Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling

Rikard, S. Michaela; Athey, Thomas L.; Nelson, Anders R.; Christiansen, Steven L.; Lee, Jia-Jye; Holmes, Jeffrey W.; Peirce, Shayn M.; Saucerman, Jeffrey J.

doi:10.3389/fphys.2019.01481

Cited by 36 publications

(30 citation statements)

References 70 publications

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“…An additional limitation of our model is that signaling dynamics were encoded as simple time delays for cadherin expression after cell contacts were made. In the future, it will be worthwhile to incorporate more realistic models of intracellular signaling, as has been done in recently published multiscale ABMs that integrate information across intracellular and intercellular scales (59)(60)(61)(62). The need to solve differential equations describing reaction kinetics in each cell within the ABM will greatly increase the computational resources needed to simulate the system as well as the number of model parameters.…”

Section: Discussionmentioning

confidence: 99%

A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

Sivakumar

Warner

Peirce

et al. 2021

Preprint

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Dynamically activated differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has previously been explored using synthetically engineered heterogenous multicell spheroid systems in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to explore the key parameters that drive the emergence of different patterns more systematically. Here, we developed and validated two-and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit regulating N-and P-cadherin expression. The model was used to predict how varying initial cell seeding, cadherin induction probabilities, or homotypic adhesion strengths between cells leads to different spheroid patterns, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was also used as a tool to design new synthetic cell signaling circuits based on a desired final multicell pattern.

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Section: Discussionmentioning

confidence: 99%

A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

Sivakumar

Warner

Peirce

et al. 2021

Preprint

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“…We have incorporated a simplified representation of fibroblast and collagen behaviors, and we did not incorporate collagen subtypes into this ABM nor did we incorporate the specific mechanistic action of Losartan (see method section). This generalized behavior in our model likely explains why it failed to capture the early cell dynamics; however, in future work, the model can be expanded to represent mechanistic behaviors [84,85]. However, the model's ability to predict cellular changes seen at later time points supports its utility in predicting longer-term remodeling outcomes (i.e.…”

Section: Plos Computational Biologymentioning

confidence: 93%

“…For cell types that were modeled, we incorporated relatively simple cellular behaviors and interactions through a series of probability defined rules; as opposed to modeling intracellular behaviors such as individual binding receptors, binding rates, etc. In the future, the ABM can be expanded to incorporate the biophysical environment of the cells and cell mechanics explicitly as well as coupling the ABM with signaling focused models to capture the details of molecular mechanisms in cell dynamics [83,84]. We have also assumed that the presence of growth factors is an important aspect of tissue regeneration following VML injury and have assumed evenly distributed growth factors, consistent with other ABMs [28,29,33].…”

Section: Plos Computational Biologymentioning

confidence: 95%

Agent-based model provides insight into the mechanisms behind failed regeneration following volumetric muscle loss injury

et al. 2021

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Skeletal muscle possesses a remarkable capacity for repair and regeneration following a variety of injuries. When successful, this highly orchestrated regenerative process requires the contribution of several muscle resident cell populations including satellite stem cells (SSCs), fibroblasts, macrophages and vascular cells. However, volumetric muscle loss injuries (VML) involve simultaneous destruction of multiple tissue components (e.g., as a result of battlefield injuries or vehicular accidents) and are so extensive that they exceed the intrinsic capability for scarless wound healing and result in permanent cosmetic and functional deficits. In this scenario, the regenerative process fails and is dominated by an unproductive inflammatory response and accompanying fibrosis. The failure of current regenerative therapeutics to completely restore functional muscle tissue is not surprising considering the incomplete understanding of the cellular mechanisms that drive the regeneration response in the setting of VML injury. To begin to address this profound knowledge gap, we developed an agent-based model to predict the tissue remodeling response following surgical creation of a VML injury. Once the model was able to recapitulate key aspects of the tissue remodeling response in the absence of repair, we validated the model by simulating the tissue remodeling response to VML injury following implantation of either a decellularized extracellular matrix scaffold or a minced muscle graft. The model suggested that the SSC microenvironment and absence of pro-differentiation SSC signals were the most important aspects of failed muscle regeneration in VML injuries. The major implication of this work is that agent-based models may provide a much-needed predictive tool to optimize the design of new therapies, and thereby, accelerate the clinical translation of regenerative therapeutics for VML injuries.

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“…For example, a hybrid multiscale model that coupled an agent-based model with logic-based differential equations was developed to describe the role of fibroblast signaling and movement in cardiac fibrosis. 31 Scaling a hybrid multiscale model of this nature to the whole tissue level would be methodologically challenging and computationally expensive. This type of modeling framework could provide an alternative method, compared with ODEs, to describe pharmacological and pathophysiological systems.…”

Section: Agent-based Modelingmentioning

confidence: 99%

“…Boolean network and agent‐based models could be integrated to describe multilevel organization. For example, a hybrid multiscale model that coupled an agent‐based model with logic‐based differential equations was developed to describe the role of fibroblast signaling and movement in cardiac fibrosis 31 . Scaling a hybrid multiscale model of this nature to the whole tissue level would be methodologically challenging and computationally expensive.…”

Section: Implementation Of Qsp Modeling In Drug Discovery and Developmentmentioning

confidence: 99%

Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

Bloomingdale

Karelina

Cirit³

et al. 2021

CPT Pharmacom & Syst Pharma

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Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling

Cited by 36 publications

References 70 publications

A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion

Agent-based model provides insight into the mechanisms behind failed regeneration following volumetric muscle loss injury

Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

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