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What are GRNs? How GRNs Work? Modeling and Analysis of GRNs Future Challenges Summary
04/15/23 2
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
04/15/23 3
A gene regulatory network in E. coli_ Nodes are operons. Some operons encode for transcription
factors. transcription factor s regulate other operons
Structure of a GRN
In the network Nodes are Genes Input is Transcription Factors (proteins) Output is Gene Expression Arrows show interaction
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
04/15/23 6
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
04/15/23 7
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
04/15/23 8
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
04/15/23 9
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
mRNATRANSLATION
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 sitegene Y
X Y
Bound activator
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
YY Y
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
04/15/23 13
Positive Feedback System
Gene encodes a protein activating its own expression is positive feedback
Positive feedback is important for differentiation, evolution
04/15/23 14
More Complex Feedback Systems
Gene encodes a protein activating synthesis of another protein inhibiting expression of gene: positive and negative feedback
04/15/23 15
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?
04/15/23 16
Computational Models forGRNs
Various computational models have been
developed for regulatory network analysis
Logical Models; Boolean Networks
Continuous Networks
Stochastic Gene Networks
04/15/23 17
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 state04/15/23 18
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
04/15/23 20
3) Stochastic Gene Networks
Gene expression is a stochastic process; random time intervals t between occurrence of reactions
Works on single gene expression and small synthetic
genetic networks
A function is assigned to each gene, defining the
gene's response to a combination of transcription
factors
04/15/23 21
04/15/23 22
More realistic models of gene regulation Require information on regulatory mechanisms on molecular level usually not available
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?
04/15/23 23
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 organism04/15/23 24
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
04/15/23 25