Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco CA 94158, United States.
Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco CA 94158, United States; Chan Zuckerberg Biohub, San Francisco, CA 94158, United States.
Curr Opin Microbiol. 2018 Oct;45:47-52. doi: 10.1016/j.mib.2018.01.004. Epub 2018 Feb 27.
Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach-complexity-aware simple modeling-that can bridge the gap between the small-scale and large-scale approaches.
数学模型对于深化我们对生物学的理解仍然至关重要。一方面，简单或小规模的模型有助于描绘一般的生物学原理。然而，这些模型在细节上的简约性以及它们的模块化和隔离假设使得它们对于描述定量特征不准确。另一方面，大规模和详细的模型可以定量地重现感兴趣的表型，但必须依赖许多未知参数，这使得它们往往难以从机制上进行解析，也难以用于提取一般原理。我们讨论了一种新方法——复杂性感知简单建模的一些示例，该方法可以弥合小规模和大规模方法之间的差距。
