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Register a new userUniversal collective fluctuations in gene expression dynamics from yeast to human
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In this work, the dynamics of fluctuations in gene expression time series is investigated. By using collected data of gene expression from yeast and human organisms, we found that the fluctuations of gene expression level and its average value over time are strongly correlated and obey a scaling law. As this feature is found in yeast and human organisms, it suggests that probably this coupling is a common dynamical organizing property of all living systems. To understand these observations, we propose a stochastic model which can explain these collective fluctuations, and predict the scaling exponent. Interestingly, our results indicate that the observed scaling law emerges from the self-similarity symmetry embedded in gene expression fluctuations.
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In the last years, tens of thousands gene expression profiles for cells of several organisms have been monitored. Gene expression is a complex transcriptional process where mRNA molecules are translated into proteins, which control most of the cell functions. In this process, the correlation among genes is crucial to determine the specific functions of genes. Here, we propose a novel multi-dimensional stochastic approach to deal with the gene correlation phenomena. Interestingly, our stochastic framework suggests that the study of the gene correlation requires only one theoretical assumption -Markov property- and the experimental transition probability, which characterizes the gene correlation system. Finally, a gene expression experiment is proposed for future applications of the model.
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.
Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN black box. Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of co-variability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate time-scales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle dependent transcription rates can be identified from the variability of co-regulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.
A quantum mechanical model on histone modification is proposed. Along with the methyl / acetate or other groups bound to the modified residues the torsion angles of the nearby histone chain are supposed to participate in the quantum transition cooperatively. The transition rate W is calculated based on the non-radiative quantum transition theory in adiabatic approximation. By using Ws the reaction equations can be written for histone modification and the histone modification level can be calculable from the equations, which is decided by not only the atomic group bound to the modified residue, but also the nearby histone chain. The theory can explain the mechanism for the correlation between a pair of chromatin markers observed in histone modification. The temperature dependence and the coherence-length dependence of histone modification are deduced. Several points for checking the proposed theory and the quantum nature of histone modification are suggested as follows: 1, The relationship between lnW and 1/T is same as usual protein folding. The non-Arhenius temperature dependence of the histone modification level is predicted. 2, The variation of histone modification level through point mutation of some residues on the chain is predicted since the mutation may change the coherence-length of the system. 3, Multi-site modification obeys the quantum superposition law and the comparison between multi-site transition and single modification transition gives an additional clue to the testing of the quantum nature of histone modification.
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