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Computing the structural influence matrix for biological systems. (English) Zbl 1359.92040

Summary: We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose \((i,j)\) entry indicates the sign of steady-state influence of the \(j\)th system variable on the \(i\)th variable (the output caused by an external persistent input applied to the \(j\)th variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.

MSC:

92C42 Systems biology, networks
15B35 Sign pattern matrices
34A34 Nonlinear ordinary differential equations and systems
92C40 Biochemistry, molecular biology
92E20 Classical flows, reactions, etc. in chemistry
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