V. Blanco
Chemical reaction networks (CRNs) are essential for modeling and analyzing complex systems across fields, from biochemistry to economics. Autocatalytic reaction networks are particularly significant for understanding self-replication dynamics in biological systems and serve as foundational elements in formalizing the concept of a circular economy. We define the maximum growth factor (MGF) of an autocatalytic subnetwork, develop exact mathematical optimization approaches to compute this metric, and explore its implications in the field of economics and dynamical systems. We report the results of computational experiments on synthetic CRNs and two well-known datasets, namely the Formose and E. coli reaction networks, identifying their autocatalytic subnetworks and exploring their scientific ramifications. Using advanced optimization techniques and interdisciplinary applications, our framework adds an essential resource to analyze complex systems modeled as reaction networks.
Palabras clave: Chemical Reaction Networks, Origin of Life, Mathematical Optimization, Complex Networks
Programado
Localización (GELOCA1)
11 de junio de 2025 15:30
MR 2