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Daniel Linder : Parameter Inference in Biochemical Reaction Networks (Nov 13, 2015 11:55 AM)

Systems biologists seek to understand the higher-level organizational properties that a proposed system exhibits from the interactions of its many lower level components. It is then typically beneficial to learn, in a statistical sense, the nature of these interactions from experimental data; this is sometimes called reverse engineering. In this talk I will discuss some methods found in the literature for learning the kinetic parameters from stochastic system trajectories measured at discrete time points. I will also discuss estimating the system topology, or network structure, with the stoichiometric algebraic statistical model and detail its relationship with kinetic parameter estimation. Finally, I will discuss some ongoing work aimed at improving both parameter estimation and system topology estimation.

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