Computational models reduce complexity and accelerate insight into cardiac signaling networks

Fluorescent biosensor imaging meets deterministic mathematical modelling: quantitative investigation of signalling compartmentalization

Cells execute specific responses to diverse environmental cues by encoding information in distinctly compartmentalized biochemical signalling reactions. Genetically encoded fluorescent biosensors enable the spatial and temporal monitoring of signalling events in live cells. Temporal and spatiotemporal computational models can be used to interpret biosensor experiments in complex biochemical networks and to explore hypotheses that are difficult to test experimentally. In this review, we first provide brief discussions of the experimental toolkit of fluorescent biosensors as well as computational basics with a focus on temporal and spatiotemporal deterministic models. We then describe how we used this combined approach to identify and investigate a protein kinase A (PKA) - cAMP - Ca2+ oscillatory circuit in MIN6 β cells, a mouse pancreatic β cell system. We describe the application of this combined approach to interrogate how this oscillatory circuit is differentially regulated in a nano-compartment formed at the plasma membrane by the scaffolding protein A kinase anchoring protein 79/150. We leveraged both temporal and spatiotemporal deterministic models to identify the key regulators of this oscillatory circuit, which we confirmed with further experiments. The powerful approach of combining live-cell biosensor imaging with quantitative modelling, as discussed here, should find widespread use in the investigation of spatiotemporal regulation of cell signalling.

Combining Cell Technologies With Biomimetic Tissue Engineering Applications: A New Paradigm for Translational Cardiovascular Therapies

Cardiovascular disease is a major cause of morbidity and mortality worldwide and, to date, the clinically available prostheses still present several limitations. The design of next-generation regenerative replacements either based on cellular or extracellular matrix technologies can address these shortcomings. Therefore, tissue engineered constructs could potentially become a promising alterative to the current therapeutic options for patients with cardiovascular diseases. In this review, we selectively present an overview of the current tissue engineering tools such as induced pluripotent stem cells, biomimetic materials, computational modeling, and additive manufacturing technologies, with a focus on their application to translational cardiovascular therapies. We discuss how these advanced technologies can help the development of biomimetic tissue engineered constructs and we finally summarize the latest clinical evidence for their use, and their potential therapeutic outcome.

Directed evolution of the genetically encoded zinc(II) FRET sensor ZapCY1

Zinc(II) ions (Zn²⁺) play an essential role in living systems, with their delicate concentration balance differing among the various intracellular organelles. The spatiotemporal distribution and homeostasis of Zn²⁺ can be monitored through photoluminescence imaging using zinc sensors. Among such biosensors, genetically encoded fluorescent sensor proteins are attractive tools owing to their subcellular localization advantage and high biocompatibility. However, the limited fluorescent properties of these proteins, such as their insufficient quantum yield and dynamic range, restrict their practical use. In this study, we developed an expression-screening-directed evolution system and used it to improve ZapCY1, a genetically encoded fluorescence resonance energy transfer (FRET) sensor. After four rounds of directed evolution, the FRET dynamic range of the modified sensor (designated ZapTV-EH) was increased by 1.5-1.7-fold. With its enhanced signal-to-noise ratio and ability to detect a wide Zn²⁺ concentration range, ZapTV-EH proves to be a better visualization tool for monitoring Zn²⁺ at the subcellular level. Combined with the simplified subcloning and expression steps and sufficient mutant libraries, this directed evolution system may provide a more simple and efficient way to develop and optimize genetically encoded FRET sensors through high-throughput screening.

Mechanisms of Lian-Gui-Ning-Xin-Tang in the treatment of arrhythmia: Integrated pharmacology and in vivo pharmacological assessment

BACKGROUND

Lian-Gui-Ning-Xin-Tang (LGNXT), a classical traditional Chinese medicine (TCM) formula, has been widely used in clinical practice and has shown satisfactory efficacy in the treatment of arrhythmias. However, its mechanism of action in the treatment of arrhythmias is still unknown. Moreover, the complex chemical composition and therapeutic targets of LGNXT pose a challenge in pharmacological research.

PURPOSE

To analyze the active compounds and action mechanisms of LGNXT for the treatment of arrhythmias.

METHODS

Here, we used an integrated pharmacology approach to identify the potential active compounds and mechanisms of action of LGNXT in treating arrhythmias. Potential active compounds in LGNXT were identified using ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) and the potential related targets of these compounds were predicted using an integrated in silico approach. The obtained targets were mapped onto relevant databases to identify their corresponding pathways, following the experiments that were conducted to confirm whether the presumptive results of systemic pharmacology were correct.

RESULTS

Eighty-three components were identified in herbal materials and in animal plasma using UPLC-Q-TOF/MS and were considered the potential active components of LGNXT. Thirty key targets and 57 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified as possible targets and pathways involved in LGNXT-mediated treatment using network pharmacology, with the cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/Ca2+ system pathway being the most significantly affected. This finding was validated using an adrenaline (Adr)-induced rat model of arrhythmias. Pretreatment with LGNXT delayed the occurrence, shortened the duration, and reduced the severity of arrhythmias. LGNXT exerted antiarrhythmic effects by inhibiting cAMP, PKA, CACNA1C, and RyR2.

CONCLUSIONS

The findings of this study revealed that preventing intracellular Ca2+ overload and maintaining intracellular Ca2+ homeostasis may be the primary mechanisms of LGNXT in alleviating arrhythmias. Thus, we suggest that the β-adrenergic receptor (AR)/cAMP/PKA/Ca2+ system signaling hub may constitute a promising molecular target for the development of novel antiarrhythmic therapeutic interventions. Additionally, we believe that the approach of investigation of the biological effects of a multi-herbal formula by the combination of metabolomics and network pharmacology, as used in this study, could serve as a systematic model for TCM research.

Potential Mechanism of Dingji Fumai Decoction Against Atrial Fibrillation Based on Network Pharmacology, Molecular Docking, and Experimental Verification Integration Strategy

Background: Dingji Fumai Decoction (DFD), a traditional herbal mixture, has been widely used to treat arrhythmia in clinical practice in China. However, the exploration of the active components and underlying mechanism of DFD in treating atrial fibrillation (AF) is still scarce.

Methods: Compounds of DFD were collected from TCMSP, ETCM, and literature. The targets of active compounds were explored using SwissTargetPrediction. Meanwhile, targets of AF were collected from DrugBank, TTD, MalaCards, TCMSP, DisGeNET, and OMIM. Then, the H-C-T-D and PPI networks were constructed using STRING and analyzed using CytoNCA. Meanwhile, VarElect was utilized to detect the correlation between targets and diseases. Next, Metascape was employed for systematic analysis of the mechanism of potential targets and protein complexes in treating AF. AutoDock Vina, Pymol, and Discovery Studio were applied for molecular docking. Finally, the main findings were validated through molecular biology experiments.

Results: A total of 168 active compounds and 1,093 targets of DFD were collected, and there were 89 shared targets between DFD and AF. H-C-T-D network showed the relationships among DFD, active compounds, targets, and AF. Three functional protein complexes of DFD were extracted from the PPI network. Further systematic analysis revealed that the regulation of cardiac oxidative stress, cardiac inflammation, and cardiac ion channels were the potential mechanism of DFD in treating AF. Addtionally, molecular docking verified the interactions between active compounds and targets. Finally, we found that DFD significantly increased the level of SIRT1 and reduced the levels of ACE, VCAM-1, and IL-6.

Conclusions: DFD could be utilized in treating AF through a complicated mechanism, including interactions between related active compounds and targets, promoting the explanation and understanding of the molecular biological mechanism of DFD in the treatment of AF.

Neutrophil crosstalk during cardiac wound healing after myocardial infarction

Myocardial infarction (MI) initiates an intense inflammatory response that induces neutrophil infiltration into the infarct region. Neutrophils commence the pro-inflammatory response that includes upregulation of cytokines and chemokines (e.g., interleukin-1 beta) and degranulation of pre-formed proteases (e.g., matrix metalloproteinases -8 and -9) that degrade existing extracellular matrix to clear necrotic tissue. An increase or complete depletion of neutrophils both paradoxically impair MI resolution, indicating a complex role of neutrophils in cardiac wound healing. Following pro-inflammation, the neutrophil shifts to a reparative phenotype that promotes inflammation resolution and aids in scar formation. Across the shifts in phenotype, the neutrophil communicates with other cells to coordinate repair and scar formation. This review summarizes our current understanding of neutrophil crosstalk with cardiomyocytes and macrophages during MI wound healing.

Protective Effects of 3,4-Seco-Lupane Triterpenes from Food Raw Materials of the Leaves of Eleutherococcus Senticosus and Eleutherococcus Sessiliflorus on Arrhythmia Induced by Barium Chloride

As a traditional wild vegetable and food raw material, the leaves of Eleutherococcus senticosus and Eleutherococcus sessiliflorus are rich in 3,4-seco-lupane triterpenes, including chiisanoside (CSS), divaroside (DVS), sessiloside-A (SSA), and chiisanogenin (CSG). This study was conducted to evaluate the anti-arrhythmic effects of these 3,4-seco-lupane triterpenes. Evaluation of the cytotoxicity of compounds was performed by measuring cell viability and apoptosis with the CCK-8 assay. In vivo, arrhythmia was induced by rapid injection of BaCl₂ via rat caudal vein. The occurrence time and duration of arrhythmias in rats were studied. The levels of SOD and MDA in serum, and Na⁺ -K⁺ -ATPase and Ca²⁺ -Mg²⁺ -ATPase in myocardial homogenate were detected by ELISA. The histopathological changes of rats myocardial were observed by HE staining. Changes in the expression of PKA and related proteins were detected by Western blot. The 3,4-seco-lupane triterpenes interactions with protein kinase A were analyzed by molecular docking. In the present study, we found that 3,4-seco-lupane triterpenes exhibited powerful anti-arrhythmic activity, especially DVS completely relieved the ventricular arrhythmia induced by BaCl₂ . This study suggests that the leaves of E. senticosus and E. sessiliflorus might be used as functional food materials to prevent arrhythmia, and DVS can potentially be further developed as an anti-arrhythmic drug.

Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues

Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often-conflicting cues that macrophages experience requires a network perspective. In this study, we integrate data from literature curation and mRNA expression profiles obtained from wild type C57/BL6J mice macrophages to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of nine cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, IL-4 with LPS, IFN-γ, IFN-β, or TNF-α, produced mutual inhibition of several signaling pathways, e.g., NF-κB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-γ and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., iNOS or Nos2 and Arg1, or mutually enhanced, e.g., Il4rα and Socs1, validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional cross-talk, such as mutual STAT3/STAT6-mediated enhancement of Il4rα expression. In summary, the computational model predicts that network cross-talk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios.

Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy

Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical and biomechanical factors regulate myocardial growth through a complex large-scale signaling network. Although numerous studies have investigated hypertrophic signaling pathways, less is known about hypertrophy signaling as a whole network and how this network acts in a context-dependent manner. Here, we developed a systematic approach, CLASSED (Context-specific Logic-bASed Signaling nEtwork Development), to revise a large-scale signaling model based on context-specific data and identify main reactions and new crosstalks regulating context-specific response. CLASSED involves four sequential stages with an automated validation module as a core which builds a logic-based ODE model from the interaction graph and outputs the model validation percent. The context-specific model is developed by estimation of default parameters, classified qualitative validation, hybrid Morris-Sobol global sensitivity analysis, and discovery of missing context-dependent crosstalks. Applying this pipeline to our prior-knowledge hypertrophy network with context-specific data revealed key signaling reactions which distinctly regulate cell response to isoproterenol, phenylephrine, angiotensin II and stretch. Furthermore, with CLASSED we developed a context-specific model of β-adrenergic cardiac hypertrophy. The model predicted new crosstalks between calcium/calmodulin-dependent pathways and upstream signaling of Ras in the ISO-specific context. Experiments in cardiomyocytes validated the model’s predictions on the role of CaMKII-Gβγ and CaN-Gβγ interactions in mediating hypertrophic signals in ISO-specific context and revealed a difference in the phosphorylation magnitude and translocation of ERK1/2 between cardiac myocytes and fibroblasts. CLASSED is a systematic approach for developing context-specific large-scale signaling networks, yielding insights into new-found crosstalks in β-adrenergic cardiac hypertrophy.

Pathological cardiac hypertrophy is a disease in which the heart grows abnormally in response to different motivators such as high blood pressure or variations in hormones and growth factors. The shape of the heart after its growth depends on the context in which it grows. Since cell signaling in the cardiac cells plays a key role in the determination of heart shape, a thorough understanding of cardiac cells signaling in each context enlightens the mechanisms which control response of cardiac cells. However, cell signaling in cardiac hypertrophy comprises a complex web of pathways with numerous interactions, and predicting how these interactions control the hypertrophic signal in each context is not achievable by only experiments or general computational models. To address this need, we developed an approach to bring together the experimental data of each context with a signaling network curated from literature to identify the main players of cardiac cells response in each context and attain the context-specific models of cardiac hypertrophy. By utilizing our approach, we identified the main regulators of cardiac hypertrophy in four important contexts. We developed a network model of β-adrenergic cardiac hypertrophy, and predicted and validated new interactions that regulate cardiac cells response in this context.

Biomechanics of infarcted left ventricle: a review of modelling

Mathematical modelling in biomechanics of infarcted left ventricle (LV) serves as an indispensable tool for remodelling mechanism exploration, LV biomechanical property estimation and therapy assessment after myocardial infarction (MI). However, a review of mathematical modelling after MI has not been seen in the literature so far. In the paper, a systematic review of mathematical models in biomechanics of infarcted LV was established. The models include comprehensive cardiovascular system model, essential LV pressure-volume and stress-stretch models, constitutive laws for passive myocardium and scars, tension models for active myocardium, collagen fibre orientation optimization models, fibroblast and collagen fibre growth/degradation models and integrated growth-electro-mechanical model after MI. The primary idea, unique characteristics and key equations of each model were identified and extracted. Discussions on the models were provided and followed research issues on them were addressed. Considerable improvements in the cardiovascular system model, LV aneurysm model, coupled agent-based models and integrated electro-mechanical-growth LV model are encouraged. Substantial attention should be paid to new constitutive laws with respect to stress-stretch curve and strain energy function for infarcted passive myocardium, collagen fibre orientation optimization in scar, cardiac rupture and tissue damage and viscoelastic effect post-MI in the future.

A Mathematical Model of the Mouse Atrial Myocyte With Inter-Atrial Electrophysiological Heterogeneity

Biophysically detailed mathematical models of cardiac electrophysiology provide an alternative to experimental approaches for investigating possible ionic mechanisms underlying the genesis of electrical action potentials and their propagation through the heart. The aim of this study was to develop a biophysically detailed mathematical model of the action potentials of mouse atrial myocytes, a popular experimental model for elucidating molecular and cellular mechanisms of arrhythmogenesis. Based on experimental data from isolated mouse atrial cardiomyocytes, a set of mathematical equations for describing the biophysical properties of membrane ion channel currents, intracellular Ca²⁺ handling, and Ca²⁺-calmodulin activated protein kinase II and β-adrenergic signaling pathways were developed. Wherever possible, membrane ion channel currents were modeled using Markov chain formalisms, allowing detailed representation of channel kinetics. The model also considered heterogeneous electrophysiological properties between the left and the right atrial cardiomyocytes. The developed model was validated by its ability to reproduce the characteristics of action potentials and Ca²⁺ transients, matching quantitatively to experimental data. Using the model, the functional roles of four K⁺ channel currents in atrial action potential were evaluated by channel block simulations, results of which were quantitatively in agreement with existent experimental data. To conclude, this newly developed model of mouse atrial cardiomyocytes provides a powerful tool for investigating possible ion channel mechanisms of atrial electrical activity at the cellular level and can be further used to investigate mechanisms underlying atrial arrhythmogenesis.

Cardiomyocyte calcium handling in health and disease: Insights from in vitro and in silico studies

Calcium (Ca²⁺) plays a central role in cardiomyocyte excitation-contraction coupling. To ensure an optimal electrical impulse propagation and cardiac contraction, Ca²⁺ levels are regulated by a variety of Ca²⁺-handling proteins. In turn, Ca²⁺ modulates numerous electrophysiological processes. Accordingly, Ca²⁺-handling abnormalities can promote cardiac arrhythmias via various mechanisms, including the promotion of afterdepolarizations, ion-channel modulation and structural remodeling. In the last 30 years, significant improvements have been made in the computational modeling of cardiomyocyte Ca²⁺ handling under physiological and pathological conditions. However, numerous questions involving the Ca²⁺-dependent regulation of different macromolecular complexes, cross-talk between Ca²⁺-dependent regulatory pathways operating over a wide range of time scales, and bidirectional interactions between electrophysiology and mechanics remain to be addressed by in vitro and in silico studies. A better understanding of disease-specific Ca²⁺-dependent proarrhythmic mechanisms may facilitate the development of improved therapeutic strategies. In this review, we describe the fundamental mechanisms of cardiomyocyte Ca²⁺ handling in health and disease, and provide an overview of currently available computational models for cardiomyocyte Ca²⁺ handling. Finally, we discuss important uncertainties and open questions about cardiomyocyte Ca²⁺ handling and highlight how synergy between in vitro and in silico studies may help to answer several of these issues.

Stretch-activated current in human atrial myocytes and Na+ current and mechano-gated channels' current in myofibroblasts alter myocyte mechanical behavior: a computational study

Background

The activation of stretch-activated channels (SACs) in cardiac myocytes, which changes the phases of action potential repolarization, is proven to be highly efficient for the conversion of atrial fibrillation. The expression of Na⁺ current in myofibroblasts (Mfbs) regenerates myocytes’ action potentials, suggesting that Mfbs play an active role in triggering cardiac rhythm disturbances. Moreover, the excitation of mechano-gated channels (MGCs) in Mfbs depolarizes their membrane potential and contributes to the increased risk of post-infarct arrhythmia. Although these electrophysiological mechanisms have been largely known, the roles of these currents in cardiac mechanics are still debated. In this study, we aimed to investigate the mechanical influence of these currents via mathematical modeling. A novel mathematical model was developed by integrating models of human atrial myocyte (including the stretch-activated current, Ca²⁺–force relation, and mechanical behavior of a single segment) and Mfb (including our formulation of Na⁺ current and mechano-gated channels’ current). The effects of the changes in basic cycle length, number of coupled Mfbs and intercellular coupling conductance on myocyte mechanical properties were compared.

Results

Our results indicated that these three currents significantly regulated myocyte mechanical parameters. In isosarcometric contraction, these currents increased segment force by 13.8–36.6% and dropped element length by 12.1–31.5%. In isotonic contraction, there are 2.7–5.9% growth and 0.9–24% reduction. Effects of these currents on the extremum of myocyte mechanical parameters become more significant with the increase of basic cycle length, number of coupled Mfbs and intercellular coupling conductance.

Conclusions

The results demonstrated that stretch-activated current in myocytes and Na⁺ current and mechano-gated channels’ current in Mfbs significantly influenced myocyte mechanical behavior and should be considered in future cardiac mechanical mathematical modeling.

Mechanical regulation of gene expression in cardiac myocytes and fibroblasts

The intact heart undergoes complex and multiscale remodelling processes in response to altered mechanical cues. Remodelling of the myocardium is regulated by a combination of myocyte and non-myocyte responses to mechanosensitive pathways, which can alter gene expression and therefore function in these cells. Cellular mechanotransduction and its downstream effects on gene expression are initially compensatory mechanisms during adaptations to the altered mechanical environment, but under prolonged and abnormal loading conditions, they can become maladaptive, leading to impaired function and cardiac pathologies. In this Review, we summarize mechanoregulated pathways in cardiac myocytes and fibroblasts that lead to altered gene expression and cell remodelling under physiological and pathophysiological conditions. Developments in systems modelling of the networks that regulate gene expression in response to mechanical stimuli should improve integrative understanding of their roles in vivo and help to discover new combinations of drugs and device therapies targeting mechanosignalling in heart disease.

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.

Genetically Encoded Fluorescent Biosensors Illuminate the Spatiotemporal Regulation of Signaling Networks

Cellular signaling networks are the foundation which determines the fate and function of cells as they respond to various cues and stimuli. The discovery of fluorescent proteins over 25 years ago enabled the development of a diverse array of genetically encodable fluorescent biosensors that are capable of measuring the spatiotemporal dynamics of signal transduction pathways in live cells. In an effort to encapsulate the breadth over which fluorescent biosensors have expanded, we endeavored to assemble a comprehensive list of published engineered biosensors, and we discuss many of the molecular designs utilized in their development. Then, we review how the high temporal and spatial resolution afforded by fluorescent biosensors has aided our understanding of the spatiotemporal regulation of signaling networks at the cellular and subcellular level. Finally, we highlight some emerging areas of research in both biosensor design and applications that are on the forefront of biosensor development.

Cardiac macrophage biology in the steady-state heart, the aging heart, and following myocardial infarction

Macrophages play critical roles in homeostatic maintenance of the myocardium under normal conditions and in tissue repair after injury. In the steady-state heart, resident cardiac macrophages remove senescent and dying cells and facilitate electrical conduction. In the aging heart, the shift in macrophage phenotype to a proinflammatory subtype leads to inflammaging. Following myocardial infarction (MI), macrophages recruited to the infarct produce both proinflammatory and anti-inflammatory mediators (cytokines, chemokines, matrix metalloproteinases, and growth factors), phagocytize dead cells, and promote angiogenesis and scar formation. These diverse properties are attributed to distinct macrophage subtypes and polarization status. Infarct macrophages exhibit a proinflammatory M1 phenotype early and become polarized toward an anti-inflammatory M2 phenotype later post-MI. Although this classification system is oversimplified and needs to be refined to accommodate the multiple different macrophage subtypes that have been recently identified, general concepts on macrophage roles are independent of subtype classification. This review summarizes current knowledge about cardiac macrophage origins, roles, and phenotypes in the steady state, with aging, and after MI, as well as highlights outstanding areas of investigation.

An Introduction to Computational Modeling of Cardiac Electrophysiology and Arrhythmogenicity

Mathematical modeling is a powerful tool to study the complex and orchestrated biological process of cardiac electrical activity. By integrating experimental data from key components of cardiac electrophysiology, systems biology simulations can complement empirical findings, provide quantitative insight into physiological and pathophysiological mechanisms of action, and guide new hypotheses to better understand this complex biological system to develop novel cardiotherapeutic approaches. In this chapter, we briefly introduce in silico methods to describe the dynamics of physiological and pathophysiological single-cell and tissue-level cardiac electrophysiology. Using a "bottom-up" approach, we first describe the basis of ion channel mathematical models. Next, we discuss how the net flux of ions through such channels leads to changes in transmembrane voltage during cardiomyocyte action potentials. By applying these fundamentals, we describe how action potentials propagate in models of cardiac tissue. In addition, we provide case studies simulating single-cell and tissue-level arrhythmogenesis, as well as promising approaches to circumvent or overcome such adverse events. Overall, basic concepts and tools are discussed in this chapter as an accessible introduction to nonmathematicians to foster an understanding of electrophysiological modeling studies and help facilitate communication with dry lab colleagues and collaborators.

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling

Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Common stresses such as high blood pressure or heart attack can lead to heart failure, which afflicts over 25 million people worldwide. These stresses cause cardiomyocytes to grow and remodel, which may initially be beneficial but ultimately worsen heart function. Current heart failure drugs such as beta-blockers counteract biochemical cues prompting cardiomyocyte growth, yet mechanical cues to cardiomyocytes such as stretch are just as important in driving cardiac dysfunction. However, no pharmacological treatments have yet been approved that specifically target mechano-signaling, in part because it is not clear how cardiomyocytes integrate signals from multiple mechano-responsive sensors and pathways into their decision to grow. To address this challenge, we built a systems-level computational model that represents 125 interactions between 94 stretch-responsive signaling molecules. The model correctly predicts 134 of 172 previous independent experimental observations, and identifies the key regulators of stretch-induced cardiomyocyte remodeling. Although cardiomyocytes have many mechano-signaling pathways that function largely independently, we find that cooperation between them is necessary to cause growth and remodeling. We identify mechanisms by which a recently approved heart failure drug pair affects mechano-signaling, and we further predict additional pairs of drug targets that could be used to help reverse heart failure.

Whole heart modeling - Spatiotemporal dynamics of electrical wave conduction and propagation

Cardiac electrical activities are varying in both space and time. Human heart consists of a fractal network of muscle cells, Purkinje fibers, arteries and veins. Whole-heart modeling of electrical wave conduction and propagation involves a greater level of complexity. Our previous work developed a computer model of the anatomically realistic heart and simulated the electrical conduction with the use of cellular automata. However, simplistic assumptions and rules limit its ability to provide an accurate approximation of real-world dynamics on the complex heart surface, due to sensitive dependence of nonlinear dynamical systems on initial conditions. In this paper, we propose new reaction-diffusion methods and pattern recognition tools to simulate and model spatiotemporal dynamics of electrical wave conduction and propagation on the complex heart surface, which include (i) whole heart model; (ii) 2D isometric graphing of 3D heart geometry; (iii) reaction-diffusion modeling of electrical waves in 2D graph, and (iv) spatiotemporal pattern recognition. Experimental results show that the proposed numerical solution has strong potentials to model the space-time dynamics of electrical wave conduction in the whole heart, thereby achieving a better understanding of disease-altered cardiac mechanisms.

The promises of quantitative systems pharmacology modelling for drug development

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Knowledge gaps to understanding cardiac macrophage polarization following myocardial infarction

Following myocardial infarction (MI), macrophages coordinate both pro-inflammatory and reparative responses of the left ventricle (LV) by reacting to and secreting cytokines, chemokines, and growth factors and by stimulating endothelial cells and fibroblasts to modulate neovascularization and scar formation. Healing of the infarcted LV can be divided into three distinct, but overlapping phases: inflammatory, proliferative, and maturation. Macrophages are involved in all phases. Despite macrophages being a major leukocyte cell type in the post-MI LV, how this cell type regulates LV remodeling over the post-MI time continuum is not completely understood. In this review, we summarize the current literature as a foundation to discuss the major knowledge gaps that remain. Defining the post-MI temporal macrophage phenotypes to establish a classification system is the first step in exploring how macrophage phenotypes are regulated, how temporal stimulation and secretion profiles evolve, and how best to modify stimuli to yield predictable cell responses. This article is part of a Special Issue entitled: The role of post-translational protein modifications on heart and vascular metabolism edited by Jason R.B. Dyck & Jan F.C. Glatz.

Conceptual Models of Frailty: Accumulation of Deficits

Frailty was introduced to explain why people of the same age have varying degrees of risk. The deficit accumulation approach shows that as people age, they accumulate health deficits, and that more deficits confer greater risk. Frailty results because not everyone of the same age has the same number of deficits. This is readily quantified using a frailty index, which has been translated to preclinical models. The frailty index grades risk without requiring special instrumentation. It allows a central clinical challenge to be addressed, which is that with age, diseases rarely travel alone.

Computational modeling of cardiac fibroblasts and fibrosis

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation

Cardiac fibroblasts support heart function, and aberrant fibroblast signaling can lead to fibrosis and cardiac dysfunction. Yet how signaling molecules drive myofibroblast differentiation and fibrosis in the complex signaling environment of cardiac injury remains unclear. We developed a large-scale computational model of cardiac fibroblast signaling in order to identify regulators of fibrosis under diverse signaling contexts. The model network integrates 10 signaling pathways, including 91 nodes and 134 reactions, and it correctly predicted 80% of independent previous experiments. The model predicted key fibrotic signaling regulators (e.g. reactive oxygen species, tissue growth factor β (TGFβ) receptor), whose function varied depending on the extracellular environment. We characterized how network structure relates to function, identified functional modules, and predicted cross-talk between TGFβ and mechanical signaling, which was validated experimentally in adult cardiac fibroblasts. This study provides a systems framework for predicting key regulators of fibroblast signaling across diverse signaling contexts.

A model model: a commentary on DiFrancesco and Noble (1985) 'A model of cardiac electrical activity incorporating ionic pumps and concentration changes'

This paper summarizes the advances made by the DiFrancesco and Noble (DFN) model of cardiac cellular electrophysiology, which was published in Philosophical Transactions B in 1985. This model was developed at a time when the introduction of new techniques and provision of experimental data had resulted in an explosion of knowledge about the cellular and biophysical properties of the heart. It advanced the cardiac modelling field from a period when computer models considered only the voltage-dependent channels in the surface membrane. In particular, it included a consideration of changes of both intra- and extracellular ionic concentrations. In this paper, we summarize the most important contributions of the DiFrancesco and Noble paper. We also describe how computer modelling has developed subsequently with the extension from the single cell to the whole heart as well as its use in understanding disease and predicting the effects of pharmaceutical interventions. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.

Modeling calcium regulation of contraction, energetics, signaling, and transcription in the cardiac myocyte

Calcium (Ca(2+)) plays many important regulatory roles in cardiac muscle cells. In the initial phase of the action potential, influx of Ca(2+) through sarcolemmal voltage-gated L-type Ca(2+) channels (LCCs) acts as a feed-forward signal that triggers a large release of Ca(2+) from the junctional sarcoplasmic reticulum (SR). This Ca(2+) drives heart muscle contraction and pumping of blood in a process known as excitation-contraction coupling (ECC). Triggered and released Ca(2+) also feed back to inactivate LCCs, attenuating the triggered Ca(2+) signal once release has been achieved. The process of ECC consumes large amounts of ATP. It is now clear that in a process known as excitation-energetics coupling, Ca(2+) signals exert beat-to-beat regulation of mitochondrial ATP production that closely couples energy production with demand. This occurs through transport of Ca(2+) into mitochondria, where it regulates enzymes of the tricarboxylic acid cycle. In excitation-signaling coupling, Ca(2+) activates a number of signaling pathways in a feed-forward manner. Through effects on their target proteins, these interconnected pathways regulate Ca(2+) signals in complex ways to control electrical excitability and contractility of heart muscle. In a process known as excitation-transcription coupling, Ca(2+) acting primarily through signal transduction pathways also regulates the process of gene transcription. Because of these diverse and complex roles, experimentally based mechanistic computational models are proving to be very useful for understanding Ca(2+) signaling in the cardiac myocyte.

Emerging Novel Therapies for Heart Failure

Heart function fails when the organ is unable to pump blood at a rate proportional to the body’s need for oxygen or when this function leads to elevated cardiac chamber filling pressures (cardiogenic pulmonary edema). Despite our sophisticated knowledge of heart failure, even so-called ejection fraction-preserved heart failure has high rates of mortality and morbidity. So, novel therapies are sorely needed. This review discusses current standard therapies for heart failure and launches an exploration into emerging novel treatments on the heels of recently-approved sacubitril and ivbradine. For example, Vasoactive Intestinal Peptide (VIP) is protective of the heart, so in the absence of VIP, VIP knockout mice have dysregulation in key heart failure genes: 1) Force Generation and Propagation; 2) Energy Production and Regulation; 3) Ca⁺² Cycling; 4) Transcriptional Regulators. VIP administration leads to coronary dilation in human subjects. In heart failure patients, VIP levels are elevated as a plausible endogenous protective effect. With the development of elastin polymers to stabilize VIP and prevent its degradation, VIP may therefore have a chance to satisfy the unmet need as a potential treatment for acute heart failure.

Systems approaches in integrative cardiac biology: illustrations from cardiac heterocellular signalling studies

Understanding the complexity of cardiac physiology requires system-level studies of multiple cardiac cell types. Frequently, however, the end result of published research lacks the detail of the collaborative and integrative experimental design process, and the underlying conceptual framework. We review the recent progress in systems modelling and omics analysis of the heterocellular heart environment through complementary forward and inverse approaches, illustrating these conceptual and experimental frameworks with case studies from our own research program. The forward approach begins by collecting curated information from the niche cardiac biology literature, and connecting the dots to form mechanistic network models that generate testable system-level predictions. The inverse approach starts from the vast pool of public omics data in recent cardiac biological research, and applies bioinformatics analysis to produce novel candidates for further investigation. We also discuss the possibility of combining these two approaches into a hybrid framework, together with the benefits and challenges. These interdisciplinary research frameworks illustrate the interplay between computational models, omics analysis, and wet lab experiments, which holds the key to making real progress in improving human cardiac wellbeing.

Bioinformatics of cardiovascular miRNA biology

MicroRNAs (miRNAs) are small ~22 nucleotide non-coding RNAs and are highly conserved among species. Moreover, miRNAs regulate gene expression of a large number of genes associated with important biological functions and signaling pathways. Recently, several miRNAs have been found to be associated with cardiovascular diseases. Thus, investigating the complex regulatory effect of miRNAs may lead to a better understanding of their functional role in the heart. To achieve this, bioinformatics approaches have to be coupled with validation and screening experiments to understand the complex interactions of miRNAs with the genome. This will boost the subsequent development of diagnostic markers and our understanding of the physiological and therapeutic role of miRNAs in cardiac remodeling. In this review, we focus on and explain different bioinformatics strategies and algorithms for the identification and analysis of miRNAs and their regulatory elements to better understand cardiac miRNA biology. Starting with the biogenesis of miRNAs, we present approaches such as LocARNA and miRBase for combining sequence and structure analysis including phylogenetic comparisons as well as detailed analysis of RNA folding patterns, functional target prediction, signaling pathway as well as functional analysis. We also show how far bioinformatics helps to tackle the unprecedented level of complexity and systemic effects by miRNA, underlining the strong therapeutic potential of miRNA and miRNA target structures in cardiovascular disease. In addition, we discuss drawbacks and limitations of bioinformatics algorithms and the necessity of experimental approaches for miRNA target identification. This article is part of a Special Issue entitled 'Non-coding RNAs'.

Computational Hemodynamic Modeling of the Cardiovascular System

Computational methods and modeling are widely used in many fields to study the dynamic behaviour of different phenomena. Currently, the use of these models is an accepted practice in the biomedical field. One of the most significant efforts in this direction is applied to the simulation and prediction of pathophysiological conditions that can affect different systems of the human body. In this work, the design and development of a computational model of the human cardiovascular system is proposed. The structure of the model has been built from a physiological base, considering some of the mechanisms associated to the cardiovascular system. Thus, the aim of the model is the prediction, heartbeat by heartbeat, of some hemodynamic variables from the cardiovascular system, in different pathophysiological cardiac situations. A modular approach to development of the model has been considered in order to include new knowledge that could force the model's hemodynamic. The model has been validated comparing the results obtained with hemodynamic values published by other authors. The results show the usefulness and applicability of the model developed. Thus, different simulations of some cardiac pathologies and physical exercise situations are presented, together with the dynamic behaviors of the different variables considered in the model.

PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling

β-Adrenergic signaling is spatiotemporally heterogeneous in the cardiac myocyte, conferring exquisite control to sympathetic stimulation. Such heterogeneity drives the formation of protein kinase A (PKA) signaling microdomains, which regulate Ca(2+) handling and contractility. Here, we test the hypothesis that the nucleus independently comprises a PKA signaling microdomain regulating myocyte hypertrophy. Spatially-targeted FRET reporters for PKA activity identified slower PKA activation and lower isoproterenol sensitivity in the nucleus (t50=10.6±0.7 min; EC50=89.0 nmol/L) than in the cytosol (t50=3.71±0.25 min; EC50=1.22 nmol/L). These differences were not explained by cAMP or AKAP-based compartmentation. A computational model of cytosolic and nuclear PKA activity was developed and predicted that differences in nuclear PKA dynamics and magnitude are regulated by slow PKA catalytic subunit diffusion, while differences in isoproterenol sensitivity are regulated by nuclear expression of protein kinase inhibitor (PKI). These were validated by FRET and immunofluorescence. The model also predicted differential phosphorylation of PKA substrates regulating cell contractility and hypertrophy. Ca(2+) and cell hypertrophy measurements validated these predictions and identified higher isoproterenol sensitivity for contractile enhancements (EC50=1.84 nmol/L) over cell hypertrophy (EC50=85.9 nmol/L). Over-expression of spatially targeted PKA catalytic subunit to the cytosol or nucleus enhanced contractile and hypertrophic responses, respectively. We conclude that restricted PKA catalytic subunit diffusion is an important PKA compartmentation mechanism and the nucleus comprises a novel PKA signaling microdomain, insulating hypertrophic from contractile β-adrenergic signaling responses.

β-adrenergic stimulation activates early afterdepolarizations transiently via kinetic mismatch of PKA targets

Sympathetic stimulation regulates cardiac excitation-contraction coupling in hearts but can also trigger ventricular arrhythmias caused by early afterdepolarizations (EADs) in pathological conditions. Isoproterenol (ISO) stimulation can transiently cause EADs which could result from differential kinetics of L-type Ca current (ICaL) vs. delayed rectifier potassium current (IKs) effects, but multiple PKA targets complicate mechanistic analysis. Utilizing a biophysically detailed model integrating Ca and β-adrenergic signaling, we investigate how different phosphorylation kinetics and targets influence β-adrenergic-induced transient EADs. We found that: 1) The faster time course of ICaL vs. IKs increases recapitulates experimentally observed ISO-induced transient EADs (which are due to ICaL reactivation). These EADs disappear at steady state ISO and do not occur during more gradual ISO application. 2) This ICaL vs. IKs kinetic mismatch with ISO can also induce transient EADs due to spontaneous sarcoplasmic reticulum (SR) Ca release and Na/Ca exchange current. The increased ICaL, SR Ca uptake and action potential duration (APD) raise SR Ca to cause spontaneous SR Ca release, but eventual IKs activation and APD shortening abolish these EADs. 3) Phospholemman (PLM) phosphorylation decreases both types of EADs by increasing outward Na/K-ATPase current (INaK) for ICaL-mediated EADs, and reducing intracellular Na and Ca loading for SR Ca-release-mediated EADs. Slowing PLM phosphorylation kinetics abolishes this protective effect. 4) Blocking phospholamban (PLB) phosphorylation has little effect on ICaL-mediated transient EADs, but abolishes SR Ca-release-mediated transient EADs by limiting SR Ca loading. 5) RyR phosphorylation has little effect on either transient EAD type. Our study emphasizes the importance of understanding non-steady state kinetics of several systems in mediating β-adrenergic-induced EADs and arrhythmias.

Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development

Congenital heart disease (CHD) occurs in ∼1% of newborns. CHD arises from many distinct etiologies, ranging from genetic or genomic variation to exposure to teratogens, which elicit diverse cell and molecular responses during cardiac development. To systematically explore the relationships between CHD risk factors and responses, we compiled and integrated comprehensive datasets from studies of CHD in humans and model organisms. We examined two alternative models of potential functional relationships between genes in these datasets: direct convergence, in which CHD risk factors significantly and directly impact the same genes and molecules and functional convergence, in which risk factors significantly impact different molecules that participate in a discrete heart development network. We observed no evidence for direct convergence. In contrast, we show that CHD risk factors functionally converge in protein networks driving the development of specific anatomical structures (e.g., outflow tract, ventricular septum, and atrial septum) that are malformed by CHD. This integrative analysis of CHD risk factors and responses suggests a complex pattern of functional interactions between genomic variation and environmental exposures that modulate critical biological systems during heart development.

Phospholemman is a negative feed-forward regulator of Ca2+ in β-adrenergic signaling, accelerating β-adrenergic inotropy

Sympathetic stimulation enhances cardiac contractility by stimulating β-adrenergic signaling and protein kinase A (PKA). Recently, phospholemman (PLM) has emerged as an important PKA substrate capable of regulating cytosolic Ca(2+) transients. However, it remains unclear how PLM contributes to β-adrenergic inotropy. Here we developed a computational model to clarify PLM's role in the β-adrenergic signaling response. Simulating Na(+) and sarcoplasmic reticulum (SR) Ca(2+) clamps, we identify an effect of PLM phosphorylation on SR unloading as the key mechanism by which PLM confers cytosolic Ca(2+) adaptation to long-term β-adrenergic receptor (β-AR) stimulation. Moreover, we show that phospholamban (PLB) opposes and overtakes these actions on SR load, forming a negative feed-forward loop in the β-adrenergic signaling cascade. This network motif dominates the negative feedback conferred by β-AR desensitization and accelerates β-AR-induced inotropy. Model analysis therefore unmasks key actions of PLM phosphorylation during β-adrenergic signaling, indicating that PLM is a critical component of the fight-or-flight response.

Bigger, better, faster: principles and models of AKAP anchoring protein signaling

A kinase anchoring proteins (AKAPs) bind multiple signaling proteins and have subcellular targeting domains that allow them to greatly impact cellular signaling. AKAPs localize, specify, amplify, and accelerate signal transduction within the cell by bringing signaling proteins together in space and time. AKAPs also organize higher-order network motifs such as feed forward and feedback loops that may create complex network responses, including adaptation, oscillation, and ultrasensitivity. Computational models have begun to provide an insight into how AKAPs regulate signaling dynamics and cardiovascular pathophysiology. Models of mitogen-activated protein kinase and epidermal growth factor receptor scaffolds have revealed additional design principles and new methods for representing signaling scaffolds mathematically. Coupling computational modeling with quantitative experimental approaches will be increasingly necessary for dissecting the diverse information processing functions performed by AKAP signaling complexes.

Genomes, proteomes, and the central dogma

Systems biology, with its associated technologies of proteomics, genomics, and metabolomics, is driving the evolution of our understanding of cardiovascular physiology. Rather than studying individual molecules or even single reactions, a systems approach allows integration of orthogonal data sets from distinct tiers of biological data, including gene, RNA, protein, metabolite, and other component networks. Together these networks give rise to emergent properties of cellular function, and it is their reprogramming that causes disease. We present 5 observations regarding how systems biology is guiding a revisiting of the central dogma: (1) It deemphasizes the unidirectional flow of information from genes to proteins; (2) it reveals the role of modules of molecules as opposed to individual proteins acting in isolation; (3) it enables discovery of novel emergent properties; (4) it demonstrates the importance of networks in biology; and (5) it adds new dimensionality to the study of biological systems.

Aging is a primary risk factor for cardiac arrhythmias: disruption of intracellular Ca2+ regulation as a key suspect

Aging is an inevitable time-dependent progression associated with a functional decline of the cardiovascular system even in 'healthy' individuals. Age positively correlates with an increasing risk of cardiac problems including arrhythmias. Not only the prevalence but also the severity of arrhythmias escalates with age. The reasons for this are multifactorial but dysregulation of intracellular calcium within the heart is likely to play a key role in initiating and perpetuating these life-threatening events. We now know that several aspects of cardiac calcium regulation significantly change with advancing age - changes that could produce electrical instability. Further development of knowledge of the mechanisms underlying these changes will allow us to reduce what currently is an inevitable increase in the incidence of arrhythmias in the elderly.

Automated image analysis identifies signaling pathways regulating distinct signatures of cardiac myocyte hypertrophy

Cardiac hypertrophy is controlled by a complex signal transduction and gene regulatory network, containing multiple layers of crosstalk and feedback. While numerous individual components of this network have been identified, understanding how these elements are coordinated to regulate heart growth remains a challenge. Past approaches to measure cardiac myocyte hypertrophy have been manual and often qualitative, hindering the ability to systematically characterize the network's higher-order control structure and identify therapeutic targets. Here, we develop and validate an automated image analysis approach for objectively quantifying multiple hypertrophic phenotypes from immunofluorescence images. This approach incorporates cardiac myocyte-specific optimizations and provides quantitative measures of myocyte size, elongation, circularity, sarcomeric organization, and cell-cell contact. As a proof-of-concept, we examined the hypertrophic response to α-adrenergic, β-adrenergic, tumor necrosis factor (TNFα), insulin-like growth factor-1 (IGF-1), and fetal bovine serum pathways. While all five hypertrophic pathways increased myocyte size, other hypertrophic metrics were differentially regulated, forming a distinct phenotype signature for each pathway. Sarcomeric organization was uniquely enhanced by α-adrenergic signaling. TNFα and α-adrenergic pathways markedly decreased cell circularity due to increased myocyte protrusion. Surprisingly, adrenergic and IGF-1 pathways differentially regulated myocyte-myocyte contact, potentially forming a feed-forward loop that regulates hypertrophy. Automated image analysis unlocks a range of new quantitative phenotypic data, aiding dissection of the complex hypertrophic signaling network and enabling myocyte-based high-content drug screening.

Cardiac myocytes and local signaling in nano-domains

It is well known that calcium-induced calcium-release in cardiac myocytes takes place in spatially restricted regions known as dyads, where discrete patches of junctional sarcoplasmic reticulum tightly associate with the t-tubule membrane. The dimensions of a dyad are so small that it contains only a few Ca²⁺ ions at any given time. Ca²⁺ signaling in the dyad is therefore noisy, and dominated by the Brownian motion of Ca²⁺ ions in a potential field. Remarkably, from this complexity emerges the integrated behavior of the myocyte in which, under normal conditions, precise control of Ca²⁺ release and muscle contraction is maintained over the life of the cell. This is but one example of how signal processing within the cardiac myocyte and other cells often occurs in small "nano-domains" where proteins and protein complexes interact at spatial dimensions on the order of ∼1-10 nm and at time-scales on the order of nanoseconds to perform the functions of the cell. In this article, we will review several examples of local signaling in nano-domains, how it contributes to the integrative behavior of the cardiac myocyte, and present computational methods for modeling signal processing within these domains across differing spatio-temporal scales.