# Predicting the diversity of photosynthetic light-harvesting using thermodynamics and machine learning

## TL;DR

This paper uses thermodynamics and machine learning to predict how photosynthetic light-harvesting systems might evolve under different light conditions, including on exoplanets.

## Contribution

A novel thermodynamic model and evolutionary algorithm predict light-harvesting structures in diverse light environments and on exoplanets.

## Key findings

- The model reproduces the pigment composition and structure of real photosynthetic organisms.
- Photosynthesis could evolve around low mass stars, with anoxygenic types more efficient around cool M-dwarfs.
- Common physical principles underlie the development of diverse antenna systems in different light environments.

## Abstract

Oxygenic photosynthesis is responsible for nearly all biomass production on Earth, and may have been a prerequisite for establishing a complex biosphere rich in multicellular life. Life on Earth has evolved to perform photosynthesis in a wide range of light environments, but with a common basic architecture of a light-harvesting antenna system coupled to a photochemical reaction centre. Using a generalized thermodynamic model of light-harvesting, coupled with an evolutionary algorithm, we predict the type of light-harvesting structures that might evolve in light of different intensities and spectral profiles. We reproduce qualitatively the pigment composition, linear absorption profile and structural topology of the antenna systems of multiple types of oxygenic photoautotrophs, suggesting that the same physical principles underlie the development of distinct antenna structures in various light environments. Finally we apply our model to representative light environments that would exist on Earth-like exoplanets, predicting that both oxygenic and anoxygenic photosynthesis could evolve around low mass stars, though the latter would seem to work better around the coolest M-dwarfs. We see this as an interesting first step toward a general evolutionary model of basic biological processes and proof that it is meaningful to hypothesize on the nature of biology beyond Earth.

Photosynthesis is responsible for supplying most of the energy for complex life on Earth. While there are multiple types of photosynthesis, they all have a common feature: an antenna system which exists to absorb light and transfer energy to a reaction centre where chemical energy is produced. The antennae of photosynthetic organisms have evolved differently to capture more light based on the kind of light they receive. We have developed a physical model of these antenna systems, along with a machine learning algorithm which is given a light input and evolves the antennae to better capture that light. We apply our algorithm to various kinds of light found in different places on Earth, finding that it predicts antennae similar to those found in real organisms. This suggests that common physical principles govern the development of diverse biological structures. We note that photosynthesis presents one of the clearest signals of life (a biosignature) on Earth that can be observed from space, called the vegetation red edge. Future astronomical missions will search for these biosignatures on exoplanets. We therefore apply our algorithm to other kinds of starlight, and make predictions about what kinds of photosynthesis might evolve on other planets.