Djordjevic Marko, Rodic Andjela, Graovac Stefan
Faculty of Biology, Institute of Physiology and Biochemistry, University of Belgrade, Belgrade, Serbia.
Interdisciplinary PhD Program in Biophysics, University of Belgrade, Belgrade, Serbia.
Eur Biophys J. 2019 Jul;48(5):413-424. doi: 10.1007/s00249-019-01366-3. Epub 2019 Apr 9.
Recent decades brought a revolution to biology, driven mainly by exponentially increasing amounts of data coming from "'omics" sciences. To handle these data, bioinformatics often has to combine biologically heterogeneous signals, for which methods from statistics and engineering (e.g. machine learning) are often used. While such an approach is sometimes necessary, it effectively treats the underlying biological processes as a black box. Similarly, systems biology deals with inherently complex systems, characterized by a large number of degrees of freedom, and interactions that are highly non-linear. To deal with this complexity, the underlying physical interactions are often (over)simplified, such as in Boolean modelling of network dynamics. In this review, we argue for the utility of applying a biophysical approach in bioinformatics and systems biology, including discussion of two examples from our research which address sequence analysis and understanding intracellular gene expression dynamics.
近几十年来，生物学经历了一场革命，主要驱动力是来自“组学”科学的数据量呈指数级增长。为了处理这些数据，生物信息学常常需要整合生物学上异质的信号，为此经常使用统计学和工程学（如机器学习）方法。虽然这种方法有时是必要的，但它实际上将潜在的生物学过程视为一个黑箱。同样，系统生物学处理的是本质上复杂的系统，其特点是有大量的自由度以及高度非线性的相互作用。为了应对这种复杂性，潜在的物理相互作用常常被（过度）简化，比如在网络动力学的布尔建模中。在本综述中，我们论证了在生物信息学和系统生物学中应用生物物理方法的实用性，包括讨论我们研究中的两个例子，它们涉及序列分析和理解细胞内基因表达动态。
