Biological Computation: How does nature compute?

When viewed as information technologies, it is apparent biological systems have an enormous capability as control systems for agility or regulation, for pattern recognition, adaptability, information storage, sensor fusion and other information-handling tasks of great interest to computer scientists, computer engineers and anyone else interested in IT-related research. In these and other domains, biology performs at levels many orders of magnitude better than silicon-based systems. We believe that research at the interface of biology and information technology may lead to important new information systems (algorithms and software) and computer technologies (hardware). The question is what and how can we learn and understand from the biological systems, and how can we adopt them and adapt them to develop these new computer technologies. The objective of this short treatise is to define what we mean by Biological Computation in view of other work in this area and then develop the idea to serve as a basis for future discussion.

Biocomputation has been used as a catch-all term for research at the interface of biology and computation, but that term is used in so many ways and for such different subsets of this intersection as to cause confusion. To help guide discussions, we offer that the general area of biocomputation can be divided into four major categories: Biomolecular Computation, Computational Biology, Bioinformatics, and Biological Computation. We define the four categories as follows:

Biomolecular Computation This category includes efforts to exploit biological macromolecules to implement relatively standard methods of computation. Examples are DNA computing, storage media using bacteria rhodopsin and biologically altered cells that do rudimentary operations within the paradigm of traditional computation.
Computational Biology This category includes any effort to solve biological problems by the application of computational methods and tools to model biology and solve detailed mathematical expressions of biological behaviors. Examples include methods of calculating from first principles such as Ab Initio, Monte Carlo or other simulation programs applied to looking at protein-protein interactions, protein folding, drug binding site elucidation, etc.
Bioinformatics This category includes the application of data management, data mining, data modeling and algorithmic techniques to biological databases, such as genome databases and related sequencing information. Examples include in silico models as a predictive method for gene function and data mining for inferring and determining sequence homology information.
Biological Computation This category includes efforts to determine how biology does information technology from the sub-cellular level to the systems and population level. The three main categories that are expanded below are in silico systems for fundamental understanding; hybrid systems to reverse engineer the biology and systems biology at the multi-cellular level and beyond.

Back to Lila Kari's homepage