In this work, the normative framework of active inference is integrated with belief propagation for inverting a probabilistic causal model using data generated from planned interactions between a Bayesian modeling agent and a biological system. Thompson sampling of parameter distributions is used to estimate the free energy of the expected future when beliefs about beliefs are rolled over a planning horizon. Learning a probabilistic model for maximizing biomass production in the well-known Baker’s yeast example is used as an ex-ample. The prior parameter distributions in the system model of a fed-batch cultivation are updated as new observations are obtained. Planned action sequences aim to excite the yeast metabolism by introducing changes in the feed rate of two nutrients (glucose and nitrogen). Results obtained demonstrate that by maximizing the model evidence, the proposed approach constraints biological system dynamics to relevant trajectories for improved parametric precision in the preferred region of physiological states that favor biomass productivity.
Información general
Fecha de exposición:octubre 2021
Fecha de publicación:2021
Idioma del documento:Inglés
Evento:VII Simposio Argentino de Ciencia de Datos y GRANdes DAtos (AGRANDA 2021) - JAIIO 50 (Modalidad virtual)
Institución de origen:Sociedad Argentina de Informática e Investigación Operativa
Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)