Next generation approaches to connect models and quantitative data

简单的数学模型在帮助我们理解生物学的关键过程方面非常成功。传统上，模型的用途是通过在数学框架中编码极其简单的生物学描述来测试生物学假设。然后用数学分析和计算机模拟来检验模型的定性预测是否与实验观测相符。然而，生物学已经发展到这样一个阶段，实验研究人员可以以以前只能梦想的分辨率生成令人惊叹的细胞和组织图像。例如，能够可视化单个mrna和蛋白质随时间的动态，意味着我们现在可以对大型基因调控网络或细胞和组织的功能产生极其复杂的假设。因此，我们开发的用于测试生物学假设的数学模型在规模和复杂性上都在迅速增长。特别是，所谓的基于主体的模型已经成为现代生命科学中流行的工具。这使得建模者能够，例如，跟踪单个细胞的命运和相互作用，同时，包括基因调控和信号传导的影响。为了让这些基于主体的模型真正有用，为了让它们指导实验工作，甚至最终取代对某些实验的需求，我们需要使用定量数据来校准它们。然而，这种简单的需求对建模界提出了一系列艰巨的挑战：(i)模型有许多必须估计的参数；（ii）数据复杂，有多种不同类型，并且很少（如果有的话）测量或跟踪所有相关细胞或蛋白质；（iii）数据被噪声所掩盖，这些噪声既是测量过程固有的，也是实验过程中引入的。拟议的研究将产生新的数学和计算工具来克服这些挑战。它将使现代生命科学和生物医学科学的科学家能够校准模型，然后选择最合适的模型，从而区分相互竞争的生物学假设。为了确保它们与生物学相关，这些新工具将在研究关键生物学问题的同时开发。为了确保这些工具可以被该领域的其他研究人员重用和扩展，我们所有的计算代码和资源都将免费提供。

Simple mathematical models have been remarkably successful in helping us understand key processes in biology. Traditionally, the utility of models has been to test biological hypotheses by encoding extremely simple descriptions of the biology in a mathematical framework. Mathematical analysis and computer simulation are then used to test whether qualitative predictions of the model match experimental observations. However, biology has advanced to the stage where experimental researchers can generate stunning images of cells and tissues at a level of resolution previously only dreamt of. Being able to visualise, for example, the dynamics of individual mRNAs and proteins over time, means that we can now generate extremely sophisticated hypotheses for how large gene regulatory networks or cells and tissues function. As a result, the mathematical models we develop to test biological hypotheses are quickly growing in size and complexity. In particular, the so-called agent-based models have become a popular tool in the modern life sciences. These allow the modeller to, for example, follow the fates and interactions of individual cells and, at the same time, include the effects of gene regulation and signalling. For these agent-based models to be truly useful, for them to direct experimental efforts or even, eventually, replace the need for some experiments, we need to calibrate them using quantitative data. This simply stated need, however, poses a formidable set of challenges for the modelling community: (i) the models have many parameters that must be estimated; (ii) the data is complex, of multiple different types and rarely, if ever, are all the relevant cells or proteins measured or tracked, for example; (iii) the data are obscured by noise that is both intrinsic to the measured processes and introduced during the experiments. The proposed research will generate new mathematical and computational tools to overcome these challenges. It will enable scientists in the modern life and biomedical sciences to calibrate models, then select the most appropriate model(s), and hence distinguish between competing biological hypotheses. To make sure they are relevant for biology, these new tools will be developed whilst investigating key biological questions. To ensure that the tools are available for re-use and extension by other researchers in the field, all of our computational codes and resources will be made freely available.