Putting Intentions into Cell Biochemistry: An Artificial Intelligence Perspective
✍ Scribed by CATHOLIJN M. JONKER; JACKY L. SNOEP; JAN TREUR; HANS V. WESTERHOFF; WOUTER C.A. WIJNGAARDS
- Publisher
- Elsevier Science
- Year
- 2002
- Tongue
- English
- Weight
- 471 KB
- Volume
- 214
- Category
- Article
- ISSN
- 0022-5193
No coin nor oath required. For personal study only.
✦ Synopsis
The living cell exists by virtue of thousands of nonlinearly interacting processes. This complexity greatly impedes its understanding. The standard approach to the calculation of the behaviour of the living cell, or part thereof, integrates all the rate equations of the individual processes. If successful extremely intensive calculations often lead the calculation of coherent, apparently simple, cellular &&decisions'' taken in response to a signal: the complexity of the behavior of the cell is often smaller than it might have been. The &&decisions'' correspond to the activation of entire functional units of molecular processes, rather than individual ones. The limited complexity of signal and response suggests that there might be a simpler way to model at least some important aspects of cell function. In the "eld of Arti"cial Intelligence, such simpler modelling methods for complex systems have been developed. In this paper, it is shown how the Arti"cial Intelligence description method for deliberative agents functioning on the basis of beliefs, desires and intentions as known in Arti"cial Intelligence, can be used successfully to describe essential aspects of cellular regulation. This is demonstrated for catabolite repression and substrate induction phenomena in the bacterium Escherichia coli. The method becomes highly e$cient when the computation is automated in a Prolog implementation. By de"ning in a qualitative way the food supply of the bacterium, the make-up of its catabolic pathways is readily calculated for cases that are su$ciently complex to make the traditional human reasoning tedious and error prone.