Spatio-temporal mechanistic modeling of whole-cell tumor metabolism

全细胞肿瘤代谢的时空机制模型

• 批准号: 10645919
• 负责人: Ilija Dukovski
• 金额: $ 19.28万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10645919
• 关键词: 3-Dimensional; Address; Affect; Amino Acids; Area; Biochemistry; Biomass; Biophysics; Cancer Model; Cancer cell line; Cells; Cellular Metabolic Process; Characteristics; Complex; Computer software; Data; Data Set; Databases; Development; Differential Equation; Dimensions; Ecosystem; Environment; Equilibrium; Future; Gene Expression; Genes; Geometry; Glucose; Growth; Heterogeneity; Human; Image; Knowledge; Malignant Neoplasms; Metabolic; Metabolism; Modeling; Morphology; Neoplasm Metastasis; Outcome; Patients; Play; Population; Predictive Cancer Model; Research; Role; Severities; Site; Structure; Surface; Testing; Therapeutic; Therapeutic Intervention; Time; Tissues; Translating; Vascularization; Warburg Effect; Work; biophysical model; cell type; combinatorial; complex data; computational platform; follow-up; genome-wide; human model; in silico; in vivo; mathematical model; metabolic phenotype; microbial; microbiome; models and simulation; open source; screening; simulation; spatiotemporal; tool; tumor; tumor growth; tumor heterogeneity; tumor metabolism; tumor progression; uptake

Abstract Understanding the metabolic characteristics of tumors and their environments is crucial for elucidating the mechanisms of cancer development and for developing therapeutic strategies. Despite the increasing availability of 3D gene expression and other high-throughput data, a major unresolved challenge is how to translate complex datasets and knowledge of human metabolism and cellular biophysics into forecasts of tumor growth dynamics, spatial structure and severity, and possible therapeutic strategies. Our highly interdisciplinary project will leverage existing computational approaches to address this challenge, establishing a new avenue for performing spatio-temporal modeling and simulations of whole-cell cancer metabolism in its microenvironment. Previous work has explored 3D mathematical models of cancer growth based on simplified descriptions of cell populations, e.g. through differential equations. In parallel, based on the approach of flux balance analysis, detailed tumor metabolism models have been used to predict all steady state fluxes in the cell, and the effects of perturbations of target genes. While in principle possible, models combining 3D spatio-temporal dynamics with detailed genome-scale metabolism, have not been developed yet. Here, we propose to repurpose our free and open- access software platform for computation of microbial ecosystems in time and space (COMETS) towards the study of tumor growth dynamics. Specifically: Aim 1: We will generate omics-data-constrained genome scale models of specific cancer cell lines, and import them into COMETS. We will then simulate overall tumor growth dynamics, and test our capacity to accurately predict key metabolic phenotypes, such as growth curves, glucose and amino acid uptake, and lactate secretion. Aim 2: We will build upon our capacity to accurately simulate with COMETS fine details of multicellular dynamics in 2D to generate and test predictions of tumor growth on a surface. We will vary tumor geometry and microenvironment composition, and experimentally test predictions using a cancer on-chip approach. Aim 3: Using the advanced capabilities of COMETS, we will explore tumor heterogeneity, and extend our detailed biophysical model for biomass propagation to 3D realistic microenvironments (with gradients and vascularization), in search for metabolic characteristics associated with morphological features of 3D tumors. We expect that results generated through this project will pave the way for predictive modeling of cancer growth and metabolism, applicable to the study of in vivo tumors. Gradual application of new COMETS capabilities will allow us to extend initial models to more complex scenarios and configurations, including interactions between different cell types, detailed modeling of specific tumor geometries based on imaging data, predictions of metastasis and metabolic adaptation in tissues other than the tissue of origin, simulations of interactions with the microbiome, and the implementation of in silico testing of thousands of combinatorial therapeutic strategies.

抽象的 了解肿瘤及其环境的代谢特征对于阐明 癌症发展的机制和制定治疗策略。尽管供应量增加 在3D基因表达和其他高通量数据中，一个主要未解决的挑战是如何翻译复合物 数据集以及对人类代谢和细胞生物物理学的了解，以预测肿瘤生长动力学， 空间结构和严重性以及可能的治疗策略。我们高度跨学科的项目将 利用现有的计算方法来应对这一挑战，建立了一条新的途径 整个细胞癌代谢在其微环境中的时空建模和模拟。以前的 工作探索了基于细胞群体的简化描述的癌症生长的3D数学模型， 例如通过微分方程。同时，基于通量平衡分析的方法，详细的肿瘤 代谢模型已用于预测细胞中的所有稳态通量，以及扰动的影响 靶基因的。虽然原则上可能，将3D时空动力学与详细的模型结合在一起 基因组规模的代谢尚未开发。在这里，我们建议重新利用我们的自由和开放 - 访问软件平台用于计算时空生态系统（彗星）的微生物生态系统 研究肿瘤生长动力学。具体来说：目标1：我们将生成OMICS-DATA约束基因组量表 特定癌细胞系的模型，并将其导入彗星。然后，我们将模拟整体肿瘤生长 动力学并测试我们准确预测关键代谢表型的能力，例如生长曲线，葡萄糖 和氨基酸摄取和乳酸分泌。目标2：我们将基于准确模拟的能力 彗星在2D中的多细胞动力学细节，以产生和测试A上肿瘤生长的预测 表面。我们将改变肿瘤的几何形状和微环境组成，并通过实验测试预测 使用挑战片的方法。目标3：使用彗星的高级功能，我们将探索肿瘤 异质性，并将生物量传播的详细生物物理模型扩展到3D现实 微环境（具有梯度和血管化），以寻找与 3D肿瘤的形态特征。我们希望通过该项目产生的结果将为 癌症生长和代谢的预测建模，适用于体内肿瘤的研究。渐进 新彗星功能的应用将使我们能够将初始模型扩展到更复杂的场景和 配置，包括不同细胞类型之间的相互作用，特定肿瘤几何的详细建模 基于成像数据，在组织以外的组织中的转移和代谢适应的预测 起源，与微生物组相互作用的模拟以及成千上万的计算机测试的实施 组合治疗策略。