Abstract

Computational techniques and methods are ubiquitous in biology. Virtually all biological systems and processes (genetic, developmental, evolutionary, ecological, etc.) can be analysed and simulated computationally. Also, the other way round, computational techniques are frequently inspired in biological knowledge (i.e., genetic algorithms, evolutionary computation, etc.). Far from being of interest only to computer scientists, many biologically-inspired algorithms have been employed, in their turn, to model and understand biological phenomena. Finally, and perhaps most notably, computation in biology is not only conceived as a mean to predict the behaviour of a system: biological systems are often interpreted as capable of performing computations in themselves. This is an important assumption for those research programmes that are oriented to the recreation and control of biological processes (like Artificial Life or synthetic biology). My goal in this talk is to offer a systematic classification of these different approaches to computation in biology and discuss some conceptual issues resulting from this taxonomy.