Modelling of tissue level carcinogenesis through hybrid, biophysical, and executable approaches

通过混合、生物物理和可执行方法对组织水平致癌作用进行建模

• 批准号: MC_UU_12022/9
• 负责人: Benjamin Hall
• 金额: $ 110.73万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Intramural
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MC_UU_12022%2F9
• 关键词: Modelling; tissue; level; carcinogenesis; through

Cancer is a major health concern, with one in three adults developing the disease over their lifetime. However, on a cellular level, the probability of a cell developing into a tumour is very low. Cells prevent the development of tumours through a combination of mechanisms, which operate over different scales. Within a single cell, proteins and genes form signalling pathways which reliably interpret and respond to both the cell state and the cell environment, including specific signals passed from other cells. At the same time, within a tissue, the movements and locations of cells can add a further layer of control, by limiting the communication between cells and mixing populations of cells. I will build mathematical representations (a “model”) of the early stages of tumour formation in cancer which bridges these two phenomena. By running simulations using this model, I will learn more about how the signalling and physical components combine to control cell growth and death in the oesophageal epithelium. I will further mutate and wound my model, to explore how these events can cause a breakdown of the controlling mechanisms and lead to tumour growth.

癌症是一个主要的健康问题，三分之一的成年人在一生中都会患上这种疾病。然而，在细胞水平上，细胞发展成肿瘤的概率非常低。细胞通过多种机制的组合来防止肿瘤的发展，这些机制在不同的范围内发挥作用。在单个细胞内，蛋白质和基因形成信号通路，可靠地解释和响应细胞状态和细胞环境，包括从其他细胞传递的特定信号。同时，在组织内，细胞的运动和位置可以通过限制细胞之间的通信和混合细胞群来增加进一步的控制层。我将建立癌症肿瘤形成早期阶段的数学表示（“模型”），以连接这两种现象。通过使用该模型进行模拟，我将更多地了解信号传导和物理成分如何结合起来控制食管上皮细胞的生长和死亡。我将进一步突变和破坏我的模型，以探索这些事件如何导致控制机制崩溃并导致肿瘤生长。

项目成果

Notch1 mutations drive clonal expansion in normal esophageal epithelium but impair tumor growth.

• DOI: 10.1038/s41588-022-01280-z
• 发表时间: 2023-03
• 期刊: NATURE GENETICS
• 影响因子: 30.8
• 作者: Abby, Emilie;Dentro, Stefan C. C.;Hall, Michael W. J.;Fowler, Joanna C. C.;Ong, Swee Hoe;Sood, Roshan;Herms, Albert;Piedrafita, Gabriel;Abnizova, Irina;Siebel, Christian W. W.;Gerstung, Moritz;Hall, Benjamin A. A.;Jones, Philip H. H.
• 通讯作者: Jones, Philip H. H.

Bringing LTL Model Checking to Biologists

为生物学家提供 LTL 模型检查

• DOI: 10.17863/cam.8964
• 发表时间: 2017
• 作者: Ahmed Z

Drug target optimization in chronic myeloid leukemia using innovative computational platform.

• DOI: 10.1038/srep08190
• 发表时间: 2015-02-03
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Chuang R;Hall BA;Benque D;Cook B;Ishtiaq S;Piterman N;Taylor A;Vardi M;Koschmieder S;Gottgens B;Fisher J
• 通讯作者: Fisher J