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Thus a yeast cell, finding itself in a sugar solution, will turn on genes to make enzymes that process the sugar to alcohol. This is how the yeast cell makes its living, gaining energy to multiply, which under normal circumstances would enhance its survival prospects.
Edges directed from node X to node Y indicate that the transcription factor encoded in X regulates operon Y.
Regulatory genome acts as a logical processing system; receives multiple inputs and processes them as combinations of logic functions Causality in the regulatory genome; (some functions aren’t planned) Network substructure; GRNs are composed of different kinds of sub-circuits, each performing a specific kind of function Reengineering genomic control systems; To redesign these most potent of all biological control systems, it is necessary to understand thoroughly the flow of causality in a genomically encoded gene regulatory network
The 0–1 vector that describes the levels of all entities is called the system’s state, or the global state.
Each gene, each input, and each output is represented by a node in a directed graph in which there is an arrow from one node to another if and only if there is a causal link between the two nodes. Each node in the graph can be in one of two states: on or off. For a gene, &quot;on&quot; corresponds to the gene being expressed; for inputs and outputs, &quot;on&quot; corresponds to the substance being present. Time is viewed as proceeding in discrete steps. At each step, the new state of a node is a Boolean function of the prior states of the nodes with arrows pointing towards it.
Gene regulatory networks
What are GRNs?
How GRNs Work?
Modeling and Analysis of GRNs
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
In multicellular organisms regulatory networks control
transcription, cell signaling and development
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
Reengineering genomic control systems
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
X1 X2 X3
Signal 1 Signal 2 Signal 3 Signal 4 Signal N
gene 1 gene 2 gene 3 gene 4 gene 5 gene 6... gene k
The mapping between environmental signals, transcription factors
inside the cell and the genes that they regulate
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
Gene expression can be Suppressed or Enhanced
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.
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 binding site
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
Bound repressor 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
Positive Feedback System
Gene encodes a protein activating its own expression
is positive feedback
Positive feedback is important for differentiation,
More Complex Feedback Systems
Gene encodes a protein activating synthesis of
another protein inhibiting expression of gene: positive
and negative feedback
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
How robust is the system under extreme conditions?
Computational Models for
Various computational models have been
developed for regulatory network analysis
Logical Models; Boolean Networks
Stochastic Gene Networks
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
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
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
A function is assigned to each gene, defining the
gene's response to a combination of transcription
More realistic models of gene regulation
Require information on regulatory mechanisms on molecular level
usually not available
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?
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
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
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
Systems Biology: From Physiology to Gene Regulation, By Mustafa Khammash
and Hana El-Samad