Within a fully microscopic setting, we derive a variational principle for the non-equilibrium steady states of chemical reaction networks, valid for time-scales over which chemical potentials can be taken to be slowly varying: at stationarity the system minimizes a global function of the reaction fluxes with the form of a Hopfield Hamiltonian with Hebbian couplings, that is explicitly seen to correspond to the rate of decay of entropy production over time. Guided by this analogy, we show that reaction networks can be formally re-cast as systems of interacting reactions that optimize the use of the available compounds by competing for substrates, akin to agents competing for a limited resource in an optimal allocation problem. As an illustration, we analyze the scenario that emerges in two simple cases: that of toy (random) reaction networks and that of a metabolic network model of the human red blood cell. © 2012 De Martino et al.
Reaction networks as systems for resource allocation: A variational principle for their non-equilibrium steady states / de Martino, A.; de Martino, D.; Mulet, R.; Uguzzoni, G.. - In: PLOS ONE. - ISSN 1932-6203. - 7:7(2012), p. e39849. [10.1371/journal.pone.0039849]
Reaction networks as systems for resource allocation: A variational principle for their non-equilibrium steady states
de Martino A.;Uguzzoni G.
2012
Abstract
Within a fully microscopic setting, we derive a variational principle for the non-equilibrium steady states of chemical reaction networks, valid for time-scales over which chemical potentials can be taken to be slowly varying: at stationarity the system minimizes a global function of the reaction fluxes with the form of a Hopfield Hamiltonian with Hebbian couplings, that is explicitly seen to correspond to the rate of decay of entropy production over time. Guided by this analogy, we show that reaction networks can be formally re-cast as systems of interacting reactions that optimize the use of the available compounds by competing for substrates, akin to agents competing for a limited resource in an optimal allocation problem. As an illustration, we analyze the scenario that emerges in two simple cases: that of toy (random) reaction networks and that of a metabolic network model of the human red blood cell. © 2012 De Martino et al.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2976765