Quantifying Evolution: a platform for the synthetic evolution of molecular ecosystems.

Descripción del proyecto

Trazar la evolución a través de una cadena de ADN a la vez

La evolución es uno de los procesos más básicos de la naturaleza, pero a su vez uno de los menos comprendidos. Resulta difícil comprenderlo a través de leyes universales debido a la complejidad de los sistemas biológicos y a las numerosas variables de los modelos experimentales. El proyecto Q-EVO, financiado por el Consejo Europeo de Investigación, aborda esta cuestión con la plataforma de evolución sintética del ADN basada en la afinidad (ADSE, por sus siglas en inglés), que examina la evolución a nivel molecular. Al utilizar secuencias de ADN como sustitutos de organismos, la plataforma ADSE recrea interacciones ecológicas como la competencia, el mutualismo y el parasitismo a través de la hibridación predecible de ADN-ADN. El equipo de Q-EVO combina todo ello con biosensores basados en matrices, ciencia de datos y ecología teórica para supervisar cientos de experimentos evolutivos paralelos a lo largo de múltiples generaciones. Mediante el mapeo de la dinámica poblacional, las rutas filogenéticas y las redes ecológicas, su objetivo es proporcionar la primera descripción cuantitativa completa de la aptitud, la especiación y la evolución impulsada por el medio ambiente, con todos los datos a disposición del público.

Objetivo

Among the fundamental processes governing Nature, evolution is one of the most thoroughlAmong the fundamental processes governing Nature, evolution is one of the most thoroughly described and less formalized. The grand goal of describing evolution via clean universal laws is made difficult by the inherent complexity of both the biological systems and the available experimental models, which generally involve a large number of potentially relevant variables of difficult quantification.
This project is built around the Affinity-based DNA Synthetic Evolution (ADSE) platform, an experimental strategy to generate and study evolution at a molecular level that enables full quantitative description of the eco-evolutionary processes. ADSE is inspired by the in-vitro molecular selection protocol “SELEX” and enables observing the evolution across generations of large populations of DNA oligomers whose identity is given by their sequence. The key innovation is that here evolutionary mechanisms and ecological interactions (selection of the fittest, resource gathering capacity, parasitism, mutualism etc…) are given by molecular interactions based on DNA-DNA hybridization, a process that is well known and predictable from sequences.
Q-EVO is based on a combination of ADSE with an innovative array-based biosensor, approaches of data science and notions of theoretical ecology. Q-EVO aims at providing a (still missing) quantitative description of the key concepts of fitness and speciation, and of the relationship between a given environment and the evolution of its ecosystem. I propose a program capable to produce hundreds of parallel evolutions for tens of generations exploring a vast variety of conditions. Raw data, formed by hundreds of millions of reads, will be analysed to follow population dynamics, extract phylogenetic paths, parametrizing the network of ecological interactions (e.g. competitors, mutualists, parasites) and will be made public in a repository created ad hoc.