Mathematical models provide a powerful framework for analysing the dynamics of biochemical reaction networks (BCRNs); however, effective modelling of complex biological systems requires balancing complexity and accuracy. As such, the rigorous and principled reduction of BCRNs via systematic, model-independent methods, while ensuring that key dynamics are preserved, is of vital importance....
Modelling biological systems imposes the challenge to balance realism and complexity. Precise mechanistic models are often difficult to analyse and thus yield little insight, whereas simpler conceptual models tend to lack justification, as they rely on implicit descriptions of the underlying mechanisms. Model reduction via timescale separation can mitigate this trade-off by allowing to derive...
Lumping methods reduce biochemical reaction networks by aggregating species concentrations via algebraic conditions that hold globally. We study linear lumping for parameter-dependent mass action systems, asking how lumpability depends on rate constants. Our first result shows that for generic parameters---those ranging over a non-empty open subset of parameter space---exact linear lumping...
For over a century, the Michaelis–Menten (MM) equation has provided the foundation for modeling enzyme kinetics in biochemistry and pharmacology. Despite its remarkable success, MM relies on assumptions that may break down in physiologically relevant regimes. In this talk, we revisit these limitations and introduce the total quasi-steady-state approximation (tQSSA) as a principled...
Biochemical reaction networks (BCRNs) form the backbone of systems biology. Effectively modeling complex biological systems requires a delicate balance between complexity and accuracy, motivating the need for rigorous, principled BCRN reduction. This minisymposium addresses the demand for systematic and algorithmic, model-independent reduction methods. Featured topics include lumping,...
