A new approach to Science at the Life Sciences Interface

下一个世纪的关键科学挑战要求我们从根本上推进当前对复杂自然系统的理解-从细胞如何工作以及它们为什么/如何出错，到气候和生态系统的相互作用如何调节地球的生命支持系统。我们希望了解这些系统在功能层面上的行为，以及这种行为是如何产生的结果，高度动态的，强烈的非线性，紧密耦合的组件过程之间的相互作用发生在多个空间和时间尺度。为了应对这些挑战，需要大量新型的探索性和预测性模型以及研究策略。需要新一代的理论方法和工具来识别和利用协同作用和相似性，从而可以在不同的科学问题领域之间转换解决方案。生物学理论往往是根据不完美的数据集进行评估的。有必要明智地选择和验证用于评估任何模型的试验台，并开发新技术，以收集对复杂系统行为至关重要的新数据。对系统级行为的研究需要确定生物学假设，而这些假设不能仅仅通过观察个体现象来表达。前所未有的复杂程度意味着这些定量模型在分析上将是难以处理的，只有通过计算方法才有可能探索它们的行为。简而言之，需要一种新的计算方法和环境来进行这种科学研究-而这在今天的学术界并不存在。要取得进步，就需要在开发数学和计算模型、工具和软件的方式上进行文化和技术变革，同时也需要在培训科学家群体开发和使用这些方法的方式上进行相应的变革。这项工作只能在真实的生物学问题的背景下进行。这一建议汇集了来自学术界和工业界的一批合作伙伴，每个合作伙伴都已开始侧重于这些问题的不同但互补的方面。这些中心是尝试这种文化转变的理想社区，因为它们已经致力于培养下一代科学家，他们将成为这种科学的先驱。

The key scientific challenges of the next century require that we fundamentally advance current understanding of complex natural systems - ranging from how cells work and why/how they go wrong, to how the interaction of climate and ecosystems regulates the planet's life support system. We wish to understand how these systems behave at the functional level, and how this behaviour arises as a result of highly dynamic, strongly non-linear, tightly coupled interactions between component processes occurring across multiple spatial and temporal scales. Entirely new kinds of exploratory and predictive models and research strategies are needed to address these challenges. A new generation of theoretical methods and tools are needed to enable the recognition and exploitation of synergies and similarities that allow the translation of solutions between different scientific problem domains. Biological theories are evaluated against often imperfect data sets. There is a need for judicious selection and validation of the test bed against which any model is evaluated and the development of novel technologies for gathering new data identified as critical to complex system behaviour. The investigation of systems-level behaviour requires the identification of biological hypotheses that could not have been expressed by looking at individual phenomena alone. The unprecedented degree of complexity will mean that these quantitative models will be analytically intractable, and exploring their behaviour will be possible only through a computational approach. In short, a novel computational approach and environment is needed for doing this kind of science - and this does not exist in academia today. Progress requires both a cultural and technological change in the way in which mathematical and computational models, tools and software are developed, and a concomitant change in the way in which groups of scientists are trained to develop and use these approaches. This work can only be done in the context of real biological problems. This proposal brings together a consortium of partners from academia and industry, each of whom has begun to focus on differing but complementary aspects of these problems. These centres are the ideal community to attempt such a cultural shift since they are already dedicated to training the next generation of scientists who will pioneer this kind of science.