Cells have a limited repertoire of behaviors and interactions. They grow, divide, die, stick to each other, send and receive signals, change shape, polarize, differentiate (change behaviors), form sheets, secrete, absorb, pull on and remodel extracellular material, and migrate in response to signals in their environment.
How can they help us understand proteins?
Graphs, or networks, have been widely adopted in computational biology, with examples including protein-protein interaction networks, gene regulatory networks, and residue interaction networks in proteins, to name a few.
Set objectives and follow through
Having engineered several scientific software applications for public consumption, the authors know from experience that the process offers unique challenges. Typically, the algorithms being implemented are complex; the process involves numerous developers with various backgrounds and skill sets; and it all takes place in a fast-paced environment where new methods must be prototyped and tested regularly.
Advances in computational power and algorithms have led to longer and more accurate molecular dynamics simulations of protein folding.
An explanation of geometric constraints in computational biomechanics
How precise an image can fluorescence microscopy provide?
As modern optics and cell biology have flourished in recent years, they’ve each driven innovation in the other. Yet commonly employed imaging techniques, such as fluorescence microscopy, have run up against fundamental limits of precision. We want to measure ever-smaller objects at ever-shorter time intervals, but the relatively long wavelengths of visible light are a barrier to how precisely we can observe the minute and rapidly changing biological world.
Unlike most classical engineering materials, biological tissues can adapt to external stimuli by growing in volume: Skin grows in response to wounding; muscles grow in response to exercise; cancer cells grow into tumors; and heart muscles become enlarged in response to high blood volume. To understand these adaptive processes and their role in various chronic diseases, it can be useful to study them in predictive computer models of cells, organs, organ systems and whole organisms.
The Fall 2005 “Under the Hood” column discussed the curse of dimensionality—too many numerical components for each data point—and the curse of dataset sparsity—too few data points. One way to treat these problems in concert is to examine the geometric relationships between the data points, and represent the data with fewer descriptors that retain the salient structure.