Artificial Life and Protocells

Abstract

The transition from non-living chemistry to life is one of the greatest unsolved mysteries in science. Artificial Life (A-Life) seeks to understand this transition by creating life-like systems "from scratch" in software, hardware, or wetware. This Masters in Computer Science proposal outlines a computational research program to investigate the emergence of metabolism and information storage—two key hallmarks of life—in a simulated "primordial soup." The goal is to develop a new A-Life simulation framework where simple, autocatalytic chemical networks can spontaneously evolve to become self-replicating entities (protocells) that store information about their environment.

Key Research Questions in Origins of Life Simulation

1. Emergence of Autocatalysis: Starting from a random collection of simple "digital molecules," what are the minimal conditions (e.g., reaction rules, energy sources) required for the spontaneous emergence of autocatalytic sets—chemical networks that collectively catalyze their own formation?
2. Metabolism-First vs. Genetics-First: Can a stable, self-sustaining "metabolism" (an autocatalytic network) emerge *before* an information-carrying molecule like RNA or DNA? Or is a genetic template a prerequisite? This research will test these competing hypotheses in a simulated environment.
3. The Origin of Information: How does a molecule originally used for catalysis or structure gain the ability to store and transmit information? The simulation will explore how certain polymers within the autocatalytic network might evolve into information-bearing templates.
4. The Role of Compartmentalization: How does enclosing a chemical network within a simple boundary (a "membrane") change its evolutionary trajectory? The research will investigate how the formation of protocells affects the stability and evolvability of the emergent life forms.

Proposed Masters in Computer Science Research: A "Digital Primordial Soup" Simulation

This thesis will involve the design and implementation of a large-scale computational chemistry simulation.

• Develop the Simulation Framework: Create a new, open-source A-Life simulation platform. In this world, simple string-based "molecules" will interact based on a defined set of "chemistry" rules. The rules will allow for molecules to catalyze the formation or cleavage of other molecules.
• Run Evolutionary Experiments: The simulation will be "seeded" with a random assortment of basic molecular building blocks and a constant influx of energy. The simulation will run for billions of time steps, and the emergent chemical networks will be analyzed for signs of life-like properties.
• Analyze Emergent Properties: Develop new analysis tools to automatically detect the formation of autocatalytic cycles, the emergence of information-storing polymers, and the spontaneous formation of self-enclosed compartments.
• Explore the "Phase Space" of Life: Systematically vary the fundamental "laws of physics" of the simulation (e.g., reaction rates, energy levels) to understand which conditions are most conducive to the emergence of life. This will help us understand if life is a likely outcome of chemistry, or a rare fluke.

Impact for Ghana and Africa

Understanding the origin of life is a fundamental scientific pursuit that expands human knowledge. For Ghana, engaging in such frontier science is a mark of intellectual leadership. However, the practical implications are also significant. The simulation frameworks and analysis techniques developed in this research could be directly applied to more immediate problems. For instance, the models of complex chemical networks could be used to design new industrial catalysts or to understand the complex metabolic interactions within the human gut microbiome. The algorithms for analyzing emergent systems could be used in fields from ecology to economics. This research would provide a Ghanaian scientist with world-class expertise in complex systems modeling, a skill that is critically needed to tackle interconnected challenges like public health, climate change, and economic development. It would demonstrate that the deepest questions about our origins can be explored from Africa, contributing to global scientific knowledge while building local intellectual capital.