The Modelling Apprentice: A tool to aid the formation of cell signalling models

计算机科学技术对微生物学的影响导致了在线数据库的创建，这些数据库现在包含数百种生物的完整基因组序列，以及各种细胞过程的详细信息。计算机也可以作为模拟器来模拟这些过程的动态行为以及它们之间的相互作用。模拟可以为科学家选择有用的实验提供指导，还可以在实验成本高昂且难以执行的情况下提供预测。系统生物学是一门快速发展的科学，旨在捕捉这些过程和相互作用的知识，而创建模拟模型是一项中心活动。一个中期目标是建立一个整个细胞的模型，在这个模型中，通常单独研究的系统的相互作用可以被分析。计算科学发现是另一个新兴学科，人工智能(AI)的技术被用来自动化或极大地简化将实验结果和数据转化为科学知识的困难过程。这一点尤其重要，因为数据量远远超过了没有人工辅助的解释能力。就系统生物学而言，科学发现通常涉及对实验结果提供解释的计算机模型的构建和验证。重要的是，得到的模型必须准确地解释结果，并且在生物学上是有效的，即这些知识对人类专家来说是有意义的。机器学习是人工智能的一个分支，它见证了计算机程序的发展，这些程序可以从数据中生成解释。在过去的十年或更长时间里，越来越多地使用机器学习技术来获取生物学知识。然而，一个主要的缺陷阻碍了更广泛的生物学界对计算科学发现的更广泛接受，那就是有效使用这些技术和技术所必需的学习曲线。许多系统生物学科学家发现有必要成为机器学习和模型模拟数学方面的专家，以及细胞生物学方面的专家。模型学徒试图克服这些障碍，提供一种易于使用、易于理解的工具，通过消除科学家理解或甚至与基础数学知识表示和机器学习交互的需要，帮助构建、验证和改进生物模型。这是通过：1)直观的图形用户界面，其中明确地显示分子和化学相互作用，以及2)将科学知识与与知识进行推理的机器学习技术分离。其中的第二个还允许建模学徒通过构建一个充当插件的库来轻松地适应研究其他科学应用程序。模特学徒将寻求改进新开发的Justaid程序，该程序已经包含了这些功能。作为测试案例，将利用剑桥和阿伯丁的专家生物学家的知识建立酵母的MAPK细胞信号网络模型。细胞信号是细胞对外界和环境刺激做出反应的过程，对这些网络的研究对于理解人类疾病，如癌症、糖尿病、免疫和退行性疾病至关重要。细胞信号的建模也没有新陈代谢等其他生物过程进展得那么快。然后，专家生物学家将评估建模学徒和新的MAPK模型库的适用性，他们将使用它来评估他们最新的实验结果。从这次测试中获得的见解将被用来进一步改进模特学徒。

The impact of computer science technology in microbiology has lead to the creation of online databases which now contain complete genome sequences for several hundred organisms, as well as detailed information for a wide variety of cell processes. Computers can also act as simulators to model the dynamic behaviour of these processes and the interactions between them. Simulation can provide guidance to scientists in the selection of useful experiments and can also provide predictions where experimentation is costly and difficult to perform. Systems biology is a rapidly advancing science that aims to capture knowledge of these processes and interactions and the creation of simulation models is a central activity. A medium term goal is the construction of a model of the whole cell, where the interactions of systems that are normally studied separately can be analysed. Computational Scientific Discovery is another emerging discipline where techniques from Artificial Intelligence (AI) are used to automate or greatly ease the difficult process of translating experimental results and data into scientific knowledge. This is especially important as the quantity of data far exceeds the ability of unaided human interpretation. In terms of systems biology scientific discovery often involves the construction and validation of computer models that provide explanations of experimental results. It is important that the resulting model accurately explains the results and is also biologically valid, i.e. the knowledge makes sense to a human expert. Machine Learning, a branch of AI, has seen the development of computer programs that can generate explanations from data. The last decade or more has seen increasing use of machine learning techniques for the acquisition of biological knowledge. However, a major drawback, preventing even wider acceptance of computational scientific discovery by the more general biology community, is the learning curve necessary for efficient use of the techniques and technology. Many systems biology scientists find it necessary to become experts in the mathematics of machine learning and model simulation as well as being experts in cell biology. The Modelling Apprentice seeks to overcome these obstacles by providing an easy to use, understandable tool to aid the construction, validation and improvement of biological models by removing the need for the scientist to understand or even interact with the underlying mathematical knowledge representation and machine learning. This is achieved by; 1) an intuitive graphical user interface where molecular and chemical interactions are displayed explicitly, and 2) separation of the scientific knowledge from the machine learning techniques that reason with the knowledge. The second of these also allows the Modelling Apprentice to be easily adapted to investigate other scientific applications by constructing a library that acts as a plug-in. The Modelling Apprentice will seek to improve the newly developed program Justaid - which already incorporates these features. As a test case, a model of the MAPK cell signalling network of yeast will be built using knowledge from expert biologists in Cambridge and Aberdeen. Cell signalling is the process by which cells respond to external and environmental stimuli and study of these networks is crucial to the understanding of human diseases such as cancer, diabetes, and immune and degenerative disorders. Modelling of cell signalling has also not progressed as fast as other biological processes such as metabolism. Suitability of the Modelling apprentice and the new MAPK model library will then be assessed by expert biologists who will use it to evaluate their latest experimental results. Insights gained from this testing will be used to further improve the Modelling Apprentice.