An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Dipak Barua,Jennifer L Reed,Joonhoon Kim

doi:10.1371/journal.pcbi.1000970

Dipak Barua, Jennifer L Reed + Show 1 more

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https://doi.org/10.1371/journal.pcbi.1000970

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Abstract

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions.

Highlights

A current challenge in systems biology is reconstructing transcriptional regulatory networks from experimental data, due to the complexity of interactions in these networks and the limited information on network components and interactions for most organisms [1,2]
Regulatory rule correction algorithm, GeneForce We developed an automated mixedinteger linear programming (MILP) approach, GeneForce, to identify problematic Boolean regulatory rules in an integrated metabolic and transcriptional regulatory model
The basic idea of the GeneForce algorithm is to allow the integrated metabolic and regulatory model to violate a minimal set of transcriptional regulatory rules so that growth can occur in a particular condition

Summary

Introduction

A current challenge in systems biology is reconstructing transcriptional regulatory networks from experimental data (e.g. gene expression, genome sequence, and DNA-protein interaction), due to the complexity of interactions in these networks and the limited information on network components and interactions for most organisms [1,2]. Methods for iterative validation and refinement of regulatory reconstructions are needed in order to assess new experimental datasets as they emerge [5,6] Such approaches need to identify and eliminate inconsistencies between the reconstructed network and new experimental data, and to include newly discovered network interactions [3]. We present an approach that allows for the automated adjustment of an integrated genome-scale metabolic and transcriptional regulatory network model, by comparing the emergent properties of the integrated networks to cellular growth phenotypes. These adjustments result in testable hypotheses about transcriptional regulation and metabolism in organisms

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