Biography
Professor Sarathy's research interest is in developing sustainable
energy technologies with decreased net environmental impact. A major
thrust of research is simulating the combustion chemistry of
transportation fuels. He develops fundamental chemical kinetic models
that can be used to simulate fuel combustion and pollutant formation in
energy systems. Engine designers then use these chemical kinetic models
to achieve various performance targets using computational simulations.
In addition, these models can be used to determine how the chemical
structure of a fuel affects pollutant formation.
Professor
Sarathy's research in combustion chemistry modeling includes quantum
chemistry based kinetic rate calculations, comprehensive mechanism
development, combustion cyberinfrastructure development, computer
generated detailed and reduced mechanisms, and simulation of
multi-dimensional reacting flows.
In addition, he obtains data
from fundamental combustion experiments to elucidate reaction pathways
of combustion, and to generate experimental data needed to validate
detailed chemical kinetic models. These experimental techniques include
perfectly stirred reactors, plug flow reactors, and diffusion flames.
The chemistry in these reactors is probed using advanced analytical
chemistry techniques such as molecular beam time-of-flight mass
spectrometry, laser absorption spectroscopy, Fourier transform infrared
spectroscopy, and a variety of gas and liquid chromatography methods.
The
goal of Professor Sarathy's research is study conventional and
alternative fuels (e.g., biofuels, synthetic fuels, etc.), so the
environmental impact of combustion systems can be reduced. He also
applies chemical kinetics expertise to study a wide range of chemical
engineering systems including biomass energy, catalysis, and drinking
water treatment.
Abstract
Chemical kinetic insights into brain lactate metabolism
Authors: Dimitris G. Patsatzis1, Efstathios Al. Tingas2, Dimitris A. Goussis1,3, S. Mani Sarathy2
Presenters: Efstathios Al. Tingas and S. Mani Sarathy
1) Department of Mechanics, School of Applied Mathematics and Physical Sciences, National Technical University of Athens, 15780 Athens, Greece
2) King Abdullah University of Science and Technology, Clean Combustion Research Center, 23955-6900 Thuwal, Saudi Arabia
3) Department of Mechanical Engineering, Khalifa University of Science, Technology and Research, 127788 Abu Dhabi, United Arab Emirates
The role of lactate in the brain is considered important as a fuel and signaling molecule in neuronal activity, especially during neuronal activation. The metabolic coupling of neurons and astrocytes establishes the Astrocyte to Neuron Lactate Shuttle (ANLS), in which astrocytes provide lactate to neurons. The mathematical models governing the processes of brain metabolism are multi-scale in character, due to the wide range of time scales characterizing the various sub-processes. In such multi-scale models, it is often challenging to identify the important processes and time-scales influencing the dynamics of the system. Here, a we provide unique insights into complex brain lactate kinetic models using Computational Singular Perturbation (CSP) algorithm. Our goal is to provide underlying physical understandings of the various features exhibited in brain metabolism. CSP is used here to identify (i) the processes generating the fast and slow time scales, (ii) the processes contributing to the establishing equilibrium states, (iii) and the processes that control the evolution of the chemical species. The ability to manipulate neuronal activation is also investigated by identifying the components of the model that influence the duration of the neuronal activation time and the desired levels of selected species. We demonstrate that our new algorithmic approach of asymptotic analysis can provide interesting insights into brain metabolism under conditions of exercise, brain disorder, and drug therapy.
All sessions by Professor Mani Sarathy
4:40 PM
"Chemical kinetic insights into brain lactate metabolism"
Session 4: Novel Imaging Techniques and Modeling