Elena S. Dimitrova

Mathematical Sciences
Clemson University
Martin Hall O-110
Clemson, SC 29634, USA

OFFICE: Martin Hall O-303

EMAIL: edimit@clemson.edu
PHONE: +1 (864) 656-1524

Research interests
Systems biology, Computational algebra, Reverse-engineering methods, Polynomial maps over finite fields, Discrete models of biochemical networks, Gröbner fans of zero-dimensional ideals and Chaos theory application to biology

Selected publications

  • Data Identification for Improving Gene Network Inference using Computational Algebra (with B. Stingler)
    Bulletin of Mathematical Biology, 76(11), pp. 2923-40, 2014

    ABSTRACT: Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.
  • Nested canalyzing depth and network stability (with L. Layne and M. Macauley)
    Bulletin of Mathematical Biology, 74(2), pp. 422-433, 2012

    ABSTRACT: We introduce the nested canalyzing depth of a function, which measures the extent to which it retains a nested canalyzing structure. We characterize the structure of functions with a given depth and compute the expected activities and sensitivities of the variables. This analysis quantifies how canalyzation leads to higher stability in Boolean networks. It generalizes the notion of nested canalyzing functions (NCFs), which are precisely the functions with maximum depth. NCFs have been proposed as gene regulatory network models, but their structure is frequently too restrictive and they are extremely sparse. We find that functions become decreasingly sensitive to input perturbations as the canalyzing depth increases, but exhibit rapidly diminishing returns in stability. Additionally, we show that as depth increases, the dynamics of networks using these functions quickly approach the critical regime, suggesting that real networks exhibit some degree of canalyzing depth, and that NCFs are not significantly better than functions of sufficient depth for many applications of the modeling and reverse engineering of biological networks.
  • Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks (with I. Mitra and A.S. Jarrah)
    EURASIP J. on Bioinformatics and Systems Biology, 2011:1, pp. 1-33.

    ABSTRACT: Elucidating the structure and/or dynamics of gene regulatory networks from experimental data is a major goal of systems biology. Stochastic models have the potential to absorb noise, account for un-certainty, and help avoid data overfitting. Within the frame work of probabilistic polynomial dynamical systems, we present an algorithm for the reverse engineering of any gene regulatory network as a discrete, probabilistic polynomial dynamical system. The resulting stochastic model is assembled from all minimal models in the model space and the probability assignment is based on partitioning the model space according to the likeliness with which a minimal model explains the observed data. We used this method to identify stochastic models for two published synthetic network models. In both cases, the generated model retains the key features of the original model and compares favorably to the resulting models from other algorithms.


  • MATH 3190 - Introduction to Proof

    Introduces mathematical proofs with topics that include proof techniques, elementary logic, induction, sets, functions, and relations.