Enriching Metabolic PATHwaY models with evidence from the literature (EMPATHY)

为了了解生命系统，生物学家已经开始建立系统的预测模型，使他们能够运行计算实验，从而减少传统的、基于实验室的实验的数量，而这些实验以前是获得这种理解所必需的。这种方法沿用了目前在工程领域司空见惯的做法，例如，航空工程师将开发复杂的飞机模型，并在计算机上测试拟议设计的安全性，而这远远早于开发飞机本身（甚至把它放在风洞里）。这种生物建模方法被命名为“系统生物学”，并已成功地应用于许多领域。这个建议的重点是模拟新陈代谢。新陈代谢是相互关联的化学反应的集合，它允许细胞从它们消耗的营养物质中提取能量和物质并生长。所有自由生物都必然有这样的代谢系统。因此，对人体新陈代谢进行建模将使我们能够了解人体的健康状态，例如衰老的功能，并有助于设计能够维持人体健康的化学物质（无论是营养素还是药物）。同样，代谢模型也被用于细胞工厂的发展，这些细胞工厂能够生产与工业有关的化学品，这些化学品通常是由化学工业通过更传统的方法生产的，并且经常涉及使用石油作为原料。这种方法（被称为发酵或“工业生物技术”）并不新鲜——我们用酵母细胞发酵来生产酒精已经有几千年的历史了——但传统的发酵改进，在青霉素的例子中持续了几十年，涉及随机突变和选择，通常伴随着有害的“乘客”突变的结合。然而，最近的研究表明，代谢网络建模方法提供了一个合理的途径，无论是成熟的发酵和新的，如生物异戊二烯可持续汽车轮胎生产。因此，这些方法对生物燃料和精细化学品等重要物质的可持续生物生产具有重要价值。因此，代谢模型在即将到来的世纪对健康和环境的可持续性有很大的希望。然而，建立这些模型所需的大部分信息都存在于教科书、专利和科学期刊中，在将这些信息纳入模型之前，需要大量的研究人员来搜索、判断和提取这些信息。因此，这类模型的传统开发目前遵循（并且需要）一个耗时且昂贵的手工过程。现代的方法使这项工作自动化。文本挖掘方法的应用可以极大地促进从文献中提取信息的过程。文本挖掘应用复杂的算法来识别隐藏在文本中的相关术语和句子，并且可以经过训练来识别可能与给定应用程序相关的大量文档中的文本段落。在这项工作中，我们将利用文本挖掘从每天发表的大量科学文章中提取构建代谢网络模型所需的信息。这些分析的结果将呈现给模型开发者，他们将判断和提取这些信息，以进一步开发现有的代谢模型。将开发一个特定的易于使用的web应用程序，以便允许多个用户为这个模型构建过程做出贡献，而不考虑他们的背景和以前的计算模型构建经验。这项工作的结果将是更完整的代谢模型，这将使研究人员能够提高对一系列生物体代谢的理解，从而将这些增加的知识用于健康和环境可持续性的应用。

In order to understand living systems, biologists have taken to generating predictive models of the system, allowing them to run computational experiments that reduce the number of more traditional, lab-based experiments that would previously be necessary to gain such an understanding. This approach follows that which is now commonplace in engineering, in which, for instance, aeronautical engineers will develop sophisticated models of aircraft and test safety aspects of the proposed design in a computer, long before developing the aircraft itself (or even putting it in a wind tunnel).This biological modelling approach is named "systems biology" and has been employed successfully in a number of areas. The focus of this proposal is in modelling metabolism. Metabolism is the collection of interconnected chemical reactions that allow cells to extract energy and material from the nutrients that they consume and to grow. All free-living organisms necessarily have such metabolic systems. Thus, modelling human metabolism will allow us to understand the human body's healthy state, for instance as a function of ageing, and aid in the design of chemicals (whether nutrients or drugs) that can maintain human health.In a similar vein, metabolic modelling is also being used in the development of cell factories, which are able to produce industrially relevant chemicals, which are commonly produced by the chemical industry through more traditional means, and often involve the use of oil as a feedstock. This approach (known as fermentation or "industrial biotechnology") is not new - we have been fermenting yeast cells to produce alcohol for thousands of years - but traditional fermentation improvements, lasting decades in the case of penicillins, involved random mutation and selection, often coupled to the incorporation of harmful 'passenger' mutations. However, recent research has shown that metabolic network modelling methods provide a rational approach, both for mature fermentations and for new ones such as bio-isoprene for sustainable car tyre production. Thus, these methods have great value for the sustainable bioproduction of important substances, such as biofuels and fine chemicals.Metabolic modelling therefore has much promise for health and environmental sustainability in this coming century. However, much of the information necessary for the building of these models is held in textbooks, patents and scientific journals, and large teams of researchers are required to search for, judge and extract this information before including it in the models. Thus, the traditional development of such models currently follows (and requires) a time consuming and expensive manual process. Modern methods allow this to be automated.This process of extracting information from the literature can be greatly facilitated by the application of the methods of text mining. Text mining applies sophisticated algorithms to recognise relevant terms and sentences buried in text, and can be trained to recognise those passages of text within a large number of documents that may be relevant to a given application.In this work, we will utilise text mining to extract information necessary for the construction of metabolic network models from the large number of scientific articles that are published daily. The results of these analyses will be presented to model developers, who will judge and extract this information to develop existing metabolic models further. A specific easy-to-use web application will be developed in order to allow a multiple users to contribute towards this model building process, irrespective of their background and previous experience of computational model building.The results of this work will be more complete metabolic models, which will allow researchers to improve understanding of metabolism in a range of organisms, and therefore use this increased knowledge in applications of health and environmental sustainability.