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已经导致了在线数据库的创建，这些数据库现在包含了数百种生物体的完整基因组序列，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，也可以在实验成本高和难以进行的地方提供预测。系统生物学是一门快速发展的科学，旨在获取这些过程和相互作用的知识，而模拟模型的创建是一项核心活动。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）技术被用于自动化或大大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，因为数据的数量远远超过了人类独立解释的能力。就系统生物学而言，科学发现通常涉及计算机模型的构建和验证，这些模型可以解释实验结果。重要的是，所得到的模型准确地解释了结果，并且在生物学上是有效的，即知识对人类专家来说是有意义的。作为人工智能的一个分支，机器学习已经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的时间里，人们越来越多地使用机器学习技术来获取生物知识。然而，阻碍计算科学发现被更普遍的生物界更广泛接受的一个主要缺点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的数学专家以及细胞生物学的专家。建模学徒旨在通过提供一个易于使用，易于理解的工具来帮助构建，验证和改进生物模型，从而消除科学家理解甚至与底层数学知识表示和机器学习交互的需要，从而克服这些障碍。这是通过；1)直观的图形用户界面，明确显示分子和化学相互作用，2)科学知识与机器学习技术的分离。第二个也允许建模学徒很容易适应调查其他科学应用程序通过构建一个库，作为一个插件。《模特学徒》将寻求改进新开发的Justaid项目，该项目已经包含了这些功能。作为一个测试案例，将利用剑桥和阿伯丁的专业生物学家的知识建立酵母的MAPK细胞信号网络模型。细胞信号传导是细胞对外部和环境刺激作出反应的过程，对这些网络的研究对于理解癌症、糖尿病、免疫和退行性疾病等人类疾病至关重要。细胞信号传导的建模也没有像新陈代谢等其他生物过程那样进展迅速。建模学徒和新的MAPK模型库的适用性将由专家生物学家评估，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用于进一步提高模特学徒。

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.