BACKGROUND Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited. RESULTS Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. CONCLUSIONS We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

中文翻译：

---

数据驱动的多尺度建模揭示了代谢耦合对酵母菌落时空生长动态的作用。

背景技术诸如哺乳动物组织或微生物生物膜之类的多细胞实体通常表现出适应其特定功能或环境的复杂空间排列。这些结构是细胞间信号传导以及与环境相互作用的结果，使相同基因型的细胞能够分化成组织良好的多样化细胞群落。尽管它很重要，但我们对这种细胞与细胞和代谢耦合如何导致功能优化结构的理解仍然有限。结果在这里，我们提出了一个数据驱动的空间框架，通过计算研究酵母菌落作为多细胞结构依赖于代谢能力的发展。为此，我们首先根据均质液体培养基条件的实验数据开发并参数化了酵母的动态细胞状态和生长模型。随后将推断的模型用于菌落发育的空间粗粒度模型中，通过使用最先进的统计技术从菌落生长的实验时间过程数据校准空间参数来研究代谢耦合的影响，以消除模型的不确定性和参数估计。该模型最终通过具有独特代谢特征的替代酵母菌株的独立实验数据进行了验证，并说明了代谢耦合对结构形成的影响。结论我们引入了一种新的酵母菌落形成模型，提出了一种以数据驱动的方式进行模型校准的统计方法，并演示了如何通过对遗传上不同的酵母菌株的独立测量进行验证，使用已建立的模型来生成跨尺度的预测。