Cecilia Diniz Behn
Colorado School of Mines
Department of Applied Mathematics and Statistics
1500 Illinois Street
Golden, Colorado
80401
303.273.3872

cdinizbe@mines.edu
 

Research Interests

My research applies multiscale mathematical modeling to investigate key research questions in metabolism, sleep, and circadian rhythms. Specifically, I model key dynamics in whole-body metabolism including changes in glucose, glycerol, and insulin; sleep and circadian (~24 h) neurophysiology; and the diverse interactions among these systems. Dysregulation of metabolism and/or sleep has dramatic implications for human health, and the complex ways in which these systems interact, both on a mechanistic and on a behavioral level, are just beginning to be understood. My research in mathematical and computational neuroscience focuses on understanding neurophysiologic mechanisms for sleep/wake regulation and my work in whole-body glucose-insulin dynamics focuses on insulin resistance in adolescents.

Mathematically, my research contributes to the development of novel techniques to understand high-dimensional multiscale systems of differential equations; analyze connections between structure and dynamics of general networks; and investigate dynamics at the interface of deterministic and stochastic behavior.

During the course of my graduate training in the Department of Mathematics and the Center for BioDynamics at Boston University, my postdoctoral training in the Division of Sleep Medicine at Harvard Medical School and the Department of Mathematics at the University of Michigan, and my faculty appointments, I have developed a strong, externally-funded research program in applied math with vital connections to experimentalists and clinicians.

I am an active member of the AMS Math Biology Research Group.



Diniz Behn Research Group

Front row (left to right): Nora Stack, Cecilia Diniz Behn, Kai Bartlette; Back row (left to right): Alicia Colclasure, Kate Bubar, and Logan Weinman).

My research group currently includes


  • PhD student Kai Bartlette
  • PhD student Alicia Colclasure
  • PhD student Nora Stack
  • Undergraduate Kate Bubar
  • Undergraduate Logan Weinman

Recent graduates of my group are Jacqueline Simens (MS 2015), Sean Lopp (BS 2015), Abigail Branch (BS 2015), Kelsey Kalmbach (MS 2016), Mollie Murray (BS 2016), and Nick Koprowicz (BS 2017).



Current Research Projects

Multiple time scales in sleep-wake modeling

Sleep-wake behavior is produced by the interaction of many processes occurring on a range of spatial and temporal scales: individual neurons spiking on a millisecond time scale coordinate their activity to promote states of wake and sleep that are modulated by processes, such as the approximately 24-hour circadian drive, that act on time scales of days. Mathematical modeling provides a key tool for integrating anatomic and physiologic data across these scales to probe the dynamic interactions of these elements. Current and ongoing work in this area involves the application of reduction of dimension and fast-slow techniques to identify mechanisms of state transition; establishing numerical criteria to compare and contrast the existence and robustness of REM/non-REM cycling produced by different putative REM-generating network structures; computing 1-D piecewise continuous maps that capture key elements of sleep/wake regulatory dynamics; and integrating molecular and electrophysiological models to investigate modulation and signaling of the circadian clock<. Projects include work in both human and nonhuman animal models.

Applying mathematical modeling to protocol design and interpretation of circadian data

Experimental investigation of the human circadian system is very resource-intensive, and it is challenging to optimize experimental protocols in the lab. In recent work, we have been applying mathematical models of the circadian clock to optimize protocol design and to provide insights into the role of inter-individual differences in data collection and interpretation. These projects have involved Markov Chain Monte Carlo (MCMC) techniques for parameter estimation associated with novel data mining approaches.

Glucose-insulin dynamics and tissue-specific insulin resistance

In recent years, my research program has increasingly addressed questions of whole-body metabolism. From a physiological perspective, the common role of the hypothalamus provides a unified substrate for sleep/circadian and metabolic investigation. However, my current research considers metabolism in contexts outside, as well as inside, the brain. My collaborators are pioneering novel experimental protocols involving multiple stable isotope tracers to provide less-invasive techniques to assess tissue-specific insulin resistance (IR) in at-risk adolescent populations. We also seek to combine these measures of IR with characterization of sleep and circadian measures including obstructive sleep apnea indices and melatonin secretion profiles. My research in this area focuses on developing the novel mathematical tools that are necessary for optimal mining and interpretation of these complex data sets.

Orexin/hypocretin neurons and their role in stabilizing sleep-wake behavior

In normal sleep-wake behavior, the neuropeptide orexin (also known as hypocretin) helps to stabilize transitions between wake and sleep, and dysregulation of orexin is associated with the sleep disorder narcolepsy. Since the state instability that characterizes narcolepsy reflects an alteration of the network dynamics producing sleep-wake behavior, mathematical approaches aimed at understanding the stability of the network provide vital complementary techniques to traditional sleep research. In previous work I have developed higher-order statistical and signal-processing techniques to analyze the altered dynamics of sleep-wake behavior in mice with disrupted orexinergic systems; modeled the action of orexin at the network level; and described a Hodgkin-Huxley-type model orexin neuron. Recent work has analyzed the role of progressive orexin cell loss in a novel rodent model of narcolepsy and characterization of narcolepsy in children. Orexin neurons are also glucose-sensing and, therefore, they are ideally situated to integrate information between sleep-wake and metabolic systems.

Stochastic and deterministic contributions to the fine architecture of sleep-wake behavior

The fine architecture of sleep-wake behavior reflects the underlying deterministic and stochastic processes that produce it and provides an important constraint for models seeking to represent this system. Systematic analysis of the effects of different kinds of noise on resulting dynamics has provided insights into the interaction between deterministic and stochastic elements of the system and revealed some fundamental asymmetries in the network dynamics producing states of wake and sleep. Current work includes assessing the relative contributions of stochastic and deterministic elements to fundamental features of sleep-wake behavior including comparative analyses across different mammalian species and includes collaborations with several labs investigating sleep in mice, rats, cats, degus, and humans.