Bienvenidos a CienciaPR, una red de recursos para todos los interesados en las ciencias y en Puerto Rico.
In May of 2013 I earned a PhD degree in Physiology from the University of Puerto Rico Medical Sciences Campus under the supervision and mentoring of Dr. Carlos Jiménez-Rivera. Currently I’m collaborating with Dr Jiménez-Rivera in the areas of dopamine neuron electrophysiology, H-current and cocaine addiction. The majority of my experimental work is driven by computational and mathematical models of neurons.
Since 2004 to present I have been teaching undergraduate courses in areas of calculus, differential equations and neurobiology at the Universidad del Sagrado Corazón in San Juan PR. My focus has been to teach students how to acquire the skill to create mathematical models for biological phenomena. For instance, I teach calculus students how to simulate Lotka-Volterra population dynamics, the Na/K Pump, the action potential and the heart using models found in scholarly publications.
Why to incorporate computational modeling in the modern biology curricula?
Biological processes are characterized by their great complexity that ranges from patterns of molecular interactions or gene activity to networks of communicating cells and individual and social animal behavior. More often than not complex systems manifest functional attributes that are inherent to their existence yet these same properties are not found in its elementary constituents. This phenomenon is termed emergence and remarkable examples of this are seen in the human brain. One of the fundamental abilities of this organ is to store and recalled explicit pieces of information (e.g. your home address). Individual neurons, however, do not operate as data storing devices that accumulate detailed descriptions of our past history. Consequently, although our brains can act as a log of our experiences this ability to keep record of past events are not seen in single nerve cells.
Emergent properties are by definition not present in the smallest building blocks of a complex system. As a result the underlying mechanisms that explain the emergent phenomena are likely not to be found by experimental approaches that dissect a biological system to its most fundamental particles. Instead, the knowledge gained through this type of experimentation is used to formulate a mathematical model that is able to explain the system behavior. Moreover, as is often the case, these types of models result in testable predictions that drive further experimentation. Once this bond between mathematical theory and experimentation is created the model may also lead to the understanding of emergent properties. In effect, classical physics had shown that this approach is well suited for the elucidation of the inner workings of emergence. For instance, mathematical models of the atomic interactions among liquid particles are able to explain macroscopic properties such as viscosity and pressure which are attributes not present at the molecular scale. I believe, therefore, that the modern student should develop the ability to address biological problems with the aid of computational modeling.
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