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The article focuses on mechanisms of compartmentation of cyclic adenosine monophosphate (cAMP). The initial measurements of such compartmentation were done via radioimmunoassay and cellular fractionation. The use of patchclamp electrophysiology led to more direct measurement of the compartmentation in living cells.

Abstract: 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 Ca2+ 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) Ca2+ clamps, we identify an effect of PLM phosphorylation on SR unloading as the key mechanism by which PLM confers cytosolic Ca2+ 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. [Copyright &y& Elsevier]

Abstract: Calmodulin (CaM) acts as a common Ca2+ sensor for many signaling pathways, transducing local Ca2+ signals into specific cellular outcomes. Many of CaM''s signaling functions can be explained by its unique biochemical properties, including high and low affinity Ca2+-binding sites with slow and fast kinetics, respectively. CaM is expected to have a limited spatial range of action, emphasizing its role in local Ca2+ signaling. Interactions with target proteins further fine-tune CaM signal transduction. Here, we focus on only three specific cellular targets for CaM signaling in cardiac myocytes: the L-type Ca2+ channel, the ryanodine receptor, and the IP3 receptor. We elaborate a working hypothesis that each channel is regulated by two distinct functional populations of CaM: dedicated CaM and promiscuous CaM. Dedicated CaM is typically tethered to each channel and directly regulates channel activity. In addition, a local pool of promiscuous CaM appears poised to sense local Ca2+ signals and trigger downstream pathways such as Ca2+/CaM dependent-protein kinase II and calcineurin. Understanding how promiscuous CaM coordinates multiple distinct signaling pathways remains a challenge, but is aided by the use of mathematical modeling and a new generation of fluorescent biosensors. This article is part of a special issue entitled "Local Signaling in Myocytes." [Copyright &y& Elsevier]

Background: New approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models. Results: We developed a logic-based differential equation modeling approach for cell signaling networks based on normalized Hill activation/inhibition functions controlled by logical AND and OR operators to characterize signaling crosstalk. Using this approach, we modeled the cardiac β1-adrenergic signaling network, including 36 reactions and 25 species. Direct comparison of this model to an extensively characterized and validated biochemical model of the same network revealed that the new model gave reasonably accurate predictions of key network properties, even with default parameters. Normalized Hill functions improved quantitative predictions of global functional relationships compared with prior logic-based approaches. Comprehensive sensitivity analysis revealed the significant role of PKA negative feedback on upstream signaling and the importance of phosphodiesterases as key negative regulators of the network. The model was then extended to incorporate recently identified protein interaction data involving integrin-mediated mechanotransduction. Conclusions: The normalized-Hill differential equation modeling approach allows quantitative prediction of network functional relationships and dynamics, even in systems with limited biochemical data. [ABSTRACT FROM AUTHOR]

The article presents a discussion on the developed three dimensional (3-D) continuum model used for the laboratory observation of calcium ion (Ca2+) properties on rat myocytes. It provides information on the geometrical model and structural data observed and gathered on the rat's ventricular muscle cell. It also investigates mechanisms involving the basic principles of excitation-contraction (EC) coupling propagation in the observed ventricular myocytes. The study also reveals that the local Ca2+ spatiotemporal features signals that relies on the axial and cell surface diffusion distances.

Cardiac myocyte intracellular calcium varies beat-to-beat and calmodulin (CaM) transduces Ca2+ signals to regulate many cellular processes (e.g. via CaM targets such as CaM-dependent kinase and calcineurin). However, little is known about the dynamics of how CaM targets process the Ca2+ signals to generate appropriate biological responses in the heart. We hypothesized that the different affinities of CaM targets for the Ca2+-bound CaM (Ca2+-CaM) shape their actions through dynamic and tonic interactions in response to the repetitive Ca2+ signals in myocytes. To test our hypothesis, we used two fluorescence resonance energy transfer-based biosensors, BsCaM-45 (Kd = ∼45 nM) and BsCaM-2 (Kd = ∼2 nM), to monitor the real time Ca2+-CaM dynamics at low and high affinity CaM targets in paced adult ventricular myocytes. Compared with BsCaM-2, BsCaM-45 tracks the beat-to-beat Ca2+-CaM alterations more closely following the Ca2+ oscillations at each myocyte contraction. When pacing frequency is raised from 0.1 to 1.0 Hz, the higher affinity BsCaM-2 demonstrates significant elevation of diastolic Ca2+-CaM binding compared with the lower affinity BsCaM-45. Biochemically detailed computational models of Ca2+-CaM biosensors in beating cardiac myocytes revealed that the different Ca2+-CaM binding affinities of BsCaM-2 and BsCaM-45 are sufficient to predict their differing kinetics and diastolic integration. Thus, data from both experiments and computational modeling suggest that CaM targets with low versus high Ca2+-CaM affinities (like CaM-dependent kinase versus calcineurin) respond differentially to the same Ca2+ signal (phasic versus integrating), presumably tuned appropriately for their respective and distinct Ca2+ signaling pathways. [ABSTRACT FROM AUTHOR]

A traditional approach to investigate the genetic basis of complex diseases is to identify genes with a global change in expression between diseased and healthy individuals. However, population heterogeneity may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we investigate individual changes of gene expression when inducing hypertrophy and heart failure in 100 + strains of genetically distinct mice from the Hybrid Mouse Diversity Panel (HMDP). We find that genes whose expression fold-change correlates in a statistically significant way with the severity of the disease are either up or down-regulated across strains, and therefore missed by a traditional population-wide analysis of differential gene expression. Furthermore, those "fold-change" genes are enriched in human cardiac disease genes and form a dense co-regulated module strongly interacting with the cardiac hypertrophic signaling network in the human interactome. We validate our approach by showing that the knockdown of Hes1, predicted as a strong candidate, induces a dramatic reduction of hypertrophy by 80–90% in neonatal rat ventricular myocytes. Our results demonstrate that individualized approaches are crucial to identify genes underlying complex diseases as well as to develop personalized therapies. Personalized medicine: uncovering missed disease genes A multitude of genes associated with complex diseases are revealed by a novel personalized, as opposed to population-level, analysis of differential gene expression. While traditional investigations of the genetic basis of complex diseases assume homogeneity across individuals and identify genes differentially expressed between a diseased and a healthy population, Northeastern University and University of California Los Angeles researchers have identified a different class of disease genes that exhibit heterogeneous up and down-regulation across 100 genetically distinct mouse strains subject to a stressor inducing heart failure, but show no significant change of expression at the population level. The results, validated by in vitro knockdown, demonstrate that individualized approaches are crucial to unmask all genes involved in complex diseases, opening new avenues for the development of personalized therapies. [ABSTRACT FROM AUTHOR]

Growth and remodeling in the heart is driven by a combination of mechanical and hormonal signals that produce different patterns of growth in response to exercise, pregnancy, and various pathologies. In particular, increases in afterload lead to concentric hypertrophy, a thickening of the walls that increases the contractile ability of the heart while reducing wall stress. In the current study, we constructed a multiscale model of cardiac hypertrophy that connects a finite-element model representing the mechanics of the growing left ventricle to a cell-level network model of hypertrophic signaling pathways that accounts for changes in both mechanics and hormones. We first tuned our model to capture published in vivo growth trends for isoproterenol infusion, which stimulates β-adrenergic signaling pathways without altering mechanics, and for transverse aortic constriction (TAC), which involves both elevated mechanics and altered hormone levels. We then predicted the attenuation of TAC-induced hypertrophy by two distinct genetic interventions (transgenic Gq-coupled receptor inhibitor overexpression and norepinephrine knock-out) and by two pharmacologic interventions (angiotensin receptor blocker losartan and β-blocker propranolol) and compared our predictions to published in vivo data for each intervention. Our multiscale model captured the experimental data trends reasonably well for all conditions simulated. We also found that when prescribing realistic changes in mechanics and hormones associated with TAC, the hormonal inputs were responsible for the majority of the growth predicted by the multiscale model and were necessary in order to capture the effect of the interventions for TAC. [ABSTRACT FROM AUTHOR]

Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil. Author summary: Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we compare the cardiotoxic effects of 3 drugs, 5-fluorouracil, acetaminophen, and doxorubicin, using transcriptomic and metabolomic data. By integrating these data into a genome-scale metabolic network reconstruction of the heart, we were able to identify metabolic adaptations in response to cardiotoxicity that avoided detection in traditional gene enrichment and metabolomic techniques. Using this approach, we confirm known mechanisms of doxorubicin-induced cardiotoxicity and provide hypotheses and potential mechanisms for metabolic adaptations in cardiotoxicity for 5-fluorouracil, doxorubicin, and acetaminophen. We hope future work using our novel paired transcriptomic and metabolic characterization of in vitro cardiotoxicity can help improve the current network heart model and further characterize the role of chemotherapeutics in cardiotoxicity. [ABSTRACT FROM AUTHOR]

To identify how cardiomyocyte mechanosensitive signaling pathways are regulated by anisotropic stretch, micropatterned mouse neonatal cardiomyocytes were stretched primarily longitudinally or transversely to the myofiber axis. Four hours of static, longitudinal stretch induced differential expression of 557 genes, compared with 30 induced by transverse stretch, measured using RNA-seq. A logic-based ordinary differential equation model of the cardiac myocyte mechanosignaling network, extended to include the transcriptional regulation and expression of 784 genes, correctly predicted measured expression changes due to anisotropic stretch with 69% accuracy. The model also predicted published transcriptional responses to mechanical load in vitro or in vivo with 63-91% accuracy. The observed differences between transverse and longitudinal stretch responses were not explained by differential activation of specific pathways but rather by an approximately twofold greater sensitivity to longitudinal stretch than transverse stretch. In vitro experiments confirmed model predictions that stretch-induced gene expression is more sensitive to angiotensin II and endothelin-1, via RhoA and MAP kinases, than to the three membrane ion channels upstream of calcium signaling in the network. Quantitative cardiomyocyte gene expression differs substantially with the axis of maximum principal stretch relative to the myofilament axis, but this difference is due primarily to differences in stretch sensitivity rather than to selective activation of mechanosignaling pathways. [ABSTRACT FROM AUTHOR]

Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high-content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFß and/or IL-1ß, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high-content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4- 023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models. We apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis. [ABSTRACT FROM AUTHOR]

Cardiac hypertrophy is a common response of cardiac myocytes to stress and a predictor of heart failure. While in vitro cell culture studies have identified numerous molecular mechanisms driving hypertrophy, it is unclear to what extent these mechanisms can be integrated into a consistent framework predictive of in vivo phenotypes. To address this question, we investigate the degree to which an in vitro- based, manually curated computational model of the hypertrophy signaling network is able to predict in vivo hypertrophy of 52 cardiac-specific transgenic mice. After minor revisions motivated by in vivo literature, the model concordantly predicts the qualitative responses of 78% of output species and 69% of signaling intermediates within the network model. Analysis of four double-transgenic mouse models reveals that the computational model robustly predicts hypertrophic responses in mice subjected to multiple, simultaneous perturbations. Thus the model provides a framework with which to mechanistically integrate data from multiple laboratories and experimental systems to predict molecular regulation of cardiac hypertrophy. [ABSTRACT FROM AUTHOR]

A correction to the article "Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP" that was published online on February 26, 2012 is presented.

Familial cardiomyopathy is a precursor of heart failure and sudden cardiac death. Over the past several decades, researchers have discovered numerous gene mutations primarily in sarcomeric and cytoskeletal proteins causing two different disease phenotypes: hypertrophic (HCM) and dilated (DCM) cardiomyopathies. However, molecular mechanisms linking genotype to phenotype remain unclear. Here, we employ a systems approach by integrating experimental findings from preclinical studies (e.g. , murine data) into a cohesive signaling network to scrutinize genotype to phenotype mechanisms. We developed an HCM/DCM signaling network model utilizing a logic-based differential equations approach and evaluated model performance in predicting experimental data from four contexts (HCM, DCM, pressure overload, and volume overload). The model has an overall prediction accuracy of 83.8%, with higher accuracy in the HCM context (90%) than DCM (75%). Global sensitivity analysis identifies key signaling reactions, with calcium-mediated myofilament force development and calcium-calmodulin kinase signaling ranking the highest. A structural revision analysis indicates potential missing interactions that primarily control calcium regulatory proteins, increasing model prediction accuracy. Combination pharmacotherapy analysis suggests that downregulation of signaling components such as calcium, titin and its associated proteins, growth factor receptors, ERK1/2, and PI3K-AKT could inhibit myocyte growth in HCM. In experiments with patient-specific iPSC-derived cardiomyocytes (MLP-W4R;MYH7-R723C iPSC-CMs), combined inhibition of ERK1/2 and PI3K-AKT rescued the HCM phenotype, as predicted by the model. In DCM, PI3K-AKT-NFAT downregulation combined with upregulation of Ras/ERK1/2 or titin or Gq protein could ameliorate cardiomyocyte morphology. The model results suggest that HCM mutations that increase active force through elevated calcium sensitivity could increase ERK activity and decrease eccentricity through parallel growth factors, Gq-mediated, and titin pathways. Moreover, the model simulated the influence of existing medications on cardiac growth in HCM and DCM contexts. This HCM/DCM signaling model demonstrates utility in investigating genotype to phenotype mechanisms in familial cardiomyopathy. [Display omitted] • HCM/DCM signaling network model links mutations to cardiomyocyte morphological changes. • The computational model predicts higher efficiency for combination pharmacotherapies in familial cardiomyopathy. • Experiments on iPSC-CMs showed rescue of HCM phenotype by combined inhibition of ERK1/2 and PI3K-AKT predicted by the model. [ABSTRACT FROM AUTHOR]

Despite clinical observations of cardiotoxicity among cancer patients treated with tyrosine kinase inhibitors (TKIs), the molecular mechanisms by which these drugs affect the heart remain largely unknown. Mechanistic understanding of TKI-induced cardiotoxicity has been limited in part due to the complexity of tyrosine kinase signaling pathways and the multi-targeted nature of many of these drugs. TKI treatment has been associated with reactive oxygen species generation, mitochondrial dysfunction, and apoptosis in cardiomyocytes. To gain insight into the mechanisms mediating TKI-induced cardiotoxicity, this study constructs and validates a computational model of cardiomyocyte apoptosis, integrating intrinsic apoptotic and tyrosine kinase signaling pathways. The model predicts high levels of apoptosis in response to sorafenib, sunitinib, ponatinib, trastuzumab, and gefitinib, and lower levels of apoptosis in response to nilotinib and erlotinib, with the highest level of apoptosis induced by sorafenib. Knockdown simulations identified AP1, ASK1, JNK, MEK47, p53, and ROS as positive functional regulators of sorafenib-induced apoptosis of cardiomyocytes. Overexpression simulations identified Akt, IGF1, PDK1, and PI3K among the negative functional regulators of sorafenib-induced cardiomyocyte apoptosis. A combinatorial screen of the positive and negative regulators of sorafenib-induced apoptosis revealed ROS knockdown coupled with overexpression of FLT3, FGFR, PDGFR, VEGFR, or KIT as a particularly potent combination in reducing sorafenib-induced apoptosis. Network simulations of combinatorial treatment with sorafenib and the antioxidant N -acetyl cysteine (NAC) suggest that NAC may protect cardiomyocytes from sorafenib-induced apoptosis. • Computational model of cardiomyocyte apoptosis signaling network. • The model is validated against a wide range of experimental data. • Virtual screen predicts regulators of tyrosine kinase inhibitor-induced apoptosis. • Inhibition of ROS is predicted to block apoptosis from tyrosine kinase inhibitors. [ABSTRACT FROM AUTHOR]

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 andmRNA 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-g, IFN-b, or TNF-a, produced mutual inhibition of several signaling pathways, e.g., NF-kB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-g and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., iNOS or Nos2 and Arg1, or mutually enhanced, e.g., Il4ra 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 Il4ra 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. [ABSTRACT FROM AUTHOR]

Cardiac myocytes transduce changes in mechanical loading into cellular responses via interacting cell signalling pathways. We previously reported a logicbased ordinary differential equation model of the myocyte mechanosignalling network that correctly predicts 78% of independent experimental results not used to formulate the original model. Here, we use Monte Carlo and polynomial chaos expansion simulations to examine the effects of uncertainty in parameter values, model logic and experimental validation data on the assessed accuracy of that model. The prediction accuracy of the model was robust to parameter changes over a wide range being least sensitive to uncertainty in time constants and most affected by uncertainty in reaction weights. Quantifying epistemic uncertainty in the reaction logic of the model showed that while replacing 'OR' with 'AND' reactions greatly reduced model accuracy, replacing 'AND' with 'OR' reactions was more likely to maintain or even improve accuracy. Finally, data uncertainty had a modest effect on assessment of model accuracy. [ABSTRACT FROM AUTHOR]

Abstract Over 5 million people in the United States suffer from heart failure, due to the limited ability to regenerate functional cardiac tissue. One potential therapeutic strategy is to enhance proliferation of resident cardiomyocytes. However, phenotypic screening for therapeutic agents is challenged by the limited ability of conventional markers to discriminate between cardiomyocyte proliferation and endoreplication (e.g. polyploidy and multinucleation). Here, we developed a novel assay that combines automated live-cell microscopy and image processing algorithms to discriminate between proliferation and endoreplication by quantifying changes in the number of nuclei, changes in the number of cells, binucleation, and nuclear DNA content. We applied this assay to further prioritize hits from a primary screen for DNA synthesis, identifying 30 compounds that enhance proliferation of human induced pluripotent stem cell-derived cardiomyocytes. Among the most active compounds from the phenotypic screen are clinically approved L-type calcium channel blockers from multiple chemical classes whose activities were confirmed across different sources of human induced pluripotent stem cell-derived cardiomyocytes. Identification of compounds that stimulate human cardiomyocyte proliferation may provide new therapeutic strategies for heart failure. Graphical abstract Unlabelled Image Highlights • Novel automated hybrid live/fixed assay for cardiomyocyte proliferation. • Assay distinguishes proliferation from multinucleation and polyploidization. • Assay applied for high-content screening of compounds. • L-type calcium channel blockers induce cardiomyocyte proliferation. [ABSTRACT FROM AUTHOR]

Abstract In yeast, the Atg2-Atg18 complex regulates Atg9 recycling from phagophore assembly site during autophagy; their function in higher eukaryotes remains largely unknown. In a targeted screening in Drosophila melanogaster , we show that Mef2-GAL4 -RNAi-mediated knockdown of Atg2, Atg9 or Atg18 in the heart and indirect flight muscles led to shortened healthspan (declined locomotive function) and lifespan. These flies displayed an accelerated age-dependent loss of cardiac function along with cardiac hypertrophy (increased heart tube wall thickness) and structural abnormality (distortion of the lumen surface). Using the Mef2-GAL4-MitoTimer mitochondrial reporter system and transmission electron microscopy, we observed significant elongation of mitochondria and reduced number of lysosome-targeted autophagosomes containing mitochondria in the heart tube but exaggerated mitochondrial fragmentation and reduced mitochondrial density in indirect flight muscles. These findings provide the first direct evidence of the importance of Atg2-Atg18/Atg9 autophagy complex in the maintenance of mitochondrial integrity and, regulation of heart and muscle functions in Drosophila , raising the possibility of augmenting Atg2-Atg18/Atg9 activity in promoting mitochondrial health and, muscle and heart function. Highlights • Knockdown of Atg2, Atg9 or Atg18 in muscle and heart leads to impaired negative geotaxis and a short lifespan in Drosophila. • Knockdown of Atg2, Atg18 or Atg9 causes age-dependent impairment of cardiac structure and function. • Atg2, Atg18 and Atg9 are required in the heart to maintain mitochondrial structure and Drp1 and Mfn2 mRNA expression. • Atg2, Atg18 and Atg9 are required for maintenance of mitochondrial structure and density in IFM. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]