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Palsson, B. O.

Publications and source records attributed to Palsson, B. O..

21 records · Page 2Linked to original sources

Topological and Kinetic Determinants of the Modal Matrices of Dynamic Models of Metabolism

Linear analysis of kinetic models of metabolism can help in understanding the dynamic response of metabolic networks. Central to linear analysis of these models are two key matrices: the Jacobian matrix (J) and its modal matrix (M-1). The modal matrix contains dynamically independent motions of the kinetic model, and it is sparse in practice. Understanding the sparsity structure of the modal matrix provides insight into metabolic network dynamics. In this study, we analyze the relationship between J and M-1. First, we show that diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures within M-1. Dominant diagonal elements in J approximate the eigenvalues corresponding to these simple modes, in which a single metabolite is driven back to its reference state on a characteristic timescale. Second, we analyze more complicated mode structures in M-1, in which two or more variables move in a certain ratio relative to one another on defined time scales. We show that complicated modes originate from sub-matrices of topologically connected elements of similar magnitude in J. Third, we describe the origin of these mode structure features based on the network stoichiometric matrix S and the reaction kinetic gradient matrix G. We demonstrate that the topologically-connected reaction sensitivities of similar magnitude in G play a central role in determining the mode structure. Ratios of these reaction sensitivities represent equilibrium balances of half reactions that are defined by linearization of the bilinear mass action rate laws followed by enzymatic reactions. These half-reaction equilibrium ratios are key determinants of modal structure for both simple and complicated modes. The work presented here helps to establish a foundation for understanding the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions.

systems biology

COBRAme: A Computational Framework for Building and Manipulating Models of Metabolism and Gene Expression

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable new and exciting insights that are fundamental to understanding the basis of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives virtually identical solutions to previous E. coli ME-models while using [1/4] the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be reconstructed and edited most efficiently using the software.

systems biology

Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP

Two major transcriptional regulators of carbon metabolism in bacteria are Cra and CRP. CRP is considered to be the main mediator of catabolite repression. Unlike for CRP, available in vivo DNA binding information of Cra is scarce. Here we generate and integrate ChIP-exo and RNA-seq data to identify 39 binding sites for Cra and 97 regulon genes that are regulated by Cra in Escherichia coli. An integrated metabolic-regulatory network was formed by including experimentally-derived regulatory information and a genome-scale metabolic network reconstruction. Applying analysis methods of systems biology to this integrated network showed that Cra enables the optimal bacterial growth on poor carbon sources by redirecting and repressing the glycolysis flux, by activating the glyoxylate shunt pathway, and by activating the respiratory pathway. In these regulatory mechanisms, the overriding regulatory activity of Cra over CRP is fundamental. Thus, elucidation of interacting transcriptional regulation of core carbon metabolism in bacteria by two key transcription factors was possible by combining genome-wide experimental measurement and simulation with a genome-scale metabolic model.

systems biology