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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:

 

  1. IGIPT - Integrated Genomic Island Prediction Tool, Ruchi Jain, Sandeep Ramineni, Nita Parekh, Bioinformation, 7(6), (2011).
  2. Identification of Genomic Islands by Pattern Discovery, Nita Parekh, chapter in the book titled ''Pattern Discovery Using Sequence Data Mining: Applications and Studies'', Ed. Pradeep Kumar, P. Radha Krishnan, and S. Bapi Raju, Chap. 10, pg. 166 – 181, IGI Global Press, USA (2011).

Available at: http://www.igi-global.com/book/pattern-discovery-using-sequence-data/51937 (to be published).

  1. Integrated Genomic Island Prediction Tool (IGIPT), Ruchi Jain, Sandeep Ramineni, Nita Parekh, icit, pp.131-132, International Conference on Information Technology, 2008.

Available at: http://doi.ieeecomputersociety.org/10.1109/ICIT.2008.42

  1. Genomic Islands Identification in Prokaryotic Genomes, Ruchi Jain and Nita Parekh, poster presentation in International Conference on Bioinformatics & Drug Discovery, Dec 20 - 22, 2007, Hyderabad. (Awarded 2nd best poster prize).

 

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:

 

  1. CompreSNPdb: Comprehensive data-mining workflow for SNPs, Genes, Diseases and Pathways, Anshu Bhardwaj, Ajeet Pandey, P. Krishna Manjari, V. Kiran Kumar, Nita Parekh and Shrish Tiwari, poster presentation at, HUGO's 13th Human Genome Meeting (HGM2008), 27 - 30 Sept 2008, Hyderabad, India.

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.