2The fibroblast is a key mediator of wound healing in the heart and other organs, yet how 2 3 it integrates multiple time-dependent paracrine signals to control extracellular matrix 2 4 synthesis has been difficult to study in vivo. Here, we extended a computational model to 2 5 simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in 2 6 response to time-dependent data for nine paracrine stimuli. This computational model 2 7was validated against dynamic collagen expression and collagen area fraction data from 2 8 post-infarction rat hearts. The model predicted that while many features of the fibroblast 2 9 phenotype at inflammatory or maturation phases of healing could be recapitulated by 3 0 single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking 3 1 of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual 3 2 overexpression screens with static cytokine pairs and after myocardial infarction 3 3 predicted phase-specific regulators of collagen expression. Several regulators increased 3 4 (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the 3 5 proliferative phase. NADPH oxidase overexpression sustained collagen expression from 3 6 proliferative to maturation phases, driven by TGFβ and endothelin positive feedback 3 7 loops. Interleukin-1 overexpression suppressed collagen via NFκB and BAMBI (BMP 3 8 and activin membrane-bound inhibitor) incoherent feedforward loops, but it then later 3 9 sustained collagen expression due to the TGFβ positive feedback loop. These model-4 0 based predictions reveal network mechanisms by which the dynamics of paracrine stimuli 4 1 and interacting signaling pathways drive the progression of fibroblast phenotypes and 4 2 fibrosis after myocardial infarction. 4 3 4 4 4 5Wound healing is a complex process that involves a dynamic interplay between 4 6 inflammatory and proliferative signaling. This process is especially important following 4 7 injury to the heart, where cardiomyocytes are unable to regenerate. Scar formation and 4 8 the preservation of viable heart muscle are important for continued cardiac function[1]. 4 9 8 1 understanding how fibroblasts respond during the different phases of wound healing 8 2 could identify mechanisms by which fibrosis develops in other organs. 8 3 Myocardial infarct healing is notoriously difficult to investigate because it 8 4 involves many dynamic and interacting signaling processes. Fibroblasts are particularly 8 5 difficult to study in situ during wound healing because they can differentiate from many 8 6 different cell types and there is no clear consensus on fibroblast markers[20].8 7Computational modeling has been a useful method for investigating complex dynamic 8 8 processes in many areas of biology. Although models have been constructed to study the 8 9 wound healing process post-MI[21], no such model has yet been applied to study 9 0 5 fibroblast intracellular signaling and phenotypic changes during m...

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and αsmooth muscle actin expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drugtarget mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

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The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual overexpression screens with static cytokine pairs and after myocardial infarction predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the proliferative phase. NADPH oxidase overexpression sustained collagen expression from proliferative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression suppressed collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feedforward loops, but it then later sustained collagen expression due to the TGFβ positive feedback loop. These modelbased predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction..

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-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.