Background

Artificial chemistry is a branch of artificial life that aims to capture the essential features of chemistry in abstract models. These models can be used to understand emergent phenomena in chemistry, as test beds for understanding the origin of life, and as topics of study in their own right. The Mathis Group has recently reimplemented a classic model of artificial chemistry, AlChemy, in Rust. This implementation enables an exciting new set of experiments but the current bottleneck is in analysis, visualization and exploration. To move past this, we want a team of Biocomputing Scholars to 1) continue the development of robust Python wrappers around the existing Rust code, 2) develop new interactive visualizations of the systems, 3) conduct simulations and analysis of the current model, and 4) extend the current model to include spatial diffusion and organization.

Research Goals

The scholar team will (1) read and understand the original papers on AlChemy from Fontana & Buss, (2) read recent reanalysis of the model from the Mathis Group, with a focus on understanding the immediate scientific questions, (3) read, summarize, and critically engage with relevant relevant prebiotic chemistry literature, including descriptions of chemical experiments, (4) plan and execute simulations based on analogies to cutting edge prebiotic chemistry, (5) analyze those simulations using existing tools and propose new simulations or visualization approaches, and (6) draft and original manuscript based on the results of simulations.

Skills Needed

Required: interest in reading and discussing diverse scientific results; solid Python programming

Beneficial: experience with data visualization libraries (e.g., matplotlib, bokeh); operational understanding of SQL; strong grasp of systems programming (Rust or C++); basic understanding of lambda calculus

Skills Gained

Students will gain experience developing and refining interactive dashboards for conveying and understanding complex data, be exposed to data analysis methods including time-series analysis and clustering, and gain first-hand experience working on an exceptionally interdisciplinary project that spans the fields of theoretical computer science, prebiotic chemistry, and software engineering.