Bayesian Rate Inference for Sequence Motif Dynamics in Systems of Reactive Nucleic Acids

TL;DR

This paper introduces a Bayesian inference framework to estimate reaction rate parameters for sequence motif dynamics in RNA systems, bridging complex simulations and experimental data to understand life's origins.

Contribution

It presents a novel Bayesian method for inferring motif rate parameters from ligation data, enabling better modeling of RNA reaction dynamics and connecting theory with experiments.

Findings

Framework accurately infers rate parameters from simulated ligation data

Enables estimation of reaction rates with uncertainty quantification

Facilitates linking complex RNA models to experimental measurements

Abstract

The RNA world hypothesis suggests a pathway of how life emerged on early earth. It assumes that life started with RNA based systems, capable of storing, transmitting and replicating information, envisioning that monomers and short RNA oligomers interact to form longer strands, eventually becoming catalytically active ribozymes. Key reactions in RNA pools are hybridization, dehybridization, templated ligation, and cleavage. Those reactions depend on many environmental parameters and the wide range of possible configurations among interacting strands. In order to scan such high dimensional parameter spaces, efficient descriptions are needed. Motif rate equations project complex strand reactor dynamics onto sequence motif space. Here we present a Bayesian inference framework to infer their parameters from ligation count data produced by strand reactor simulations. This provides a framework to match the simpler motif rate equations to more complex simulations. Additionally, it is a step towards inferring reaction rate constants directly from experimental data, including rigorous uncertainty estimation. This could be an essential procedure to connect theory and experiment, and deepen our understanding of the essential features necessary for life to emerge.