Track genomics, neuroscience and molecular biology.
Search papers, labs, and topics across Lattice.
This paper introduces a computational and machine-learning framework to discover and characterize parameter regimes exhibiting approximate multistationarity and multistability in mass-action systems, addressing limitations of purely structural theory. The approach combines Latin hypercube and adaptive sampling with interpretable surrogate modeling (logistic regression and neural networks) to efficiently explore parameter spaces and identify regions of interest. The framework successfully identifies interpretable empirical descriptions of these regimes using Buckingham-Pi groups and CN2 rule induction, demonstrating significant gains in detection efficiency for complex biological models compared to global sampling.
A machine learning framework efficiently uncovers interpretable parameter regions for multistationarity in complex chemical reaction networks, outperforming brute-force sampling.
Identifying kinetic parameters and conserved quantities that support multistationarity and multistability remains challenging in chemical reaction network analysis. Structural theory provides existence and exclusion results, but does not always yield explicit, interpretable descriptions of the corresponding parameter regimes. We present a computational and machine-learning framework for discovering and characterizing parameter regimes with numerical evidence of multistationarity and multistability in mass-action systems. We refer to these numerically detected behaviors as approximate multistationarity and approximate multistability. The framework combines Latin hypercube sampling, adaptive sampling, and interpretable surrogate modeling. Logistic-regression and neural-network surrogates are trained separately on accumulated detection labels and independently guide later sampling toward regions with high scores for approximate multistationarity or multistability. Boundary refinement targets transitions between detected and undetected numerical classes. Buckingham--Pi groups are then used with CN2 rule induction to obtain interpretable empirical descriptions of detected regimes. Illustrative networks yield rules consistent with known multistationarity conditions, while the Schl\"ogl model demonstrates adaptive enrichment of multistationarity detections. In the hybrid histidine kinase model and the sequential dual phosphorylation cycle, adaptive sampling substantially increased the number of parameter - total pairs with detected multistability relative to global sampling under equal sampling budgets. The framework complements analytical chemical reaction network theory by providing efficient, parameter-resolved discovery and characterization of multistationary and multistable regimes.