Nikki Meshkat


Math 341: Applied Differential Equations, Spring 2014

Math 114: Finite Mathematics, Fall 2013

Math 341: Applied Differential Equations, Spring 2013

Math 303: Linear Analysis, Fall 2012


I study parameter identifiability, which concerns finding which unknown parameters of a model can be quantified from given input-output data. Many biological models are unidentifiable, which means that the parameters can take on an infinite number of values, yet yield the same input-output data. The goal is then to find identifiable parameter combinations to reparameterize the model. My work has been focused on the differential algebra approach to identifiability and on an algorithm I wrote for finding identifiable parameter combinations using Groebner Bases.

Here is my CV.

Papers in Preparation

Identifiability of linear compartment models (with Marisa Eisenberg and Seth Sullivant).


N. Meshkat and S. Sullivant, Identifiable reparametrizations of linear compartment models, Journal of Symbolic Computation 63 (2014) 46-67. ScienceDirect arXiv

A. Mahdi, N. Meshkat, and S. Sullivant, Structural identifiability of viscoelastic mechanical systems, PloS ONE 9(2) (2014): e86411. PLoS ONE arXiv

N. Meshkat, C. Anderson, and J. J. DiStefano III, Alternative to Ritt’s Pseudodivision for finding the input-output equations of multi-output models, Math. Biosci. 239 (2012) 117-123. ScienceDirect arXiv

N. Meshkat, C. Anderson, and J. J. DiStefano III, Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations, Math. Biosci. 233 (2011) 19-31. ScienceDirect

N. Meshkat, M. Eisenberg, and J. J. DiStefano III, An algorithm for finding globally identifiable parameter combinations of nonlinear ODE models using Groebner Bases, Math. Biosci. 222 (2009) 61-72. PubMed