The Holland Broadcast Language and the Modeling of Biochemical Networks

The article presents the use of signal regulatory networks (SRNs), a biologically inspired model based on gene regulatory networks. SRNs are a way of understanding a class of self-organizing IT systems, signal-regulated systems (SRSs). This article builds on the theory of SRSs and introduces some formalisms to clarify the discussion. An exemplar SRS that can be evaluated using SRNs is presented. Finally, an implementation of an adaptive and robust solution, built on a theory of SRSs and analyzed as a SRN, is shown to be plausible.

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“…The probabilistic functions of the P-SRM are an extension of the HBL [32]. HBL consists of a set of condition-action rules.…”

Section: Figurementioning

confidence: 99%

“…One selforganizing solution proposed by Holland is that of complex signal networks [29][30][31]. Holland proposed the implementation of these networks through the use of classifier systems and the Holland broadcast language (HBC) [32]. The idea of artificial signal networks has been extended by Decraene et al [33,34].…”

Section: Introductionmentioning

confidence: 99%

Signal‐regulated systems and networks

Zyl

Ehlers

2010

Complexity

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“…The broadcast language presented by Holland in the original text omitted a number of interactions between broadcast devices, which could in certain cases present us with ambiguities regarding the expected action to be performed. Holland discussed some of these semantical conflicts [14], while the remaining ambiguities were addressed in [7,8]. However in the context of this paper, this is not an important consideration.…”

Section: The Symbolsmentioning

confidence: 99%

“…In [8], it was demonstrated that the Broadcast Language can model Genetic Regulatory Networks (GRNs). This was due to the ability of the Broadcast Language to mirror Boolean networks which illustrates the wide ranging processing power that Broadcast Systems are capable of.…”

Section: Fusing Mcs and The Broadcast Languagementioning

confidence: 99%

Evolving Artificial Cell Signaling Networks: Perspectives and Methods

Decraene

Mitchell

McMullin

2007

Advances in Biologically Inspired Information Systems

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Abstract-Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.

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