We describe an approach to model biochemical reaction networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that depart from the usual chemical kinetics setup and includes spatial and temporal density fluctuations in a most natural way.  A particular but otherwise generally applicable choice for the microscopic transition rates of the models also makes them of independent interest. To illustrate the formalism, we investigate  some stationary state properties of the repressilator, a synthetic three-gene network of transcriptional regulators that possesses a rich dynamical behaviour .
We describe an approach to model genetic regulatory networks at the level of promotion-inhibition circuitry through a class of stochastic spin models that includes spatial and temporal density fluctuations in a natural way. The formalism can be viewed as an agent-based model formalism with agent behavior ruled by a classical spin-like pseudo-Hamiltonian playing the role of a local, individual objective function. A particular but otherwise generally applicable choice for the microscopic transition rates of the models also makes them of independent interest. To illustrate the formalism, we investigate (by Monte Carlo simulations) some stationary state properties of the repressilator, a synthetic three-gene network of transcriptional regulators that possesses oscillatory behavior .
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biochemical reaction
genetic regulatory
D
depart from the usual chemical kinetics setup and
D
most
A
The formalism can be viewed as an agent-based model formalism with agent behavior ruled by a classical spin-like pseudo-Hamiltonian playing the role of a local, individual objective function.
A
(by Monte Carlo simulations)
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a rich dynamical behaviour
oscillatory behavior