and output genes, using cumulated distributions that are equivalent to rank-degree  plots.
due to the inherent directionality of the links, we separately analyzed the number of regulating tfs per regulated gene  and the number of regulated genes per tf , to determine if their distributions are best approximated by exponential-like  <cit>  or power-law  <cit>  models.
we find that the distribution of the number of incoming links per node, kin, displays an exponential decay , as previously described  <cit> , while that of outgoing links shows an intermediate behavior between exponential-like- and power-law decay models .
interestingly, the outgoing links for input tfs closely approximate an exponentially decaying degree distribution, , while a few of the intermediate tfs are unexpectedly large hubs resembling more closely the power-law models.
also, the outdegrees of intermediate tfs tend to be larger than those of input nodes .
taken together, the cumulative in- and outdegree distributions suggest that the yeast tr network belongs to a mixed class of networks , where the number of connections per node is likely to be constrained both by the limited size of a target gene's promoter region  <cit> , and perhaps by the biosynthetic costs of maintaining regulatory interactions  <cit> .
distribution of graph motifs in the yeast tr network
the effects of many external and internal signals are manifested by altered tf activity, followed by the propagation of the perturbation to nodes of lower layers.
small circuits  play a key role in this propagation; they often connect nodes of different regulatory layers to each other.
of these, overrepresented subgraphs  are likely to enhance the versatility of information processing in a tr network  <cit> , and may have become abundant due to the overall functional robustness they provide during evolutionary adaptation to changing environmental conditions .
to elucidate the type and information processing role of such overrepresented subgraphs, we examined the abundance of three-node subgraphs in the s. cerevisiae tr network.
using a standard link-randomization algorithm  we found that the feed-forward loop , the single regulatory interaction with mutual regulation  and the convergence with mutual regulation  are overrepresented, i.e., they are motifs , while the divergence , cascade  and convergence  subgraphs are underrepresented, i.e., they are anti-motifs  <cit>  .
we also examined the position of these 3-node subgraphs with respect to individual origons, and found that  similarly to the e. coli tr network  <cit> , only a subset of origons contains ffl, smr, and cmr motifs , and  the majority  of cnv subgraphs perform signal integration: they receive regulatory signals  from two different sources  and transmit the joint signal to a single node .
motifs are marked, and only subgraphs with at least  <dig> occurrences in the original network are listed.
after link randomization the numbers of ffl, smr and cmr subgraphs decrease, while those of div, cas, and cnv subgraphs are maintained with slight increases, indicating that ffl, smr and cmr are motifs in the tr network, while div, cas and cnv are anti-motifs  <cit> .
functional cartography of the yeast tr network
external signals, conveyed by various signaling mechanisms, may be perceived by signal-specific tfs or relatively non-specific tfs.
to understand how the responses to these signals are encoded into the topology of the tr network we first examined the degree of overlap among the genes regulated by input- and intermediate tfs.
as shown in figure 2a – where the width of a link between two tfs is proportional to the number of outputs  they both regulate – the targets of different tfs extensively overlap , suggesting that most genes are combinatorially regulated by several tfs.
in contrast, direct regulatory interactions among tfs are more limited : the largest connected component of the network of direct regulatory interactions among tfs  is sparse, and  <dig> of the remaining  <dig> tfs have no regulatory interactions with other tfs at all, i.e., they act in isolation.
to characterize the type of combinatorial regulation performed by each tf, we color coded each of the  <dig> tfs according to the function of the genes they regulate.
to this end, we resorted to the  <dig> go slim biological process terms  <cit> , which we grouped into eight go slim categories described in the methods.
it is evident, that all tfs regulate genes with various functions .
for example, genes within two overlapping origons – defined by the input tfs ino <dig> and stb <dig> – display a multitude of functions .
stb <dig> takes part in the regulation of transcription at the g1/s transition  <cit> , while ino <dig> is a positive regulator of phospholipid biosynthesis  <cit> .
similarly to stb <dig>  the two intermediate tfs, swi <dig> and ndd <dig>  regulate temporal expression patterns: ndd <dig> is essential for the activation of many late s-phase specific genes  <cit> , while swi <dig> activates genes in the g <dig> phase and at the m/g <dig> boundary  <cit> .
notably, in the overlap of the origons ino <dig> and stb <dig> two major regulatory tasks are integrated .
among the genes contained exclusively by the ino <dig> origon participation in metabolism is very common, while only one gene is known to perform a cell-cycle related function.
for genes contained exclusively by origon stb <dig> this relation is reversed, while in the overlap of the two origons both functions are common.
thus, the overlap of these two origons illustrates the coordination of a temporally regulated event  with another general task .
for a concise analysis of regulatory task integration by overlapping origons, in each of the  <dig> overlapping origon pairs , we listed the go slim biological process terms for the regions a^b , a\b and b\a .
we found that the distribution of go slim biological processes in the set a^b is in general significantly similar  to the distribution deduced from the sets a\b and b\a summed together .
thus, we infer that in the tr network of s. cerevisiae overlapping pairs of origons significantly integrate regulatory tasks.
topological organization of signal integration in the yeast tr network
complex environmental signals are decomposed into more elementary signals that eventually elicit an integrated transcriptional response in the context of the cell's own internal state.
since intermediate tfs  transmit signals from input to output nodes and provide connections among all tfs , the topological organization of their interactions is likely to play a key role in developing such integrated responses.
to examine their relationships, we decomposed the tr network by an iterative peeling algorithm , where the top and bottom layers of the network have been successively removed until only  <dig> small isolated graph components  remained.
then these cores were consolidated by adding back their nearest up- and downstream intermediate regulators .
after this decomposition procedure we found that the 45-node intermediate tf subnetwork naturally segregated into three internally densely-connected groups of tfs , as well as several isolated tf nodes .
in contrast, the connections between organizers are sparse : organizers o <dig> and o <dig> are connected by one interaction , and o <dig> and o <dig> have only two connections .
of note, all three inter-organizer connections transfer a signal from the 'top'  of one organizer to the 'bottom' of the other.
we also find that input tfs often co-regulate intermediate tfs located in one or two organizers, but never in all three of them.
note, that as an alternative approach we also performed computational search for partially overlapping communities  <cit>  in the tr network.
this analysis yielded highly similar results , suggesting that the concept of organizers is valid irrespective of data stringency , or the analytical technique used for their identification.
currently, on the global scale the dynamical utilization of signal-specific transcription regulatory subnets can be best tested with microarray expression data  <cit> .
to analyze the dynamical role of organizers, for each of the  <dig> intermediate tfs we have defined the tf and the list of its targets as a group of genes, and computed the transcriptional response of this group to a given external or internal signal .
under hyperosmotic shock , the tfs  in organizer o <dig> displayed by far the strongest average response, as measured by the double z score  <cit>  :  <dig> , compared to - <dig>  and - <dig>  in organizers o <dig> and o <dig>  respectively.
within this group the set of genes regulated by intermediate tfs hap <dig>  sok <dig>  phd <dig>  and rox  <dig> show the strongest response.
all these tfs are regulated by input tf, skn <dig>  suggesting that this input tf is one of the main sensors of hyperosmotic shock in s. cerevisiae, in agreement with previous results  <cit> .
a similar conclusion can be drawn for all other environmental stimuli tested , suggesting that only a subnet of organizer are activated upon simple or complex environmental stimuli.
discussion
the multitude of cellular tasks makes it necessary for cellular components to be hierarchically organized into modules based on functional association  <cit> .
one well-studied aspect of this functional organization is the 'static map' of a tr network, i.e., the list of all possible transcription regulatory  interactions within a cell.
small numbers of individual tr nodes  are known to be arranged into overrepresented, specifically wired information processing units   <cit> , which in turn participate in a series of sequentially embedded higher order structures  <cit> .
in an actual response, however, from all topological  possibilities in the tr network the cell utilizes only limited sets of these interactions  <cit> .
these interactions are often signal-specific  <cit> , though there are also many tr nodes that are known to be generic responders  <cit> .
however, tr interactions represent only a subset of regulatory interactions.
in fact, protein-protein- and protein-metabolite interactions represent the majority of information processing interactions of a cell .
when taking this into account, additional heterogeneous interaction patterns can be uncovered at various hierarchical scales  <cit> .
nevertheless, tr interactions represent the 'slow component' of the overall network, whose behavior determines long-range response  <cit> .
thus, it is of great importance to understand how the large-scale structure of a tr network reflects the integration of the vast variety of individual external signals with each other and with the cell's internal state.
detailed methods, a supplementary table and supplementary figures are also available .
CONCLUSIONS
from the analyses presented here the system-level picture arising for the integration of tr signals suggests the presence of a small number of large-scale signal integration 'pools'  in the yeast tr network, along which signals are processed and transmitted towards all target genes .
regulatory connections inside organizers are dense, while inter-organizer connections are sparse.
in addition to this topological separation, the target genes of different organizers also elicit remarkably different transcriptional responses .
moreover, due to the slowness of the interactions  a given signal can elicit subsequent waves of transcriptional regulatory events that are usually integrated through feedbacks of rapid interactions .
for example, transcriptional regulation in response to decreasing concentration of oxygen  is carried out mainly by two tfs, fnr and arca in e. coli.
although arca can be transcriptionally activated by fnr , fnr is conformationally activated at a lower oxygen level than arca.
thus, arca-specific genes are activated first, followed by a subsequent wave of activation of a second set of genes  that partially overlaps with genes activated during the first wave  <cit> .
in turn, rapid non-transcriptional feedback, such as phosporylation of tfs, may alter the activity of other intermediate tfs.
this may initiate additional sets of 'transcriptional waves' leading to the comprehensive response of the cell observed upon the aerobic-anaerobic shift .
what explains the evolution of the observed topological architecture?
the tr network appears to grow by node duplication  <cit> , resulting in structurally related tf protein families, in which diversification is both a result of tf structural evolution  <cit>  and the evolution of dna binding motifs  <cit> .
the subsequent natural selection of motifs and higher order structures might have been driven by their ability to provide reliable information processing functions to the cell, including robustness against mutations  <cit> , noise  <cit> , and oscillating signals  <cit> , while simultaneously allowing rapid response to common signals in an overall highly variable environment  <cit> .
the future availability of additional types of interaction maps, such as those of phosphoproteins  <cit> , together with an improved understanding of the behavior of fast- , slow-  and combined circuits  <cit>  will probably further explain the emergence of the observed small and large-scale topological structures of the cell's information processing network.

 living cells continuously process information about their environment, and based on this information and their own internal state mount appropriate responses to these signals. this information processing is carried out by various regulatory networks functioning in a highly crowded, viscous cellular interior, with characteristic times spanning several orders of magnitude. the fastest among these are signal transduction networks: they range from simple two-component pathways in prokaryotes to the highly complex signal transduction networks of mammalian cells. fast signaling, however, is frequently followed by slower transcriptional regulatory events, during which regulatory gene products, such as transcription factors and regulatory rnas, alter the rate of transcription of other genes, reorganizing gene expression to achieve new metabolic states, or initiate cellular programs, such as the cell cycle, sporulation, or differentiation. understanding the system-level properties of these networks requires both experimental and computational efforts that start with mapping out potential regulatory interactions that exist in a given cell type. in the yeast saccharomyces cerevisiae and in the bacterium escherichia coli, the static'wiring diagrams' of potential tf-mediated interactions have been mapped out to such a degree that their system-level characteristics and function can be investigated. subsequent computational analyses have shown that in both tr networks the regulatory interactions between tfs and the regulated genes are often organized into basic information processing subgraphs, called motifs that can form even larger potential information processing units, such as motif clusters, themes and thematic maps, and transcriptional modules. it is also evident that the tr network is utilized in a condition-specific manner, perhaps through the activation of distinct, signal-specific subnetworks. in spite of these advances the principles along which regulatory networks process signals, encode the relevant signals at different layers of the network, and integrate them with other signals remain poorly understood. here we show that regulatory interactions among an intermediate layer of transcription factors is a key determinant of information transfer within the s. cerevisiae tr network, and that this layer naturally segregates into distinct, sparsely communicating subnets in which tfs are densely interlinked in a hierarchical manner. we also show that tfs and the genes regulated by them respond to external signals by utilizing various fractions of these subnetworks. the identified features suggest a model in which successive waves of transcriptional regulation of gene expression via multiple interferences at various levels of tf interaction hierarchy constitute a key feature of developing robust integrated responses to complex stimuli.  hierarchies and signal-specific subnets in the s. cerevisiae tr network with the exception of a few mutually regulating pairs, the links of the s. cerevisiae tr network are unidirectional, and its nodes can be arranged into three main layers based on their position, regulation, and function. the layers reflect the flow of information from the input nodes, through intermediate tfs to the output nodes; a path from an input to an output node contains usually to steps, and the maximum length is steps. in the s. cerevisiae tr network each tf regulates a limited number of target genes, with an average number of. as described recently for the tr network of e. coli, the genes directly or indirectly regulated by a given input tf form a signal-specific subnet, or origon, and the nodes at the intermediate and output layers of the origons are often shared by two or more origons. figure 1a illustrates two overlapping origons, originating from the input tfs yap and skn since the network contains input tfs, there is a total of origons in the s. cerevisiae tr network, of which only two are isolated from the rest of the network. classification of the yeast tr network based on its global topological properties to gain insight into the overall yeast tr network organization we first assessed the connectivity distribution of all nodes, and separately those of input tfs, intermediate tfs, and output genes, using cumulated distributions that are equivalent to rank-degree plots. due to the inherent directionality of the links, we separately analyzed the number of regulating tfs per regulated gene and the number of regulated genes per tf, to determine if their distributions are best approximated by exponential-like or power-law models. we find that the distribution of the number of incoming links per node, kin, displays an exponential decay, as previously described, while that of outgoing links shows an intermediate behavior between exponential-like- and power-law decay models. interestingly, the outgoing links for input tfs closely approximate an exponentially decaying degree distribution,, while a few of the intermediate tfs are unexpectedly large hubs resembling more closely the power-law models. also, the outdegrees of intermediate tfs tend to be larger than those of input nodes. taken together, the cumulative in- and outdegree distributions suggest that the yeast tr network belongs to a mixed class of networks, where the number of connections per node is likely to be constrained both by the limited size of a target gene's promoter region, and perhaps by the biosynthetic costs of maintaining regulatory interactions. distribution of graph motifs in the yeast tr network the effects of many external and internal signals are manifested by altered tf activity, followed by the propagation of the perturbation to nodes of lower layers. small circuits play a key role in this propagation; they often connect nodes of different regulatory layers to each other. of these, overrepresented subgraphs are likely to enhance the versatility of information processing in a tr network, and may have become abundant due to the overall functional robustness they provide during evolutionary adaptation to changing environmental conditions. to elucidate the type and information processing role of such overrepresented subgraphs, we examined the abundance of three-node subgraphs in the s. cerevisiae tr network. using a standard link-randomization algorithm we found that the feed-forward loop, the single regulatory interaction with mutual regulation and the convergence with mutual regulation are overrepresented, i.e., they are motifs, while the divergence, cascade and convergence subgraphs are underrepresented, i.e., they are anti-motifs. we also examined the position of these 3-node subgraphs with respect to individual origons, and found that similarly to the e. coli tr network, only a subset of origons contains ffl, smr, and cmr motifs, and the majority of cnv subgraphs perform signal integration: they receive regulatory signals from two different sources and transmit the joint signal to a single node. motifs are marked, and only subgraphs with at least occurrences in the original network are listed. after link randomization the numbers of ffl, smr and cmr subgraphs decrease, while those of div, cas, and cnv subgraphs are maintained with slight increases, indicating that ffl, smr and cmr are motifs in the tr network, while div, cas and cnv are anti-motifs. functional cartography of the yeast tr network external signals, conveyed by various signaling mechanisms, may be perceived by signal-specific tfs or relatively non-specific tfs. to understand how the responses to these signals are encoded into the topology of the tr network we first examined the degree of overlap among the genes regulated by input- and intermediate tfs. as shown in figure 2a – where the width of a link between two tfs is proportional to the number of outputs they both regulate – the targets of different tfs extensively overlap, suggesting that most genes are combinatorially regulated by several tfs. in contrast, direct regulatory interactions among tfs are more limited: the largest connected component of the network of direct regulatory interactions among tfs is sparse, and of the remaining tfs have no regulatory interactions with other tfs at all, i.e., they act in isolation. to characterize the type of combinatorial regulation performed by each tf, we color coded each of the tfs according to the function of the genes they regulate. to this end, we resorted to the go slim biological process terms, which we grouped into eight go slim categories described in the methods. it is evident, that all tfs regulate genes with various functions. for example, genes within two overlapping origons – defined by the input tfs ino and stb – display a multitude of functions. stb takes part in the regulation of transcription at the g1/s transition, while ino is a positive regulator of phospholipid biosynthesis. similarly to stb the two intermediate tfs, swi and ndd regulate temporal expression patterns: ndd is essential for the activation of many late s-phase specific genes, while swi activates genes in the g phase and at the m/g boundary. notably, in the overlap of the origons ino and stb two major regulatory tasks are integrated. among the genes contained exclusively by the ino origon participation in metabolism is very common, while only one gene is known to perform a cell-cycle related function. for genes contained exclusively by origon stb this relation is reversed, while in the overlap of the two origons both functions are common. thus, the overlap of these two origons illustrates the coordination of a temporally regulated event with another general task. for a concise analysis of regulatory task integration by overlapping origons, in each of the overlapping origon pairs, we listed the go slim biological process terms for the regions a^b, a\b and b\a. we found that the distribution of go slim biological processes in the set a^b is in general significantly similar to the distribution deduced from the sets a\b and b\a summed together. thus, we infer that in the tr network of s. cerevisiae overlapping pairs of origons significantly integrate regulatory tasks. topological organization of signal integration in the yeast tr network complex environmental signals are decomposed into more elementary signals that eventually elicit an integrated transcriptional response in the context of the cell's own internal state. since intermediate tfs transmit signals from input to output nodes and provide connections among all tfs, the topological organization of their interactions is likely to play a key role in developing such integrated responses. to examine their relationships, we decomposed the tr network by an iterative peeling algorithm, where the top and bottom layers of the network have been successively removed until only small isolated graph components remained. then these cores were consolidated by adding back their nearest up- and downstream intermediate regulators. after this decomposition procedure we found that the 45-node intermediate tf subnetwork naturally segregated into three internally densely-connected groups of tfs, as well as several isolated tf nodes. in contrast, the connections between organizers are sparse: organizers o and o are connected by one interaction, and o and o have only two connections. of note, all three inter-organizer connections transfer a signal from the'top' of one organizer to the'bottom' of the other. we also find that input tfs often co-regulate intermediate tfs located in one or two organizers, but never in all three of them. note, that as an alternative approach we also performed computational search for partially overlapping communities in the tr network. this analysis yielded highly similar results, suggesting that the concept of organizers is valid irrespective of data stringency, or the analytical technique used for their identification. currently, on the global scale the dynamical utilization of signal-specific transcription regulatory subnets can be best tested with microarray expression data. to analyze the dynamical role of organizers, for each of the intermediate tfs we have defined the tf and the list of its targets as a group of genes, and computed the transcriptional response of this group to a given external or internal signal. under hyperosmotic shock, the tfs in organizer o displayed by far the strongest average response, as measured by the double z score:, compared to - and - in organizers o and o respectively. within this group the set of genes regulated by intermediate tfs hap sok phd and rox show the strongest response. all these tfs are regulated by input tf, skn suggesting that this input tf is one of the main sensors of hyperosmotic shock in s. cerevisiae, in agreement with previous results. a similar conclusion can be drawn for all other environmental stimuli tested, suggesting that only a subnet of organizer are activated upon simple or complex environmental stimuli. discussion the multitude of cellular tasks makes it necessary for cellular components to be hierarchically organized into modules based on functional association. one well-studied aspect of this functional organization is the'static map' of a tr network, i.e., the list of all possible transcription regulatory interactions within a cell. small numbers of individual tr nodes are known to be arranged into overrepresented, specifically wired information processing units, which in turn participate in a series of sequentially embedded higher order structures. in an actual response, however, from all topological possibilities in the tr network the cell utilizes only limited sets of these interactions. these interactions are often signal-specific, though there are also many tr nodes that are known to be generic responders. however, tr interactions represent only a subset of regulatory interactions. in fact, protein-protein- and protein-metabolite interactions represent the majority of information processing interactions of a cell. when taking this into account, additional heterogeneous interaction patterns can be uncovered at various hierarchical scales. nevertheless, tr interactions represent the'slow component' of the overall network, whose behavior determines long-range response. thus, it is of great importance to understand how the large-scale structure of a tr network reflects the integration of the vast variety of individual external signals with each other and with the cell's internal state. detailed methods, a supplementary table and supplementary figures are also available.  from the analyses presented here the system-level picture arising for the integration of tr signals suggests the presence of a small number of large-scale signal integration'pools' in the yeast tr network, along which signals are processed and transmitted towards all target genes. regulatory connections inside organizers are dense, while inter-organizer connections are sparse. in addition to this topological separation, the target genes of different organizers also elicit remarkably different transcriptional responses. moreover, due to the slowness of the interactions a given signal can elicit subsequent waves of transcriptional regulatory events that are usually integrated through feedbacks of rapid interactions. for example, transcriptional regulation in response to decreasing concentration of oxygen is carried out mainly by two tfs, fnr and arca in e. coli. although arca can be transcriptionally activated by fnr, fnr is conformationally activated at a lower oxygen level than arca. thus, arca-specific genes are activated first, followed by a subsequent wave of activation of a second set of genes that partially overlaps with genes activated during the first wave. in turn, rapid non-transcriptional feedback, such as phosporylation of tfs, may alter the activity of other intermediate tfs. this may initiate additional sets of'transcriptional waves' leading to the comprehensive response of the cell observed upon the aerobic-anaerobic shift. what explains the evolution of the observed topological architecture? the tr network appears to grow by node duplication, resulting in structurally related tf protein families, in which diversification is both a result of tf structural evolution and the evolution of dna binding motifs. the subsequent natural selection of motifs and higher order structures might have been driven by their ability to provide reliable information processing functions to the cell, including robustness against mutations, noise, and oscillating signals, while simultaneously allowing rapid response to common signals in an overall highly variable environment. the future availability of additional types of interaction maps, such as those of phosphoproteins, together with an improved understanding of the behavior of fast-, slow- and combined circuits will probably further explain the emergence of the observed small and large-scale topological structures of the cell's information processing network. 
by studying the tr network of the yeast saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. as transcriptional regulation represents the'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the tr network control robust integrated responses to complex stimuli. in these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets.
 signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. however, the network topological basis of developing such integrated responses remains poorly understood.  by studying the tr network of the yeast saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. in these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets.  as transcriptional regulation represents the'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the tr network control robust integrated responses to complex stimuli. 