George G. Mitchell

Evolving Artificial Cell Signaling Networks: Perspectives and Methods

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. In this paper we introduce an abstraction of Cell Signaling Networks focusing 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. Following this we describe a novel class of Artificial Chemistry named Molecular Classifier Systems (MCS) to simulate ACSNs. The MCS can be regarded as a special purpose derivation of Hollands Learning Classifier System (LCS). We propose an instance of the MCS called the MCS.b that extends the precursor of the LCS: the broadcast language. We believe the MCS.b can offer a general purpose tool that can assist in the study of real CSNs in Silico 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.

Evolving artificial cell signaling networks using molecular classifier systems

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 Hollands 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

Crosstalk and the cooperation of collectively autocatalytic reaction networks

We examine a potential role of signalling crosstalk in Artificial Cell Signalling Networks (ACSNs). In this research, we regard these ACSNs as subsets of collectively autocatalytic (i.e., organizationally closed) reaction networks being able to both self-maintain and to carry out a distinct signal processing function. These signalling crosstalk phenomena occur naturally when different biochemical networks become mixed together where a given molecular species may contribute simultaneously to multiple ACSNs. It has been reported in the biological literature, that crosstalk may have effects that are both constructive (e.g., coordinating cellular activities, multi-tasking) and destructive (e.g., premature programmed cell death). In this paper we demonstrate how crosstalk may enable distinct closed ACSNs to cooperate with other. From a theoretical point of view, this work may give new insights for the understanding of crosstalk in natural biochemical networks. From a practical point view, this investigation may provide novel applications of crosstalk in engineered ACSNs.

Quality time tradeoff operator for designing efficient multi level genetic algorithms

We present a novel cost benefit operator that assists multi levelgenetic algorithm searches. Through the use of the cost benefitoperator, it is possible to dynamically constrain the search of thebase level genetic algorithms, to suit the users requirements. We note that the current literature has abundant studies on metaevolutionary GAs, however these approaches have not identifiedan efficient approach to the termination of base GA searchs or ameans to balance practical consideration such as quality ofsolution and the expense of computation. Our Quality timetradeoff operator (QTT) is user defined, and acts as a base leveltermination operator and also provides a fitness value for themeta-level GA. In this manner, the amount of computation timespent on less encouraging configurations can be specified by theuser. Our approach was applied to a computationally intensive test problem which evaluates a large set of configuration settings forthe base GAs to find suitable configuration settings (populationsize, crossover operator and rate, mutation operator and rate,repair or penalty and the use of adaptive mutation rates) forselected TSP problems.

A cost benefit operator for efficient multi level genetic algorithm searches

In this paper we present a novel cost benefit operator that assists multi level genetic algorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level genetic algorithm, to suit the users requirements. Initially we review meta-evolutionary (multi-level genetic algorithm) approaches. We note that the current literature has abundant studies on meta-evolutionary GAs. However these approaches have not identified an efficient approach to termination of base GA search or a means to balance practical consideration such as quality of solution and the expense of computation. Our quality time tradeoff operator (QTT) is user defined, and acts as a base level termination operator and also provides a fitness value for the meta-level GA. In this manner the amount of computation time spent on less encouraging configurations can be specified by the user. Our approach has been applied to a computationally intensive test problem which evaluates a large set of configuration settings for the base GAs. This approach should be applicable across a wide range of practical problems (e.g. routing, logistic and biomedical applications).