Programming Multistationarity in Chemical Replication Networks

Abstract

Systems Chemistry generates platforms for studying the emergence of function in networks operating far from equilibrium. In this area, we have previously used experiments and simulations towards characterization and manipulation of small peptide-based networks, driven by reversible self-replication processes, that exhibit bistability. We now show how coupling two such networks, each exhibiting bistability, yields new dynamic systems that reach multiple (up to four) steady states. Furthermore, we demonstrate how such multistationarity, rarely analyzed before, can be systematically programmed and tuned. Our results suggest that the key to mimic biological complexification lies not only in the applied network size, the number of molecules involved, or even in the emergence of elaborate structures, but also in the network nature and topology.