A gene regulatory network (GRN) is a set of genes that interact to control cellular functions like development and environmental responses. GRNs can be identified in plants using several approaches like genome sequencing, mutant resources, gene expression analysis, and epigenetic modifications. Computational modeling of GRNs, including Boolean, continuous, and stochastic models, is needed to understand the complex networks and answer questions about gene interactions. Key plant species whose GRNs have been studied include Arabidopsis, rice, and poplar.

1. A SYSTEM APPROACHES TO
IDENTIFING GENE RGULATORY
NETWORK IN PLANTS

2. OUTLINE:
What is Gene Regualating Network(GNR)What is Gene Regulating Network(GRN) ?
How GRN work
Identifying the GRN
Modelling and Analysis of GRN

3. Gene Regulating Network
 A gene regulatory network is a set of genes, or parts of genes, that interact with
each other to control a specific cell function. Gene regulatory networks are
important in development, differentiation and responding to environmental
cues[1].
 In unicellular organism regulatory networks respond to the external environmental
to survival of the cell.
 In case of multicellular organisms regulatory network control transcription cell
signalling and development.

4. https://www.researchgate.net/figure/Gene-regulatory-network-GRN-construction-pipeline-flowchart-A-
visualization-of-how-an_fig7_237015827

6. Feedback system of Network:
Positive feedback
 Gene encodes a protein activating it own
expression is positive feedback.
Negative feedback
 Gene encoding a protein inhibiting its own
expression is Negative feedback

7. Identifying the GRN
 We can identify Gene Regulatory Network by different approaches:
 Genome Sequencing- System study has been defined as the study of nature
complex system.
 However only three plant genome only sequenced:
1. Arabidopsis (The Arabidopsis Initiative 2000)
2. Rice (The Rice Genome Seq. proj. 2005)
3. Poplar (Tuskan et al. 2006)
 Mutant Resource-Once the genome is sequenced and genes are predicted, the next
goal is to determine the biological function of each gene. Systems biology is greatly
facilitated by collections of sequence-indexed, genome-wide mutants.
 using mutant resource one can predict the function of genes using reverse genetic
approach.

8. Cont.
 Large scale gene expression analysis - Microarray technology has
revolutionized our ability to monitor output the transcription regulation at the level
of whole genome.
 we can do at cell, tissue and organ level at different mutant background.

9. Cont.
Phylogenetic Analysis:-
comparative genomics have also
contributed to our understanding
of gene regulatory networks.
Figure 1. Phylogenetic tree of FLC
homologues from Brassica , Arabidopsis ,
Raphanus , and Sinapis species. BnFLC
homologues are

10. Cont.
 Epigenetic Modification:-
 Methylation of cytosine has been mapped throughout the Arabidopsis genome. (X.
Zhang et al. 2006, Zilberman et al. 2007).
 Histone methylation, particularly trimethylation of lysine 27 of histone H3 (H3K27me3),
plays an important role in regulating animal development.
http://www.plantcell.org/content/20/3/580

11. Cont.
 Post Transcription Modification-
 Plant miRNAs are a large class of 20 to 24 base-pair noncoding RNAs.
 They bind to complementary sequences on target RNA and cause
posttranscriptional gene silencing (PTGS) by cleaving or inhibiting translation of
target mRNAs.
 miRNAs are a significant component of gene regulatory networks because they
often modify the expression of transcription factors.
 more than 870 miRNAs have been identified in more than 70 plant species
through genetic screens

12. Cont.
 Some database for miRNA:

http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index

13. Modelling of Gene Regulatory Network
 In the network
 Nodes are Genes
 Input is Transcription factor(proteins)
 Output is Gene Expression
 Arrow shows the interaction

14.  Extremely complex network needs computation tool
which can answer various question-
Logical model: Boolean Model
Continuous network
Stochastic Gene Network

15. 1.Boolean model
 Simplest modelling methodology-
 In Boolean network an entity attiain two level:
 Active(1) or inactive(0)
 a gene can be described as active or inactive at any time

16. 2.Contineous model
 An extension of Boolean network.
 Gene display continuous range of activity levels, continuous
network capture several properties of gene regulatory netork
which is not present in Boolean model.

17. 3.Stochastic gene netork
 Gene expression is stochastic process; random time interval
t between occurrence of reaction.
 Work on single gene expression and small synthetic genetic
networks.
 A function assigned to each defining the gene response to a
combination of transcription factors.

18. References
 https://www.nature.com/Unravelling miRNA regulation in yield of rice (Oryza
sativa) based on differential network model. [published on 31-may-2018].
 https://www.researchgate.net/figure/Gene-regulatory-network-GRN-model-
underlying-cell-fate-determination.
 https://www.sciencedirect.com/science/computation-approaches- identifying the
regulatory of plant stress.