Grand challenges in mathematical biology: Integrating multi-scale modeling and data

Eftimie, Raluca

doi:10.3389/fams.2022.1010622

Cited by 8 publications

(6 citation statements)

References 59 publications

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“…However, to predict durotaxis, a more accurate biomechanical model of focal adhesions is required (Rens and Merks, 2020). Highly detailed mechanical models aim to comprehensively describe the physics of the cytoskeleton, membrane deformation, and adhesion to the ECM (He and Jiang, 2017;Kim et al, 2018;2022). These efforts, however, come at a price of a large amount of unknown parameters which limits model interpretability, and considerable computational cost of simulation.…”

Section: Mechanisms Of Cell Migrationmentioning

confidence: 99%

“…Models that include the ECM are intrinsically more complex due to at least one additional variable in the model (or more if including specific components of the ECM) and their associated properties. The implementation of large, allencompassing models is something many aspire to, and work is ongoing in the field of mathematical biology to improve and perfect these models to elucidate the most relevant and important information (Eftimie, 2022).…”

Section: Open Challengesmentioning

confidence: 99%

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Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Crossley,

Johnson,

Tsingos

et al. 2024

Front. Cell Dev. Biol.

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The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.

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Section: Mechanisms Of Cell Migrationmentioning

confidence: 99%

Section: Open Challengesmentioning

confidence: 99%

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Crossley,

Johnson,

Tsingos

et al. 2024

Front. Cell Dev. Biol.

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“…Grand challenges in biology have required tools with increasingly higher resolution and throughput while also sampling across spatial and temporal axes (Lähnemann et al, 2020; Eftimie, 2022; Bindea et al, 2013; Johnson et al, 2021). In particular, dynamic data from live cell imaging is positioned to become the next “omics” (Lelek et al, 2021; Bagheri et al, 2022) with lower-level resolution data, like scRNAseq, contributing a more nuanced understanding of individual system components across space (Hwang et al, 2018; Heumos et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, dynamic data from live cell imaging is positioned to become the next “omics” (Lelek et al, 2021; Bagheri et al, 2022) with lower-level resolution data, like scRNAseq, contributing a more nuanced understanding of individual system components across space (Hwang et al, 2018; Heumos et al, 2023). Our ability to utilize high resolution data has often lagged behind our ability to generate said data (Ji et al, 2017; Lähnemann et al, 2020; Eftimie, 2022). Parity between computational and experimental approaches will allow researchers to synergistically utilize computational models to explain nonintuitive observations, identify “rules of life”, and design model-driven experiments that test new hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Cain,

Evarts,

et al. 2024

Preprint

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Motivation: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies—e.g. live cell imaging, scRNAseq, agent-based models—requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Results: Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. Availability: All source codes for the agent-based model, emulation, and analyses are publicly available at github.com/bagherilab/ARCADE, github.com/bagherilab/emulation, and github.com/bagherilab/ emulation_analysis, respectively.

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“…Such models which will be digital twins of real-life processes that are sought by mathematical scientists in biology [23] and other sciences will not be obtained by interpreting data with new mathematics but rather by translating the universal non-mathematical language of pure thought into the necessary mathematical equivalents. And when paths have been forged to the empirical alternatives of all logically-deduced theories in each field of science, the result will be the disappearance of the deep-seated incompatibilities in our system of concepts [15] and the unification of knowledge which was not only Einstein's dream but also that of Herbert Spencer, the scientist who was commemorated with "On the method of theoretical physics" at Oxford in 1933 [2].…”

mentioning

confidence: 99%

The Method of Resolving the Inconsistencies that Stymie Scientific fields

Ola¹

2023

Preprint

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The history of physics teaches us that the resolution of inconsistencies that stymie scientific fields is the reliable path to breakthroughs. What it does not teach us is the method by which Albert Einstein resolved inconsistencies in the process of developing General Relativity and how this method can be employed to resolve other inconsistencies that stymie scientific fields. Upon acquiring the capacity to use the method to resolve the inconsistencies that stymie public health after 13 years of the necessary philosophical and empirical immersion, it was found to be one in which the scientist forges a path to knowledge of reality by means of pure thought rather than assumptions about reality with the goal of giving greater explanatory and predictive power to theories. It was discovered that mathematics is not a “microscope” that has the capacity to uncover knowledge of reality by illuminating experimental results but rather a language into which the universal language of thought that begins in experimental results (pure thought) must be translated or in which such thought must be conducted if the doubtlessness of each step taken towards knowledge of reality will be ascertained before arrival at concepts and the principles that interrelate them. Thus, the mathematical equivalent of the universal language of pure thought, such as the non-Euclidean geometry of General Relativity, which increases the likelihood that the scientist will forge a path to empirical knowledge is analogous to the pictorial language in maps by which ancient voyagers ascertained the doubtlessness of their steps and increased the likelihood of success long before arrival at their destinations where such doubtlessness is confirmed. Together, these results reveal that the focus of scientists that aim to resolve the inconsistencies that stymie their fields must be fluency in the non-mathematical language of pure thought which must be achieved when the realities to be understood are those to which paths cannot be forged in any known mathematics, such as quantum reality which Einstein sought in the bid to unify knowledge in physics.

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Grand challenges in mathematical biology: Integrating multi-scale modeling and data

Abstract: September CITATION Eftimie R () Grand challenges in mathematical biology: Integrating multi-scale modeling and data .

Cited by 8 publications

References 59 publications

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

The Method of Resolving the Inconsistencies that Stymie Scientific fields

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