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Details of the Ongoing Research Activities
Dynamical Systems Analysis - Application to Physical,
Chemical and Biological Systems
Introduction
Synchronization and control of
spatiotemporal dynamics: Networks of coupled
dynamical systems have been used to model biological oscillators, excitable
media, neural networks, genetic control networks and many other self-organizing
systems. In general, the connection topology is assumed to be either completely
regular (e.g., diffusively-coupled system) or completely random. However, most
biological networks lie somewhere between these two extremes, having mainly
short-range interactions with a few long-range interactions. These systems,
called small-world networks, can be highly clustered, like regular lattices,
yet have small characteristic path lengths, like random graphs. From the
perspective of nonlinear dynamics, it would be interesting to understand how a
network of interacting dynamical systems - be they neurons, chemical concentrations,
or species population - behave collectively, given their individual dynamics
and coupling architecture. Currently we are analyzing the dynamics of a simple
dynamical system, logistic map embedded on of various networks: regular, small-world,
scale-free and random and analyze the synchronizability and controllability of
the network dynamics.
Earlier work
involves the characterization, synchronization and control of spatiotemporal
dynamics and spatiotemporal chaos by subsystem analysis, control of
self-replicating patterns, control of population dynamics by constant external
pinning.
Analysis of the topology and structure of Airport Network of India
(ANI) using graph theoretic approach: Analysis of the topological features
is useful not only in planning the infrastructure and expansion of the air
traffic connectivity, but also in managing the flow of transportation during
emergencies. Recently it has been observed that densely connected air-transportation
networks play a major role in the spread of infectious diseases, viz., Avian
Influenza, Swine-Flu, etc., turning from epidemic into pandemic in a short
period of time. Knowledge of the connectivity pattern for reduction of flights
on certain routes can help to contain the spread of the disease.
Associated people:
Manasi Sapre (post-BSc dual degree, 2011), Raina Arora
(post-BSc dual degree, 2010).
Related Publication:
1.
Analysis
of Centrality Measures of Airport Network of India, Manasi Sapre and
Nita Parekh, in S.O. Kuznetsov, D.P. Mandal, M.K. Kundu, S.K. Pal (Eds.), Lecture Notes in Computer Science, vol.
6744, pg. 376-381, (2011).
Available at:
http://www.springerlink.com/index/35249L23N0702311.pdf
2.
Analysis
of Airport Network of India (ANI), Manasi Sapre and Nita
Parekh, poster presentation at Grace Hopper Celebration of Women in
Computing, Dec 7 - 9, 2010, Bangalore, India.
3.
Controlling Dynamical Networks, Raina Arora and Nita Parekh, proceedings of CHAOS2010, The 3rd Chaotic Modeling and
Simulation International Conference, June 1-4, 2010 Chania, Crete, Greece.
Available at:
http://www.cmsim.net/sitebuildercontent/sitebuilderfiles/Arora_Parekh-Controllin
g_D ynamical_Networks-CHAOS2010-Paper.pdf.
4.
Controlling Spatiotemporal Dynamics on Small World Topology, Raina Arora and Nita Parekh, paper presentation at ICARUS at IIT, Kanpur, 26-28 March,
2010.
5.
Controlling Dynamics in Different Topological Networks, Raina Arora and Nita Parekh, poster presentation at International Conference on Frontiers of
Interface between Statistics and Sciences, 30 Dec 2009 - 2 Jan 2010,
Hyderabad, India.
6.
Dynamical Networks,
Raina Arora and Nita Parekh, poster presentation at Symposium on Theoretical and Mathematical Biology, October 10 - 11,
2009, IISER, Pune, India.
7.
Controllability of Spatially Extended Systems Using the
Pinning Approach, Nita Parekh and S. Sinha, Physica A 318, 200-212 (2003).
8.
Controlling Dynamics in Spatially Extended Systems, Nita Parekh and S. Sinha, Phys. Rev. E. 65, 036227-1 to 9 (2002).
9.
Suppression of Spatiotemporal Chaos in Coupled Map Lattices, Nita Parekh and S. Sinha in Nonlinear Dynamics and Brain Functioning, eds. N. Pradhan, P.E.
Rapp and R. Sreenivasan [Nova Science Publishers, NY, 1999], ISBN 1560726482.
10.
Global and Local Control of Spatiotemporal Chaos in Coupled
Map Lattices, Nita Parekh and S. Sinha, poster
presentation at The 5th
Experimental Chaos Conference, Orlando, Florida, USA, 28 June - 1 July
1999.
11.
Global and Local Control of Spatiotemporal Chaos in Coupled
Map Lattices, Nita Parekh, S. Parthasarthy and S.
Sinha, Phys. Rev. Lett. 81, 1401 (1998).
12.
Synchronization and Control of Spatiotemporal Chaos Using
Time-Series Data from Local Regions,
Nita Parekh, V. Ravi Kumar and B.D. Kulkarni, Chaos 8, 300 (1998).
13.
Control of Spatiotemporal Chaos: A Study with an
Autocatalytic Reaction-Diffusion System,
Nita Parekh, V. Ravi Kumar and B.D. Kulkarni, Pramana - J. Phys. 48, 303 (1997)
14.
Analysis and Characterization of Spatio-Temporal Patterns in
Nonlinear Reaction-Diffusion systems,
Nita Parekh, V. Ravi Kumar, and B.D. Kulkarni, Physica A 224, 369 (1996)
15.
Control of Self-Replicating Patterns in a Model
Reaction-Diffusion System, Nita Parekh,
V. Ravi Kumar and B.D. Kulkarni, Phys.
Rev. E 52, 5100 (1995).
Biological
Network Analysis - Using Graph Theoretic Approaches
Introduction
Graph theory is a branch of discrete
mathematics applied to the study of various real-world networks and their
properties including biological networks. Here we explore various graph
properties such as centrality measures, modularity, etc. for the analysis of
biological networks, viz., protein
contact networks, metabolic networks of Arabidopsis
thaliana, comparative analysis of metabolic networks of bacterial species,
and infectious disease transmission network.
Protein Contact Networks: Protein structures have been
extensively studied using graph theory wherein the Ca or Cb forms the nodes (vertices) connected by noncovalent interactions
(edges) defined by spatial proximity. The structure of proteins is governed to
a large extent by non-covalent interactions, and graph theory captures this
3-dimensional topology providing insights into the structure of proteins. We
have carried out graph spectral analysis of protein contact networks for
identifying tandem structural repeats and domains. We are also interested
investigating various graph centrality measures for the identification of
residues ''critical'' for the stability and function of proteins.
Metabolic Networks: The metabolic network, a complex
network including all metabolites and enzyme catalyzed reactions occurring
within a living cell, as well as the interactions between the reactants and
enzymes, is an abstract representation of cellular metabolism. The topology of
metabolic networks reflects the dynamics of their formation and evolution and
graph theory have proved to be useful in such analysis. Various graph
properties can also be used in identifying important metabolites and enzymes
and pathways conserved over evolution. We are presently carrying out
graph-based analysis of substrate-centric and enzyme-centric metabolic networks
of Arabidopsis thaliana.
Associated people:
Broto Chakrabarty (MS by Research), Kasthuri Viswanathan (MS
by Research), Y. Hari Krishna (Ms
by Research, 2009), B. Sreekanth (MTech 2009), Ruchi Jain (Ms by Research, 2008).
Related Publications:
1.
Construction
and Analysis of Enzyme Centric Network of A.
thaliana using Graph Theory, Kasthuribai Viswanathan and
Nita Parekh, in proceedings of International
Workshop on Soft Computing Applications and Knowledge Discovery
(SCAKD-2011), June 2011, Moscow, Russia.
Available
at: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-758/
2.
Construction
and Analysis of Metabolic Network of Arabidopsis
thaliana Pathways, Kasthuribai Viswanathan and Nita Parekh, in proceedings of The 2011 International Conference on
Bioinformatics & Computational Biology (BIOCOMP'11), July 18-21, 2011,
USA.
3.
Computational Approaches to Protein
Domain Identification, Nita Parekh, chapter in the book
titled ''Recent Trends in Computational Biology and Computational Statistics
Applied in Biotechnology and Bioinformatics'', ed. A.K. Roy, (to be published).
4.
Identifying Structural Repeats in
Proteins using Graph Centrality Measures, Ruchi Jain, Hari Krishna
Yalamanchili and Nita Parekh, published in the IEEE proceedings of World Congress on Nature & Biologically
Inspired Computing (NaBIC), 110 - 115, 2009; Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=
&arnumber=5393609&isnumber =5393306.
5. Graph Spectral Approach for Identifying Protein Domains, Hari Krishna Yalamanchili and
Nita Parekh, published in the book ''Bioinformatics and Computational
Biology'', S. Rajasekaran
(Ed.): BICoB 2009, LNBI 5462, pp. 437-448, LNCS, Springer Verlag Berling
Heidelberg 2009;
Available at:
http://www.springerlink. com/content/2671816416j54078/.
6. Comparative Analysis of Metabolic Networks, Shubhi Gupta and Nita Parekh,
poster presentation at International
Conference on Frontiers of Interface between Statistics and Sciences, 30
Dec 2009 - 2 Jan 2010, Hyderabad, India.
7. Graph Spectral Approach for Identifying Protein Domains, Hari Krishna Yalamanchili and
Nita Parekh, poster presentation at National
Symposium on Cellular and Molecular Biophysics, 22-24 January, 2009, CCMB
Hyderabad India; poster presentation at Symposium
on Theoretical and Mathematical Biology, October 10 - 11, 2009, IISER,
Pune, India.
8. Graph Spectral Analysis of Protein Repeat Families, Nita Parekh, published in the
proceedings of the AICTE sponsored National
Conference on Recent Developments and Applications of Probability Theory,
Random Process and Random Variables in Computer Science, 27 - 29 August
2008, MACFAST, Tiruvalla, India.
Identifying
Repeats in Protein Sequences
Introduction
Identifying tandem repeats in the
proteome of any organism is important not only for understanding the structure
and function of the proteins but also for analyzing the association of abnormal
expansion of repeat regions with disorders. We have developed an efficient tool
for identifying periodic repeats, viz., tandem peptide repeats (TPRs),
single amino acid repeats (SAARs), and periodic occurrences of single amino
acids (POSAAs). The tool reports the consensus repeat pattern, the complete
repeat region, the score and alignment of the consensus with the repeat region
along with percentage mismatch and insertions/deletions. A database of periodic
peptide repeats (DRiPS) has been developed using the tool, PEPPER.
Available at: http://bioinf.iiit.ac.in/PEPPER.
Associated people:
Vicky Khanna (M.Tech. 2011), Rima Kumari (MS by Research
2008), Krishna Manjari and V. Kiran Kumar (MTech 2008), K. Kasturi Kiran and
Radhika B. (M.Tech 2006).
Related Publications:
1.
Analysis of Periodic Repeats in Protein Sequences:
Distribution, Function and Disease Association, Rima Kumari and Nita Parekh, published as a monograph by
LAP LAMBERT Academic Publishing GmbH & Co. KG, Germany, (Oct 2010). ISBN:
978-3-8433-6005-0
Available at:
http://www.amazon.com/Analysis-Periodic-Repeats-Protein-Sequences/dp/384336
0057/ref=sr_1_1?ie=UTF8&s=books &qid=1291888949&sr=1-1
2.
Database of Repeats in Protein Sequences (DRiPS), Rima Kumari and Nita Parekh, poster paper in 19th International Conference on
Genome Informatics (GIW2008), held at
Gold Coast Australia, 1-3 Dec 2008.
Available at: http://mlaa.com.au/giw2008/GIW Posters.htm.
3.
Protein Tandem Repeat DataBase (PTRDB), Krishna Manjari, V. Kiran Kumar, Rima Kumari and Nita
Parekh, poster presentation at the International
Conference on Bioinformatics & Drug Discovery, Hyderabad Central
University, Dec 20-22, 2007. (Awarded 5th best poster prize).
4.
PEPPER - A Tool for Identifying Peptide Periodic
Repeats, poster presentation in the International Conference in Bioinformatics,
Dec 18-20, 2006, New Delhi.
Identifying
Genomic Islands and Pathogenicity Islands
Introduction
In recent years genomic islands have
been discovered in a variety of pathogenic as well as non-pathogenic bacteria.
Because they promote genetic variability, genomic islands (GIs) play an
important role in microbial evolution. We have developed a web based integrated
platform for the identification of genomic islands in which various measures
that capture bias in nucleotide compositions have been implemented, viz., GC content (both at the whole
genome and at three codon positions in genes), genomic signature, k-mer distribution (k=2-6), codon usage bias and amino acid usage bias. The tool
carries out analysis in sliding windows (default size 10Kb) and compares with
the genomic average for each measure to identify probable genomic islands. The
output is displayed in a tabular format for each window which may be filtered
if the values of the measures differ by 1.5σ (standard deviations) from
the genomic average. The tool also provides option to extract flanking regions
of predicted GIs for further analysis.
Availability:
http://bioinf.iiit.ac.in/IGIPT.
Associated people:
Ruchi Jain (Ms by Research, 2008), Sandeep Ramineni (Project
student), Tulasi and Keerthija (MTech 2009).
Related Publications:
Available
at: http://www.igi-global.com/book/pattern-discovery-using-sequence-data/51937 (to
be published).
Available at: http://doi.ieeecomputersociety.org/10.1109/ICIT.2008.42
An
Integrated Tool for SNP Function Analysis
Introduction
Single nucleotide polymorphisms (SNPs)
are commonly used for association studies to find genes responsible for complex
genetic diseases. The complex diseases may involve many genes and hundreds of
alleles but only a small portion of them are functional polymorphisms that
contribute to disease phenotypes. Assessment of the risk requires access to a
variety of heterogeneous biological databases and analytical tools. A web
server is developed to facilitate the functional analysis of SNPs by mining
data from various resources and providing a detailed report for the query.
Associated people:
Ajeet
Pandey (MTech 2007), Krishna Manjari & V. Kiran Kumar (MTech 2008), Anshu
Bharadwaj (Phd Student, CCMB), Shrish Tiwari (Sct., CCMB).
Related Publications:
Available at:
http://hgm2008.hugo-international.org/Abstracts/Publish/WorkshopPosters/WorkshopPosters10/hgm381.html.
Development
of Comprehensive Gene Database
Introduction:
An important pattern recognition
problem in biological sequences is gene prediction - the region that codes for
proteins. What are the important conserved patterns or motifs in exonic and
intronic regions of eukaryotic genes, splice site recognition, promoters &
regulatory sequences found in the vicinity of genic regions, etc. are some of
the important questions in gene prediction. Developing a specialized database
of genes would greatly facilitate in this analysis. The Comprehensive Gene
Database (CGD) of mammals was developed by integrating information from various
NCBI resources (obsolete now).
Associated people:
G.
Madhukar Reddy (MSIT 2002), Ch. Jagan Mohan Reddy (MSIT 2002), Sai Deepthi
(MSIT 2002), Kasturi Nadella (MSIT 2002), B. Subramanyam Sarath (M.Tech 2004),
Ramesh Narella (M.Tech 2005), Shrish Tiwari (Sct., CCMB), Nita Parekh
Related Publication:
1.
Gene Identification in silico, Nita Parekh, in proceedings of the National Workshop on Bioinformatics Computing, 6 - 7 March, 2009,
Sri Sathya Sai University, Prasanthinilayam, Puttaparthy.
2.
Gene Prediction in silico, Nita Parekh, invited lecture at National Seminar on Bioinformatics and Functional Genomics,
Bioinformatics Centre, Pondicherry University, Feb 15 - 17, 2005.
3.
Computational Issues in Gene Prediction, Nita Parekh, invited lecture at 40th National Convention of Computer Society India,
hosted by CSI Hyderabad Chapter, Nov 9 - 12, 2005.
4.
Tool to find Absolute Location of Genes in Human Genome, G. Madhukar Reddy, Ch. Jagan Mohan Reddy and Nita Parekh,
oral presentation at the National Seminar
on Systems Approach to Bioinformatics, conducted by Bioinformatics Centre,
Pondicherry University, Feb 18 - 20, 2004.