Putting Intentions into Cell Biochemistry: An Artificial Intelligence Perspective

Abstract

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

BDI-modelling of complex intracellular dynamics

“…The BDImodels available in the literature do not adequately address the dynamics of the internal state properties over time, nor do they specify in which order and at what time the appropriate beliefs, desires and intentions are generated in relation to environmental conditions. Since Jonker et al (2002) dealt with steady states, this limitation of the BDI-model was harmless, and relating the steady states to different environmental circumstances fitted well to the logic of the BDI-modelling strategy which abstracts from internal dynamics.…”

“…It is an extension of the complex steady-state example of the bacterium Escherichia coli presented in (Jonker et al, 2002), to (dynamically) varying environments:…”

“…As compared to that in (Jonker et al, 2002), the denotation has been adjusted for readability. The chemical criteria and the corresponding logical notation for the intentional state properties are both given, again improved with respect to those in (Jonker et al, 2002). The correspondence between the chemical and the intentional terms is shown in Figure 1.…”

“…One sort of these agent models are BDI-models describing agents in terms of internal state properties such as Beliefs, Desires and Intentions (e.g., Rao and Georgeff, 1991;Jonker, Treur and Wijngaards, 2003). In (Jonker, Snoep, Treur, Westerhoff, and Wijngaards, 2002) the BDI-modelling approach is used to identify and analyse steady states within the cell in relation to environmental circumstances. The BDImodels available in the literature do not adequately address the dynamics of the internal state properties over time, nor do they specify in which order and at what time the appropriate beliefs, desires and intentions are generated in relation to environmental conditions.…”

The Living Cell as a Multi-agent Organisation: A Compositional Organisation Model of Intracellular Dynamics

“…These properties serve as constraints on the dynamics of the respective roles and interactions. In (Jonker, Treur, and Wijngaards, 2002) an executable temporal language was introduced to specify the different types of dynamic properties.…”