From Annotated Genomes to Metabolic Flux Models and Kinetic Parameter Fitting

Abstract

Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steadystate models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.

OptStrain: A computational framework for redesign of microbial production systems

“…Whenever the elemental balance of a reaction could not be restored, the reaction was removed from consideration. We expect that this step will become less time-consuming as automated tools for reaction database testing and verification (Segre et al 2003) are becoming available. Furthermore, the purely stoichiometric representation of metabolic pathways in microbial models used can lead to unrealistic flux distributions by not accounting for kinetic barriers and regulatory interactions (e.g., allosteric regulation).…”

“…These models, already available for Helicobacter pylori (Schilling et al 2002), Escherichia coli (Edwards and Palsson 2000;Reed et al 2003), Saccharomyces cerevisiae (Forster et al 2003), and other microorganisms (Van Dien and Lidstrom 2002;David et al 2003;Valdes et al 2003), provide successively refined abstractions of the microbial metabolic capabilities. An automated process to expedite the construction of stoichiometric models from annotated genomes (Segre et al 2003) promises further to accelerate the metabolic reconstructions of several microbial organisms. At the same time, individual reactions are deposited in databases such as KEGG, EMP, MetaCyc, UM-BBD, and many more (Selkov Jr. et al 1998;Overbeek et al 2000;Karp et al 2002;Ellis et al 2003;Kanehisa et al 2004;Krieger et al 2004), forming encompassing and growing collections of the biotransformations for which we have direct or indirect evidence of existence in different species.…”

Computational Methods to Interpret and Integrate Metabolomic Data

“…Thus, in vitro kinetic rates and in vitro kinetic parameters describe enzymatic behaviors that may not truly represent the observed physiological kinetic behavior in the cell. Several methods have been proposed to address this issue by incorporating in vivo measurements in constructing kinetic models (Visser & Heijnen, 2003) or estimating kinetic parameters in biochemical networks using measured variables (Lei & Jorgensen, 2001;Moles et al, 2003;Segre et al, 2003). These methods require considerable mathematical efforts or utilize nonlinear optimization techniques.…”

Flux Balance Analysis of Mycolic Acid Pathway: Targets for Anti-tubercular Drugs

“…MOMA [18,47] is a technique similar to FBA, particular to the analysis of perturbed systems and has been reported to outperform FBA in certain cases. MOMA circumvents the use of an objective function for optimisation under perturbed conditions and rather solves for a flux distribution closest to the unperturbed system, subject to the new constraints imposed, minimising the metabolic adjustment of the system.…”

“…Constructing such models forms an important step in understanding the underlying molecular mechanisms of disease, and facilitates rational approaches to drug design. Several computational methods have emerged in recent years to simulate biochemical models, which aid in the systems approach to understanding pathways, processes, and wholecell metabolism [12][13][14][15][16][17][18]. Flux balance analysis (FBA), a stoichiometric analysis technique, has been applied to study the metabolic capabilities of several systems [19,20], which has provided useful insights into cellular behaviour, including response to perturbations such as gene deletions [21,22].…”