Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows
Keywords: systems biology, generative modeling, normalizing flows, Bayesian statistics, model selection
TL;DR: We demonstrate how to use normalizing flows on systems biology math models that can be simulated forward to approximate an intractable likelihood function using experimental data.
Abstract: Systems biology relies on mathematical models that often involve complex and intractable likelihood functions, posing challenges for efficient inference and model selection. Generative models, such as normalizing flows, have shown remarkable ability in approximating complex distributions in various domains. However, their application in systems biology for approximating intractable likelihood functions remains unexplored. Here, we elucidate a framework for leveraging normalizing flows to approximate complex likelihood functions inherent to systems biology models. By using normalizing flows in the Simulation-based inference setting, we demonstrate a method that not only approximates a likelihood function but also allows for model inference in the model selection setting. We showcase the effectiveness of this approach on real-world systems biology problems, providing practical guidance for implementation and highlighting its advantages over traditional computational methods.
Submission Number: 31
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