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Register a new userA stochastic approach to multi-gene expression dynamics
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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.
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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.
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.
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.
A toggle switch consists of two genes that mutually repress each other. This regulatory motif is active during cell differentiation and is thought to act as a memory device, being able to choose and maintain cell fate decisions. In this contribution, we study the stability and dynamics of a two-stage gene expression switch within a probabilistic framework inspired by the properties of the Pu/Gata toggle switch in myeloid progenitor cells. We focus on low mRNA numbers, high protein abundance and monomeric transcription factor binding. Contrary to the expectation from a deterministic description, this switch shows complex multi-attractor dy- namics without autoactivation and cooperativity. Most importantly, the four attractors of the system, which only emerge in a probabilistic two-stage description, can be identified with committed and primed states in cell differentiation. We first study the dynamics of the system and infer the mechanisms that move the system between attractors using both the quasi-potential and the probability flux of the system. Second, we show that the residence times of the system in one of the committed attractors are geometrically distributed and provide an analytical expression of the distribution. Most importantly we find that the mean residence time increases linearly with the mean protein level. Finally, we study the implications of this distribution for the stability of a switch and discuss the influence of the stability on a specific cell differentiation mechanism. Our model explains lineage priming and proposes the need of either high protein numbers or long term modifications such as chromatin remodeling to achieve stable cell fate decisions. Notably we present a system with high protein abundance that nevertheless requires a probabilistic description to exhibit multistability, complex switching dynamics and lineage priming.
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.
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