Dr. Bori Mazzag serves as the Associate Dean of Academic Affairs in the College of Natural Resources and Sciences. Dr. Mazzag received her Ph.D. in Applied Mathematics in 2002 from UC Davis, then after a postdoctoral position at the College of William and Mary and the University of Utah, she joined the Math faculty in 2005. Dr. Mazzag's research interests are mathematical biology and mathematical modeling. She served as the Chair of Mathematics and Computer Science Departments from 2017 to 2021. Since 2021, she has been actively involved in the Cal Poly Humboldt self-study and implementation teams. She is passionate about exploring interdisciplinary connections between applied mathematics and areas of application. Her recent work has focused on providing equitable classroom and research experiences for students, aiming to connect higher education to meaningful careers in STEM. She has been an active member of the Strategic Planning Council of the CSU Education and Research for Biotechnology (CSUPERB).
Calcium modeling: My postdoctoral position (under the direction of Dr. Gregory Conradi Smith) focused on the computational and mathematical modeling of the dynamics of calcium-gated calcium channels. Calcium is an important second messanger that is present in many systems, including cardiac muscle cells, neurons and oocytes. My postdoctoral work investigaged the dynamics of calcium accumulating near the calcium-gated channels and its feedback on the channel open probability. My work explored this question both in a spatially homogeneous and a spatially heterogeneous domain. I continued this work in my tenure-track position at Humboldt State University. With funding from a California State University-wide organization (CSUPERB), I supported two undergraduate research students to examine how entire release sites made up of a collection of calcium-regulated ion channels behavesand how the calcium accumulating near the release site changes the collective dynamics. We published our findings.
Mechanical signal transduction in endothelial cells: I first started a project on endothelial cell mechanotransduction with Dr. Abul Barakat while I was a graduate student at UC Davis. Endothelial cells (EC) form a protective layer in blood vessels. It has been clearly established that ECs respond in a genetic, physiological and morphological way to distinct flow regimes. Whether the EC response is pathological or protective is an important first step in the development of various cardiovascular diseases from atherosclerosis to high blood pressure. Because early EC responses to changes in blood flow are fast (on the order of milliseconds) and because the signal is a physical (changes in shear, pressure and tension), it is thought to be very important to consider how mechanical signals are transmitted in endothelial cells. I had worked on three related projects in this area that all took a similar modeling approach: the EC is represented as a network of connected viscoelastic elements with distinct material properties.
Neuroanatomy of zebrafish sensorimotor pathway: Applying graph theoretical approaches to neuroanatomy is a relatively new field that has been aided by various technological advances in neuroscience that allow the visualization of large portions of the nervous systems of various organisms. Graph theoretical approaches have been used to identify features that all neural networks share but there are still many gaps in our knowledge and disagreements about the extent to which various neural networks share an organizational structure. My collaboration on the zebrafish sensorimotor pathway with Gahtan lab fits into this larger context of inquiry.
In my work with graduate students in the (now suspended) Mathematical Modeling Option of the Environmental System Gratudate program involved many different systems and approaches. Several of the projects with individual-based models or IBMs. This is a relatively new modeling framework that has a lot of local expertise through the work of Dr. Steven Railsback. In usual population models, an average behavior is assumed for all individuals in a population and this average behavior is modeled. In the IBM framework, it is assumed that individuals in a population have unique characteristics and their behavior is limited by these characteristics. For example, when foraging, an individual’s behavior may depend on its relative dominance, level of hunger, ability to move certain distances, etc. The IBM framework has been used in many projects in conservation biology in an attempt to link individual behavior to patterns that emerge on a population level.