Kreikemeyer, Justin N. and Burrage, Kevin and Uhrmacher, Adelinde M. (2024) Discovering Biochemical Reaction Models by Evolving Libraries. In: 22nd International conference on Computational Methods in System Biology (CMSB 2024), 16-18 Sep 2024, Pisa, Italy.
Full text not available from this repository.Abstract
In a time of data abundance, automatic methods increasingly support manual modeling. To this end, the Sparse Identification of Non-linear Dynamics (SINDy) provides a solid foundation for identifying non-linear dynamical systems in the form of differential equations. In biochemistry, reaction networks imply coupled differential equations. It has recently been demonstrated how this intrinsic coupling can be achieved within the SINDy framework, providing a straightforward interpretation of the learned equations as reaction systems with mass- action kinetics. However, this extension inherits from SINDy the requirement to enumerate all candidate reactions in a library, resulting in ill-posed optimization problems and long model descriptions, limiting its utility for identifying models with many species. Here, we elaborate on the recent advances in bringing SINDy to the biochemical domain by considering the sub-sampling of reaction libraries as part of an evolutionary optimization scheme. This enables the generation of parsimonious models, as well as the inclusion of model-level constraints, and allows the consideration of large numbers of candidate reactions. We evaluate the approach on two smaller case studies and the recovery of a large Wnt signaling model.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | accepted, to appear |
