• Dr. Vikram Sunkara

    Vikram

    Post-doc

    2015-                Post-doctoral research at “Systems Pharmacology & Disease Control” group, Dep. of Mathematics, Freie Universität Berlin, Germany and The Konrad Zuse Institute Berlin.

    2014-2015     Post-doctoral researcher at University of Adelaide (Australia), Department of Mathematical Sciences.

    2012-2014    Post-doctoral researcher at Karlsruhe Institute of Technology (Germany), Institute for Applied  and Numerical Mathematics.

    2010- 2012     PhD at the Australian National University (Canberra Australia). School of Mathematical Sciences.

    2004-2009      Undergraduate Studies at the University of Wollongong (Australia). Studying Advanced Mathematics (Honours)

    Research Interests

    1. Numerical Analysis
    2. Stochastic Processes
    3. Systems Biology
    4. High Performance Computing

    Research Focus

    The key focus is on constructing probability distributions of jump Markov processes that arise in Biology. Solving the Chemical Master Equation (CME) is one method in which we can construct a pdf for such processes. Knowing the probability distribution gives vital information about the state the biological system is likely to be in and where it is likely to go. Computing accurate approximations of the CME is a difficult task when there are a variety of interactions in the biological process being modeled. I work in the following key research topics to tackle these problems and make the CME tractable:

     

    • Domain selection techniques for high dimensional CME.
    • Parameter Estimation methods for Large Bio-Chemical Network Systems.
    • Hybrid methods to reduce complexity of the CME.
    • Numerical methods for Stochastic processes in Biology.

    Teaching

    Applied Numerics in Systems Biology (19403511)

    Numerik für Bioinformatiker (19400601-2)

    Publications

    Vikram Sunkara
    Algebraic Expressions of Conditional Expectations in Gene Regulatory Networks. (Submitted 2018)

    Nigel Bean, Giang T Nguyen, Malgorzata O’Reilly and Vikram Sunkara.
    A Discontinuous Galerkin Method for Approximating the Stationary Distribution of Stochastic Fluid-Fluid Processes (Submitted 2018)

    Haase T+, Sunkara V+ , … , von Kleist.M* , Ertel W* (+shared first, *shared corres. authorship)
    Discerning the spatio-temporal disease patterns of surgically induced OA mouse models. (Submitted 2018)

    Sulav Duwal, Vikram Sunkara and Max Von Kleist.
    Multiscale Systems‐Pharmacology Pipeline to Assess the Prophylactic Efficacy of NRTIs Against HIV‐1.
    ECPT: pharmacometrics & systems pharmacology, 2016/7/1

    Nigel Bean, Giang T Nguyen, Malgorzata O’Reilly and Vikram Sunkara.
    A numerical framework for computing the limiting distribution of a stochastic fluid-fluid process.
    Ninth International Conference on Matrix-Analytic Methods in Stochastic Models, 2016

    Vikram Sunkara and Max Von Kleist.
    Coupling cellular phenotype and mechanics to understand extracellular matrix formation and homeostasis in osteoarthritis.
    IFAC-PapersOnLine, 2016/1/1

    Stefan Engblom, Vikram Sunkara.
    Preconditioned Metropolis sampling as a strategy to improve efficiency in Posterior exploration.
    IFAC-PapersOnLine, 2016/1/1

    Tobias Jahnke and Vikram Sunkara.
    Error bound for hybrid models of two-scaled stochastic reaction systems.
    Extraction of Quantifiable Information from Complex Systems, 2014/1/1

    Vikram Sunkara, Markus Hegland.
    Parallelising the finite state projection method
    ANZIAM Journal, 2011

    Vikram Sunkara, Markus Hegland.
    An optimal finite state projection method
    Procedia Computer Science, 2010 – Elsevier

    Vikram Sunkara.
    The Chemical Master Equation With Respect To Reaction Counts
    Proc. 18th World IMACS/MODSIM Congress, 2009

    Thesis.
    Analysis and Numerics of the Chemical Master Equation
    Supervised by Professor Markus Hegland at the Australian National University.

    Software

    PyME is a python based FSP CME solver. The software was designed to adaptively select the domain to help compute an accurate approximation to the solution of large dimensional CME. There are key additions to this package which give very fast computation times, the algorithms are based on methods proposed in my thesis.