SysBioMed report: Advancing systems biology for medical applications
SysBioMed report: Advancing systems biology for medical applications
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DOI：
10.1049/iet-syb.2009.0005
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发表时间：
2009-05-01
影响因子：
2.3
通讯作者：
van Leeuwen, I.
中科院分区：
文献类型：
--
作者：
Wolkenhauer, O.;Fell, D.;van Leeuwen, I.
以下报告选择并总结了一系列研讨会和讨论中产生的一些结论和建议，这些研讨会和讨论导致了《科学政策简报》(SPB) Nr. 的出版。 35，由欧洲科学基金会出版。 （本文的大部分内容直接基于 ESF SPB。这里没有给出有关特定应用领域的详细建议，但可以在 SPB 中找到。本文中更详细地讨论了与数学建模相关的问题，包括培训和支持建模的基础设施的需求。）有关快速发展的系统生物学领域的进展的大量报告和出版物导致了关键概念的大量替代定义。在这里，作者所说的“数学建模”是指主要使用动力系统理论的方法对亚细胞、细胞和宏观尺度现象进行建模和模拟。此类模型的目的是编码和测试有关细胞功能基础机制的假设。典型的例子是分子网络模型，其中细胞的行为以转录本和基因产物水平的定量变化来表达。生物信息学提供了必要的补充工具，包括模式识别程序、机器学习、统计建模（测试差异、搜索关联和相关性）以及从数据库中提取的二手数据。动态系统理论是研究表现出非线性时空行为的复杂生物系统的自然语言。然而，生成适合参数化、校准和验证此类模型的实验数据通常既耗时又昂贵，甚至以当今可用的技术是不可能的。在我们的报告中，当数学模型与生物信息学资源生成的信息相补充时，我们使用术语“计算模型”。因此，“模型”实际上是来自各种（可能是异构的）来源的数据和模型的集成集合。本报告重点关注选定的主题，这些主题被确定为医疗系统生物学的适当案例研究，并采用了作者认为重要的特定观点。我们坚信，数学建模代表了一种自然语言，可以用来整合各个层面的数据，从而提供对复杂疾病的洞察：建模需要明确的假设陈述，这一过程通常可以增强对生物系统的理解，并可以揭示缺乏理解的关键点。模拟可以揭示复杂系统中隐藏的模式和/或反直觉的机制。理论思维和数学建模构成了强大的工具，可以整合和理解正在生成的生物和临床信息，以及更多重要的是，产生可以在实验室中进行测试的新假设。最近在欧洲各地开展的医疗系统生物学项目表明需要采取行动：虽然数学建模和跨学科合作的需求在生物科学中得到广泛认可，这对该领域内的培训和研究资助机制具有重大影响，但医学科学尚未追随这一趋势。为了在医疗系统生物学方面取得重大突破，需要重新考虑大型项目的现有学术资助计划。制药行业对系统生物学研究的重大投资必须得到解决。应鼓励领先的医学期刊推广数学建模。
The following report selects and summarises some of the conclusions and recommendations generated throughout a series of workshops and discussions that have lead to the publication of the Science Policy Briefing (SPB) Nr. 35, published by the European Science Foundation. (Large parts of the present text are directly based on the ESF SPB. Detailed recommendations with regard to specific application areas are not given here but can be found in the SPB. Issues related to mathematical modelling, including training and the need for an infrastructure supporting modelling are discussed in greater detail in the present text.)The numerous reports and publications about the advances within the rapidly growing field of systems biology have led to a plethora of alternative definitions for key concepts. Here, with 'mathematical modelling' the authors refer to the modelling and simulation of subcellular, cellular and macro-scale phenomena, using primarily methods from dynamical systems theory. The aim of such models is encoding and testing hypotheses about mechanisms underlying the functioning of cells. Typical examples are models for molecular networks, where the behaviour of cells is expressed in terms of quantitative changes in the levels of transcripts and gene products. Bioinformatics provides essential complementary tools, including procedures for pattern recognition, machine learning, statistical modelling (testing for differences, searching for associations and correlations) and secondary data extracted from databases.Dynamical systems theory is the natural language to investigate complex biological systems demonstrating nonlinear spatio-temporal behaviour. However, the generation of experimental data suitable to parameterise, calibrate and validate such models is often time consuming and expensive or not even possible with the technology available today. In our report, we use the term 'computational model' when mathematical models are complemented with information generated from bioinformatics resources. Hence, 'the model' is, in reality, an integrated collection of data and models from various (possibly heterogeneous) sources. The present report focuses on a selection of topics, which were identified as appropriate case studies for medical systems biology, and adopts a particular perspective which the authors consider important. We strongly believe that mathematical modelling represents a natural language with which to integrate data at various levels and, in doing so, to provide insight into complex diseases:Modelling necessitates the statement of explicit hypotheses, a process which often enhances comprehension of the biological system and can uncover critical points where understanding is lacking.Simulations can reveal hidden patterns and/or counter-intuitive mechanisms in complex systems.Theoretical thinking and mathematical modelling constitute powerful tools to integrate and make sense of the biological and clinical information being generated and, more importantly, to generate new hypotheses that can then be tested in the laboratory.Medical Systems Biology projects carried out recently across Europe have revealed a need for action:While the need for mathematical modelling and interdisciplinary collaborations is becoming widely recognised in the biological sciences, with substantial implications for the training and research funding mechanisms within this area, the medical sciences have yet to follow this lead.To achieve major breakthroughs in Medical Systems Biology, existing academic funding schemes for large-scale projects need to be reconsidered.The hesitant stance of the pharmaceutical industry towards major investment in systems biology research has to be addressed.Leading medical journals should be encouraged to promote mathematical modelling.