Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells

A major challenge in computational biology is constraining free parameters in mathematical models. Adjusting a parameter to make a given model output more realistic sometimes has unexpected and undesirable effects on other model behaviors. Here, we extend a regression-based method for parameter sensitivity analysis and show that a straightforward procedure can uniquely define most ionic conductances in a well-known model of the human ventricular myocyte. The model's parameter sensitivity was analyzed by randomizing ionic conductances, running repeated simulations to measure physiological outputs, then collecting the randomized parameters and simulation results as “input” and “output” matrices, respectively. Multivariable regression derived a matrix whose elements indicate how changes in conductances influence model outputs. We show here that if the number of linearly-independent outputs equals the number of inputs, the regression matrix can be inverted. This is significant, because it implies that the inverted matrix can specify the ionic conductances that are required to generate a particular combination of model outputs. Applying this idea to the myocyte model tested, we found that most ionic conductances could be specified with precision (R2 > 0.77 for 12 out of 16 parameters). We also applied this method to a test case of changes in electrophysiology caused by heart failure and found that changes in most parameters could be well predicted. We complemented our findings using a Bayesian approach to demonstrate that model parameters cannot be specified using limited outputs, but they can be successfully constrained if multiple outputs are considered. Our results place on a solid mathematical footing the intuition-based procedure simultaneously matching a model's output to several data sets. More generally, this method shows promise as a tool to define model parameters, in electrophysiology and in other biological fields

Calcium Sparks and Homeostasis in a Minimal Model of Local and Global Calcium Responses in Quiescent Ventricular Myocytes

We present a minimal whole cell model that accounts for both local and global aspects of Ca signaling in quiescent ventricular myocytes..

Calcium Homeostasis in a Local/Global Whole Cell Model of Permeabilized Ventricular Myocytes with a Langevin Description of Stochastic Calcium Release

Population density approaches to modeling local control of Ca2+ induced Ca2+ release in cardiac myocytes can be used to construct minimal whole cell models that accurately represent heterogeneous local Ca2+ signals. Unfortunately, the computational complexity of such local/global whole cell models scales with the number of Ca2+ release unit (CaRU) states, which is a rapidly increasing function of the number of ryanodine receptors (RyRs) per CaRU. Here we present an alternative approach based on a Langevin description of the collective gating of RyRs coupled by local Ca2+ concentration ([Ca2+]). The computational efficiency of this approach no longer depends on the number of RyRs per CaRU. When the RyR model is minimal, Langevin equations may be replaced by a single Fokker-Planck equation, yielding an extremely compact and efficient local/global whole cell model that reproduces and helps interpret recent experiments that investigate Ca2+ homeostasis in permeabilized ventricular myocytes. Our calculations show that elevated myoplasmic [Ca2+] promotes elevated network sarcoplasmic reticulum (SR) [Ca2+] via SR Ca2+ -ATPase-mediated Ca2+ uptake. However, elevated myoplasmic [Ca2+] may also activate RyRs and promote stochastic SR Ca2+ release, which can in turn decrease SR [Ca2+]. Increasing myoplasmic [Ca2+] results in an exponential increase in spark-mediated release and a linear increase in nonspark-mediated release, consistent with recent experiments. The model exhibits two steady-state release fluxes for the same network SR [Ca2+] depending on whether myoplasmic [Ca2+] is low or high. In the later case, spontaneous release decreases SR [Ca2+] in a manner that maintains robust Ca2+ sparks

The role of beta-adrenergic system remodeling in human heart failure: A mechanistic investigation

[EN] ß-adrenergic receptor antagonists (ß-blockers) are extensively used to improve cardiac performance in heart failure (HF), but the electrical improvements with these clinical treatments are not fully understood. The aim of this study was to analyze the electrophysiological effects of ß-adrenergic system remodeling in heart failure with reduced ejection fraction and the underlying mechanisms. We used a combined mathematical model that integrated ß-adrenergic signaling with electrophysiology and calcium cycling in human ventricular myocytes. HF remodeling, both in the electrophysiological and signaling systems, was introduced to quantitatively analyze changes in electrophysiological properties due to the stimulation of ß-adrenergic receptors in failing myocytes. We found that the inotropic effect of ß-adrenergic stimulation was reduced in HF due to the altered Ca2+ dynamics resulting from the combination of structural, electrophysiological and signaling remodeling. Isolated cells showed proarrhythmic risk after sympathetic stimulation because early afterdepolarizations appeared, and the vulnerability was greater in failing myocytes. When analyzing coupled cells, ß-adrenergic stimulation reduced transmural repolarization gradients between endocardium and epicardium in normal tissue, but was less effective at reducing these gradients after HF remodeling. The comparison of the selective activation of ß-adrenergic isoforms revealed that the response to ß2-adrenergic receptors stimulation was blunted in HF while ß1-adrenergic receptors downstream effectors regulated most of the changes observed after sympathetic stimulation. In conclusion, this study was able to reproduce an altered ß-adrenergic activity on failing myocytes and to explain the mechanisms involved. The derived predictions could help in the treatment of HF and guide in the design of future experiments.This work was partially supported by the "Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016" from the Ministerio de Economía, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE), by the "Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020" from the Ministerio de Ciencia e Innovación y Universidades (PID2019-104356RB-C41/AEI/10.13039/5011000110 33), and by the "Programa de Ayudas de Investigación y Desarrollo (PAID-01-17)" from the Universitat Politècnica de València.Mora-Fenoll, MT.; Gong, JQX.; Sobie, EA.; Trenor Gomis, BA. (2021). The role of beta-adrenergic system remodeling in human heart failure: A mechanistic investigation. 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(2001). Norepinephrine induces action potential prolongation and early afterdepolarizations in ventricular myocytes isolated from human end-stage failing hearts. European Heart Journal, 22(11), 955-963. doi:10.1053/euhj.2000.2499Wang, Y., Yuan, J., Qian, Z., Zhang, X., Chen, Y., Hou, X., & Zou, J. (2015). β2 adrenergic receptor activation governs cardiac repolarization and arrhythmogenesis in a guinea pig model of heart failure. Scientific Reports, 5(1). doi:10.1038/srep07681Lowe, M. D. (2001). beta2 Adrenergic receptors mediate important electrophysiological effects in human ventricular myocardium. Heart, 86(1), 45-51. doi:10.1136/heart.86.1.45Nikolaev, V. O., Bünemann, M., Schmitteckert, E., Lohse, M. J., & Engelhardt, S. (2006). Cyclic AMP Imaging in Adult Cardiac Myocytes Reveals Far-Reaching β 1 -Adrenergic but Locally Confined β 2 -Adrenergic Receptor–Mediated Signaling. Circulation Research, 99(10), 1084-1091. doi:10.1161/01.res.0000250046.69918.d5A.D. Loucks, T. O'Hara, N.A. Trayanova, Degradation of T-tubular microdomains and altered cAMP Compartmentation Lead to emergence of Arrhythmogenic triggers in heart failure Myocytes: an in silico study, Front. Physiol. 9 (2018) 1–12. doi:https://doi.org/10.3389/fphys.2018.01737.Rocchetti, M., Alemanni, M., Mostacciuolo, G., Barassi, P., Altomare, C., Chisci, R., … Zaza, A. (2008). Modulation of Sarcoplasmic Reticulum Function by PST2744 [Istaroxime; (E,Z)-3-((2-Aminoethoxy)imino) Androstane-6,17-dione Hydrochloride)] in a Pressure-Overload Heart Failure Model. Journal of Pharmacology and Experimental Therapeutics, 326(3), 957-965. doi:10.1124/jpet.108.138701Dong, X., & Thomas, D. D. (2014). Time-resolved FRET reveals the structural mechanism of SERCA–PLB regulation. Biochemical and Biophysical Research Communications, 449(2), 196-201. doi:10.1016/j.bbrc.2014.04.166Lucia, C. de, Eguchi, A., & Koch, W. J. (2018). New Insights in Cardiac β-Adrenergic Signaling During Heart Failure and Aging. 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A network medicine approach to build a comprehensive atlas for the prognosis of human cancer

The Cancer Genome Atlas project has generated multi-dimensional and highly integrated genomic data from a large number of patient samples with detailed clinical records across many cancer types, but it remains unclear how to best integrate the massive amount of genomic data into clinical practice. We report here our methodology to build a multi-dimensional subnetwork atlas for cancer prognosis to better investigate the potential impact of multiple genetic and epigenetic (gene expression, copy number variation, microRNA expression and DNA methylation) changes on the molecular states of networks that in turn affects complex cancer survivorship. We uncover an average of 38 novel subnetworks in the protein-protein interaction network that correlate with prognosis across four prominent cancer types. The clinical utility of these subnetwork biomarkers was further evaluated by prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumor stratification and independent validation. Some pathways including the dynactin, cohesion and pyruvate dehydrogenase-related subnetworks are identified as promising new targets for therapy in specific cancer types. In conclusion, this integrative analysis of existing protein interactome and cancer genomics data allows us to systematically dissect the molecular mechanisms that underlie unexpected outcomes for cancer, which could be used to better understand and predict clinical outcomes, optimize treatment and to provide new opportunities for developing therapeutics related to the subnetworks identified

Mechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics

Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation–contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration–sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model‐based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm