Martyn Amos

Manchester Metropolitan University, Manchester, England, United Kingdom

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Publications (51)27.87 Total impact

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    ABSTRACT: The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination of problem instance and solution method is highly challenging, and few concrete guidelines exist on how and when such tuning may be performed. Previous work tends to either focus on a specific algorithm or use benchmark problems, and both of these restrictions limit the applicability of any findings. Here, we examine a number of different algorithms, and study them in a "problem agnostic" fashion (i.e., one that is not tied to specific instances) by considering their performance on fitness landscapes with varying characteristics. Using this approach, we make a number of observations on which algorithms may (or may not) benefit from tuning, and in which specific circumstances.
    05/2013;
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    ABSTRACT: Recent efforts in synthetic biology have focussed on the implementation of logical functions within living cells. One aim is to facilitate both internal "re-programming" and external control of cells, with potential applications in a wide range of domains. However, fundamental limitations on the degree to which single cells may be re-engineered have led to a growth of interest in multicellular systems, in which a "computation" is distributed over a number of different cell types, in a manner analogous to modern computer networks. Within this model, individual cell type perform specific sub-tasks, the results of which are then communicated to other cell types for further processing. The manner in which outputs are communicated is therefore of great significance to the overall success of such a scheme. Previous experiments in distributed cellular computation have used global communication schemes, such as quorum sensing (QS), to implement the "wiring" between cell types. While useful, this method lacks specificity, and limits the amount of information that may be transferred at any one time. We propose an alternative scheme, based on specific cell-cell conjugation. This mechanism allows for the direct transfer of genetic information between bacteria, via circular DNA strands known as plasmids. We design a multi-cellular population that is able to compute, in a distributed fashion, a Boolean XOR function. Through this, we describe a general scheme for distributed logic that works by mixing different strains in a single population; this constitutes an important advantage of our novel approach. Importantly, the amount of genetic information exchanged through conjugation is significantly higher than the amount possible through QS-based communication. We provide full computational modelling and simulation results, using deterministic, stochastic and spatially-explicit methods. These simulations explore the behaviour of one possible conjugation-wired cellular computing system under different conditions, and provide baseline information for future laboratory implementations.
    PLoS ONE 01/2013; 8(6):e65986. · 3.53 Impact Factor
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    ABSTRACT: We present GPU implementations of two different nature-inspired optimization methods for well-known optimization problems. Ant Colony Optimization (ACO) is a two-stage population-based method modelled on the foraging behaviour of ants, while P systems provide a high-level computational modelling framework that combines the structure and dynamic aspects of biological systems (in particular, their parallel and non-deterministic nature). Our methods focus on exploiting data parallelism and memory hierarchy to obtain GPU factor gains surpassing 20x for any of the two stages of the ACO algorithm, and 16x for P systems when compared to sequential versions running on a single-threaded high-end CPU. Additionally, we compare performance between GPU generations to validate hardware enhancements introduced by Nvidia’s Fermi architecture.
    The Journal of Supercomputing 01/2013; 63(3). · 0.92 Impact Factor
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    ABSTRACT: Graphics Processing Units (GPUs) have evolved into highly parallel and fully programmable architecture over the past five years, and the advent of CUDA has facilitated their application to many real-world applications. In this paper, we deal with a GPU implementation of Ant Colony Optimization (ACO), a population-based optimization method which comprises two major stages: tour construction and pheromone update. Because of its inherently parallel nature, ACO is well-suited to GPU implementation, but it also poses significant challenges due to irregular memory access patterns. Our contribution within this context is threefold: (1) a data parallelism scheme for tour construction tailored to GPUs, (2) novel GPU programming strategies for the pheromone update stage, and (3) a new mechanism called I-Roulette to replicate the classic roulette wheel while improving GPU parallelism. Our implementation leads to factor gains exceeding 20x for any of the two stages of the ACO algorithm as applied to the TSP when compared to its sequential counterpart version running on a similar single-threaded high-end CPU. Moreover, an extensive discussion focused on different implementation paths on GPUs shows the way to deal with parallel graph connected components. This, in turn, suggests a broader area of inquiry, where algorithm designers may learn to adapt similar optimization methods to GPU architecture.
    Journal of Parallel and Distributed Computing 01/2013; 73(1):42-51. · 1.12 Impact Factor
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    Henry Dorrian, Jon Borresen, Martyn Amos
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    ABSTRACT: In many types of network, the relationship between structure and function is of great significance. We are particularly interested in community structures, which arise in a wide variety of domains. We apply a simple oscillator model to networks with community structures and show that waves of regular oscillation are caused by synchronised clusters of nodes. Moreover, we show that such global oscillations may arise as a direct result of network topology. We also observe that additional modes of oscillation (as detected through frequency analysis) occur in networks with additional levels of topological hierarchy and that such modes may be directly related to network structure. We apply the method in two specific domains (metabolic networks and metropolitan transport) demonstrating the robustness of our results when applied to real world systems. We conclude that (where the distribution of oscillator frequencies and the interactions between them are known to be unimodal) our observations may be applicable to the detection of underlying community structure in networks, shedding further light on the general relationship between structure and function in complex systems.
    PLoS ONE 01/2013; 8(10):e75569. · 3.53 Impact Factor
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    Naomi Jacobs, Martyn Amos
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    ABSTRACT: A significant amount of high-impact contemporary scientific research occurs where biology, computer science, engineering and chemistry converge. Although programmes have been put in place to support such work, the complex dynamics of interdisciplinarity are still poorly understood. In this paper we highlight potential barriers to effective research across disciplines, and suggest, using a case study, possible mechanisms for removing these impediments.
    11/2012;
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    Angel Goni-Moreno, Martyn Amos
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    ABSTRACT: In bacterial populations, cells are able to cooperate in order to yield complex collective functionalities. Interest in population-level cellular behaviour is increasing, due to both our expanding knowledge of the underlying biological principles, and the growing range of possible applications for engineered microbial consortia. Researchers in the field of synthetic biology - the application of engineering principles to living systems - have, for example, recently shown how useful decision-making circuits may be distributed across a bacterial population. The ability of cells to interact through small signalling molecules (a mechanism known as it quorum sensing) is the basis for the majority of existing engineered systems. However, horizontal gene transfer (or conjugation) offers the possibility of cells exchanging messages (using DNA) that are much more information-rich. The potential of engineering this conjugation mechanism to suit specific goals will guide future developments in this area. Motivated by a lack of computational models for examining the specific dynamics of conjugation, we present a simulation framework for its further study. We present an agent-based model for conjugation dynamics, with realistic handling of physical forces. Our framework combines the management of intercellular interactions together with simulation of intracellular genetic networks, to provide a general-purpose platform. We validate our simulations against existing experimental data, and then demonstrate how the emergent mixing patterns of multi-strain populations can affect conjugation dynamics. Our model of conjugation, based on a probability distribution, may be easily tuned to correspond to the behaviour of different cell types. Simulation code and movies are available at http://code.google.com/p/discus/.
    11/2012;
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    ABSTRACT: A significant challenge in nature-inspired algorithmics is the identification of specific characteristics of problems that make them harder (or easier) to solve using specific methods. The hope is that, by identifying these characteristics, we may more easily predict which algorithms are best-suited to problems sharing certain features. Here, we approach this problem using fitness landscape analysis. Techniques already exist for measuring the "difficulty" of specific landscapes, but these are often designed solely with evolutionary algorithms in mind, and are generally specific to discrete optimisation. In this paper we develop an approach for comparing a wide range of continuous optimisation algorithms. Using a fitness landscape generation technique, we compare six different nature-inspired algorithms and identify which methods perform best on landscapes exhibiting specific features.
    10/2012;
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    Angel Goni-Moreno, Martyn Amos
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    ABSTRACT: BACKGROUND: Engineering genetic Boolean logic circuits is a major research theme of synthetic biology. By altering or introducing connections between genetic components, novel regulatory networks are built in order to mimic the behaviour of electronic devices such as logic gates. While electronics is a highly standardized science, genetic logic is still in its infancy, with few agreed standards. In this paper we focus on the interpretation of logical values in terms of molecular concentrations. RESULTS: We describe the results of computational investigations of a novel circuit that is able to trigger specific differential responses depending on the input standard used. The circuit can therefore be dynamically reconfigured (without modification) to serve as both a NAND/NOR logic gate. This multi-functional behaviour is achieved by a) varying the meanings of inputs, and b) using branch predictions (as in computer science) to display a constrained output. A thorough computational study is performed, which provides valuable insights for the future laboratory validation. The simulations focus on both single-cell and population behaviours. The latter give particular insights into the spatial behaviour of our engineered cells on a surface with a non-homogeneous distribution of inputs. CONCLUSIONS: We present a dynamically-reconfigurable NAND/NORgenetic logic circuit that can be switched between modes of operation via a simple shift in input signal concentration. The circuit addresses important issues in genetic logic that will have significance for more complex synthetic biology applications.
    BMC Systems Biology 09/2012; 6(1):126. · 2.98 Impact Factor
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    Angel Goñi-Moreno, Martyn Amos
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    ABSTRACT: In this paper we consider the problem of representation and measurement in genetic circuits, and investigate how they can affect the reliability of engineered systems. We propose a design scheme, based on the notion of continuous computation, which addresses these issues. We illustrate the methodology by showing how a concept from computer architecture (namely, branch prediction) may be implemented in vivo, using a distributed approach. Simulation results confirm the in-principle feasibility of our method, and offer valuable insights into its future laboratory validation.
    Bio Systems 02/2012; 109(1):52-6. · 1.27 Impact Factor
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    Martyn Amos, Jack Coldridge
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    ABSTRACT: In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. By constructing a simulator for the game, we compare the performance of the GA with that of the A* algorithm. We show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. By highlighting relevant features of the game we hope to stimulate further work on its study, and we conclude by presenting several possible areas for future research. KeywordsGenetic algorithm–Transport puzzle–NP-complete–Game A*
    Natural Computing 01/2012; · 0.68 Impact Factor
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    Daniel Richards, Nick Dunn, Martyn Amos
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    ABSTRACT: We present a developmental genotype-phenotype growth process, or embryogeny, which is used to evolve, in silico, efficient three-dimensional structures that exhibit real-world architectural performance. The embryogeny defines a sequential assembly of architectural components within a three-dimensional volume, and indirectly establishes a regulatory network of components based on the principles of gene regulation. The implicitly regulated phenotypes suggest advances for the automatic design of physical structures, by improving scalability of the genotype encoding and embedding real-world constraints. We demonstrate that our model can evolve novel, yet efficient, architectural structures which exhibit emergent shape, topology and material distribution. Finally, we compare evolved structures against a "hand-coded" solution to illustrate that our model produces competitive results without prior knowledge of the design solution or direct human guidance.
    01/2012;
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    Robin Houston, Joseph White, Martyn Amos
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    ABSTRACT: Zen Puzzle Garden (ZPG) is a one-player puzzle game. In this paper, we prove that deciding the solvability of ZPG is NP-complete.
    Information Processing Letters. 06/2011; 112(3).
  • Angel Goñi-Moreno, Martyn Amos
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    ABSTRACT: Genetic oscillators are a major theme of interest in the emerging field of synthetic biology. Until recently, most work has been carried out using intra-cellular oscillators, but this approach restricts the broader applicability of such systems. Motivated by a desire to develop large-scale, spatially distributed cell-based computational systems, we present an initial design for a population-level oscillator which uses three different bacterial strains. Our system is based on the client-server model familiar to computer science, and uses quorum sensing for communication between nodes. Importantly, it is robust to perturbation and noise. We present the results of extensive in silico simulation tests, which confirm the feasibility of our design.
    Bio Systems 06/2011; 105(3):286-94. · 1.27 Impact Factor
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    ABSTRACT: Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is there- fore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: Tour construction and Pheromone update. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for both stages of the ACO algorithm on the GPU. We propose an alternative data-based parallelism scheme for Tour construction, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the Pheromone update stage. Our results show a total speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone update, and suggest that ACO is a potentially fruitful area for future research in the GPU domain.
    Computing Research Repository - CORR. 01/2011;
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    Matthew Crossley, Martyn Amos
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    ABSTRACT: In this paper we describe a general method for the conversion of an equation-based model to an agent-based simulation. We illustrate the process by converting a well-known recent case-study in epidemiology (zombie infection), and show how we may obtain qualitatively similar results, whilst gaining access to the many benefits of an agent-based implementation.
    Agent and Multi-Agent Systems: Technologies and Applications - 5th KES International Conference, KES-AMSTA 2011, Manchester, UK, June 29 - July 1, 2011. Proceedings; 01/2011
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    Naomi Jacobs, Martyn Amos
    [Show abstract] [Hide abstract]
    ABSTRACT: A significant amount of high-impact contemporary scientific research occurs where biology, computer science, engineering and chemistry converge. Although programmes have been put in place to support such work, the complex dynamics of interdisciplinarity are still poorly understood. In this paper we interrogate the nature of interdisciplinary research and how we might measure its "success", identify potential barriers to its implementation, and suggest possible mechanisms for removing these impediments.
    12/2010;
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    J. Coldridge, M. Amos
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    ABSTRACT: In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. We then compare the performance of the genetic algorithm with that of the A* algorithm. Our results show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. We conclude with a brief discussion of the implications of our findings for game solving and other “real world” problems.
    Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on; 10/2010
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    Angel Goni-Moreno, Martyn Amos
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic oscillators are a major theme of interest in the emerging field of synthetic biology. Until recently, most work has been carried out using intra-cellular oscillators, but this approach restricts the broader applicability of such systems. Motivated by a desire to develop large-scale, spatially-distributed cell-based computational systems, we present an initial design for a population-level oscillator which uses three different bacterial strains. Our system is based on the client-server model familiar to computer science, and uses quorum sensing for communication between nodes. We present the results of extensive in silico simulation tests, which confirm that our design is both feasible and robust. Comment: Submitted
    07/2010;
  • Source
    Jack Coldridge, Martyn Amos
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as the Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. We then compare the performance of the genetic algorithm with that of the A* algorithm. Our results show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. We conclude with a brief discussion of the implications of our findings for game solving and other "real world" problems. Comment: Submitted
    05/2010;

Publication Stats

217 Citations
27.87 Total Impact Points

Institutions

  • 2007–2013
    • Manchester Metropolitan University
      • School of Computing,Mathematics and Digital Technology
      Manchester, England, United Kingdom
    • Huazhong University of Science and Technology
      Wu-han-shih, Hubei, China
    • China Three Gorges University
      Tung-hu, Hubei, China
  • 2006
    • University of Exeter
      • Department of Engineering
      Exeter, England, United Kingdom
  • 1998–2002
    • University of Liverpool
      • Department of Computer Science
      Liverpool, England, United Kingdom
  • 1996
    • The University of Warwick
      Coventry, England, United Kingdom