## 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).

**Publications: **

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

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

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

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