The brain is the source of thoughts, perceptions, emotions, memories and actions. Neural signaling, the foundation of brain activity, must be precisely regulated to prevent neuronal disorders that may cause Parkinson's disease, schizophrenia, compulsive behaviors and addiction. Such a precise regulation is achieved by key signaling proteins, voltage-gated sodium and potassium channels for electrical signaling and calcium - bound synaptotagmin for chemical signaling. Here, innovations in computer simulation techniques will be used to investigate the molecular mechanism of neural firing induced by voltage-gated sodium and potassium channels and membrane fusion triggered by synaptotagmin.
Spotlight: Better NAMD Electrostatics (May 2015)
Long-range electrostatic interactions control macromolecular processes
within living cells as prominent charges appear everywhere,
such as in DNA or RNA,
in membrane lipid head groups, and in ion channels. Reliable and
efficient description of electrostatic interactions is crucial in
molecular dynamics simulations of such processes. Recently a new
mathematical approach for calculating electrostatic interactions, known as
multilevel summation method (MSM),
has been developed and
Compared to the earlier decades-long approach,
the particle-mesh Ewald (PME) method, MSM provides more flexibility
as it permits non-periodic simulations like ones
with asymmetric charge distributions
across a membrane
or of a water droplet with a protein folding inside. Furthermore,
MSM is ideally suited for modern parallel computers, running,
for example, simulations
of large virus particles. More information
Biological visuo-motor control of a pneumatic robot arm.
Michael Zeller, K. R. Wallace, and Klaus Schulten. In Dagli et al., editors, Intelligent Engineering Systems Through Artificial Neural Networks, volume 5, pp. 645-650, New York, 1995. American Society of Mechanical Engineers.
A "neural gas" network learns topologies.
Thomas Martinetz and Klaus Schulten. In Teuvo Kohonen, Kai Mäkisara, Olli Simula, and Jari Kangas, editors, Artificial Neural Networks, pp. 397-402. Elsevier, Amsterdam, 1991.