Bayesian Estimation of Muscle Mechanisms and Therapeutic Targets Using Variational Autoencoders.

Autor:	Tune T; Department of Biology, University of Washington.; Center for Transnational Muscle Research, University of Washington., Kooiker KB; Center for Transnational Muscle Research, University of Washington.; Division of Cardiology, Department of Medicine, University of Washington., Davis J; Center for Transnational Muscle Research, University of Washington.; Department of Bioengineering, University of Washington.; Department of Laboratory Medicine and Pathology, University of Washington.; Center for Cardiovascular Biology, University of Washington., Daniel T; Department of Biology, University of Washington.; Center for Transnational Muscle Research, University of Washington.; Washington Research Foundation., Moussavi-Harami F; Center for Transnational Muscle Research, University of Washington.; Division of Cardiology, Department of Medicine, University of Washington.; Department of Laboratory Medicine and Pathology, University of Washington.; Center for Cardiovascular Biology, University of Washington.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Oct 01. Date of Electronic Publication: 2024 Oct 01.
DOI:	10.1101/2024.05.08.593035
Abstrakt:	Cardiomyopathies, often caused by mutations in genes encoding muscle proteins, are traditionally treated by phenotyping hearts and addressing symptoms post irreversible damage. With advancements in genotyping, early diagnosis is now possible, potentially introducing earlier treatment. However, the intricate structure of muscle and its myriad proteins make treatment predictions challenging. Here we approach the problem of estimating therapeutic targets for a mutation in mouse muscle using a spatially explicit half sarcomere muscle model. We selected 9 rate parameters in our model linked to both small molecules and cardiomyopathy-causing mutations. We then randomly varied these rate parameters and simulated an isometric twitch for each combination to generate a large training dataset. We used this dataset to train a Conditional Variational Autoencoder (CVAE), a technique used in Bayesian parameter estimation. Given simulated or experimental isometric twitches, this machine learning model is able to then predict the set of rate parameters which are most likely to yield that result. We then predict the set of rate parameters associated with twitches from control mice with the cardiac Troponin C (cTnC) I61Q variant and control twitches treated with the myosin activator Danicamtiv, as well as model parameters that recover the abnormal I61Q cTnC twitches. Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.
Databáze:	MEDLINE
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