BioPreDyn

From Data to Models
New Bioinformatics Methods and Tools for Data-Driven, Predictive Dynamic Modelling in Biotechnological Applications

´┐╝Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach (2015)

Systems biology models can be used to test new hypo- theses formulated on the basis of previous knowledge or new expe- rimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters.


In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dyna- mic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regula- tory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alterna- tive to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presen- ted here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells.

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