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Skills:
Skills:
Built a custom chemical evolution engine simulating environmental cycles, catalytic reactions, and energy dynamics like prebiotic Earth.
Encoded hypotheses about origins-of-life chemistry (e.g., wet-dry cycles, catalysis, UV-driven energy input) in a quantitative framework.
Applied object-oriented and procedural design using Python packages; implemented performance-efficient molecular dynamics logic.
Modeled probabilistic reactions (polymerization, hydrolysis, degradation) using Monte Carlo methods for chemical noise and variability.
Plots visualize molecular complexity, energy flow, and environmental cycling with a focus on interpretability
Accounted for thermodynamic constraints by integrating energy conservation and usage rules into reaction logic, including molecule size-dependent energetic costs.
Reason about interacting variables in complex, nonlinear systems.
My code starts with a pool of some basic monomers (labelled A, B, C) that represent simple prebiotic molecules. Then, I simulate three key processes in the prebiotic chemistry that may be responsible for the generation of early life:
Polymerization: Molecules joining together to form longer chains
Hydrolysis: Longer molecules splitting randomly
Degradation: Molecules can decay and be removed from the "pool"
This simplified prebiotic chemical system is then exposed to alternating wet–dry environmental cycles, intended to approximate conditions on early Earth or even potentially Mars. The monomers then undergo stochastic polymerization, hydrolysis, and degradation reactions governed by phase-dependent probabilities. This code tracks the average molecular length, as a proxy here for complexity, over time.
Time series plots showing how molecular complexity may evolve and possibly mimicking a simplification of the early steps toward life:
Maximum molecule length set to 20
Maximum molecule length set to 100
Those plots excellently represent the net accumulation we would expect towards molecular complexity in early life. However, they miss some of the complexities I sought to add.
Energy availability constrains polymerization, with costs proportional to product length. Energy is replenished only during "wet" (sunlit) phases, while "dry" phases favor hydrolysis and net molecular breakdown. Catalysts (defined molecule types) probabilistically enhance polymerization rates.
Over time, the model tracks changes in molecular complexity (via average polymer length) and system energy, revealing how environmental cycling, catalysis, and thermodynamic constraints can shape the emergence of complex chemical assemblies in non-equilibrium settings.
Molecular complexity demonstrated by the average molecule length and system energy over time. Wet and dry cycles with energy cost not proportional to molecule length.
Molecular complexity demonstrated by the average molecule length and system energy over time. Wet and dry cycles with energy cost proportional to molecule length.
While this decrease in complexity may seem farther from an accurate model, it is actually to be expected based on the parameters I set. For example, in this model molecules do not persist or self-replicate, meaning there's no mechanism to favor stability or inheritance of complex forms. Therefore, complex molecules are constantly built and destroyed with no long-term accumulation.
In a system like this, increasing complexity is not automatic and requires sufficient and sustained energy input, mechanisms for molecular persistence or replication, and possibly selection pressures that favor longer or functional molecules. All of which would have been more common in Early Earth conditions than my model accounts for.
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