Gunawan:Thanneer Malai

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Singapore 117576 <BR>
Singapore 117576 <BR>
Tel:+65 6516 7859
Tel:+65 6516 7859
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I work for [http://openwetware.org/wiki/Gunawan  Rudiyanto Gunawan] at National University of Singapore (NUS).
I work for [http://openwetware.org/wiki/Gunawan  Rudiyanto Gunawan] at National University of Singapore (NUS).
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;''Dynamical Sensitivity Analysis for Chemical and Biological Systems''
;''Dynamical Sensitivity Analysis for Chemical and Biological Systems''
:<p align='justify' CLASS="indented">Complexity of chemical and biological systems often limits human intuition in understanding the functional regulations and emergent behaviours. To this end, mathematical models have commonly been used to simulate their dynamical behaviour, to which a quantitative model analysis tool can be applied to gain insights. Most of the existing tools, like classical parametric sensitivity, bifurcation analysis and structured singular values, quantify the system dependence on model parameters, by introducing static perturbations. The dynamical aspects of the system are not immediately apparent from these analyses. The reason stems from the fact that the effected perturbations are on system parameters and are persistent on system behavior. To overcome this draw back, we develop novel sensitivity analysis methodologies, for analyzing the dynamics of both deterministic and stochastic models of chemical and biological systems.</p>
:<p align='justify' CLASS="indented">Complexity of chemical and biological systems often limits human intuition in understanding the functional regulations and emergent behaviours. To this end, mathematical models have commonly been used to simulate their dynamical behaviour, to which a quantitative model analysis tool can be applied to gain insights. Most of the existing tools, like classical parametric sensitivity, bifurcation analysis and structured singular values, quantify the system dependence on model parameters, by introducing static perturbations. The dynamical aspects of the system are not immediately apparent from these analyses. The reason stems from the fact that the effected perturbations are on system parameters and are persistent on system behavior. To overcome this draw back, we develop novel sensitivity analysis methodologies, for analyzing the dynamics of both deterministic and stochastic models of chemical and biological systems.</p>
 +
:Methods developed include:
:Methods developed include:
::* Green’s Function Matrix (GFM) based sensitivity
::* Green’s Function Matrix (GFM) based sensitivity
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::* Pathway Parametric Sensitivity Analysis (PathPSA)
::* Pathway Parametric Sensitivity Analysis (PathPSA)
::* Molecular Distribution based Finite Perturbation (MDFP) analysis
::* Molecular Distribution based Finite Perturbation (MDFP) analysis
 +
:<p align='justify' CLASS="indented">Tools developed in this work offers dynamical insights on the functional regulation and signal propagation in the networks of interest. The results give a reaction-by-reaction, molecule-by-molecule and pathway-by pathway account of how networks function or output behavior is accomplished. The efficacy of the tools developed is evaluated on both synthetic and real networks of chemical and biological systems. Other than analyzing the dynamics, the knowledge gained will have applications from robustness-fragility analysis to identify robustness causing mechanisms to model reduction and validation to design of future experiments. In addition, the biological knowledge gained can assist drug discovery efforts in the identification of potential drug targets, the understanding of drug efficacy and specificity, and finally the optimization of drug dosing and timing. The development of these methods represents a concrete step towards robustness-based drug design through systems biology and systems medicine.</p>
:<p align='justify' CLASS="indented">Tools developed in this work offers dynamical insights on the functional regulation and signal propagation in the networks of interest. The results give a reaction-by-reaction, molecule-by-molecule and pathway-by pathway account of how networks function or output behavior is accomplished. The efficacy of the tools developed is evaluated on both synthetic and real networks of chemical and biological systems. Other than analyzing the dynamics, the knowledge gained will have applications from robustness-fragility analysis to identify robustness causing mechanisms to model reduction and validation to design of future experiments. In addition, the biological knowledge gained can assist drug discovery efforts in the identification of potential drug targets, the understanding of drug efficacy and specificity, and finally the optimization of drug dosing and timing. The development of these methods represents a concrete step towards robustness-based drug design through systems biology and systems medicine.</p>
:<p align='justify'>'''''Key words:''''' Sensitivity/Robustness analysis, Computational Systems Biology, Mathematical modeling, Systems Oriented Drug Discovery </p>
:<p align='justify'>'''''Key words:''''' Sensitivity/Robustness analysis, Computational Systems Biology, Mathematical modeling, Systems Oriented Drug Discovery </p>

Revision as of 06:28, 27 June 2011

Chemical and Biological Systems Engineering Laboratory

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Thanneer Malai Perumal

Thanneer Malai Perumal

4 Engineering Drive 4 Block E5 #B-05
National University of Singapore
Singapore 117576
Tel:+65 6516 7859

I work for Rudiyanto Gunawan at National University of Singapore (NUS).

Education

  • 2006, BE (Chem), National Institute of Technology Karanataka, Surathkal.

Awards/Accolades

  • 2008-2012, Recipient of Singapore Millennium Foundation (SMF) scholarship
  • 2007-2008, NUS Research Scholarship

Research interests

In Chemical and Biological Systems Engineering Lab (CABSL)
Dynamical Sensitivity Analysis for Chemical and Biological Systems

Complexity of chemical and biological systems often limits human intuition in understanding the functional regulations and emergent behaviours. To this end, mathematical models have commonly been used to simulate their dynamical behaviour, to which a quantitative model analysis tool can be applied to gain insights. Most of the existing tools, like classical parametric sensitivity, bifurcation analysis and structured singular values, quantify the system dependence on model parameters, by introducing static perturbations. The dynamical aspects of the system are not immediately apparent from these analyses. The reason stems from the fact that the effected perturbations are on system parameters and are persistent on system behavior. To overcome this draw back, we develop novel sensitivity analysis methodologies, for analyzing the dynamics of both deterministic and stochastic models of chemical and biological systems.

Methods developed include:
  • Green’s Function Matrix (GFM) based sensitivity
  • Impulse Parametric Sensitivity Analysis (iPSA)
  • Pathway Parametric Sensitivity Analysis (PathPSA)
  • Molecular Distribution based Finite Perturbation (MDFP) analysis

Tools developed in this work offers dynamical insights on the functional regulation and signal propagation in the networks of interest. The results give a reaction-by-reaction, molecule-by-molecule and pathway-by pathway account of how networks function or output behavior is accomplished. The efficacy of the tools developed is evaluated on both synthetic and real networks of chemical and biological systems. Other than analyzing the dynamics, the knowledge gained will have applications from robustness-fragility analysis to identify robustness causing mechanisms to model reduction and validation to design of future experiments. In addition, the biological knowledge gained can assist drug discovery efforts in the identification of potential drug targets, the understanding of drug efficacy and specificity, and finally the optimization of drug dosing and timing. The development of these methods represents a concrete step towards robustness-based drug design through systems biology and systems medicine.

Key words: Sensitivity/Robustness analysis, Computational Systems Biology, Mathematical modeling, Systems Oriented Drug Discovery

Journal Articles

  • Perumal TM and Gunawan R: Understanding dynamics using sensitivity analysis: caveat and solution. BMC Syst Biol, 5:41, 2011
  • Perumal TM, Wu Y, and Gunawan R: Dynamical analysis of cellular networks based on the Green’s function matrix. J. theor Biol, 261:248-259, 2009

Peer reviewed Conference Proceedings

  • Perumal TM and Gunawan R. (2011) Impulse Parametric Sensitivity Analysis, In Proceedings of the 18th World Congress - The International Federation of Automatic Control (IFAC), Milano, Italy, August 28 - September 2 (Accepted)
  • Perumal TM and Gunawan R. (2010) Dynamical Analysis and Model Reduction of Complex Systems, In Proceedings of the 13th Asia Pacific Confederation of Chemical Engineering Congress (APCChE), Taipei, October 5 - 8
  • Perumal TM and Gunawan R. (2009) Information-theoretic global robustness analysis of cellular systems: A molecular perturbation approach, In Third International Conference on Foundations of Systems Biology in Engineering (FOSBE), Denver, Colorado, USA, August 9 - 12, PP: 52 - 55
  • Perumal TM, Yan W and Gunawan R. (2008) Robustness Analysis of Cellular Systems for In Silico Drug Discovery, In 17th World Congress - The International Federation of Automatic Control (IFAC), Seoul, Korea, July 6-11, PP: 12607-12612

Oral and Poster Presentations

  • Perumal TM and Gunawan R. (2010) Caveats of Parametric Sensitivity Analysis (PSA): In analyzing the dynamics of biological systems. In AIChE Annual Meeting, Salt Lake City, UT, USA, November 7-12
  • Perumal TM and Gunawan R. (2010) Dynamical Model Reduction of Large Reaction Mechanisms: A Green’s Function Matrix (GFM) Based Approach. In AIChE Annual Meeting, Salt Lake City, UT, USA, 2010, November 7-12
  • Perumal TM and Gunawan R. (2010) In analyzing the complex dynamics of biochemical pathways, In Satellite Conference of the International Congress of Mathematics, Hyderabad, India, August 16 - 18
  • Perumal TM and Gunawan R. (2009) Information Transfer in Biological Network Motifs, In 10th International Conference on Systems Biology (ICSB), Standford, California, USA, 2009, August 30 - September 4 (poster)
  • Perumal TM, Yan W and Gunawan R. (2008) In Silico Dynamical Analysis of Cellular Systems: A Molecular Perturbation Approach, In 12th Annual International Conference on Research in Computational Molecular Biology (RECOMB), Singapore, March 30 - April 2 (poster)
  • Perumal TM, Yan W and Gunawan R. (2008) In Silico Molecular Analysis: Application to Fas-Induced Apoptotic Pathway, In 10th International Conference on Molecular Systems Biology (ICMSB), Manila, Philippines, February 25-28 (poster)
  • Perumal TM, Yan W and Gunawan R. (2007) In Silico Molecular Analysis: Application to the Fas-induced Apoptotic Pathway, In 8th International Conference on Systems Biology (ICSB), California, USA, October 1-6 (poster)

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