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.
Living cells organize many functions: molecular import and export, signaling, transcription of genes into proteins, movement, building, repair, and more. These functions are realized through a complex architecture of the cell interior reflected in a system of labyrinthine membranes forming manifold cellular organelles from tubes, vesicles and many other shapes. Accordingly, cells need to sculpt their membrane in never ceasing processes and have at their disposal a wide range of mechanisms. A key sculpting mechanism is furnished by proteins, so-called BAR domains, that apparently form lattice-like scaffolds adhering to membrane surfaces. Such scaffolds have been observed through electron microscopy and they are now also being described through molecular dynamics simulations using NAMD. As recently reported, the simulations, carried out at four different levels of resolution (from an atomic to a continuum level), revealed that different arrangements of BAR domains lead to different curvatures. The simulations help to explain why BAR domains working in teams, i.e., in lattice formation, sculpt intra-cellular membranes into different shapes, depending on the exact arrangement. An arrangement of BAR domains that is particularly efficient in bending membranes was identified. More information can be found on our BAR domain web page.
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.