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