Gene Regulatory Networks

1. Gene Regulatory
Networks (GRNs)

2. Outline
 What are GRNs?
 How GRNs Work?
 Modeling and Analysis of GRNs
 Future Challenges
 Summary
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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
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4. A gene regulatory network in E. coli_ Nodes are operons. Some operons encode for transcription
factors. transcription factor s regulate other operons

5. Structure of a GRN
 In the network
 Nodes are Genes
 Input is Transcription Factors (proteins)
 Output is Gene Expression
 Arrows show interaction

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
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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
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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
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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
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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. 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. 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. 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
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14. Positive Feedback System
 Gene encodes a protein activating its own expression
is positive feedback
 Positive feedback is important for differentiation,
evolution
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15. More Complex Feedback Systems
 Gene encodes a protein activating synthesis of
another protein inhibiting expression of gene: positive
and negative feedback
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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?
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17. Computational Models for
GRNs
 Various computational models have been
developed for regulatory network analysis
 Logical Models; Boolean Networks
 Continuous Networks
 Stochastic Gene Networks
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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
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19. 04/29/15 19

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
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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
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 More realistic models of gene regulation
 Require information on regulatory mechanisms on molecular level
usually not available

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?
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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
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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
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