Computational systems mechanobiology of growth and remodeling: Integration of tissue mechanics and cell regulatory network dynamics

Sree, Vivek D.; Tepole, Adrián Buganza

doi:10.1016/j.cobme.2020.01.002

Cited by 17 publications

(13 citation statements)

References 40 publications

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“…Cell-ECM mechanobiology is fundamental to morphogenesis, homeostasis, growth and regeneration [Sree and Tepole, 2020, Walma and Yamada, 2020]. An outstanding challenge in the field is relating measurable macroscale mechanics to microscale properties of cells and ECM cross-talk [Sree and Tepole, 2020].…”

Section: Discussionmentioning

confidence: 99%

“…By design, these approaches focus on the ECM’s material properties, and are thus limited in two respects. First, they oversimplify biological behavior of cells that are embedded into and interact with the fiber network [Sree and Tepole, 2020]. Second, they struggle with the mesoscale: They can only capture cell-ECM mechanobiology when cell density is very low [Eichinger et al, 2021, Guo et al, 2022], or, conversely, when cell density is very high such that multicellular collectives can be approximated by a continuum [Guo et al, 2022].…”

Section: Introductionmentioning

confidence: 99%

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Modelling the mechanical cross-talk between cells and fibrous extracellular matrix using hybrid cellular Potts and molecular dynamics methods

Tsingos

Bakker

Keijzer

et al. 2022

Preprint

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Cell-based modelling frameworks such as cellular Potts are well-established tools to spatially model cell behavior and tissue morphogenesis. These models generally represent the extracellular matrix (ECM) with mean-field approaches, which assume substrate homogeneity. This assumption breaks down with fibrous ECM, which has non-trivial topology and mechanics. Here, we extend the cellular Potts software library Tissue Simulation Toolkit with the molecular mechanics framework hoomd-blue. We model cells mechanically interacting through discrete focal adhesion-like sites with an ECM fiber network modelled as bead-spring chains. Using a case study of an isolated contractile cell straining a randomly-oriented fiber network, we demonstrate agreement with experimental fiber densification and displacement dynamics. Further, we apply in silico atomic force microscopy to show local cell-induced network stiffening consistent with experiments. Our model overcomes the limitation of mean-field approaches to modelling ECM, and lays the foundation to investigate biomechanical cell-ECM interactions in a multicellular context.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling the mechanical cross-talk between cells and fibrous extracellular matrix using hybrid cellular Potts and molecular dynamics methods

Tsingos

Bakker

Keijzer

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Among others, we have found phenomenological models to be useful in generating and testing diverse hypotheses fundamental to arterial adaptations [ 5 , 22 ], in studying arterial disease progression [ 71 , 72 ], and in the design of tissue engineered constructs and their clinical usage [ 73 , 74 ]. Nevertheless, tissue-level manifestations arise from molecular and cellular level changes [ 75 – 78 ]. There is, therefore, a pressing need for models that enable one to examine changes in cell phenotype and ECM turnover in terms of cell signaling pathways.…”

Section: Discussionmentioning

confidence: 99%

Cell signaling model for arterial mechanobiology

Irons

Humphrey

2020

PLoS Comput Biol

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Arterial growth and remodeling at the tissue level is driven by mechanobiological processes at cellular and sub-cellular levels. Although it is widely accepted that cells seek to promote tissue homeostasis in response to biochemical and biomechanical cues-such as increased wall stress in hypertension-the ways by which these cues translate into tissue maintenance, adaptation, or maladaptation are far from understood. In this paper, we present a logic-based computational model for cell signaling within the arterial wall, aiming to predict changes in extracellular matrix turnover and cell phenotype in response to pressure-induced wall stress, flow-induced wall shear stress, and exogenous sources of angiotensin II, with particular interest in mouse models of hypertension. We simulate a number of experiments from the literature at both the cell and tissue level, involving single or combined inputs, and achieve high qualitative agreement in most cases. Additionally, we demonstrate the utility of this modeling approach for simulating alterations (in this case knockdowns) of individual nodes within the signaling network. Continued modeling of cellular signaling will enable improved mechanistic understanding of arterial growth and remodeling in health and disease, and will be crucial when considering potential pharmacological interventions.

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“…We have demonstrated two examples of SINDy that could be used in cardiovascular flow modelling. Systems biology models (system of ordinary differential equations governing biological reaction kinetics) are an essential part of multiscale mechanobiology models of disease growth [113,114]. SINDy provides a framework to derive such models from experimental data or identify reduced-order systems biology models from higher-order models.…”

Section: Opportunities and Challengesmentioning

confidence: 99%

Data-driven cardiovascular flow modelling: examples and opportunities

Arzani

Dawson

2021

J. R. Soc. Interface.

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High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

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Computational systems mechanobiology of growth and remodeling: Integration of tissue mechanics and cell regulatory network dynamics

Cited by 17 publications

References 40 publications

Modelling the mechanical cross-talk between cells and fibrous extracellular matrix using hybrid cellular Potts and molecular dynamics methods

Modelling the mechanical cross-talk between cells and fibrous extracellular matrix using hybrid cellular Potts and molecular dynamics methods

Cell signaling model for arterial mechanobiology

Data-driven cardiovascular flow modelling: examples and opportunities

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