Enhancement of Prebiotic Peptide Formation in Cyclic Environments.

The dynamic behaviors of prebiotic reaction networks may be critically important to understanding how larger biopolymers could emerge, despite being unfavorable to form in water. We focus on understanding the dynamics of simple systems, prior to the emergence of replication mechanisms, and what role they may have played in biopolymer formation. We specifically consider the dynamics in cyclic environments using both model and experimental data. Cyclic environmental conditions prevent a system from reaching thermodynamic equilibrium, improving the chance of observing interesting kinetic behaviors. We used an approximate kinetic model to simulate the dynamics of trimetaphosphate (TP)-activated peptide formation from glycine in cyclic wet-dry conditions. The model predicts that environmental cycling allows trimer and tetramer peptides to sustain concentrations above the predicted fixed points of the model due to overshoot, a dynamic phenomenon. Our experiments demonstrate that oscillatory environments can shift product distributions in favor of longer peptides. However, experimental validation of certain behaviors in the kinetic model is challenging, considering that open systems with cyclic environmental conditions break many of the common assumptions in classical chemical kinetics. Overall, our results suggest that the dynamics of simple peptide reaction networks in cyclic environments may have been important for the formation of longer polymers on the early Earth. Similar phenomena may have also contributed to the emergence of reaction networks with product distributions determined not by thermodynamics, but rather by kinetics.

The ecology-evolution continuum and the origin of life.

Prior research on evolutionary mechanisms during the origin of life has mainly assumed the existence of populations of discrete entities with information encoded in genetic polymers. Recent theoretical advances in autocatalytic chemical ecology establish a broader evolutionary framework that allows for adaptive complexification prior to the emergence of bounded individuals or genetic encoding. This framework establishes the formal equivalence of cells, ecosystems and certain localized chemical reaction systems as autocatalytic chemical ecosystems (ACEs): food-driven (open) systems that can grow due to the action of autocatalytic cycles (ACs). When ACEs are organized in meta-ecosystems, whether they be populations of cells or sets of chemically similar environmental patches, evolution, defined as change in AC frequency over time, can occur. In cases where ACs are enriched because they enhance ACE persistence or dispersal ability, evolution is adaptive and can build complexity. In particular, adaptive evolution can explain the emergence of self-bounded units (e.g. protocells) and genetic inheritance mechanisms. Recognizing the continuity between ecological and evolutionary change through the lens of autocatalytic chemical ecology suggests that the origin of life should be seen as a general and predictable outcome of driven chemical ecosystems rather than a phenomenon requiring specific, rare conditions.

Action functional gradient descent algorithm for estimating escape paths in stochastic chemical reaction networks.

We first derive the Hamilton-Jacobi theory underlying continuous-time Markov processes, and then we use the construction to develop a variational algorithm for estimating escape (least improbable or first passage) paths for a generic stochastic chemical reaction network that exhibits multiple fixed points. The design of our algorithm is such that it is independent of the underlying dimensionality of the system, the discretization control parameters are updated toward the continuum limit, and there is an easy-to-calculate measure for the correctness of its solution. We consider several applications of the algorithm and verify them against computationally expensive means such as the shooting method and stochastic simulation. While we employ theoretical techniques from mathematical physics, numerical optimization and chemical reaction network theory, we hope that our work finds practical applications with an inter-disciplinary audience including chemists, biologists, optimal control theorists and game theorists.

An ecological framework for the analysis of prebiotic chemical reaction networks.

It is becoming widely accepted that very early in life's origin, even before the emergence of genetic encoding, reaction networks of diverse small chemicals might have manifested key properties of life, namely self-propagation and adaptive evolution. To explore this possibility, we formalize the dynamics of chemical reaction networks within the framework of chemical ecosystem ecology. To capture the idea that life-like chemical systems are maintained out of equilibrium by fluxes of energy-rich food chemicals, we model chemical ecosystems in well-mixed compartments that are subject to constant dilution by a solution with a fixed concentration of input chemicals. Modelling all chemical reactions as fully reversible, we show that seeding an autocatalytic cycle with tiny amounts of one or more of its member chemicals results in logistic growth of all member chemicals in the cycle. This finding justifies drawing an instructive analogy between an autocatalytic cycle and a biological species. We extend this finding to show that pairs of autocatalytic cycles can exhibit competitive, predator-prey, or mutualistic associations just like biological species. Furthermore, when there is stochasticity in the environment, particularly in the seeding of autocatalytic cycles, chemical ecosystems can show complex dynamics that can resemble evolution. The evolutionary character is especially clear when the network architecture results in ecological precedence, which makes a system's trajectory historically contingent on the order in which cycles are seeded. For all its simplicity, the framework developed here helps explain the onset of adaptive evolution in prebiotic chemical reaction networks, and can shed light on the origin of key biological attributes such as thermodynamic irreversibility and genetic encoding.