Computational design of genomic transcriptional networks with adaptation to varying environments

Abstract

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...

An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of Escherichia coli

“…A crucial tool for integrative modeling is network inference algorithms, both unsupervised and supervised, which can be used to generate topological models and consensus networks from data (Basso et al , 2005 ; Faith et al , 2007 ; Mordelet & Vert, 2008 ; Taylor et al , 2008 ; Zare et al , 2009 ; Marbach et al , 2010 , 2012 ). Several methods have targeted the integration of models across the transcriptional, proteomic, signal transduction, and metabolomics layers (Reed et al , 2003 , 2006 ; Covert et al , 2004 ; Duarte et al , 2004 ; Beltran et al , 2006 ; Joyce & Palsson, 2006 ; Kresnowati et al , 2006 ; Becker et al , 2007 ; Feist et al , 2007 ; Andersen et al , 2008 ; Feist & Palsson, 2008 ; Herrgard et al , 2008 ; Carrera et al , 2012a , b ).…”

Automated design of bacterial genome sequences

“…To maximize the modularity of the system and thus simplify the TRN, we defined a measure based on the entropy of the genome [17]. We also aimed to maximize the similarity of the expression profiles of the wild-type and refactored genomes for a set of extreme environments and for a set of critical genes that guarantee the functionality of the refactored system.…”

“…Furthermore, it is possible to construct regulatory network models that accurately predict the global transcriptional profile for some organisms [ 15 , 16 ]. These regulatory network models can be used to predict the growth of cells with modified transcriptional networks, thereby providing the fitness function required to evaluate their performance under diverse environmental conditions [ 17 ].…”

Why Build Whole-Cell Models?

“…These approaches rely on the construction of networks that encompass key biological processes, and may incorporate combinations of probabilistic representations [5], machine learning-based algorithms [6], mechanistic constraint-based models [7], or large-scale ordinary differential equations [8]. …”