Engineering Analysis of Physiological Systems (BIEN 223, Graduate course)
Physiology is the science of life. It is the branch of biology that aims to understand the functions of living organisms, from the basis of cell function at the ionic and molecular level to the integrated behavior of the whole body and the influence of the external environment. Understanding the mechanisms and the computations underlying these functions is critical for developing new drugs, therapies and bioengineering technologies to fight diseases. This course studies basic engineering techniques and control analytical tools that can be used to model physiological systems. The number of physiological systems is vast and reviewing all of them in detail is not realistic in this short course. Therefore, we address biological cellular control systems and use engineering methodology to evaluate the system of interest for solving particular problems. The course primarily focuses on presenting control theory techniques that can be used to model biological systems. The students have the chance to explore other biological control systems through a quarter-long project.
Introduction to Neuroimaging with MRI (BIEN/PSYCH 276, Graduate course)
How do we reach and grasp to a glass of water? What happens when we think? How do we decide what to have for lunch? What are the causes of mental illness? Do we have free will? For centuries, scientists and philosophers have been struggled to unlock the mystery of the human brain by answering these and other similar questions. However, until recently the brain was viewed as nearly incomprehensible. The main reason was the lack of a technology that could monitor non-invasively brain activity while individuals perform a variety of tasks. However, recently the brain is beginning to relinquish its secrets. What did it change? Advances in neuroimaging offered to scientists a powerful tool to assess human brain functional architecture in health, disease, and developmental states. The course begins with an introduction to basis MR instrumentation (magnet, gradient and RF coils), and signals (T1, T2, T2*). Next, we discuss the basic principles of MR image formation and review the acquisition and analysis of some of the most common neuroimaging measurements, such as anatomical, diffusion and functional signals. We will also talk on how to design experiments with fMRI, how to perform statistical analysis and functional connectivity analysis. Students will be also introduced to other neuroimaging techniques, such as magnetoencephalography and functional ultrasound imaging (fUS).
Biosystems and Signal Analysis (BIEN 120, Undergraduate course)
You have already studied an enormous amount of important information on how biological and biomedical systems act when specified at steady-state conditions. You also know how to determine system behavior under a new set of conditions. However, bioprocess outputs are frequently fluctuated due to changing inputs (e.g., viruses, allergic reaction, drug injection, change in nutrition, etc). It is often important to know how long it takes for a system to stop making significant changes. The study on how systems change with inputs or external perturbations is known as system dynamics. This applies to systems in general but can also apply to processes where you might be producing something or controlling the condition of the process. For instance, the sensorimotor cortex of your brain controls voluntary movements in different parts of your body, or the pancreas controls the output of the insulin to regulate blood glucose levels. Analyzing these types of systems is called process control. It also applies to design processes where you might want to make a product. In many cases though, if you make no adjustments, the outcome is no longer desirable. Therefore, it is necessary to adjust certain variables, so that the impact of the dynamic inputs is significantly reduced. Likewise, in many bioprocesses, it is desired to operate with certain output conditions, such as an optimum temperature or a specific pressure, so that the desired product is safely and economically obtained. Thus, the process must be corrected when changes in the input conditions occur. To account for this in the process control design, knowledge of the nature of the time-dependent system or system dynamics is paramount. This course introduces you to the classical approach to investigating system dynamic processes and their control.