Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

The Interplay between Chemistry and Mechanics in the Transduction of a Mechanical Signal into a Biochemical Function

There are many processes in biology in which mechanical forces are generated. Force-bearing networks can transduce locally developed mechanical signals very extensively over different parts of the cell or tissues. In this article we conduct an overview of this kind of mechanical transduction, focusing in particular on the multiple layers of complexity displayed by the mechanisms that control and trigger the conversion of a mechanical signal into a biochemical function. Single molecule methodologies, through their capability to introduce the force in studies of biological processes in which mechanical stresses are developed, are unveiling subtle intertwining mechanisms between chemistry and mechanics and in particular are revealing how chemistry can control mechanics. The possibility that chemistry interplays with mechanics should be always considered in biochemical studies.Comment: 50 pages, 18 figure