Gene regulatory networks

Gene Regulatory Networks (GRNs)

Outline

What are GRNs? How GRNs Work? Modeling and Analysis of GRNs Future Challenges Summary

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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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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

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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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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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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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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

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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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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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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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Computational Models forGRNs

Various computational models have been

developed for regulatory network analysis

Logical Models; Boolean Networks

Continuous Networks

Stochastic Gene Networks

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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

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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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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

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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?

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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

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