A pivoting algorithm for metabolic networks in the presence of thermodynamic constraints

Nigam, R. K.; Liang, S.

doi:10.1109/csb.2005.6

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…(20). The friction and noise are the two opposite sides of stochastic dynamics that are the ability to adaptation with friction and the ability to optimization with noise [11,30,[71][72][73][74].…”

Section: Network Dynamicsmentioning

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%

See 3 more Smart Citations

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: Network Dynamicsmentioning

confidence: 99%

Section: Network Dynamicsmentioning

confidence: 99%

Section: Thermodynamic Constraintsmentioning

confidence: 99%

Section: Thermodynamic Constraintsmentioning

confidence: 99%

See 2 more Smart Citations

Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Demi̇rel

2010

Journal of Non-Newtonian Fluid Mechanics

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“…Ideally, these constraints are derived from fundamental physical and chemical principles so that the physically realistic states of a network can be accurately identified and the unrealistic states ignored. Ensuring the conservation of mass can be achieved in a relatively straightforward manner, but it is much more challenging to incorporate the conservation of energy [ 2 - 8 ]. Methods that rely on the identification of steady state reaction cycles have been developed to achieve this [ 3 - 5 ].…”

Section: Introductionmentioning

confidence: 99%

Exhaustive identification of steady state cycles in large stoichiometric networks

Wright

Wagner

2008

BMC Syst Biol

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Background: Identifying cyclic pathways in chemical reaction networks is important, because such cycles may indicate in silico violation of energy conservation, or the existence of feedback in vivo. Unfortunately, our ability to identify cycles in stoichiometric networks, such as signal transduction and genome-scale metabolic networks, has been hampered by the computational complexity of the methods currently used.

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Algorithm for perturbing thermodynamically infeasible metabolic networks

Nigam

Liang

2007

Computers in Biology and Medicine

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A pivoting algorithm for metabolic networks in the presence of thermodynamic constraints

Cited by 3 publications

References 8 publications

Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Nonequilibrium thermodynamics modeling of coupled biochemical cycles in living cells

Exhaustive identification of steady state cycles in large stoichiometric networks

Algorithm for perturbing thermodynamically infeasible metabolic networks

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