Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action

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.

Inferring parameters of pyramidal neuron excitability in mouse models of Alzheimer’s disease using biophysical modeling and deep learning

“…Here, we have introduced and employed a deep hybrid modeling (DeepHM) framework [17,20] featuring conditional generative adversarial networks (cGANs) that can be categorized as a population of models technique. We compared the performance of cGAN and a standard Bayesian inference Markov chain Monte Carlo (MCMC) method [40] on a parameter inference task with synthetic target data where the ground truth was known.…”

“…The problem of constructing populations of deterministic models and identifying distributions of model input parameters from stochastic observations from multiple individuals in a population is known as the stochastic inverse problem (SIP). State-of-the-art methods for solving SIPs apply Bayesian inference techniques, including Markov chain Monte Carlo (MCMC) sampling, and are limited to finding a distribution for a single set of observations [11,[17][18][19]. To draw inferences about a new target dataset, the SIP would have to be solved again.…”

Generative adversarial networks for modelling clinical biomarker profiles with race/ethnicity

“…The literature on GANs in pharmacometrics is sparse. Parikh et al 15 . have used GANs to generate instances of models for cardiac mechanics in control myocytes and myocytes treated with omecamtiv mecarbil, a new drug for treating heart failure.…”

Generative Adversarial Networks for Modeling Clinical Biomarker Profiles in Under-Represented Groups

“…Parikh et al . 14 have used GANs to generate instances of models for cardiac mechanics in control myocytes and myocytes treated with omecamtiv mecarbil, a new drug for treating heart failure. The GANs were used to find model parameters for fitting the data for both groups.…”