Background

Attempts to define life, detect it, and understand its origins have been ongoing. A key element of this pursuit involves identifying aspects, properties, or measurable quantities in physical systems that distinguish living matter from inert matter. This distinction holds significant implications for both space exploration missions seeking life on distant planets and efforts to recreate life in laboratory settings. Once a relevant and effective metric for detecting life is established, several critical questions arise. It becomes essential to test this metric against a variety of samples, both biological and non-biological, to verify its accuracy in differentiating life from non-life. Recent research has revealed intriguing results — some non-living systems can generate complex patterns and behaviours, entirely in the absence of life, that closely resemble those produced by living systems. This phenomenon challenges the development of reliable metrics, as the life-like behaviour in inert matter complicates the ability to clearly distinguish between living and non-living systems.

Research Goals

This project aims to explore the extent to which the structure of connections in chemical reaction systems can produce complex behaviours that mimic life-like features. Participating students will utilize techniques such as evolutionary algorithms (e.g., genetic algorithms) applied to an existing chemical system model to investigate whether specific structures can lead to the formation of complex molecules.

Skills Needed

Students should possess a foundational understanding of basic algebra and proficiency in at least one high-level programming language, such as Python or Julia. While prior knowledge of evolutionary algorithms and graph theory would be advantageous, it is not a strict requirement.

Skills Gained

Participants will enhance their programming abilities, gain experience conducting extensive numerical simulations, and engage with High Performance Computing clusters—skills highly valued in the research community. Additionally, students will become familiar with the comprehensive scientific process, spanning from experimental design to result analysis.