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Thermodynamics of random reaction networks
Jakob Fischer1, Axel Kleidon2, Peter Dittrich3
1Bio Systems Analysis Group, Institute of Computer Science, Jena Centre for Bioinformatics and Friedrich Schiller University, Jena, Germany; Max-Planck-Institute for Biogeochemistry, Jena, Germany; International Max Planck Research School for Global Biogeochemical Cycles, Jena, Germany.
Plos One
|February 28, 2015
Summary
Artificial reaction networks reveal how topology impacts thermodynamic properties and disequilibrium. Linear networks exhibit higher flow than nonlinear ones, with flow decreasing as inflow/outflow species distance increases.
Area of Science:
- Chemistry
- Systems Biology
- Earth System Science
Background:
- Thermodynamic disequilibrium is crucial in many reaction systems.
- General thermodynamic properties of reaction networks are not well understood.
- Sparse thermodynamic data hinders analysis.
Purpose of the Study:
- Investigate non-equilibrium steady states of artificial reaction networks.
- Understand the impact of network topology on thermodynamic properties.
- Provide a systematic method to study reaction network thermodynamics.
Main Methods:
- Generated artificial linear and nonlinear reaction networks using four complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha).
- Compared topological properties of artificial networks with real reaction networks.
- Analyzed steady state flow, entropy production, and chemical potential distributions under various boundary fluxes.
Main Results:
- Linear networks showed higher steady-state flow (approx. one order of magnitude) than nonlinear networks.
- Flow decreased with increasing distance between inflow and outflow species; Watts-Strogatz networks had a smaller slope.
- Entropy production distribution followed a power law (exponent ~ -1.5 to -1.66); elevated entropy production occurred in reactions with weakly connected species.
Conclusions:
- The relationship between dissipation distribution, network topology, and disequilibrium is complex.
- Artificial reaction networks provide a viable approach for systematic thermodynamic analysis.
- Findings highlight the influence of network structure on system dynamics and energy dissipation.
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