Automatic learning is a complex, multidisciplinary field of research and development, involving theoretical and applied methods from statistics, computer science, artificial intelligence, biology and psychology. Its applications to engineering problems, such as those encountered in electrical power systems, are therefore challenging, while extremely promising. More and more data have become available, collected from the field by systematic archiving, or generated through computer-based simulation. To handle this explosion of data, automatic learning can be used to provide systematic approaches, without which the increasing data amounts and computer power would be of little use.
The cell interior is another world that we are only beginning to explore. Although there are a number of approaches for examining the inner workings of the cell, the reductionist approach of building up complexity appeals to many with physical science and engineering backgrounds. This volume of Methods in Cell Biology spans a range of spatial scales from single protein molecules to vesicle and cell sized structures capable of complex behaviors. Contributions include; methods for combining different motors and cytoskeletal components in defined ways to produce more complex behaviors; methods to combine cytoskeletal assemblies with fabricated devices such as chambers or pillar arrays; reconstituting membrane fission and fusion; reconstituting important biological processes that normally take place on membrane surfaces; and methods for encapsulating protein machines within vesicles or droplets.
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