PROJECT 5

这个项目的目标是将遗传学理论与多尺度生理模型联系起来，以解决和理解种群背景下的生理基因组学。个性化医学中的许多挑战反映了对基因-表型(GP)图(见图1.1)的缺乏理解，即不同遗传变异星座(基因类型)的不同长度和不同时间尺度的表型效应的聚合。 我们的方法的动机是这样一个事实，即理解复杂性状的GP图可能需要对遗传变异的表型效应进行机械性的模型描述，即与遗传信息有明确联系的高级多系列生理模型。我们使用术语因果凝聚型-表型模型来描述遗传变异如何在不同的系统水平上表现为表型变异，直到组织、器官和整个生物体水平[19]。因果凝聚的基因型表型模型是从复合映射中产生的电子表示，首先是从基因型到模型参数，其次是从模型到表型，这些表型又可能是覆盖的模型结构中的参数。(参见图1.1。)这一方法使我们有可能以经典统计方法所不能及的方式来研究一系列复杂的现象，如遗传显性、上位性、外显性、多效性、隐蔽变异和基因/环境相互作用。结合这个项目中将要开发的非常先进的模型，这种方法将提供关于遗传变异和复杂疾病之间关系的新的和重要的信息。

This project targets the challenge of connecting genetics theory with multi-scale physiological models, to address and understand physiological genomics in a population context. Many challenges in personalized medicine reflect a lack of understanding of the genotype-to-phenotype (GP) map (see Figure 1.1), i.e. the aggregated phenotypic effects across different length and time scales of different constellations of genetic variation (genotypes). Our approach is motivated by the fact that understanding the GP map for a complex trait is likely to require mechanistic model descriptions of the phenotypic effects of genetic variation, i.e. advanced multiseale physiological models with an explicit link to genetic information. We use the term causally cohesive genotype-phenotype models for models describing how genetic variation manifests in phenotypic variation at various systemic levels up to the tissue, organ and whole-organism level [19]. Causally cohesive genotype phenotype models are in silico representations that emerge from a composite mapping, first from genotypes to model parameters, and second from models to phenotypes, which may in turn be parameters in overlying model structures. (See Figure 1.1.) This approach makes it possible to address a whole range of complex phenomena, like genetic dominance, epistasis, penetrance, plelotropy, cryptic variation and genotype/environment interactions in ways that are beyond reach of classical statistical approaches [16-22]. In combination with the very advanced models to be developed in this project, this approach will provide novel and important information about the relationships between genetic variation and complex diseases.