Araudia

Araudia is a stochastic simulation platform to study the origins of regulation and agency in evolving (proto)cell ecologies.

Specifically, the platform implements a microbial consumer-resource model with the following extensions:

• The chemostat containing the microbial ecology can have time-dependent nutrient sources applied.

• Evolutionary operators are implemented so that organisms can adapt over time (via minor mutations or more major innovations).

• Organisms can possess an internal regulatory network (implemented as a neural ODE) which can evolve to dynamically change the nutrient preference of the organism over short time scales, dependent on prevailing internal and external conditions. Thus, organisms can learn better short-term survival strategies.

• The simulation runs with stochastic dynamics.

Araudia was originally developed for Our Paper concerned with the origins of regulatory mechanisms in prebiotic protocells at the origins of life. However, the platform can be also used in a wider context – for example, it can be used as a standard microbial consumer-resource model with/without evolution. See the Supplementary Information of Our Paper for a full technical description of the model.

The software is written in Python and is available as an open source repository on Bitbucket under a GPL v3 License. The current version is v1.0.

To get a sense of the capabilities of the platform, follow the Installation instructions and try some of the Illustrative Demos.

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💡 Developed in Donostia-San Sebastián, Spain, “Araudia” is a Basque word translated as a ‘set of rules/regulations’ or ‘code of conduct’. The platform investigates under what conditions protocells can evolve appropriate codes of conduct to dynamically deal with environmental changes over short time scales.

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Contents:

• Installation
  • HPC Cluster Installation
• Illustrative Demos
  • Preliminary Notes
  • Demo 1: Chemical Pulses Running Through Chemostat
  • Demo 2: Single Population Sustaining in Chemostat
  • Demo 3: Single Population under Evolution (Major Innovations Enabled)
  • Demo 4: Single Population Evolving Lac-Operon-Like Behaviour
  • Demo 5: Testing an Evolved Regulatory Network
  • Demo 6: Cross-Feeding Ecology Emerging from Initial Condition
  • Demo 7: Cross-Feeding Ecology under Evolution (Major Innovations Enabled)
  • Demo 8: Cross-Feeding Ecology under Evolution + Varying Nutrient Sources
  • Demo 9: Cross-Feeding Ecology under Evolution + Varying Nutrient Sources + Regulation
  • Paper Case Studies
  • Simulation Challenges
• Simulation Settings
  • General Parameters
  • Chemostat Parameters
  • Metabolic Network Parameters
  • Regulatory Network Parameters
  • Evolutionary Parameters
  • Secondary Parameters
  • Format of Nutrient Feed Forcing CSV Files
• Analysis Settings
• Known Issues
  • Simulations
  • Data Analysis
• Notes on Platform Architecture
  • Simulation Engine Main Loop
  • Neural ODE Implementation
  • Main Groups of Scripts
• Our Paper