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Biological structure and function depend on complex regulatory interactionsbetween many genes. A wealth of gene expression data is available fromhigh-throughput genome-wide measurement technologies, but effective generegulatory network inference methods are still needed. Model-based methodsfounded on quantitative descriptions of gene regulation are among the mostpromising, but many such methods rely on simple, local models or on ad hocinference approaches lacking experimental interpretability. We propose anexperimental design and develop an associated statistical method for inferringa gene network by learning a standard quantitative, interpretable, predictive,biophysics-based ordinary differential equation model of gene regulation. Wefit the model parameters using gene expression measurements from perturbedsteady-states of the system, like those following overexpression or knockdownexperiments. Although the original model is nonlinear, our design allows us totransform it into a convex optimization problem by restricting attention tosteady-states and using the lasso for parameter selection. Here, we describethe model and inference algorithm and apply them to a synthetic six-genesystem, demonstrating that the model is detailed and flexible enough to accountfor activation and repression as well as synergistic and self-regulation, andthe algorithm can efficiently and accurately recover the parameters used togenerate the data.

Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach