The effect of transposable elements on phenotypic variation: insights from plants to humans

Transposable elements (TEs), originally discovered in maize as controlling elements, are the main components of most eukaryotic genomes. TEs have been regarded as deleterious genomic parasites due to their ability to undergo massive amplification. However, TEs can regulate gene expression and alter phenotypes. Also, emerging findings demonstrate that TEs can establish and rewire gene regulatory networks by genetic and epigenetic mechanisms. In this review, we summarize the key roles of TEs in fine-tuning the regulation of gene expression leading to phenotypic plasticity in plants and humans, and the implications for adaption and natural selection.     

Gene and epigene networks: Two levels in organizing the hereditary system

Intracellular governing gene networks consisting of genes and regulatory bonds among them are considered as the first level in organizing the hereditary system. We give examples of both prokaryotic gene network that controls the development of the λ-phage and eukaryotic gene network that controls the early Drosophila ontogenesis. Using the method of generalized threshold models kinetic curves are shown for some gene products of these networks. Gene networks that govern ontogenetic processes can be envisioned as epigene networks, the networks of the subsequent level in organizing the hereditary systems. Based on the mathematical model our computer experiments show that even the simplest hypothetical two-epigene network is capable of ensuring divergent determination, conservation of determinate states and reproduction of the initial “zygotic” functional state. In addition, the experimental results are given on construction an artificial epigene.     

Towards precise reconstruction of gene regulatory networks by data integration

Background: More and more high-throughput datasets are available from multiple levels of measuring gene regulations. The reverse engineering of gene regulatory networks from these data offers a valuable research paradigm to decipher regulatory mechanisms. So far, numerous methods have been developed for reconstructing gene regulatory networks. Results: In this paper, we provide a review of bioinformatics methods for inferring gene regulatory network from omics data. To achieve the precision reconstruction of gene regulatory networks, an intuitive alternative is to integrate these available resources in a rational framework. We also provide computational perspectives in the endeavors of inferring gene regulatory networks from heterogeneous data. We highlight the importance of multi-omics data integration with prior knowledge in gene regulatory network inferences. Conclusions: We provide computational perspectives of inferring gene regulatory networks from multiple omics data and present theoretical analyses of existing challenges and possible solutions. We emphasize on prior knowledge and data integration in network inferences owing to their abilities of identifying regulatory causality.     

Perturbation avalanches and criticality in gene regulatory networks

Boolean networks are simplified models of gene regulatory networks. We derive an approximation of the size distribution of perturbation avalanches in Boolean networks based on known results in the theory of branching processes. We show numerically that the approximation works well for different kinds of Boolean networks. It has been suggested that gene regulatory networks may be dynamically critical. To study this, as an application of the presented theory we present a novel method for estimating an order parameter from microarray data. According to the available data and our method, we find that gene regulatory networks appear to be stable and reside near the phase transition between order and chaos.     

Modeling of Hysteresis in Gene Regulatory Networks

Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.     

非编码RNA与基因表达调控

近年来,随着对基因组的深入研究,发现真核生物中存在许多形态和功能各异的非编码RNA分子,这类RNA分子并不表达蛋白质,但它们在基因转录水平、转录后水平及翻译水平起了重要的调控作用。具有调控作用的RNA分子种类非常丰富,如长链非编码RNA(long non-coding RNA,lncRNA)、miRNA、PIWI相互作用RNA(PIWI-interacting RNA,piRNA)、内源性小干扰RNA(endogenous small interfering RNA,endo-siRNA)、竞争性内源RNA(competitive endogenous RNA,ceRNA)等,它们使基因表达过程更为丰富、严谨和有序。本文综述几类典型的非编码RNA对基因表达的调节作用,以助于理解细胞中RNA分子调节网络的功能和机制。     

Learning about gene regulatory networks from gene deletion experiments

Gene regulatory networks are a major focus of interest in molecular biology. A crucial question is how complex regulatory systems are encoded and controlled by the genome. Three recent publications have raised the question of what can be learned about gene regulatory networks from microarray experiments on gene deletion mutants. Using this indirect approach, topological features such as connectivity and modularity have been studied.