Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models.

IF 2 4区生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Physical biology Pub Date : 2024-02-19 DOI:10.1088/1478-3975/ad2777

Navid Mohammad Mirzaei, Leili Shahriyari

引用次数: 0

Abstract

Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ordinary differential equation models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.

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癌症进展建模：将数据驱动的动力学模型扩展到生物力学 PDE 模型的综合工作流程。

癌症的计算建模有助于揭示实验难以复制的动态和相互作用。由于癌症数据库和数据分析技术的进步，这些模型比以往任何时候都更加强大。从亚细胞到组织尺度，从治疗到诊断，有许多数学模型通过不同的方法研究癌症。在本研究中，我们将逐步介绍数据驱动的肿瘤微环境机理模型。我们讨论了数据采集策略、数据准备、参数估计和敏感性分析技术。此外，我们还提出了一种可能的方法，将机理 ODE 模型扩展为与机械生长耦合的 PDE 模型。本文讨论的工作流程有助于理解肿瘤微环境中细胞和细胞因子之间复杂的时空相互作用及其对肿瘤生长的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physical biology 生物-生物物理

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity. Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as: molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division systems biology, e.g. signaling, gene regulation and metabolic networks cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis cell-cell interactions, cell aggregates, organoids, tissues and organs developmental dynamics, including pattern formation and morphogenesis physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation neuronal systems, including information processing by networks, memory and learning population dynamics, ecology, and evolution collective action and emergence of collective phenomena.