Ranjit Randhawa

Wyeth, New Johnsonville, Tennessee, United States

Are you Ranjit Randhawa?

Claim your profile

Publications (8)9 Total impact

  • Source
    Ranjit Randhawa, Clifford A Shaffer, John J Tyson
    [Show abstract] [Hide abstract]
    ABSTRACT: Models of regulatory networks become more difficult to construct and understand as they grow in size and complexity. Large models are usually built up from smaller models, representing subsets of reactions within the larger network. To assist modelers in this composition process, we present a formal approach for model composition, a wizard-style program for implementing the approach, and suggested language extensions to the Systems Biology Markup Language to support model composition. To illustrate the features of our approach and how to use the JigCell Composition Wizard, we build up a model of the eukaryotic cell cycle "engine" from smaller pieces.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 01/2010; 7(2):278-87. · 2.25 Impact Factor
  • Source
    Ranjit Randhawa, Clifford A Shaffer, John J Tyson
    [Show abstract] [Hide abstract]
    ABSTRACT: Models of regulatory networks become more difficult to construct and understand as they grow in size and complexity. Modelers naturally build large models from smaller components that each represent subsets of reactions within the larger network. To assist modelers in this process, we present model aggregation, which defines models in terms of components that are designed for the purpose of being combined. We have implemented a model editor that incorporates model aggregation, and we suggest supporting extensions to the Systems Biology Markup Language (SBML) Level 3. We illustrate aggregation with a model of the eukaryotic cell cycle 'engine' created from smaller pieces. Java implementations are available in the JigCell Aggregation Connector. See http://jigcell.biol.vt.edu. shaffer@vt.edu
    Bioinformatics 10/2009; 25(24):3289-95. · 5.47 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We demonstrate how to model macromolecular regulatory networks with JigCell and the Parameter Estimation Toolkit (PET). These software tools are designed specifically to support the process typically used by systems biologists to model complex regulatory circuits. A detailed example illustrates how a model of the cell cycle in frog eggs is created and then refined through comparison of simulation output with experimental data. We show how parameter estimation tools automatically generate rate constants that fit a model to experimental data.
    Methods in molecular biology (Clifton, N.J.) 02/2009; 500:81-111. · 1.29 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: See: http://scholar.lib.vt.edu/theses/available/etd-04302008-115237/
    05/2008, Degree: PhD, Supervisor: Clifford A. Shaffer and John J. Tyson
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We describe procedures for converting a macromolecular regulatory model from the most common deterministic formulation to one suitable for stochastic simulation. To avoid error, we seek to automate as much of the process as possible. However, deterministic models often omit key information necessary to a stochastic formulation. In this paper we introduce how we implement conversion in the JigCell modeling environment. Our tool makes it easier for the modeler to include complete details. Stochastic simulations are known for being computationally intensive, and thus require high performance computing facilities to be practical. We provide the first stochastic simulation results for realistic cell cycle models, using Virginia Tech's System X supercomputer.
    Proceedings of the 2008 Spring Simulation Multiconference, SpringSim 2008, Ottawa, Canada, April 14-17, 2008; 01/2008
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Today's macromolecular regulatory network models are small compared to the amount of information known about the corresponding cellular pathways, in part because current modeling languages and tools are unable to handle significantly larger models. Most pathway models are small models of individual pathways which are relatively easy to construct and manage. The hope is someday to put these pieces together to create a more complete picture of the underlying molecular machinery. While efforts to make large models can benefit from reusing existing components, there currently exists little tool or representational support for combining or composing models. In this paper we present a tool for merging two or more models (we call this process model fusion) and a concrete proposal for implementing composition in the context of the Systems Biology Markup Language (SBML).
    Proceedings of the 2007 Spring Simulation Multiconference, SpringSim 2007, Norfolk, Virginia, USA, March 25-29, 2007, Volume 2; 01/2007
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Today's macromolecular regulatory network models are small compared to the amount of information known about a particular cellular pathway, in part because current mod- eling languages and tools are unable to handle significantly larger models. Thus, most pathway modeling work today focuses on building small models of individual pathways since they are easy to construct and manage. The hope is someday to put these pieces together to create a more com- plete picture of the underlying molecular machinery. While efforts to make large models benefit from reusing existing components, unfortunately, there currently exists little tool or representational support for combining or composing models. We have identified four distinct modeling pro- cesses related to model composition: fusion, composition, aggregation, and flattening. We present concrete proposals for implementing all four processes in the context of the
    Proceedings of the Winter Simulation Conference WSC 2006, Monterey, California, USA, December 3-6, 2006; 12/2006
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 1 Abstract Today’s pathway models are small compared to the amount of information known about a particular cellular pathway, in