Parametric evolution of a bacterial signalling system formalized by membrane computing

BackgroundMany physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation.ResultsUsing agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project.ConclusionsOur current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics.

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“…At this point, we have gathered some preliminary, yet promising results with other agent-based models [34]. …”

Section: Discussionmentioning

confidence: 99%

Simulating the decentralized processes of the human immune system in a virtual anatomy model

Sarpe

Jacob

2013

BMC Bioinformatics

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“…Similar to an auction in multi-agent systems (Wooldridge, 2009), the best broadcast solution, i.e., decision program and fitness value, would be implemented. As an extension, any population-based simulation and optimization approaches could be distributed among the OCbotics individuals and their evolution be concerted across the whole swarm [for distributed population-based optimization, see, for instance, Sarpe et al (2010) and Jacob et al (2011)]. Especially in situations with imbalanced computational loads across the swarm, following a smart distributed optimization strategy could yield an important advantage.…”

Section: Evolving Collaborative Behaviormentioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

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Similar to the Autonomous Computing initiative, which has mainly been advancing techniques for self-optimization focusing on computing systems and infrastructures, Organic Computing (OC) has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behavior of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework, which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g., by means of adapting to changing goals and conditions. Not only for the sake of generalizability but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organize themselves to fulfill spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimization as well as of the optimization of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

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“…Moreover, the way the evaluation functions are constructed was also discussed. In [19,64], the task was to find the optimal parameters for a P system model. In [19], it is discussed a continuous backward problem and the comparisons of five real-valued parameter optimization algorithms.…”

Section: Work On Admcmmentioning

confidence: 99%

“…In [19], it is discussed a continuous backward problem and the comparisons of five real-valued parameter optimization algorithms. In [64], it is applied PSO to optimize the parameters of a MC model of a synthetic autoinducer-2 (AI-2) signalling system in genetically engineered Escherichia coli bacteria. The model is a non-deterministic in silico model of Autoinducer-2 Quorum Sensing formalized by using P systems.…”

Section: Work On Admcmmentioning

confidence: 99%

Evolutionary membrane computing: A comprehensive survey and new results

Zhang

Gheorghe

Pan

et al. 2014

Information Sciences

138

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Evolutionary membrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionary membrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations.

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Parametric evolution of a bacterial signalling system formalized by membrane computing

Cited by 5 publications

References 18 publications

Simulating the decentralized processes of the human immune system in a virtual anatomy model

Simulating the decentralized processes of the human immune system in a virtual anatomy model

An Organic Computing Approach to Self-Organizing Robot Ensembles

Evolutionary membrane computing: A comprehensive survey and new results

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