Predicting species emergence in simulated complex pre-biotic networks

Abstract

An intriguing question in evolution is what would happen if one could “replay” life’s tape. Here, we explore the following hypothesis: when replaying the tape, the details (“decorations”) of the outcomes would vary but certain “invariants” might emerge across different life-tapes sharing similar initial conditions. We use large-scale simulations of an in silico model of pre-biotic evolution called GARD (Graded Autocatalysis Replication Domain) to test this hypothesis. GARD models the temporal evolution of molecular assemblies, governed by a rates matrix (i.e. network) that biases different molecules’ likelihood of joining or leaving a dynamically growing and splitting assembly. Previous studies have shown the emergence of so called compotypes, i.e., species capable of replication and selection response. Here, we apply networks’ science to ascertain the degree to which invariants emerge across different life-tapes under GARD dynamics and whether one can predict these invariant from the chemistry specification alone (i.e. GARD’s rates network representing initial conditions). We analysed the (complex) rates’ network communities and asked whether communities are related (and how) to the emerging species under GARD’s dynamic, and found that the communities correspond to the species emerging from the simulations. Importantly, we show how to use the set of communities detected to predict species emergence without performing any simulations. The analysis developed here may impact complex systems simulations in general.

Systems protobiology: origin of life in lipid catalytic networks

“…It turns out that the definition of cores/compartments fully overlaps with that of composomes (see §8), as has actually been explicitly stated in another paper by the same authors [ 27 ]. These cores/compartments are identical to what in graph theory is called communities, whose relevance to GARD is discussed in §8 and in [ 92 ].…”

“…With such a modification, most randomly formed GARD compositional assemblies will not be RAF. On the other hand, as the compounds belonging to a composome show overlap with communities within the β -matrix-defined subsets of more tightly linked network nodes [ 27 , 92 ] (see §8), it follows that composomes are much more likely to be RAFs. If true, GARD kinetic simulations could be used, in parallel to the published analytic algorithm [ 73 ], to distinguish between RAFs and non-RAFs.…”

“…However, this composome is never reached in the more bio-realistic simulations that involve periodic splitting and lead to one or more non-canonical composomes. The recent advent of a method for pre-identifying such composomes given the β matrix [ 92 ] will be of great help in future GARD analyses.…”

Automatic selection of verification tools for efficient analysis of biochemical models

“…That is, model parameters could be safely ignored while still being able to robustly predict the best simulator to use. More recently, it has been demonstrated that a similar approach (namely focussing on structure and ignoring parameters) was sufficient to predict the outcome of long stochastic chemical simulations Markovitch and Krasnogor (2018). Thus, we follow a similar approach here and focus solely on model structure and ignore model parameters for the purpose of predicting the speed at which different model checkers verify system level models.…”

Programming Multistationarity in Chemical Replication Networks

“…We show numerically, and by simulating many cases, that this yields multistationarity or multistability (Figure e), i. e., multiple stable steady‐state solutions (see formal mathematical definitions by Joshi and Shiu), where the specific points of convergence depend on the efficiency of each replicator and the system's initial conditions. As shown in various numerical studies of replication networks, the current system also displays patterns of behavior that are difficult to predict without simulation. Furthermore, the simulations reveal fundamental trends and characteristics potentially useful for design of future experiments.…”