Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111333
Title: Generative Adversarial Networks for Construction of Virtual Populations of Mechanistic Models: Simulations to Study Omecamtiv Mecarbil Action
Authors: Parikh, J.
Rumbell, T.
Butova, X.
Myachina, T.
Acero, J. C.
Khamzin, S.
Solovyova, O.
Kozloski, J.
Khokhlova, A.
Gurev, V.
Issue Date: 2022
Publisher: Springer
Springer Science and Business Media LLC
Citation: Generative Adversarial Networks for Construction of Virtual Populations of Mechanistic Models: Simulations to Study Omecamtiv Mecarbil Action / J. Parikh, T. Rumbell, X. Butova et al. — DOI 10.21538/0134-4889-2020-26-3-275-285 // Journal of Pharmacokinetics and Pharmacodynamics. — 2022. — Vol. 49. — Iss. 1. — P. 51-64.
Abstract: Biophysical models are increasingly used to gain mechanistic insights by fitting and reproducing experimental and clinical data. The inherent variability in the recorded datasets, however, presents a key challenge. In this study, we present a novel approach, which integrates mechanistic modeling and machine learning to analyze in vitro cardiac mechanics data and solve the inverse problem of model parameter inference. We designed a novel generative adversarial network (GAN) and employed it to construct virtual populations of cardiac ventricular myocyte models in order to study the action of Omecamtiv Mecarbil (OM), a positive cardiac inotrope. Populations of models were calibrated from mechanically unloaded myocyte shortening recordings obtained in experiments on rat myocytes in the presence and absence of OM. The GAN was able to infer model parameters while incorporating prior information about which model parameters OM targets. The generated populations of models reproduced variations in myocyte contraction recorded during in vitro experiments and provided improved understanding of OM’s mechanism of action. Inverse mapping of the experimental data using our approach suggests a novel action of OM, whereby it modifies interactions between myosin and tropomyosin proteins. To validate our approach, the inferred model parameters were used to replicate other in vitro experimental protocols, such as skinned preparations demonstrating an increase in calcium sensitivity and a decrease in the Hill coefficient of the force–calcium (F–Ca) curve under OM action. Our approach thereby facilitated the identification of the mechanistic underpinnings of experimental observations and the exploration of different hypotheses regarding variability in this complex biological system. © 2021, The Author(s).
Keywords: BIOPHYSICAL MODELS
GENERATIVE ADVERSARIAL NETWORKS
OMECAMTIV MECARBIL
PARAMETER INFERENCE
POPULATIONS OF MODELS
URI: http://hdl.handle.net/10995/111333
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85118314674
PURE ID: 29615496
ISSN: 1567-567X
DOI: 10.21538/0134-4889-2020-26-3-275-285
metadata.dc.description.sponsorship: This work was partially supported by the EU’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie (g.a. 764738) state Program (No. AAAA-A19-119070190064-4) and the research grant from RFBR (No. 19-31-90089).
CORDIS project card: H2020: 764738
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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