A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

Bonneau, Richard; Facciotti, Marc T.; Reiss, David J; Schmid, Amy K.; Pan, Min; Kaur, Amardeep; Thórsson, Vésteinn; Shannon, Paul; Johnson, Michael H.; Bare, J. Christopher; Longabaugh, William J.R.; Vuthoori, Madhavi; Whitehead, Kenia; Madar, Aviv; Suzuki, Lena; Mori, Tetsuya; Chang, Dong Eun; DiRuggiero, Jocelyne; Johnson, Carl H.; Hood, Leroy; Baliga, Nitin S.

doi:10.1016/j.cell.2007.10.053

Cited by 290 publications

(316 citation statements)

References 42 publications

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“…We will show that this can be done by adapting an existing ODE model for the TRN of E. coli (7) to include the required signal transduction. The evolutionary computational methodology here proposed is general, and it could be used with other ODE models for TRNs (8)(9)(10)(11)(12)(13).…”

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confidence: 99%

Computational design of genomic transcriptional networks with adaptation to varying environments

Carrera

Elena

Jaramillo

2012

Proc. Natl. Acad. Sci. U.S.A.

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Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.automated design | synthetic genomics | genome refactoring | evolutionary computation O rganisms have evolved mechanisms for regulating transcription to better adapt to changing environments. Could such regulation be engineered in a different way (1, 2)? Recent experiments investigating the evolvability of bacterial transcriptional regulatory networks (TRNs) have shown that the massive addition of new links to the network does not significantly alter cell growth. Isalan et al. (3) added transcriptional fusions of promoters with different master transcriptional regulators and showed that Escherichia coli (E. coli) tolerated almost all rewired networks; however, growth was perturbed by as much as 5% (3). This inherent predisposition of E. coli networks to dampen extreme changes in their circuitry enables the possibility of conducting genome-wide rewiring (4). Global transcription regulation could also be analyzed by comparing the regulatory models from distant organisms, provided they show a similar response to the set of studied environments. In this way, they could provide alternative regulatory models, although the lack of knowledge of species-specific selective pressures may blur the conclusions. We will propose here an alternative evolution experiment, which will be conducted computationally thanks to the availability of a quantitative model for the genom...

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confidence: 99%

Computational design of genomic transcriptional networks with adaptation to varying environments

Carrera

Elena

Jaramillo

2012

Proc. Natl. Acad. Sci. U.S.A.

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“…This has led to the idea that distinct neurogenomic states underlie distinct behaviors (1), but it is not known how these states are defined or maintained. Further, the regulatory architecture of behaviorally relevant neurogenomic states has not been studied, and it is not known whether behavior is subserved by the kinds of transcriptional regulatory networks (TRNs) known for other phenotypes (3)(4)(5)(6).…”

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confidence: 99%

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

Chandrasekaran

Ament

Eddy

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

164

237

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Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior.Apis mellifera | gene regulation | social behavior | systems biology B ehavior is influenced by both heritable and environmental factors, sometimes via massive changes in brain transcriptomes (1). An emerging insight is that these changes induce shifts in "neurogenomic states" rather than activation of particular genes only in local neural circuits (2). This has led to the idea that distinct neurogenomic states underlie distinct behaviors (1), but it is not known how these states are defined or maintained. Further, the regulatory architecture of behaviorally relevant neurogenomic states has not been studied, and it is not known whether behavior is subserved by the kinds of transcriptional regulatory networks (TRNs) known for other phenotypes (3-6).We applied tools and perspectives from molecular systems biology-used to study transcriptional regulation in the brain and elsewhere (3-6)-to transcript profiles from the BeeSpace Project, which used microarray analysis to study hereditary and environmental influences on brain gene expression and social behavior (Methods). This provided a unique aggregate dataset from a single laboratory (G.E.R.), using the same analytical platform, protocols, and analysis procedures (7). Because the natural behavioral repertoire of the honey bee (Apis mellifera) is perhaps the best studied of any nonhuman a...

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“…Although these models have been used successfully to address focused questions about individual pathways, more global network reconstructions are needed to understand differential regulation of hypertrophy and crosstalk between these pathways. Largescale integrative models have been successful in other systems such as the prediction of optimal evolution (9) and drug targets (10) in metabolic networks and prediction of the global transcriptional response to genetic and environmental perturbations (11).…”

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confidence: 99%

Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling

Ryall

Holland

Delaney

et al. 2012

Journal of Biological Chemistry

139

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Background:Little is known about how global signaling network properties influence cardiac myocyte hypertrophy. Results: New 106 species computational model exhibited enriched cross-talk motifs and modular organization, predicting Ras as the most influential hub. Conclusion: Multiple levels of network organization modulate hypertrophic outcomes. Significance: Rather than acting through isolated pathways, cardiac hypertrophy signaling is a highly integrated network.

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A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

Cited by 290 publications

References 42 publications

Computational design of genomic transcriptional networks with adaptation to varying environments

Computational design of genomic transcriptional networks with adaptation to varying environments

Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling

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