Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery

Automated design of bacterial genome sequences

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes? Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model. We used computational design techniques to design a rewired global transcriptional regulation of Escherichia coli, yet showing a similar transcriptomic response than the wild-type. Afterwards, we “compiled” the transcriptional networks into nucleotide sequences to obtain the final genome sequence. Our computational evolution procedure ensures that we can maintain the genotype-phenotype mapping during the rewiring of the regulatory network. We found that it is theoretically possible to reorganize E. coli genome into 86% fewer regulated operons. Such refactored genomes are constituted by operons that contain sets of genes sharing around the 60% of their biological functions and, if evolved under highly variable environmental conditions, have regulatory networks, which turn out to respond more than 20% faster to multiple external perturbations. Conclusions: This work provides the first algorithm for producing a genome sequence encoding a rewired transcriptional regulation with wild-type behavior under alternative environments

Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.

The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy

Hybrid systems biology: application to Escherichia coli

Dissertation presented to obtain a Master degree in BiotechnologyIn complex biological systems, it is unlikely that all relevant cellular functions can be fully described either by a mechanistic (parametric) or by a statistic (nonparametric) modelling approach. Quite often, hybrid semiparametric models are the most appropriate to handle such problems. Hybrid semiparametric systems make simultaneous use of the parametric and nonparametric systems analysis paradigms to solve complex problems. The main advantage of the semiparametric over the parametric or nonparametric frameworks lies in that it broadens the knowledge base that can be used to solve a particular problem, thus avoiding reductionism. In this M.Sc. thesis, a hybrid modelling method was adopted to describe in silico Escherichia coli cells. The method consists in a modified projection to latent structures model that explores elementary flux modes (EFMs) as metabolic network principal components. It maximizes the covariance between measured fluxome and any input “omic” dataset. Additionally this method provides the ranking of EFMs in increasing order of explained flux variance and the identification of correlations between EFMs weighting factors and input variables. When applied to a subset of E. coli transcriptome, metabolome, proteome and envirome (and combinations thereof) datasets from different E. coli strains (both wild-type and single gene knockout strains) the model is able, in general, to make accurate flux predictions. More particularly, the results show that envirome and the combination of envirome and transcriptome are the most appropriate datasets to make an accurate flux prediction (with 88.5% and 85.2% of explained flux variance in the validation partition, respectively). Furthermore, the correlations between EFMs weighting factors and input variables are consistent with previously described regulatory patterns, supporting the idea that the regulation of metabolic functions is conserved among distinct envirome and genotype variants, denoting a high level of modularity of cellular functions

Comparative transcriptomics in Yersinia pestis: a global view of environmental modulation of gene expression

<p>Abstract</p> <p>Background</p> <p>Environmental modulation of gene expression in <it>Yersinia pestis </it>is critical for its life style and pathogenesis. Using cDNA microarray technology, we have analyzed the global gene expression of this deadly pathogen when grown under different stress conditions <it>in vitro</it>.</p> <p>Results</p> <p>To provide us with a comprehensive view of environmental modulation of global gene expression in <it>Y. pestis</it>, we have analyzed the gene expression profiles of 25 different stress conditions. Almost all known virulence genes of <it>Y. pestis </it>were differentially regulated under multiple environmental perturbations. Clustering enabled us to functionally classify co-expressed genes, including some uncharacterized genes. Collections of operons were predicted from the microarray data, and some of these were confirmed by reverse-transcription polymerase chain reaction (RT-PCR). Several regulatory DNA motifs, probably recognized by the regulatory protein Fur, PurR, or Fnr, were predicted from the clustered genes, and a Fur binding site in the corresponding promoter regions was verified by electrophoretic mobility shift assay (EMSA).</p> <p>Conclusion</p> <p>The comparative transcriptomics analysis we present here not only benefits our understanding of the molecular determinants of pathogenesis and cellular regulatory circuits in <it>Y. pestis</it>, it also serves as a basis for integrating increasing volumes of microarray data using existing methods.</p

Genetic effects on molecular network states explain complex traits

The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Current Challenges in Modeling Cellular Metabolism

Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results

Fine-Tuning Tomato Agronomic Properties by Computational Genome Redesign

Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence. Inspired in a synthetic biology approach, the model was used for exploring the landscape of all possible local transcriptional changes with the aim of engineering tomato fruits with fine-tuned biotechnological properties. The method was validated by the ability of the proposed genomes, engineered for modified desired agronomic traits, to recapitulate experimental correlations between associated metabolites