Enumeration of Autocatalytic Subsystems in Large Chemical Reaction Networks

Area of Science:

• Systems Biology
• Biochemistry
• Computational Biology

Background:

• Autocatalysis is vital for organism self-maintenance in metabolic networks.
• Autocatalytic subsystems in chemical reaction networks (CRNs) are defined by algebraic conditions on the stoichiometric matrix.
• Understanding autocatalysis is key to comprehending metabolic network organization.

Purpose of the Study:

• To derive conditions for identifying irreducible autocatalytic systems in CRNs.
• To develop an efficient algorithm for enumerating autocatalytic subnetworks and cores.
• To analyze autocatalysis in specific metabolic networks and provide accessible software tools.

Main Methods:

• Derivation of sufficient algebraic conditions for autocatalysis using the bipartite König representation.
• Development of an efficient algorithm to enumerate autocatalytic subnetworks and minimal autocatalytic subnetworks (cores).
• Application of the algorithm to analyze core metabolism in *E. coli* and other organisms.

Main Results:

• Sufficient conditions for irreducible autocatalytic systems were derived.
• An efficient algorithm for enumerating autocatalytic subnetworks and cores was developed and validated.
• Autocatalysis was analyzed in the metabolic networks of *E. coli*, human erythrocytes, and *Methanosarcina barkeri*.

Conclusions:

• The study provides a robust method for analyzing autocatalysis in complex metabolic networks.
• The developed algorithm and accompanying software facilitate routine analysis of autocatalytic subsystems.
• This work enhances the understanding of metabolic network self-maintenance mechanisms.