Deciphering and targeting cancer metabolism using executable models

癌症的进展是由细胞中促进“癌症标志”的突变和错误驱动的。这些是肿瘤细胞所具有的一系列不同行为，使癌症能够存活和生长。在癌细胞中长期观察到的是瓦尔堡效应;从氧化磷酸化转变为无氧糖酵解。最近的证据表明，代谢重编程-细胞如何处理能量的大规模修改-是通过包括KRAS在内的癌基因突变实现的。这使得癌细胞能够在肿瘤中存活。此外，单个代谢酶（如富马酸水合酶）的突变可影响从上皮形态向间充质形态的转变。然而，个体代谢物和细胞行为之间的联系以及代谢网络在癌症发展中的作用仍然不清楚。这两个知识是必要的，以解释癌症的代谢变化，并确定新的药物靶点，是强大的网络effects.Computational建模提供了机会，正式网络中的元素之间的关系，并解决这个问题。然而，基于常微分方程（ODEs）和通量平衡分析（FBA）的传统方法是不合适的。ODE模型需要精确的物理化学参数，而这些参数对于人体代谢是不可用的。FBA没有这一要求，但不能模拟响应突变可能发生的代谢物积累。我建议使用一个“可执行”的建模方法来构建模型的代谢网络，并将它们连接到细胞的行为。可执行的建模方法不需要详细的物理参数，可以模拟积累事件。他们有进一步的优势，他们是服从一类模型分析称为形式验证。在这里，数学证明用于提供形式逻辑中编码的单元属性的保证。这些保证可以应用于系统的所有可能状态，因此提供了一种独特的强大方法来测试模型的正确性。这对于高度鲁棒的系统（如代谢网络）特别有用，但也可以考虑罕见事件。当考虑罕见事件如何决定癌症的发展时，后一个问题尤为重要。模型构建将通过使用机器学习方法（t-SNE，决策森林）来指导，以解决阿斯利康与我们共享的公开表达数据和代谢组学数据集。最后，这些模型将用于对代谢可以改变细胞表型进行新的预测。这些模型的开发将使我们能够了解代谢网络如何驱动癌症进展，并帮助我们识别新的致癌基因和药物靶点。

The progression of cancer is driven by mutations and errors in the cell that promote the "hallmarks of cancer". These are a set of distinct behaviours that cells in tumours possess, enabling the survival and growth of the cancer. A longstanding observation in cancer cells is the Warburg effect; the switch away from oxidative phosphorylation to anaerobic glycolysis. Recent evidence has shown that metabolic reprogramming- large scale modification of how the cells process energy- is achieved by mutations in oncogenes including KRAS. This enables cancer cell survival in the tumour. Furthermore mutations in individual metabolic enzymes, such as fumarate hydratase, can affect a transition from epithelial to a mesenchymal morphology. Both the links between individual metabolites and cell behaviour, and the role of the metabolic network in cancer development however remains unclear. Knowledge of both of these are necessary to interpret metabolic changes in cancer and to identify new drug targets that are robust to network effects.Computational modelling offers the opportunity to formalise the relationships between elements in the network and to address this issue. However, conventional approaches based on ordinary differential equations (ODEs) and flux balance analysis (FBA) are not suitable. ODE models require precise physico-chemical parameters that are not available for human metabolism. FBA does not have this requirement but is not capable of modelling the accumulation of metabolites that can occur in response to mutation. I propose to use an "executable" modelling approach to construct models of the metabolic network and link them to cellular behaviour. Executable modelling approaches do not require detailed physical parameters, and can model accumulation events. They have the further advantage that they are amenable to a class of model analysis known as formal verification. Here mathematical proofs are used to offer guarantees of cell properties that are encoded in formal logic. These guarantees can apply over all possible states of the system and so offer a uniquely powerful way to test model correctness. This is particularly useful for highly robust systems such as the metabolic network, but also in taking account of rare events. This latter issue is particularly important when considering how rare events determine the development of cancer. Model building will be guided by the use of machine learning approaches (t-SNE, decision forests) to address publicly available expression data and metabolomic datasets shared with us by AstraZeneca. Finally, these models will be used to make novel predictions about the metabolism can change cell phenotype. The development of these models will allow us to understand how metabolic networks drive cancer progression and help us identify novel oncogenes and drug targets.