Seven Challenges in the Multiscale Modelling of Multicellular Tissues

Fletcher, Alexander G.; Osborne, James M.

doi:10.20944/preprints202007.0022.v1

Cited by 6 publications

(10 citation statements)

References 71 publications

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“…Overall, this thesis contributes to all the challenges highlighted by Fletcher and Osborne [54]. Still, there is a long way to go and the journey toward multiscale modeling of multicellular systems is scattered with pitfalls.…”

Section: Discussionmentioning

confidence: 85%

“…In a recent manuscript, Fletcher et al [54] described seven current challenges in the multiscale modeling of multicellular tissues, namely (i) model construction, (ii) model calibration, (iii) numerical solution, (iv) software and hardware implementation, (v) model validation, (vi) data/code standards and benchmarks, and (vii) comparing modeling assumptions and approaches. In this chapter we detail how the papers presented in this thesis contribute to solving these challenges.…”

Section: Multiscale Modelling Of Multicellular Tissuesmentioning

confidence: 99%

“…How to choose an appropriate model is the first challenge described by Fletcher and Osborne [54]. With Paper IV, we compare three modeling levels and determine specific concrete guidelines as to which model to choose depending on the parameter regime.…”

Section: Multiscale Modeling Model Comparison and Parameter Inference For Stochastic Chemical Kineticsmentioning

confidence: 99%

“…In particular, simulating such models together is technically complex, and it is notably difficult to determine which models are adequate for the problem at hands. In a recent preprint [54], Fletcher and Osborne described seven current challenges of multiscale multicellular modeling of tissues, going from the conceptual level, such as how to build these models, down to the technical level, e.g. how to implement and simulate these models on high performance computing architectures.…”

Section: Introductionmentioning

confidence: 99%

“…Chapter 3 is an introduction to agent based modeling for cellular mechanics. Finally in Chapter 4 we detail the contributions of each paper presented in this thesis and how they contribute to solving the challenges presented in [54].…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Modeling in Systems Biology: Methods and Perspectives

Coulier¹

2021

Digital Comprehensive Summaries of Uppsala Dissertations From the Faculty of Science and Technology

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In the last decades, mathematical and computational models have become ubiquitous to the field of systems biology. Specifically, the multiscale nature of biological processes makes the design and simulation of such models challenging. In this thesis we offer a perspective on available methods to study and simulate such models and how they can be combined to handle biological processes evolving at different scales. The contribution of this thesis is threefold. First, we introduce Orchestral, a multiscale modular framework to simulate multicellular models. By decoupling intracellular chemical kinetics, cell-cell signaling, and cellular mechanics by means of operator-splitting, it is able to combine existing software into one massively parallel simulation. Its modular structure makes it easy to replace its components, e.g. to adjust the level of modeling details. We demonstrate the scalability of our framework on both high performance clusters and in a cloud environment. We then explore how center-based models can be used to study cellular mechanics in biological tissues. We show how modeling and numerical choices can affect the results of the simulation and mislead modelers into incorrect biological conclusions if these errors are not monitored properly. We then propose CBMOS, a Python framework specifically designed for the numerical study of such models. Finally, we study how spatial details in intracellular chemical kinetics can be efficiently approximated in a multiscale compartment-based model. We evaluate how this model compares to two other alternatives in terms of accuracy and computational cost. We then propose a computational pipeline to study and compare such models in the context of Bayesian parameter inference and illustrate its usage in three case studies.

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Section: Discussionmentioning

confidence: 85%

Section: Multiscale Modelling Of Multicellular Tissuesmentioning

confidence: 99%

Section: Multiscale Modeling Model Comparison and Parameter Inference For Stochastic Chemical Kineticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiscale Modeling in Systems Biology: Methods and Perspectives

Coulier¹

2021

Digital Comprehensive Summaries of Uppsala Dissertations From the Faculty of Science and Technology

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Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Lorenzo

Hormuth

et al. 2022

Biophysics Reviews

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Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

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CBMOS: a GPU-enabled Python framework for the numerical study of center-based models

2022

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Background Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments’ results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case. Results We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS’ focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases. Conclusions CBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.

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Seven Challenges in the Multiscale Modelling of Multicellular Tissues

Cited by 6 publications

References 71 publications

Multiscale Modeling in Systems Biology: Methods and Perspectives

Multiscale Modeling in Systems Biology: Methods and Perspectives

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

CBMOS: a GPU-enabled Python framework for the numerical study of center-based models

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