(3am) Simulation of Neurological Systems from Mass and Energy Balances

It is an opportune time to use chemical engineering concepts to study the brain and other complex biological systems, particularly by making links between simulations and experimental data. An abundance of information has been published concerning specific biochemical reaction rates and pathways related to neuronal processes along with justifications as to their individual importance, yet it is the collective activity that ultimately matters and that is what ultimately must be studied. Mathematical models based on mass and energy balances can be used to simulate complex systems within customizable, 3-dimensional domains using a finite element solver, allowing the details of individual neurochemical dynamics to be accounted for in the context of our existing knowledge of the brain. This creates a framework enabling the incorporation and testing of new ideas, directly showing the significance of the new concepts to the description of the overall system. This analysis of collective activity can reveal important gaps in understanding that can be filled by reverse engineering necessary explanations, and should lead to breakthroughs in our understanding of the mechanisms driving neurological function and the maintenance of complex biological systems.

Research Interests:

My use of chemical engineering as applied to neuroscience originates in my graduate work: simulating the performance of electroenzymatic biosensors for neurotransmitters in vivo. These simulations led to the production of sensors with a six-fold improvement in sensitivity and an order of magnitude reduction in response time, maintaining high selectivity.^1-3 Comparisons made to experimental data led to the identification of an important interaction within the sensor that primarily affects response time. With this information, further improvements to sensor design should lead to another order of magnitude reduction in response time down to 8 ms.⁴ This highlights the need to consider similar effects within the surrounding brain tissue as well, where small changes in chemical concentrations and electric fields or potentials could critically affect the timing of neurological processes, which operate on millisecond time scales. To model the sensors in vivo required further consideration of anisotropicity, heterogeneity, effects of sensor byproducts, and expected spatial and temporal variations in neuron firing. Simulations showed how the use of a calibration factor determined in vitro, which is how translation of sensor response to concentration has historically been done, could easily misinterpret the data over a typical range of biological and experimental conditions and requires further consideration.^5,6

Many other processes occurring in neurological systems can be investigated in a similar manner, of which two are of immediate interest to me in understanding how neuronal dynamics are affected by small changes to specific mass transfer, electronic, and geometric conditions. The first considers how the collection of protein aggregates can clutter extracellular space, first affecting the diffusion of small and macro molecules and ultimately resulting in changes to the function of a cascading system of neurons, as extracellular protein aggregation has been linked to neurodegeneration. Modeling the extracellular space around key points of a neuron or supporting astrocyte could clarify the reasons why these structures can be harmful and the mechanisms of their formation and propagation. I’m also interested in investigating how changes to the myelin layer that coats neuronal axons affects the speed and dynamics of neuronal transmission, particularly by considering variations in the myelin’s functions as an electrical insulator and how myelin can affect the composition and electric potential of a neuron. The type and extent of myelination ranges widely over the course of development or due to neurological diseases and is known to affect learning and brain plasticity. Simulations of these systems would need to consider all of these factors and influences and could provide an exciting development in our understanding of the mind and brain.

Extension of my work in these directions would consist of simulations that can be done on a scale that is both expedient and that maintains an appropriate level of complexity, where solutions can be found using commercially available numerical solvers and computational resources. Should greater resources be required, I am not unfamiliar with the grant writing process having applied for an NIH fellowship as a primary investigator, and I expect greater success in applications to come.

Publications and Manuscripts in Preparation:

2. Clay, H.G. Monbouquette. 2018. “A detailed model of electroenzymatic glutamate biosensors to aid in sensor optimization and in applications in vivo” ACS Chem. Neurosci.9(2):241–51
3. W Huang, M. Clay, S. Wang, Y. Guo, J. Nie, H.G. Monbouquette. 2020. “Electroenzymatic glutamate sensing at near the theoretical performance limit” Analyst 145, 2602-2611
4. W Huang, M. Clay, H.G. Monbouquette. “Electroenzymatic Choline Sensing at Near the Theoretical Performance Limit” (manuscript awaiting submission)
5. Clay, H.G. Monbouquette. “Effects of Adsorption in Determining Electroenzymatic Biosensor Response Time” (manuscript in preparation)
6. Clay, H.G. Monbouquette. “Theoretical Accuracy of Electroenzymatic Biosensors for Neurotransmitters in vivo” (manuscript in preparation)
7. Clay, H.G. Monbouquette. “Modeling Electroenzymatic Biosensors in 3 Dimensions” (manuscript in preparation)

Teaching Interests:

I have TA experience in transport phenomena, separations, and chemical engineering fundamentals and would be very comfortable as an instructor in these classes. As a TA, my duties included weekly lectures, office hours, and grading. I earned excellent evaluations from students in every course where I served as a TA.

I find teaching to be one of the most rewarding things that I have ever done, and firmly believe that I can improve the ways in which chemical engineering is taught by incorporating more active learning and taking better advantage of widely available and easy-to-use computational resources. By doing so, we can move quickly beyond oversimplified cases with analytical solutions in undergraduate education and can introduce students to the more realistic problems they may experience in research or in industry.

As a professor, I also plan to continue to improve my teaching strategies with involvement in education sessions at academic conferences, drawing upon cross-disciplinary networking with professors I know through my undergraduate education to improve teaching and assessment in the chemical engineering field. [C.P. Bailey, V. Minderhout, J. Loertscher. 2012. “Learning Transferable Skills in Large Lecture Halls: Implementing a POGIL Approach in Biochemistry” Biochem. Mol. Biol. Edu. 40 (1), 1-7)]