Deciphering and targeting cancer metabolism using executable models

癌症的进展是由细胞中的突变和错误推动的，这些突变和错误促进了“癌症的标志”。这些是肿瘤细胞拥有的一套独特的行为，使癌症得以存活和生长。在癌细胞中长期观察到的是Warburg效应，即从氧化磷酸化转变为厌氧糖酵解。最近的证据表明，代谢重新编程--细胞如何处理能量的大规模修改--是通过包括KRAS在内的癌基因的突变实现的。这使得癌细胞能够在肿瘤中存活。此外，个体代谢酶的突变，如富马酸水合酶，可以影响从上皮到间充质形态的转变。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不清楚。这两方面的知识对于解释癌症中的新陈代谢变化和识别对网络效应具有健壮性的新药靶点是必要的。计算模型提供了将网络中元素之间的关系正式化并解决这一问题的机会。然而，传统的基于常微分方程组(ODE)和通量平衡分析(FBA)的方法并不适用。ODE模型需要精确的物理化学参数，而这些参数对人类的新陈代谢来说是不可用的。FBA没有这一要求，但不能对突变时可能发生的代谢物积累进行建模。我建议使用一种“可执行”的建模方法来构建代谢网络的模型，并将它们与细胞行为联系起来。可执行的建模方法不需要详细的物理参数，并且可以对累积事件进行建模。它们还有一个进一步的优势，那就是它们可以接受一类被称为形式验证的模型分析。这里，数学证明被用来提供以形式逻辑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而强大的方法来测试模型的正确性。这对于新陈代谢网络等高度健壮的系统特别有用，但在考虑罕见事件时也是如此。当考虑到罕见事件如何决定癌症的发展时，后一个问题尤其重要。模型的建立将由机器学习方法(t-SNE，决策森林)的使用来指导，以处理阿斯利康与我们共享的公开可用表达数据和代谢数据集。最后，这些模型将被用来对新陈代谢可以改变细胞表型做出新的预测。这些模型的发展将使我们能够了解代谢网络如何推动癌症进展，并帮助我们识别新的癌基因和药物靶点。

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.