9,593 research outputs found
From Electric Circuits to Chemical Networks
Electric circuits manipulate electric charge and magnetic flux via a small
set of discrete components to implement useful functionality over continuous
time-varying signals represented by currents and voltages. Much of the same
functionality is useful to biological organisms, where it is implemented by a
completely different set of discrete components (typically proteins) and signal
representations (typically via concentrations). We describe how to take a
linear electric circuit and systematically convert it to a chemical reaction
network of the same functionality, as a dynamical system. Both the structure
and the components of the electric circuit are dissolved in the process, but
the resulting chemical network is intelligible. This approach provides access
to a large library of well-studied devices, from analog electronics, whose
chemical network realization can be compared to natural biochemical networks,
or used to engineer synthetic biochemical networks
Reduction of dynamical biochemical reaction networks in computational biology
Biochemical networks are used in computational biology, to model the static
and dynamical details of systems involved in cell signaling, metabolism, and
regulation of gene expression. Parametric and structural uncertainty, as well
as combinatorial explosion are strong obstacles against analyzing the dynamics
of large models of this type. Multi-scaleness is another property of these
networks, that can be used to get past some of these obstacles. Networks with
many well separated time scales, can be reduced to simpler networks, in a way
that depends only on the orders of magnitude and not on the exact values of the
kinetic parameters. The main idea used for such robust simplifications of
networks is the concept of dominance among model elements, allowing
hierarchical organization of these elements according to their effects on the
network dynamics. This concept finds a natural formulation in tropical
geometry. We revisit, in the light of these new ideas, the main approaches to
model reduction of reaction networks, such as quasi-steady state and
quasi-equilibrium approximations, and provide practical recipes for model
reduction of linear and nonlinear networks. We also discuss the application of
model reduction to backward pruning machine learning techniques
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