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Across many fields, a problem of interest is to predict the transition rates between nodes of a network, given limited stationary state and dynamical information. We give a solution using the principle of Maximum Caliber. We find the transition rate matrix by maximizing the path entropy of a random walker on the network constrained to reproducing a stationary distribution and a few dynamical averages. A main finding here is that when constrained only by the mean jump rate, the rate matrix is given by a square-root dependence of the rate, $omega_{ab} propto sqrt{p_b/p_a}$, on $p_a$ and $p_b$, the stationary state populations at nodes a and b. We give two examples of our approach. First, we show that this method correctly predicts the correlated rates in a biochemical network of two genes, where we know the exact results from prior simulation. Second, we show that it correctly predicts rates of peptide conformational transitions, when compared to molecular dynamics simulations. This method can be used to infer large numbers of rates on known networks where smaller numbers of steady-state node populations are known.
We present herein an extension of an algebraic statistical method for inferring biochemical reaction networks from experimental data, proposed recently in [3]. This extension allows us to analyze reaction networks that are not necessarily full-dimens
Proteins are an important class of biomolecules that serve as essential building blocks of the cells. Their three-dimensional structures are responsible for their functions. In this thesis we have investigated the protein structures using a network t
We present a new experimental-computational technology of inferring network models that predict the response of cells to perturbations and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from si
Cortisol, corticosterone and aldosterone activate full-length glucocorticoid receptor (GR) from elephant shark, a cartilaginous fish belonging to the oldest group of jawed vertebrates. Activation by aldosterone a mineralocorticoid, indicates partial