论文标题

数学生物学的开放问题

Open Problems in Mathematical Biology

论文作者

Vittadello, Sean T., Stumpf, Michael P. H.

论文摘要

生物学是数据丰富的,它同样丰富了概念和假设。因此,试图了解生物过程和系统的一部分是使用统计方法来面对我们的思想和假设,以确定我们的假设与现实一致的程度。但是,随着我们的假设变得越来越详细,我们的数据变得越来越复杂,以系统的方式这样做变得越来越具有挑战性。因此,数学方法在整个生命和生物医学科学中都具有重要意义。数学模型使我们能够测试自己的理解,对未来行为做出可检验的预测,并了解如何控制生物系统的行为。有人认为,数学方法对生物学家有理由有意义。但是,数学和数学家将同样受益于考虑生活系统固有的常常令人困惑的复杂性。在这里,我们提出了数学生物学中的一小部分开放问题和挑战。我们之所以选择这些开放问题,是因为它们具有生物学和数学兴趣。

Biology is data-rich, and it is equally rich in concepts and hypotheses. Part of trying to understand biological processes and systems is therefore to confront our ideas and hypotheses with data using statistical methods to determine the extent to which our hypotheses agree with reality. But doing so in a systematic way is becoming increasingly challenging as our hypotheses become more detailed, and our data becomes more complex. Mathematical methods are therefore gaining in importance across the life- and biomedical sciences. Mathematical models allow us to test our understanding, make testable predictions about future behaviour, and gain insights into how we can control the behaviour of biological systems. It has been argued that mathematical methods can be of great benefit to biologists to make sense of data. But mathematics and mathematicians are set to benefit equally from considering the often bewildering complexity inherent to living systems. Here we present a small selection of open problems and challenges in mathematical biology. We have chosen these open problems because they are of both biological and mathematical interest.

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