Research

I am a member of the Systems and Engineering Ecology group at the University of Georgia. Allmost all of my ecology related work is in collabroation with our group.

Recent Projects

  • EcoNet

    EcoNet is an ecological modelling and simulation software. Actually any process that can be represented as a stock-flow diagram, related to Ecology or not, can be implemented in EcoNet. It is written in C++ from scratch, so it is very efficient. EcoNet is an online software, so users can run their models online without downloading and installing. Please read here for more information.

  • Particle tracking method

    We developed particle tracking method, a novel algorithm for analysing ecological networks. Similar to EcoNet, the use of this method is not limited to ecological applications. We label each mass (biomass or C, N, P) or energy packet in the system, and track their locations as they flow through the network. History of each particle is recorded, so the method is computationally intensive. However, it provides detailed information on how energy or biomass is distributed, cycled and stored in the network.

    What sets it apart from agent (or individual) based models is that it is compatible with the ODE representation (and master equation) of the system. So, no artificial rules need to be defined, and causality is preserved. Implementation is simple, as it uses the same modelling language as EcoNet, so we can use Particle Tracking on any EcoNet model without modifications.

    Particle tracking method provides accurate computation of network properties such as cycling index, residence time, etc. Furthermore these properties can be computed dynamically as the system evolves. Particle tracking is an ideal tool for revisiting current ecological network properties, searching for new ecological goal functions and studying ecological thermodynamics. This work is in collaboration with E. W. Tollner.

  • Collective behavior of large biochemical networks

    Cells, the building blocks of living organisms, can be viewed as large, complex reaction systems with embedded subsystems such as gene regulatory networks, enzymatic pathways and protein-protein interactions. These complex cellular systems share many similar features in different organisms, and some aspects of their behavior can be studied in terms of abstract large networks that exhibit proper statistical properties.

    One such property is the existence of common patterns, called "network motifs", that appear in biochemical networks much more frequently than randomised networks. Another property shared by most organisms is their scale-free network structure. Another common statistical feature observed in many tissues and organisms is related to their gene expressions. The distribution of the abundances of expressed genes, which are correlated to the abundances of the corresponding proteins, obey the same power rule. Motivated by this last property, and inspired by classical works like Boltzmann's treatment of gas behavior, we focus on the statistical description of large biochemical networks in terms of the abundance distribution. This work is in collaboration with S. Ta'asan from Carnegie Mellon University.

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