Casey O. Diekman

Contact Info

Title: Assistant Professor
Email: casey.o.diekman@njit.edu
Office: 506 Cullimore Hall
Hours: TF 5:30-6:30 PM (Fall 2017)
Phone: 973-596-3497
Dept: Mathematical Sciences
Webpage: https://web.njit.edu/~diekman/

Academic Interests: dynamical systems, mathematical biology, computational neuroscience, mathematical modeling

About Me

My research uses a combination of mathematical modeling, numerical simulation, and dynamical systems analysis to gain insight into biological systems. I am currently focused on creating a mathematical framework to understand how dynamic changes in gene expression affect the electrical properties of neurons and ultimately animal behavior. Circadian (~24-hour) rhythms offer one of the clearest examples of the interplay between these different levels of organization, with rhythmic gene expression leading to daily rhythms in neural activity, physiology and behavior.
 
I have developed mathematical models of the master circadian clock in the mammalian brain. These models and the mathematical theory associated with them led to counterintuitive predictions that have since been validated experimentally by my collaborators.

The primary goal of my research program in mathematical biology is to uncover mechanisms underlying biological timekeeping, neuronal rhythm generation, and the disruption of rhythmicity associated with certain pathological conditions including sleep disorders, breathing problems, and neurodegenerative disease.

Prior to joining NJIT, I was a postdoctoral fellow at the Mathematical Biosciences Institute (MBI) located at The Ohio State University.

Education

  • Ph.D., University of Michigan, 2010
  • M.S., University of Michigan, 2005
  • B.S., Purdue University, 2002

Website

https://web.njit.edu/~diekman/

Research Interests

  • data assimilation
  • biological oscillations
  • circadian rhythms
  • respiratory rhythm
  • locomotor rhythms

Current Research

The goal of my research program is to uncover mechanisms underlying biological timekeeping and neuronal rhythm generation. As an applied mathematician, I aim to create mathematical tools and knowledge that advance biological understanding. Rhythms can be found nearly everywhere in biology and are fundamental to brain function. In my work I use data-driven mathematical modeling, numerical simulation, and dynamical systems analysis to study biological systems from the subcellular scale up to networks of thousands of neurons.

My interests in biological oscillations span a wide range of time scales and application areas, including daily (circadian) rhythms such as the sleep/wake cycle, the respiratory rhythm with several breaths per minute, and the alternations in visual perception that occur every few seconds during binocular rivalry. A common theme running throughout my research is modulation and entrainment of internally generated oscillations by sensory feedback or other forms of external forcing.

Selected Publications
 

  1. Diekman CO, Thomas P, and Wilson C (2017). Eupnea, tachypnea, and autoresuscitation in a closed-loop respiratory control model. Journal of Neurophysiology, 118:2194-2215.
     
  2. Wegner S, Belle M, Hughes A, Diekman CO, and Piggins H (2017). Delayed cryptochrome degradation asymmetrically alters the daily rhythm in suprachiasmatic clock neuron excitability. Journal of Neuroscience, 37:7824-7836.
     
  3. Diekman CO and Bose A (2016). Entrainment maps: A new tool for understanding circadian oscillator models. Journal of Biological Rhythms, 31:598-616.
     
  4. Flourakis M, Kula-Eversole E, Hutchison A, Han T, Aranda K, Moose D, White K, Dinner A, Lear B, Ren D, Diekman CO, Raman I, and Allada R (2015). A conserved bicycle model for circadian clock control of membrane excitability. Cell, 162:836-848.
     
  5. Diekman CO and Golubitsky M (2014). Network symmetry and binocular rivalry experiments. Journal of Mathematical Neuroscience, 4(12):1-29.
     
  6. Diekman CO, Dasgupta K, Nair V, and Unnikrishnan K. (2014) Discovering functional neuronal connectivity from serial patterns in spike train data. Neural Computation, 26:1263-1297.
     
  7. Terman D, Rubin J, and Diekman CO. (2013) Irregular activity arises as a natural consequence of synaptic inhibition. Chaos, 23(046110):1-20.
     
  8. Diekman CO, Belle M, Irwin R, Allen C, Piggins H, and Forger D. (2013) Causes and consequences of hyperexcitation in central clock neurons. PLOS Computational Biology, 9(8:e1003196):1-11.
     
  9. Diekman CO, Fall C, Lechleiter J, and Terman D. (2013) Modeling the neuroprotective role of enhancing astrocyte mitochondrial metabolism during stroke. Biophysical Journal, 104:1752-1763.
     
  10. Diekman CO, Golubitsky M, McMillen T, and Wang Y. (2012) Reduction and dynamics of a generalized rivalry network with two learned patterns. SIAM Journal of Applied Dynamical Systems, 11:1270-1309.
     
  11. Belle M, Diekman CO, Forger D, and Piggins H. (2009) Daily electrical silencing in the mammalian circadian clock. Science, 326:281-284.