The ambitious and ultimate research purpose in Systems Biology is the understanding and modelling of the cells system. Although a vast number of models have been developed in order to extract biological knowledge from complex systems composed of basic elements as proteins, genes and chemical compounds, a need remains for improving our understanding of dynamical features of the systems (i.e., temporal-dependence). In this article, we analyze the gene expression dynamics (i.e., how the genes expression fluctuates in time) by using a new constructive approach. This approach is based on only two fundamental ingredients: symmetry and the Markov property of dynamics. First, by using experimental data of human and yeast gene expression time series, we found a symmetry in short-time transition probability from time $t$ to time $t+1$. We call it self-similarity symmetry (i.e., surprisingly, the gene expression short-time fluctuations contain a repeating pattern of smaller and smaller parts that are like the whole, but different in size). Secondly, the Markov property of dynamics reflects that the short-time fluctuation governs the full-time behaviour of the system. Here, we succeed in reconstructing naturally the global behavior of the observed distribution of gene expression (i.e., scaling-law) and the local behaviour of the power-law tail of this distribution, by using only these two ingredients: symmetry and the Markov property of dynamics. This approach may represent a step forward toward an integrated image of the basic elements of the whole cell.
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