Abstract: Mechanistic models for biochemical networks are often constructed in the form of nonlinear ordinary or stochastic differential equation systems. Inference of such dynamic models from experimental data has attracted lots of interest in systems biology field, but the inference task is generally considered to be challenging. In this talk I will describe various differential equation and machine learning models for dynamic molecular networks, including parametric and novel non-parametric alternatives, and describe how the model parameters as well as network structure can be efficiently inferred from experimental time-course data. I will demonstrate applications of these models in biological and non-biological contexts.

Speaker: Harri Lähdesmäki

Affiliation: Professor of Computer Science, Aalto University

Place of Seminar: University of Helsinki