document: Reconstructing chemical reaction networks : data mining meets system identification We present an approach to reconstructing chemical reaction networks from time series measurements of the concentrations of the molecules involved . Our solution strategy combines techniques from numerical sensitivity analysis and probabilistic graphical models . By modeling a chemical reaction system as a Markov network ( undirected graphical model ) , we show how systematically probing for sensitivities between molecular species can identify the topology of the network . Given the topology , our approach next uses detailed sensitivity profiles to characterize properties of reactions such as reversibility , enzyme-catalysis , and the precise stoichiometries of the reactants and products . We demonstrate applications to reconstructing key biological systems including the yeast cell cycle . In addition to network reconstruction , our algorithm finds applications in model reduction and model comprehension . We argue that our reconstruction algorithm can serve as an important primitive for data mining in systems biology applications .
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