Algorithm for perturbing thermodynamically infeasible metabolic networks

Nigam, R. K.; Liang, S.

doi:10.1016/j.compbiomed.2006.01.002

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…If one deletes reactions corresponding to zero fluxes, the metabolic network is modified, and this may lead to an optimal thermodynamically feasible solution. It should, however, be noted that the FBA and EBA may still not able to constrain the metabolic network completely and lead to an infinity of flux and change in chemical potential vectors [72]. More realistic bounds on the values of fluxes are required to further constrain the system by studying the biochemistry of several pathways.…”

Section: Thermodynamic Constraintsmentioning

confidence: 99%

“…(19), indicates two time scales: the very short one characterizing the stochastic force (q, t) and the time scale on which the smooth functions of (q), degradation (friction) matrix S(q) and the translocation matrix T(q) are well defined. This corresponds to the hierarchical structure of metabolic pathways [11,[68][69][70][71][72][73][74]112]. The stochastic force (q, t) can arise from the environmental influence on the network, or from approximations such that the continuous representation of a discrete process.…”

Section: Network Dynamicsmentioning

confidence: 99%

“…Biological systems work under mass and energy conservations as well as thermodynamic constraints arising from the second law [11,[71][72][73][74]. The flux balance analysis is based on mass conservation; and the energy balance analysis (EBA) is based on the nonequilibrium network thermodynamics, which states that each internal reaction with non-zero flux must dissipate energy [25,74].…”

Section: Thermodynamic Constraintsmentioning

confidence: 99%

“…According to the second law, fluxes must flow from reactants of higher chemical potential to ones of lower chemical potential and the entropy of the reaction is always non-decreasing [21,76]. Nigam and Liang [72,73] provided an algorithm for flux and energy balance analyses that perturb the metabolic network to find a thermodynamically feasible solution. If one deletes reactions corresponding to zero fluxes, the metabolic network is modified, and this may lead to an optimal thermodynamically feasible solution.…”

Section: Thermodynamic Constraintsmentioning

confidence: 99%

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Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Demi̇rel

2010

Journal of Non-Newtonian Fluid Mechanics

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a b s t r a c tLiving cells represent open, nonequilibrium, self-organizing, and dissipative systems maintained with the continuous supply of outside and inside material, energy, and information flows. The energy in the form of adenosine triphosphate is utilized in biochemical cycles, transport processes, protein synthesis, reproduction, and performing other biological work. The processes in molecular and cellular biological systems are stochastic in nature with varying spatial and time scales, and bounded with conservation laws, kinetic laws, and thermodynamic constraints, which should be taken into account by any approach for modeling biological systems. In component biology, this review focuses on the modeling of enzyme kinetics and fluctuation of single biomolecules acting as molecular motors, while in systems biology it focuses on modeling biochemical cycles and networks in which all the components of a biological system interact functionally over time and space. Biochemical cycles emerge from collective and functional efforts to devise a cyclic flow of optimal energy degradation rate, which can only be described by nonequilibrium thermodynamics. Therefore, this review emphasizes the role of nonequilibrium thermodynamics through the formulations of thermodynamically coupled biochemical cycles, entropy production, fluctuation theorems, bioenergetics, and reaction-diffusion systems. Fluctuation theorems relate the forward and backward dynamical randomness of the trajectories or paths, bridge the microscopic and macroscopic domains, and link the time-reversible and irreversible descriptions of biological systems. However, many of these approaches are in their early stages of their development and no single computational or experimental technique is able to span all the relevant and necessary spatial and temporal scales. Wide range of experimental and novel computational techniques with high accuracy, precision, coverage, and efficiency are necessary for understanding biochemical cycles.

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Section: Thermodynamic Constraintsmentioning

confidence: 99%

Section: Network Dynamicsmentioning

confidence: 99%

Section: Thermodynamic Constraintsmentioning

confidence: 99%

Section: Thermodynamic Constraintsmentioning

confidence: 99%

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Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Demi̇rel

2010

Journal of Non-Newtonian Fluid Mechanics

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“…Loop-law thermodynamic constraints have the advantage that they do not require data on equilibrium constants and metabolite concentrations. Therefore, they have also been widely studied in the literature [3,13,16,20,28,30,32,34,35,38,47,49] The key property of lattices is that metabolic pathways can be combined by taking the union of the supports. However, if the pathways have to satisfy thermodynamic constraints, this is not always possible.…”

Section: Introductionmentioning

confidence: 99%

Generic flux coupling analysis

Reimers

Goldstein

Bockmayr

2015

Mathematical Biosciences

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a b s t r a c tFlux coupling analysis (FCA) has become a useful tool for aiding metabolic reconstructions and guiding genetic manipulations. Originally, it was introduced for constraint-based models of metabolic networks that are based on the steady-state assumption. Recently, we have shown that the steady-state assumption can be replaced by a weaker lattice-theoretic property related to the supports of metabolic fluxes. In this paper, we further extend our approach and develop an efficient algorithm for generic flux coupling analysis that works with any kind of qualitative pathway model. We illustrate our method by thermodynamic flux coupling analysis (tFCA), which allows studying steady-state metabolic models with loop-law thermodynamic constraints. These models do not satisfy the lattice-theoretic properties required in our previous work. For a selection of genome-scale metabolic network reconstructions, we discuss both theoretically and practically, how thermodynamic constraints strengthen the coupling results that can be obtained with classical FCA.A prototype implementation of tFCA is available at http://hoverboard.io/L4FC.

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A model‐based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: Application to the green algae Chlamydomonas reinhardtii

Cogne

Rügen

Bockmayr

et al. 2011

Biotechnology Progress

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A constraint-based modeling approach was developed to investigate the metabolic response of the eukaryotic microalgae Chlamydomonas reinhardtii under photoautotrophic conditions. The model explicitly includes thermodynamic and energetic constraints on the functioning metabolism. A mixed integer linear programming method was used to determine the optimal flux distributions with regard to this set of constraints. It enabled us, in particular, to highlight the existence of a light-driven respiration depending on the incident photon flux density in photobioreactors functioning in physical light limitation.

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Algorithm for perturbing thermodynamically infeasible metabolic networks

Cited by 10 publications

References 11 publications

Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Generic flux coupling analysis

A model‐based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: Application to the green algae Chlamydomonas reinhardtii

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