Metabolic networks analysis using convex optimization

Julius, A. Agung; Imieliński, Marcin; Pappas, George J.

doi:10.1109/cdc.2008.4739111

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…Such procedures have been successful in other combinatorial optimization problems. They include simulated annealing [80] , [81] , bi-level optimization [82] , [83] , OptFlux [84] , convex optimization [85] and evolutionary optimization [86] , [87] . The extent to which they can identify networks with superior design remains an important subject of future work.…”

Section: Discussionmentioning

confidence: 99%

Design Constraints on a Synthetic Metabolism

Bilgin

Wagner

2012

PLoS ONE

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A metabolism is a complex network of chemical reactions that converts sources of energy and chemical elements into biomass and other molecules. To design a metabolism from scratch and to implement it in a synthetic genome is almost within technological reach. Ideally, a synthetic metabolism should be able to synthesize a desired spectrum of molecules at a high rate, from multiple different nutrients, while using few chemical reactions, and producing little or no waste. Not all of these properties are achievable simultaneously. We here use a recently developed technique to create random metabolic networks with pre-specified properties to quantify trade-offs between these and other properties. We find that for every additional molecule to be synthesized a network needs on average three additional reactions. For every additional carbon source to be utilized, it needs on average two additional reactions. Networks able to synthesize 20 biomass molecules from each of 20 alternative sole carbon sources need to have at least 260 reactions. This number increases to 518 reactions for networks that can synthesize more than 60 molecules from each of 80 carbon sources. The maximally achievable rate of biosynthesis decreases by approximately 5 percent for every additional molecule to be synthesized. Biochemically related molecules can be synthesized at higher rates, because their synthesis produces less waste. Overall, the variables we study can explain 87 percent of variation in network size and 84 percent of the variation in synthesis rate. The constraints we identify prescribe broad boundary conditions that can help to guide synthetic metabolism design.

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Section: Discussionmentioning

confidence: 99%

Design Constraints on a Synthetic Metabolism

Bilgin

Wagner

2012

PLoS ONE

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“…This is the idea behind the proposed fastcore algorithm: We build up the set V in a greedy fashion, by computing in each iteration a new mode of the global network. Further, as a means to approximately minimize card(A), each added mode is constrained to have sparse support outside C. This is implemented via L 1 -norm minimization, which is a standard approach to computing sparse solutions to (convex) optimization problems [6,23].…”

Section: Greedy Approximation and The Fastcore Algorithmmentioning

confidence: 99%

Fast Reconstruction of Compact Context-Specific Metabolic Network Models

2014

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Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.

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“…Thus, there are n pseudo-reactions. Equation (5) can, therefore, be written compactly as [14] Sv = 0, v ≥ 0,…”

Section: A Metabolic Network Modeling At Steady Statementioning

confidence: 99%

“…2 Therefore, problem (14) can be equivalently written as 2 We write X 0 if and only if the symmetric matrix X ∈ S n belongs in the positive semidefinite cone, defined by S n + = {X ∈ S n | X 0}. …”

Section: A Uncertainty In the Biomass Compositionmentioning

confidence: 99%

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Robust flux balance analysis of metabolic networks

Zavlanos

Julius

2011

Proceedings of the 2011 American Control Conference

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Metabolic networks describe the set of biochemical reactions and regulatory interactions of metabolism that govern the phenotypical properties of a cell. Analysis of such networks is critical not only to promote biological knowledge, but also in drug discovery, where it can be used to identify and knockout the targeted pathways. Flux Balance Analysis (FBA) has been widely used to study metabolic networks. This powerful technique employs the reaction stoichiometries and reversibility constraints along with experimental measurements of phenotypical properties of the cell, e.g., biomass composition or ATP synthesis, to compute the fluxes of metabolites that are best manifested in the cell. Although FBA has been shown to satisfactorily capture cell behavior, its performance could be significantly improved if measurement uncertainty is introduced in the models. In this paper we propose Robust Flux Balance Analysis (RFBA) to determine optimal fluxes of metabolites for all phenotypical measurements in a given uncertainty set. We derive a least squares bi-criterion approximation of the uncertain problem and, using the S-procedure and tools from matrix analysis, we show that this is equivalent to a semidefinite program that can be solved optimally using available techniques. We illustrate our approach on synthetic metabolic networks and discuss the effect of regularization on the final solutions. Due to its convex nature, our approach can be applied to genome-scale networks.

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Metabolic networks analysis using convex optimization

Cited by 8 publications

References 27 publications

Design Constraints on a Synthetic Metabolism

Design Constraints on a Synthetic Metabolism

Fast Reconstruction of Compact Context-Specific Metabolic Network Models

Robust flux balance analysis of metabolic networks

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