Moein Khalighi "AI-driven stochastic modeling of eco-evolutionary dynamics"
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This talk can be viewed via zoom. (Note: this talk will be recorded)
Title: AI-driven stochastic modeling of eco-evolutionary dynamics
Description:
This talk explores the dynamics and critical transitions in microbial communities, bridging from simple monocultures to the complex realities of Antimicrobial Resistance (AMR). We begin with monocultures, where the sharing of "public goods" (such as quorum-sensing signals) often leads to a "tragedy of the commons" driven by social cheaters. Scaling up to multi-species environments, this dynamic shifts: a single species may evolve to degrade antibiotics, providing a public good that inadvertently protects vulnerable bystander species.
Currently, standard deterministic models struggle to capture the underlying mechanisms of these state shifts and stable co-existence. To move the field forward, we must upgrade our mathematical frameworks. This requires adopting Stochastic Differential Equations (SDEs) to account for natural biological noise, and critically, incorporating "memory effects"—quantifying both the evolutionary history of genetic mutants and the ecological history of communities adapting to environmental shocks over time.
Finally, the talk outlines the necessary next steps for predicting microbial behavior. By leveraging Artificial Intelligence to analyze history-dependent data, we can calculate a "prediction horizon" that defines how far into the future we can reliably forecast microbial evolution, community collapse, and the emergence of AMR.