Dr. J. Srividhya

Visiting Research Associate

Indiana University School of Informatics
Eigenmann Hall 917
1900 East Tenth Street
Bloomington, IN 47406
USA


Phone +1 (812) 855-9958
Fax +1 (812) 856-1995
Cell +1 (812) 320-6527
Skype: sri2377



• Email

  My Vision

  The ultimate aim of chemistry and biology is to understand biological systems sufficiently to enable accurate, quantitative predictions about the behaviors of the system. This includes predictions of the effects of modifications of the systems. To achieve a precise understanding about the system it is inevitable to simulate the behavior using a computer model and compare it with experiments. Interdisciplinary research alone can provide insights in the pursuit of this common goal. My career goal is to head an interdisciplinary mathematical biology/chemistry group and solve complex problems in chemistry and biology using computational and modeling approaches. I did my Ph.D. under the supervision of Prof. M. S. Gopinathan, IITM. I now work as a post doctoral associate with Dr. Santiago Schnell in Indiana University School of Informatics. I am a chemist, now conducting reserach in solving physico-chemical problems using modelling approaches.

  Education

  Master's in Chemistry from Dept of Chemistry , Indian Institute of Technology Madras , Chennai, India.

  Doctorate in Mathematical modeling of chemical and biological dynamical systems also from the Indian Institute of Technology Madras with Prof. M. S. Gopinathan (IIITMK)

  Positions

  1.Council of Scientific and Industrial Research (CSIR - India) fellow from 2000-2005

  2.Post Doctoral research associate in Indiana University School of Informatics with Dr. Santiago Schnell. (2005-current)

  Research Interests

  1. Method for inferring kinetics and network architecture (MIKANA)

     

     My primary research is developing suitable computational methodology to extract mechanistic information of biochemical pathways from data obtained from high throughput experiments. Understanding biochemical pathways are fundamental in addressing key problems in biomedical engineering. This has the potential to create valuable treatment strategies and in the development of new drugs. High-throughput experiments produce data which are fundamental for the mapping of biochemical pathways. Nuclear magnetic resonance (NMR), mass spectrometry (MS), fluorescence spectroscopy / microscopy, and fluorescence labeling combined with autoradiography on 2-D gels are some of the techniques which allows simultaneous measurement of several metabolites. With these techniques, we can obtain measures of metabolite concentrations at any instant or progressively over time. The later is called as time series data.

     Time series data is unique because it has dynamic information about the system. Several computational methods are being developed to infer reaction mechanisms from time series data . Since more data are produced new algorithmic strategies are serious requirements in order to extract information. A combination of fundamental biochemical theory and a suitable computational method will aid the determination of mechanisms from time series data. I am precisely interested in developing such a methodology. With this as my research objective, I have developed a method to infer kinetics and network architecture (MIKANA).

     The method MIKANA is based on global non-linear modeling, which identifies elementary chemical reaction steps that constitute the biochemical pathway. Elementary reaction steps are those that cannot be decomposed to reveal reaction intermediates that might themselves be identified as separate chemical entities on a biochemically relevant timescale. MIKANA depends on the selection of appropriate chemical reactions from a dictionary of elementary chemical reactions, in order to best represent experimental data. This process is aided by a cost function that penalizes the use of too many reactions to fit the data. This function is named Information Criterion (IC).

     My research has been very successful in determining several simple chemical reaction mechanisms, enzyme reaction mechanisms and the complete structure of a glycolytic pathway of a bacterium. Further, I have applied this method to optimize certain experimental design factors that can be implemented by the experimentalists. This work has explicitly revealed certain modification to be done in experiments in order to obtain accurate information about the system. This is a very important result to the experimentalists.

     Collabotrators: Dr. Edmund J. Crampin (Auckland Bioengineering Institute) , Dr. Patrick McSharry (University of Oxford) and Marcio Mauro (PhD student).

  2. Probing the mechanism of Oil/water interface

     

     I am also interested in studying complex mass transfer mechanisms occurring at the oil/water interface. Oil/water interface is a very complex system and has taken decades for scientists to understand the mechanisms at the interface. These systems are of industrial interests because they serve the separation of metals from specific organic or aqueous solvents. Yet very little is understood on the mass transfer mechanism. A part of my PhD research was devoted to the studying of the mass transfer mechanism in an oil/water interface.

     I designed an experimental set up which is very easy to reproduce and yet exhibit all the characteristic of a typical mass transfer mechanism. I also concentrated on a specific system and applied modeling approach to explain the mass transfer mechanism. The model was very successful in reproducing the experimental results. Also my experimental design is easily reproducible in contrary to other experimental designs available for these systems. The mechanism of mass transfer is more than complex and comprises of chemical interactions under suitable circumstances.Conducting experiments and modelling this phenomenon using chemical hydrodynamical principle is fascinating and under progress.

  3. Modeling Biochemical networks

     Modeling complex biochemical networks with DDEs is another area which I am interested in. Networks containing cascading phosphorylation-dephosphorylation cycles form the skeleton of a number of biological processes. In order to study the effect of delay in a network like cell cycle, its effect in a phenomenological model of a network of P-D cycles is currently undertaken. Cascading activation involving 3 variables is replaced with delayed activation between 2 variables. Introduction of delayed feedback did not change the property of the system and that the reduced model mimics the original model quantitatively. Further investigations on this system give strong results that can be used by mathematical biologists when modeling complex biochemical pathways. The applicability of the above result is evident in a cell cycle model proposed by us using delayed variable.

  4. Mechanism of Protein digestion

     A number of enzyme digestion assays show apparent first-order kinetics of reactant disappearance. There are four explanations of this phenomenon: (i) the reaction is dominated by a first-order limiting step, (ii) the digestion follows a pseudo-first order kinetics under the excess of a reactant species, (iii) the first-order kinetics is only applicable to the slow transient of the reaction, and (iv) the aggregate behavior of the reaction pathway produces behavior indistinguishable from the first-order kinetics. In this paper we theoretically investigate the kinetics for protein digestion by mathematically formulating rate equations for two proposed mechanisms namely the one by one mechanism and zipper mechanism. Our analysis shows that the kinetics of protein digestion would follow apparent first-order kinetics irrespective of the mechanism for low initial substrate concentration with respect to the initial enzyme concentration. Also our results provide an explanation for the experimental observations and suggest new experimental protocol that could reveal information on the mechanism of digestion. Futher, we are exploring possibilities to distinguish between one-by-one and zipper mechanisms. Our examination has revealed interesting facts which could me implemented experimentally.

  CV

  Find my CV here.

  Publications

  Journals

  1. Modeling experimental oscillations in liquid membranes using delay equations, J. Srividhya and M. S. Gopinathan, J. Phys. Chem. B, 2003, 107, pp 1438-1443. (pdf)

  2. Concentration of CO2 over Melting Ice Oscillates, S. Usharani, J. Srividhya, M. S. Gopinathan and T. Pradeep, Phys. Rev. Lett., 2004, 93(4), Article No: 048304, pp 1-4. (pdf)

  3. A simple time delay model for eukaryotic cell cycle, J. Srividhya and M. S. Gopinathan, J. theor. Biol., 2006, 214(3), pp 617-627. (pdf)

  4. Why substrate disappearance in enzyme digestion follows apparent first-order kinetics, J. Srividhya and Santiago Schnell, Comp. Biol. Chem., 2006, 30, pp 209-214. (pdf)

  5. Reconstructing biochemical pathways from time series data. J. Srividhya, Santiago Schnell, Edmund J Crampin and Patrick E McSharry, 2007, Proteomics, 7, 828 - 838. (invited paper) . (pdf)

  6. Effect of time delay in a model of phosphorylation-dephosphorylation network. J. Srividhya, M. S. Gopinathan and Santiago Schnell, Biophysical chemistry, 2007, 125, pp 286-297. (pdf)