The rapidly growing field of Computational Neuroscience holds great promise for enhancing the understanding of the functions of genes and proteins within nerve cells, as well as the interactions responsible for storing information and generating behavior. Pairing computer science with biomedical research facilitates deeper insight into mechanisms of complex biological systems, including the most complex-the brain. Computational Neuroscience includes mathematical modeling , computer-assisted research that enables scientists to understand complex biological systems, and also includes information management , whereby computer technology is exploited to understand, manipulate and disseminate the vast amounts of data being generated by the scientific enterprise.

Mathematical Modeling
Mathematical modeling represents in computational terms borrowed from physics and engineering, components of neural systems. One of the first examples following the early adoption of mathematical modeling is the electrical activity of the squid axon by Hodgkin and Huxley in 1952. Following the completion of the human genome, however, more emphasis has been placed on the use of mathematical modeling where equations express regulation of the genes and their encoded proteins. Computer simulations can display and even predict behavior of neural systems once mathematical models of the gene and protein networks are developed. Computational approaches will also enable an understanding of the interaction of the signaling pathways and the electrical activity of neurons. Finally, modeling will be used to examine how the interconnections of neurons lead to information processing and behavior, thus playing an essential role in developing molecular approaches to prevent and treat diseases.

Information Management

Computational approaches also present the challenge of managing the plethora of data produced and this issue has been addressed. In September of 2004, The National Institutes of Health (NIH) launched the first four National Centers for Biomedical Computing, part of the NIH Roadmap for Medical Research. The Centers include centralized data management tools and a national software engineering system, which allows scientists anywhere in the country to share and analyze data.

Training

Most Computational Biomedicine scientists were not trained in the field directly, and few undergraduate or graduate programs exist for students interested in specializing in this field.

In a collaborative effort through the Gulf Coast Consortia of Houston (GCC), institutions throughout the Houston/Galveston area have joined forces and consider as one of their top goals, training new scientists at the intersection of biological sciences with computational and physical sciences. The NRC contributes to the training initiative in Theoretical and Computational Neuroscience within the GCC, aiming to eliminate cross institution redundancies, while creating a more advanced and comprehensive graduate program.

Research

Several NRC laboratories conduct separate studies as part of this growing field. Current projects include modeling molecular networks underlying circadian rhythms and neuronal plasticity; modeling neural networks underlying simple behaviors such as reflexes, feeding and locomotion; modeling neural networks underlying vision; and modeling neural systems underlying learning and memory.  These computational models provide new insight into the operations of molecular, cellular, network and systems processes, which contribute to behavior and cognitive function.

A number of NRC members use a supercomputer cluster equivalent to 180 computers linked together, to classify, store and analyze the large amount of data produced during research. The supercomputer is housed in the School of Health and Information Sciences, but shared by various NRC members whose research involves image and signal processing, data mining and information retrieval.

Neuroscientists are utilizing computer technology to expedite the move toward a better understanding of the complex inner workings of the brain. It is the advances in this computer technology that are fundamentally changing the discovery process with still unknown, but certain beneficial outcomes to medicine, and ultimately to human health.