Identifiability of chemical reaction networks

By Gheorghe Craciun, Casian Pantea. 2008

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Causal network inference using biochemical kinetics (2014, background): “In summary, we consider a dynamical system dX/dt = fG(X,θ) where the state vector X contains the abundances of molecular species, G is a chemical reaction graph (Craciun and Pantea, 2008) that characterizes reactions in the system, fG is a kinetic model that depends on G and θ collects together all ”
Linear conjugacy of chemical reaction networks (2011, methodology): “The most comprehensive of these results is Craciun and Pantea [10] wherein the authors consider conditions under which a mass-action system does not permit uniquely identifiable rate constants in the sense that two disparate reaction networks could produce the same governing set of differential equa”
Inference of complex biological networks: distinguishability issues and optimization-based solutions (2011, methodology): “Geometric conditions for the indistinguishability of CRNs are given in [55] with a related comment in [56].”
Network Inference and Biological Dynamics (2011, background): “It is clear that chemical reaction networks (CRNs; these are graphs that give detailed descriptions of individual reactions comprising the overall system) underlying biological networks are not in general identiﬁable [Craciun and Pantea (2008)].”