Template-Based Catalysis and the Emergence of Collectively Autocatalytic Systems

Area of Science:

• Origin of Life Research
• Computational Biology
• Systems Chemistry

Background:

• Mathematical and computational models are crucial for studying the origin of life, especially where wet experiments are limited.
• Interacting polymers in prebiotic environments are key to understanding molecular replication and protocell emergence.

Purpose of the Study:

• To investigate mathematical and computational models of interacting polymers in prebiotic environments.
• To understand the emergence of molecular replication and protocell reproduction.
• To compare the Binary Polymer Model (K-BPM) with a biologically relevant variant (C-BPM).

Main Methods:

• Utilized the Binary Polymer Model (K-BPM) and introduced a catalyzed polymer model (C-BPM) where catalysis depends on polymer structure.
• Analyzed the formation of autocatalytic networks, specifically Reflexive Autocatalytic Food-generated (RAF) sets.
• Simulated protocell dynamics incorporating these networks.

Main Results:

• Demonstrated clear differences in dynamics between the K-BPM and C-BPM models.
• Showed that the presence of a RAF does not inherently guarantee protocell population survival.
• Identified that survival is possible when only a subset of species participates in the RAF.

Conclusions:

• Small autocatalytic networks may have been precursors to the larger networks observed in contemporary life.
• The structure-dependent catalysis in the C-BPM offers a more biologically relevant model for origin of life studies.
• Protocell survival is influenced by the specific composition and dynamics of the autocatalytic network.