A statistical approach to the origin of life

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A statistical approach to the origin of life
vendredi 14 janvier 2022 19:06:56

A mathematical and statistical approach to the origin of life: emergence of polymerization, autocatalysis and Darwinian evolution

Is Darwinian evolution a surprising phenomenon? Life as we know it is governed by Darwinian evolution, but there is so far no convincing demonstration that a natural or artificial physical-chemical system is capable of autonomous Darwinian evolution outside of biology. Explaining how evolution emerges from inanimate matter is therefore central to understanding the origin of life.

In this project, we propose a theoretical approach to identify conditions for the emergence of Darwinian evolution. By generating chemical reaction rules consistent with actual chemistries, we aim to estimate the probability of major transitions in the origin of life: polymerization, autocatalysis, and evolution by natural selection. This project builds on recent works of the laboratory which define general criteria to recognize autocatalysis in reaction networks and characterize its dynamics [1,2].

Artificial chemistries will be generated computationally, where chemicals and reaction rules are drawn randomly but consistently with known chemistries. We will then characterize and search for subnetworks allowing polymerization, autocatalysis or evolution in the space of all possible networks. In parallel, we will establish asymptotic mathematical analyses of the probability of such subnetworks. We wish to identify whether Darwinian evolution is possible at all, if yes, what transitions precede it, and what parameters govern the probability of such transitions. Ultimately, we will estimate whether feasible scenarios are plausible given chemical systems and environments found on early Earth and exoplanets [3].

The PhD will be co-supervised by Philippe Nghe (director of the Laboratory of Biophysics and Evolution, ESPCI Paris-PSL) and Jérémie Unterberger (mathematical physics, Institut Elie Cartan, Université de Lorraine). It is funded by the European ERC project AbioEvo. The candidate is expected to have a strong background in theoretical physics or mathematical physics or computational approaches, and a taste for discussions with experimentalists in various disciplines, from chemistry to biology and astrophysics.


[1] Blokhuis, A., Lacoste, D., & Nghe, P. (2020). Universal motifs and the diversity of autocatalytic systems. Proceedings of the National Academy of Sciences, 117(41), 25230-25236.
[2] Unterberger, J., & Nghe, P. (2021). Stoechiometric and dynamical autocatalysis for diluted chemical reaction networks. arXiv preprint arXiv:2109.01130.
[3] Tran, Q. P., Adam, Z. R., & Fahrenbach, A. C. (2020). Prebiotic reaction networks in water. Life, 10(12), 352.