plant system biology, omics approach, modelling

1. Plant System Biology – Insights
And Advancements
Sri Subalakhshmi V K I
2019608015
I Ph.D (GPB)

2. System biology
 System of interacting units are analysed as a whole
than by analysing individually
 Concept – first proposed by Ludwig von Bertalanffy
(1973)
 Further developed by Ashby (1956)

3. Characteristics
 System have hierarchial structure
 The structure is held together by numerous linkages
to construct complex networks
(Trewevas, 2006)

4. Three types
 Top down – high throughput
omics technology
 Bottom up – starts with
molecular properties
 Middle-out – starts
somewhere b/w top and
bottom and progress
towards hierarchy of model

5. Plant System biology
 study of interactions among biological components
using models or networks to integrate genes,
metabolites, proteins, regulatory elements and other
biochemical components.

6. Overview

7. Components

8. Genomics

9. Genomics
 Study of organism’s whole genome
 Era of single gene sequencing followed by whole
genome sequencing, SNPs, arrays

10. Genome Wide Association Studies
 Investigation of genetic variation in whole genome
 Comprises of NGS techniques, whole genome
nucleotide arrays, genotyping by sequencing
approach to identify phenotypic variation of complex
traits
 Genotype-phenotype relationship
 Arabidopsis, maize, rice, sorghum

11. Epigenomics
 Epigenetics – Conrad H.Waddington
 Epigenetics – study of heritable changes in gene
expression and function not respect to change in DNA
sequence
 Epigenome – description of various epigenetic
regulators across whole genome

12. Cont.
 Dynamic and varies b/w cell types, developmental
stages or in response to environmental stimuli
 Various plant physiological processes are regulated
by epigenetic mechanisms
 DNA methylation, histone modifications, non –
coding RNA based mechanism

13. DNA methylation
 Cytosine methylation – chemical modification at 5’
methyl group
 Methylation occurs symmetric (CpG or CpNpG
island) or assymetric (other cytosines) on genome
 BS-Seq for DNA methylation analysis

14. Histone modifications
 H2A, H2B, H3, H4 – compaction of chromosomes
 Modified by post-translational modifications
 DNA to transcriptional regulators
 Histone acetylation or deacetylation
 Histone methylation - may promote or suppress
transcription

15. Non-coding RNA
 miRNAs, siRNAs, snoRNAs, rasiRNAs
 21-30 nucleotides long
 Modification at transcriptional and post-
transcriptional level

16. Bioinformatic tools
 ChIP on chip (Ringo, Tilescope)
 ChIP Seq (BLAST, ELAND)
 CpG Sequence prediction (Glimmer, Grai-IEXP)
 Bisulfite sequencing (EMSE, BiQAnalyser)

17. Other Omics approaches

18. Transcriptomics
 all the genomic counterparts which are
expressed as RNA transcripts, including coding
(mRNA) and non-coding (e.g., tRNA, miRNA)
RNAs at a given time in a cell or population of
cells under a given set of environmental
conditions - Transcriptome
 Microarray and NGS – for elucidation

19. Proteomics
 Wilkins et al. 1996
 Study of entire protein complement of the system
expressed at a given time and at particular
environmental conditions – Proteome
 Systematic analysis of proteome - Proteomics

20. Cont
 analysing changes in protein expression, study of
protein structure, function and post-translational
modifications (phosphorylation and ubiquitination)
 2D gel electrophoresis, Edman sequencing, mass
spectrometric methods
 Bioinformatic tools – ECO-2DBASE, SWISS- 2DPAGE,
WORLD-2DPAGE (specially for plant protein)

21. Metabolomics
 Oliver Fiehn, 2002
 Comprehensive analysis of all the metabolites under
given set of conditions, in an organism
 High heterogeneity than genes and proteins with
respect to physical and chemical properties
 200,000 metabolites exists in plants
 NMR, LC-MS, GC-MS, IR spectroscopy, HPLC

22. Phenomics
 High throughput systematic analysis of phenotypes
Interactomics
 the comprehensive analysis of the interactions
between different macromolecules, predominantly
protein–protein interactions in an organism

23. Lipidomics
 Comprehensive study of lipid entities of an organism
Hormonomics
 Entire set of endogeneous hormones (Auxins, ABA,
GB, etc.) of an organism
Lectinomics
 Bioinformatic studies of carbohydrate binding
proteins - lectin

24. Integration of Omics data
Deposit
individual
omics data
in public
repositaries
Generate
relationship
among
datasets
Visualizati
on of
data
Application
of
bioinforma
tics and
statistical
tools

25.  The successful integration of data will depend on
appropriate experimental design, sound statistical
analysis and correct interpretation of the results.

26. Modelling and Simulation
 Bridge the gap between theory and experiment
 Crucial component – network construction and
analysis

27. Network/graph

28. Softwares and algorithms
 Omics data visualization – Sungear,MapMan
REACTOME, Cytoscape
 Pathway database for modelling system – KEGG,
BioCyc, Biocarta, COPASI
 System biology model repositories – SynBioWave,
Cell Illustrator, Moksiskan, MEMOSys, MetNet

29. Conclusion
 Knowledge about response of plants to internal and
external stimuli
 Know about individual hierarchial component and
their interactions
 Unity in diversity approach as it combines plant
biologists and computational modelers