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.
Academic Press
A BDI-based continuous-time modelling approach for intracellular dynamics is presented. It is shown how temporalised BDI-models make it possible to model intracellular biochemical processes as decision processes. By abstracting from some of the details of the biochemical pathways, the model achieves understanding in nearly intuitive terms, without losing veracity: classical intentional state properties such as Beliefs, Desires and Intentions, are founded in reality through precise biochemical relations. In an extensive example, the complex regulation of Escherichia coli vis-à-vis lactose, glucose and oxygen is simulated as a discrete-state, continuous-time temporal decision manager. Thus a bridge is introduced between two different scientific areas: the area of BDI-modelling and the area of intracellular dynamics.
In this paper reduction and its pragmatics are discussed in the light of the development in Computer Science of languages to describe processes. The design of higher-level description languages within Computer Science has had the aim of allowing for description of the dynamics of processes in the (physical) world on a higher level avoiding all (physical) details of these processes. The higher description levels developed have dramatically increased the complexity of applications that came within reach. The pragmatic attitude of a (scientific) practitioner in this area has become inherently anti-reductionist, but based on well-established reduction relations. The paper discusses how this perspective can be related to reduction in general, and to other domains where description of dynamics plays a main role, in particular, biological and cognitive domains.
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