Circuitry And Dynamics Of Human Transcription Factor Regulatory Networks Pdf
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- Constructing transcriptional regulatory networks
- Circuitry and Dynamics of Human Transcription Factor Regulatory Networks
- Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling
Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks TRNs and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs.
Constructing transcriptional regulatory networks
A gene or genetic regulatory network GRN is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis , the creation of body structures, which in turn is central to evolutionary developmental biology evo-devo. The interaction can be direct or indirect through transcribed RNA or translated protein. In general, each mRNA molecule goes on to make a specific protein or set of proteins. In some cases this protein will be structural , and will accumulate at the cell membrane or within the cell to give it particular structural properties. In other cases the protein will be an enzyme , i. Some proteins though serve only to activate other genes, and these are the transcription factors that are the main players in regulatory networks or cascades.
Circuitry and Dynamics of Human Transcription Factor Regulatory Networks
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Neph and A. Stergachis and Alex Reynolds and R.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Regulation by competing: A hidden layer of gene regulatory networks Abstract: Quantitative understanding of biological regulation is essential for studying natural biosystems and for constructing synthetic systems. Current studies on gene regulation are used to build models under the assumption that gene regulators acting on a single or few targets. However, many regulators are actually shared between multiple target species with varying binding affinities, and target molecules often exist at different copy number. If the regulator is not in excess relative to target molecule pool, targets could cross talk with each other by competing for a limited pool of sharing regulators.
With relatively low efficiency, differentiated cells can be reprogrammed to a pluripotent state by ectopic expression of a few transcription factors. An understanding of the mechanisms that underlie data emerging from such experiments can help design optimal strategies for creating pluripotent cells for patient-specific regenerative medicine. We have developed a computational model for the architecture of the epigenetic and genetic regulatory networks which describes transformations resulting from expression of reprogramming factors. Importantly, our studies identify the rare temporal pathways that result in induced pluripotent cells. Further experimental tests of predictions emerging from our model should lead to fundamental advances in our understanding of how cellular identity is maintained and transformed.
Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling
Plant responses to environmental and intrinsic signals are tightly controlled by multiple transcription factors TFs. These TFs and their regulatory connections form gene regulatory networks GRNs , which provide a blueprint of the transcriptional regulations underlying plant development and environmental responses. This review provides examples of experimental methodologies commonly used to identify regulatory interactions and generate GRNs. Additionally, this review describes network inference techniques that leverage gene expression data to predict regulatory interactions.
Biological networks are the representation of multiple interactions within a cell, a global view intended to help understand how relationships between molecules dictate cellular behavior. Recent advances in molecular and computational biology have made possible the study of intricate transcriptional regulatory networks that describe gene expression as a function of regulatory inputs specified by interactions between proteins and DNA. Here we review the properties of transcriptional regulatory networks and the rapidly evolving approaches that will enable the elucidation of their structure and dynamic behavior.
Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing scRNAseq captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast Saccharomyces cerevisiae.
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Женщина нахмурилась: - Извините, сэр.