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Network models are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of these systems are generally nonlinear, suggesting that suitable nonlinear formulations may offer gains with respect to network inference and associated prediction problems. We present a general framework for both network inference and dynamical prediction that is rooted in nonlinear biochemical kinetics. This isarXiv:1406.0063v1 fatcat:j5o23k65gbcy5dqyfvoybrs64e