M.Sc. Justin N. Kreikemeyer, research assistant at the chair of Modeling and Simulation (University of Rostock), will give a presentation in the topic
"Discovering Chemical Reaction Networks from Data"
as part of the VAC Colloquium.
Afterwards we look forward to discussions while enjoying coffee and cookies.
The event is open to anyone interested.
Abstract:
In a time of data abundance, automatic methods increasingly support manual mechanistic modeling. This support can range from data-driven calibration of some parameters up to uncovering the mechanics underlying a system from measurements. The Sparse Identification of Non-Linear Dynamics (SINDy) achieves both, allowing for the discovery of differential equations from time-series data. However, especially in biochemistry, but also in ecology and epidemiology, chemical reaction networks (CRNs) often act as target for (automatic) modeling. Their semantics require differential equations with a characteristic coupling.
In this talk, I will demonstrate how to adapt SINDy to the CRN formalism and present the findings published in our recent paper "Discovering Biochemical Reaction Models by Evolving Libraries", that was voted "best paper" at the CMSB 2024. We found that combining the extension of SINDy to CRNs with an evolutionary algorithm enables the integration of new kinds of prior knowledge and can increase the parsimony of learned models. The talk concludes by expanding the perspective to other automatic modeling methods which may support modeling in the future, e.g., inferring models from textual descriptions using large language models.
