OxDNA to study species interactions

TL;DR

This paper proposes using a Molecular Dynamics coarse-grained model to study species interactions through DNA strand competition, supported by experiments, offering a new platform for ecological research and artificial ecosystems.

Contribution

It introduces a novel DNA-based simulation approach to analyze species competition, integrating computational modeling with experimental validation.

Findings

DNA-DNA interactions can measure competitive advantages.

Complex DNA structures influence binding success.

Experimental results support simulation hypotheses.

Abstract

Molecular ecology uses molecular genetic data to answer traditional ecological questions in biogeography and biodiversity among others. Several ecological principles, such as the niche hypothesis and the competitive exclusions, are based on the fact that species compete for resources. More in general, it is now recognized that species interactions play a crucial role in determining the coexistence and abundance of species. However, experimentally controllable platforms, which allow to study and measure competitions among species, are rare and difficult to implement. In this work, we suggest to exploit a Molecular Dynamics coarse-grained model to study interactions among single strands of DNA, representing individuals of different species, which compete for binding to other oligomers considered as resources. In particular, the well-established knowledge of DNA-DNA interactions at the nanoscale allows us to test the hypothesis that the maximum consecutive overlap between pairs of oligomers measure the species competitive advantages. However, we suggest that more complex structure also plays a role in the ability of the species to successfully bind to the target resource oligomer. We complement the simulations with experiments on populations of DNA strands which qualitatively confirm our hypotheses. These tools constitute a promising starting point for further developments concerning the study of controlled, DNA-based, artificial ecosystems.