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1. Unpacking the multimodal, multi‐scale data of the fast and slow lanes of the cardiac vagus through computational modelling

   https://doi.org/10.1113/EP090865  

   Gee, Michelle M. ; Hornung, Eden ; Gupta, Suranjana ; Newton, Adam J. H. ; Cheng, Zixi ; Lytton, William W. ; Lenhoff, Abraham M. ; Schwaber, James S. ; Vadigepalli, Rajanikanth ( April 2023 , Experimental Physiology)

   What is the topic of this review?

   The vagus nerve is a crucial regulator of cardiovascular homeostasis, and its activity is linked to heart health. Vagal activity originates from two brainstem nuclei: the nucleus ambiguus (fast lane) and the dorsal motor nucleus of the vagus (slow lane), nicknamed for the time scales that they require to transmit signals.

   What advances does it highlight?

   Computational models are powerful tools for organizing multi‐scale, multimodal data on the fast and slow lanes in a physiologically meaningful way. A strategy is laid out for how these models can guide experiments aimed at harnessing the cardiovascular health benefits of differential activation of the fast and slow lanes.

   The vagus nerve is a key mediator of brain–heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat‐to‐beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high‐dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data‐derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi‐scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular‐scale data, particularly single‐cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular‐scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal‐scale physiology data to create multi‐system, multi‐scale models that enablein silicoexploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.

    

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2. Skeletal muscle: A review of molecular structure and function, in health and disease

   https://doi.org/10.1002/wsbm.1462  

   Mukund, Kavitha ; Subramaniam, Shankar ( August 2019 , WIREs Systems Biology and Medicine)

   Decades of research in skeletal muscle physiology have provided multiscale insights into the structural and functional complexity of this important anatomical tissue, designed to accomplish the task of generating contraction, force and movement. Skeletal muscle can be viewed as a biomechanical device with various interacting components including the autonomic nerves for impulse transmission, vasculature for efficient oxygenation, and embedded regulatory and metabolic machinery for maintaining cellular homeostasis. The “omics” revolution has propelled a new era in muscle research, allowing us to discern minute details of molecular cross‐talk required for effective coordination between the myriad interacting components for efficient muscle function. The objective of this review is to provide a systems‐level, comprehensive mapping the molecular mechanisms underlying skeletal muscle structure and function, in health and disease. We begin this review with a focus on molecular mechanisms underlying muscle tissue development (myogenesis), with an emphasis on satellite cells and muscle regeneration. We next review the molecular structure and mechanisms underlying the many structural components of the muscle: neuromuscular junction, sarcomere, cytoskeleton, extracellular matrix, and vasculature surrounding muscle. We highlight aberrant molecular mechanisms and their possible clinical or pathophysiological relevance. We particularly emphasize the impact of environmental stressors (inflammation and oxidative stress) in contributing to muscle pathophysiology including atrophy, hypertrophy, and fibrosis.

   This article is categorized under:

   Physiology > Mammalian Physiology in Health and Disease

   Developmental Biology > Developmental Processes in Health and Disease

   Models of Systems Properties and Processes > Cellular Models

    

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3. Modeling immune cell behavior across scales in cancer

   https://doi.org/10.1002/wsbm.1484  

   Makaryan, Sahak Z. ; Cess, Colin G. ; Finley, Stacey D. ( March 2020 , WIREs Systems Biology and Medicine)

   Detailed, mechanistic models of immune cell behavior across multiple scales in the context of cancer provide clinically relevant insights needed to understand existing immunotherapies and develop more optimal treatment strategies. We highlight mechanistic models of immune cells and their ability to become activated and promote tumor cell killing. These models capture various aspects of immune cells: (a) single‐cell behavior by predicting the dynamics of intracellular signaling networks in individual immune cells, (b) multicellular interactions between tumor and immune cells, and (c) multiscale dynamics across space and different levels of biological organization. Computational modeling is shown to provide detailed quantitative insight into immune cell behavior and immunotherapeutic strategies. However, there are gaps in the literature, and we suggest areas where additional modeling efforts should be focused to more prominently impact our understanding of the complexities of the immune system in the context of cancer.

   This article is categorized under:

   Biological Mechanisms > Cell Signaling

   Models of Systems Properties and Processes > Mechanistic Models

   Models of Systems Properties and Processes > Cellular Models

    

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4. Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection

   https://doi.org/10.1186/s12915-021-01115-z  

   Sego, T. J. ; Aponte-Serrano, Josua O. ; Gianlupi, Juliano F. ; Glazier, James A. ( September 2021 , BMC Biology)

   The biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems.

   In this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models.

   We developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes.

    

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5. COMPARING THE MESOSCALE AND MICROSCALE MECHANICAL PROPERTIES OF RAT LUNG TISSUE USING COMPUTATIONAL MODELING

   https://doi.org/10.1142/S0219519423500756  

   DIMBATH, ELIZABETH ; GEORGE, STEPHANIE ; BRÁS, LISANDRA DE ; VADATI, ALEX ( September 2023 , Journal of Mechanics in Medicine and Biology)

   Current literature reports a wide range of stiffness values and constitutive models for lung tissue across different spatial scales. Comparing the reported lung tissue stiffness values across different spatial scales may provide insights into how well those mechanical properties and the proposed constitutive models represent lung tissue’s mechanical behavior. Thus, this study applies in silico modeling to compare and potentially bridge the differences reported in lung tissue mechanical properties at different length scales. Specifically, we predicted the mesoscale mechanical behavior of rat lung tissue based on in situ and in vitro microscale test data using finite element (FE) analysis and compared those computational predictions to the reported data using mesoscale uniaxial experiments. Our simulations showed that microscale-based stiffness values differed from the mesoscale data in the simulated strain range of 0–60%, with the atomic force microscopy (AFM)-based data overestimating the mesoscale data above 15% strain. This research demonstrates that computational modeling can be used as an informative and guiding tool to investigate and potentially bridge the differences in reported lung tissue material properties across length scales.

    

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