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bioRxiv · 10.1101/2024.04.26.591317

Leveraging AI for improved reproducibility of mathematical disease models: insights from a retinitis pigmentosa case study

Thibault-Greugny, E.·Ratto, N.·Etheve, L.·Gourlet, J.-B.·Cogny, F.·Monteiro, C.
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Abstract

Mathematical modeling of disease and drug action is becoming an indispensable component of drug development, underscored by recent examples of models predicting trial results. To be able to rely on such approaches, decision-makers need to be able to verify those results independently with in silico confirmatory studies. Artificial Intelligence (AI) offers a valuable avenue for improving the reproducibility of complex models, enabling their swift and software-agnostic deployment. This paper highlights AIs impact through a case study on an Ordinary Differential Equation (ODE) model of Retinitis Pigmentosa (RP). We use a version of Chat GPT 4, a sophisticated large language model (LLM) developed by OpenAI, as customized by Mathpix company with additional capabilities. This setup facilitated the extraction of equations from the PDF and converted into a human-readable, text-based definition language called Antimony, which is part of the Python package tellurium. Subsequently, the model was converted into Systems Biology Markup Language (SBML) using tellurium and uploaded onto the jink[o] platform for simulation. The RP model was efficiently and accurately implemented using AI techniques. Furthermore, we were able to reproduce the model behavior presented in the literature. Our findings advocate for the broader application of AI in mathematical model re-implementations to ensure reliability and reproducibility of the results.

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Thibault-Greugny, E., Ratto, N., Etheve, L., Gourlet, J.-B., Cogny, F., Monteiro, C.. 2024-04-30. Leveraging AI for improved reproducibility of mathematical disease models: insights from a retinitis pigmentosa case study. https://doi.org/10.1101/2024.04.26.591317

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