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PLoS Comput Biol. Nov 2012; 8(11): e1002781.
Published online Nov 29, 2012. doi:  10.1371/journal.pcbi.1002781
PMCID: PMC3510039

Temporal Expression-based Analysis of Metabolism

Jason A. Papin, Editor

Abstract

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.

Author Summary

Understanding the dynamic response of microorganisms to environmental changes is a major challenge in systems biology. In many cases, these responses manifest themselves through changes in gene transcription, which then propagate to adjust flow through metabolism. Here, we implement a Temporal Expression-based Analysis of Metabolism (TEAM) by dynamically integrating a genome-scale model of the metabolism of S. oneidensis with high-throughput measurements of gene expression and growth data. TEAM recapitulates the complex cascade of secretion and re-uptake of intermediary carbon sources that S. oneidensis exhibits in the experimental data. We show that these complicated metabolic behaviors are best captured when TEAM explicitly accounts for each gene's unique transcriptional signature. Furthermore, by way of a newly proposed sensitivity analysis, we reveal and study the inherent difficulty of dynamic metabolic flux modeling: small changes early in a simulation can easily spread and lead to significant changes towards the end of it. We expect that further development of robust dynamic flux balance methods will need to overcome such “history-dependent” sensitivities in order to achieve increased predictive accuracy.


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