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Gene regulatory networks

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Gene Regulatory Networks

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Gene regulatory networks

  1. 1. Gene Regulatory Networks (GRNs)
  2. 2. Outline  What are GRNs?  How GRNs Work?  Modeling and Analysis of GRNs  Future Challenges  Summary 04/29/15 2
  3. 3. Gene Regulatory Network  A set of genes, proteins, small molecules which interact mutually to control rate of transcription  In unicellular organisms regulatory networks respond to the external environment, to make the cell survival (Yeast)  In multicellular organisms regulatory networks control transcription, cell signaling and development 04/29/15 3
  4. 4. A gene regulatory network in E. coli_ Nodes are operons. Some operons encode for transcription factors. transcription factor s regulate other operons
  5. 5. Structure of a GRN  In the network  Nodes are Genes  Input is Transcription Factors (proteins)  Output is Gene Expression  Arrows show interaction
  6. 6. GRNs as Control Systems  The GRNs control animal development  They regulate the expression of thousands of genes in developmental process  Regulatory genome acts as a logical processing system  Causality in the regulatory genome  Network substructure  Reengineering genomic control systems 04/29/15 6
  7. 7. How GRNs Work?  GRNs are made up of thousands of DNA sequences in a cell  Inputs are signaling pathways and regulatory proteins known as transcription factors  Signaling pathways respond to signals and activate the transcription factor proteins  Transcription factors bind to genes and make mRNA  The mRNA synthesizes the required proteins 04/29/15 7
  8. 8. X1 X2 X3 Signal 1 Signal 2 Signal 3 Signal 4 Signal N Xm gene 1 gene 2 gene 3 gene 4 gene 5 gene 6... gene k Environment Transcription factors Genes ... ... The mapping between environmental signals, transcription factors inside the cell and the genes that they regulate 04/29/15 8
  9. 9. GRNs and Protein Synthesis  Specific transcription factors interact with specific genes to pass on specific genetic information to the mRNA to synthesize specific proteins for specific purposes  Gene expression can be Suppressed or Enhanced 04/29/15 9
  10. 10. gene Y TRANSCRIPTION promoter DNA RNA polymerase GENE TRANSCRIPTIONAL REGULATION, THE BASIC PICTURE: Each gene is usually preceded by a regulatory DNA region called the promoter. The promoter contains a specific site (DNA sequence) that can bind RNA polymerase (RNAp), a complex of several proteins that forms an enzyme That can synthesize mRNA that is complementary to the genes coding sequence. The process of forming the mRNA is called transcription. The mRNA is then translated into protein. Y protein gene Y mRNA TRANSLATION
  11. 11. INCREASED TRANSCRIPTION An activator X, is a transcription- factor protein that increases the rate of mRNA transcription when it binds the promoter. The activator transits rapidly between active and inactive forms. In its active form, it has a high affinity to a specific site (or sites) on the promoter. The signal Sx increases the probability that X is in its active form X*. Thus, X* binds the promoter of gene Y to increase transcription and production of protein Y. The timescales are typically sub-second for transitions between X and X*, seconds for binding/ unbinding of X to the promoter, minutes for transcription and translation of the protein product, and tens of minutes for the accumulation of the protein, X X* Sx X* Y Y ActivatorX Y Y X binding site gene Y X Y Bound activator
  12. 12. A repressor X, is a transcription- factor protein that decreases mRNA transcription when it binds the promoter. The signal Sx increases the probability that X is in its active form X*.X* binds a specific site in the promoter of gene Y to decrease transcription and production of protein Y. Many genes show a weak (basal) transcription when repressor is bound. Bound repressor X Y X X* Sx NO TRANSCRIPTION X* Unbound repressor X Bound repressor Y Y Y Y
  13. 13. Negative Feedback System  Gene encodes a protein inhibiting its own expression is negative feedback  Negative feedback is important for homeostasis, maintenance of system near a desired state 04/29/15 13
  14. 14. Positive Feedback System  Gene encodes a protein activating its own expression is positive feedback  Positive feedback is important for differentiation, evolution 04/29/15 14
  15. 15. More Complex Feedback Systems  Gene encodes a protein activating synthesis of another protein inhibiting expression of gene: positive and negative feedback 04/29/15 15
  16. 16. Modeling and Analysis of GRNs  Extremely complex networks need computational tools which can answer various questions:  Behaviors of a system under different conditions?  Changes in the dynamics of the system if certain parts stop functioning?  How robust is the system under extreme conditions? 04/29/15 16
  17. 17. Computational Models for GRNs  Various computational models have been developed for regulatory network analysis  Logical Models; Boolean Networks  Continuous Networks  Stochastic Gene Networks 04/29/15 17
  18. 18. 1) Boolean Networks  Simplest modeling methodology; logic based  In a Boolean Network, an entity can attain two levels: active (1) or inactive (0)  A gene can be described as expressed or not expressed at any time  The level of each entity is updated according to the levels of several entities, via a specific Boolean function called the system’s state 04/29/15 18
  19. 19. 04/29/15 19
  20. 20. 2) Continuous Networks  An extension of the Boolean networks  Genes display a continuous range of activity levels, Continuous Networks capture several properties of gene regulatory networks not present in the Boolean model  Grouping of inputs to a node to show level of regulation  Continuous models allow a comparison of global state and experimental data and can be more accurate 04/29/15 20
  21. 21. 3) Stochastic Gene Networks  Gene expression is a stochastic process; random time intervals t between occurrence of reactions  Works on single gene expressionand small synthetic genetic networks  A function is assigned to each gene, defining the gene's response to a combination of transcription factors 04/29/15 21
  22. 22. 04/29/15 22  More realistic models of gene regulation  Require information on regulatory mechanisms on molecular level usually not available
  23. 23. Future Challenges  Future Challenges include:  Predicting how genes are regulated in a network?  Which proteins participate in metabolic pathways and how they interact?  How to extract and represent the knowledge of the genetic regulatory networks? 04/29/15 23
  24. 24. Summary  Discovering gene regulatory dependencies is fundamental for understanding mechanisms responsible for proper activity of a cell  As the complexity of GRNs increases so does the need for accurate modeling techniques  Once constructed, GRNs can be used to model the behavior of an organism 04/29/15 24
  25. 25. Literature  http://www.brighthub.com/science/genetics/articles/47551.aspx#ixzz192NMwLe6  The Knowledge Representation of the Genetic Regulatory Networks Based on Ontology, Ines Hamdi, and Mohamed Ben Ahmed  Intrinsic noise in gene regulatory networks, Mukund Thattai and Alexander van Oudenaarden*  Gene regulatory networks and embryonic specification, Leroy Hood* Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103  From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks, ilya shmulevich, member, ieee, edward r. dougherty, and wei zhang  Systems Biology: From Physiology to Gene Regulation, By Mustafa Khammash and Hana El-Samad 04/29/15 25