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.

摘要 了解肿瘤及其环境的代谢特征对于阐明 癌症发展的机制和开发治疗策略。尽管可用性不断提高 对于3D基因表达和其他高通量数据，一个尚未解决的主要挑战是如何将复杂的 将人体新陈代谢和细胞生物物理学的数据集和知识用于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，建立一种新的执行 全细胞癌细胞在其微环境中代谢的时空建模与模拟。上一首 这项工作探索了基于简化的细胞群体描述的癌症生长的3D数学模型， 例如通过微分方程式。并行地，基于流量平衡分析的方法，详细地描述了肿瘤 新陈代谢模型已经被用来预测细胞内的所有稳态通量，以及扰动的影响 目标基因。虽然原则上是可能的，但将3D时空动力学与详细的 基因组规模的新陈代谢，还没有开发出来。在这里，我们建议改变我们自由开放的- Access软件平台，用于计算时间和空间上的微生物生态系统(彗星) 肿瘤生长动力学研究。具体来说：目标1：我们将生成组学数据受限的基因组规模 建立特定癌细胞系的模型，并将其导入彗星。然后我们将模拟整个肿瘤的生长 动力学，并测试我们准确预测关键代谢表型的能力，如生长曲线、血糖 氨基酸的摄取和乳酸的分泌。目标2：我们将建立我们的能力，以准确地模拟 Comet 2D多细胞动力学的精细细节以生成和测试对肿瘤生长的预测 浮出水面。我们将改变肿瘤的几何形状和微环境成分，并在实验中测试预测 使用了一种芯片上癌症的方法。目标3：利用彗星的先进能力，我们将探索肿瘤 异质性，并将我们用于生物量传播的详细生物物理模型扩展到3D逼真 微环境(具有梯度和血管形成)，以寻找与以下相关的代谢特征 三维肿瘤的形态特征。我们希望通过这个项目产生的结果将为 肿瘤生长代谢预测模型，适用于体内肿瘤研究。渐进的 应用新的彗星功能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，特定肿瘤几何形状的详细建模 基于成像数据，预测肿瘤组织以外其他组织的转移和代谢适应 起源，与微生物组相互作用的模拟，以及数千人的电子测试的实施 组合治疗策略。