Tag: Natural Computing

Biocircuit design through engineering bacterial logic gates

Abstract  Designing synthetic biocircuits to perform desired purposes is a scientific field that has exponentially grown over the past
decade. The advances in genome sequencing, bacteria gene regulatory networks, as well as the further knowl…

Abstract  

Designing synthetic biocircuits to perform desired purposes is a scientific field that has exponentially grown over the past
decade. The advances in genome sequencing, bacteria gene regulatory networks, as well as the further knowledge of intraspecies
bacterial communication through quorum sensing signals are the starting point for this work. Although biocircuits are mostly
developed in a single cell, here we propose a model in which every bacterium is considered to be a single logic gate and chemical
cell-to-cell connections are engineered to control circuit function. Having one genetically modified bacterial strain per
logic process would allow us to develop circuits with different behaviors by mixing the populations instead of re-programming
the whole genetic network within a single strain. Two principal advantages of this procedure are highlighted. First, the fully
connected circuits obtained where every cellgate is able to communicate with all the rest. Second, the resistance to the noise
produced by inappropriate gene expression. This last goal is achieved by modeling thresholds for input signals. Thus, if the
concentration of input does not exceed the threshold, it is ignored by the logic function of the gate.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s11047-010-9184-2
  • Authors
    • Angel Goñi-Moreno, Universidad Politécnica de Madrid Grupo de Computación Natural, Facultad de Informática 28660 Madrid Spain
    • Miguel Redondo-Nieto, Universidad Autónoma de Madrid Depto. Biología, Facultad de Ciencias 28049 Madrid Spain
    • Fernando Arroyo, Universidad Politécnica de Madrid Depto. de Lenguajes, Proyectos y Sistemas Informáticos, Escuela Universitaria de Informática 28031 Madrid Spain
    • Juan Castellanos, Universidad Politécnica de Madrid Artificial Intelligence Department, Facultad de Informática Campus de Montegancedo, Boadilla del Monte s/n 28660 Madrid Spain

Petri nets for modelling metabolic pathways: a survey

Abstract  In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic
pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about m…

Abstract  

In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic
pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways
can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of
a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of
recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling
a metabolic pathway by a corresponding Petri net.

  • Content Type Journal Article
  • DOI 10.1007/s11047-010-9180-6
  • Authors
    • Paolo Baldan, Dipartimento di Matematica Pura e Applicata, Università di Padova, via Trieste 63, 35121 Padova, Italy
    • Nicoletta Cocco, Dipartimento di Informatica, Università Ca’ Foscari di Venezia, via Torino 155, 30172 Venezia Mestre, Italy
    • Andrea Marin, Dipartimento di Informatica, Università Ca’ Foscari di Venezia, via Torino 155, 30172 Venezia Mestre, Italy
    • Marta Simeoni, Dipartimento di Informatica, Università Ca’ Foscari di Venezia, via Torino 155, 30172 Venezia Mestre, Italy

Petri net representation of multi-valued logical regulatory graphs

Abstract  Relying on a convenient logical representation of regulatory networks, we propose a generic method to qualitatively model
regulatory interactions in the standard elementary and coloured Petri net frameworks. Logical functions gover…

Abstract  

Relying on a convenient logical representation of regulatory networks, we propose a generic method to qualitatively model
regulatory interactions in the standard elementary and coloured Petri net frameworks. Logical functions governing the behaviours
of the components of logical regulatory graphs are efficiently represented by Multivalued Decision Diagrams, which are also
at the basis of the translation of logical models in terms of Petri nets. We further delineate a simple strategy to sort trajectories
through the introduction of priority classes (in the logical framework) or priority functions (in the Petri net framework).
We also focus on qualitative behaviours such as multistationarity or sustained oscillations, identified as specific structures
in state transition graphs (for logical models) or in marking graphs (in Petri nets). Regulatory circuits are known to be
at the origin of such properties. In this respect, we present a method that allows to determine the functionality contexts
of regulatory circuits, i.e. constraints on external regulator states enabling the corresponding dynamical properties. Finally,
this approach is illustrated through an application to the modelling of a regulatory network controlling T lymphocyte activation
and differentiation.

  • Content Type Journal Article
  • Pages 727-750
  • DOI 10.1007/s11047-010-9178-0
  • Authors
    • C. Chaouiya, Instituto Gulbenkian de Ciência, Oeiras, Portugal
    • A. Naldi, INSERM U928—TAGC, Marseille, France
    • E. Remy, Institut de Mathématiques de Luminy, Marseille, France
    • D. Thieffry, INSERM U928—TAGC, Marseille, France

On aggregation in multiset-based self-assembly of graphs

Abstract  We continue the formal study of multiset-based self-assembly. The process of self-assembly of graphs, where iteratively new
nodes are attached to a given graph, is guided by rules operating on nodes labelled by multisets. In this w…

Abstract  

We continue the formal study of multiset-based self-assembly. The process of self-assembly of graphs, where iteratively new
nodes are attached to a given graph, is guided by rules operating on nodes labelled by multisets. In this way, the multisets
and rules model connection points (such as “sticky ends”) and complementarity/affinity between connection points, respectively.
We identify three natural ways (individual, free, and collective) to attach (aggregate) new nodes to the graph, and study
the generative power of the corresponding self-assembly systems. For example, it turns out that individual aggregation can
be simulated by free or collective aggregation. However, we demonstrate that, for a fixed set of connection points, collective
aggregation is rather restrictive. We also give a number of results that are independent of the way that aggregation is performed.

  • Content Type Journal Article
  • Pages 1-22
  • DOI 10.1007/s11047-010-9183-3
  • Authors
    • Francesco Bernardini, Leiden University Leiden Institute of Advanced Computer Science Leiden The Netherlands
    • Robert Brijder, Leiden University Leiden Institute of Advanced Computer Science Leiden The Netherlands
    • Matteo Cavaliere, Centre for Computational and Systems Biology (CoSBi) The Microsoft Research-University of Trento Trento Italy
    • Giuditta Franco, University of Verona Department of Computer Science Strada Le Grazie 15 37134 Verona Italy
    • Hendrik Jan Hoogeboom, Leiden University Leiden Institute of Advanced Computer Science Leiden The Netherlands
    • Grzegorz Rozenberg, Leiden University Leiden Institute of Advanced Computer Science Leiden The Netherlands

Electrostatic field framework for supervised and semi-supervised learning from incomplete data

Abstract  In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original
approach to exploiting incomplete training data with missing features, involving extensive use of electrostat…

Abstract  

In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original
approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy,
has been used. The framework supports a hybrid supervised and unsupervised training scenario, enabling learning simultaneously
from both labelled and unlabelled data using the same set of rules and adaptation mechanisms. Classification of incomplete
patterns has been facilitated by introducing a local dimensionality reduction technique, which aims at exploiting all available
information using the data ‘as is’, rather than trying to estimate the missing values. The performance of all proposed methods
has been extensively tested in a wide range of missing data scenarios, using a number of standard benchmark datasets in order
to make the results comparable with those available in current and future literature. Several modifications to the original
Electrostatic Field Classifier aiming at improving speed and robustness in higher dimensional spaces have also been introduced
and discussed.

  • Content Type Journal Article
  • Pages 921-945
  • DOI 10.1007/s11047-010-9182-4
  • Authors
    • Marcin Budka, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB UK
    • Bogdan Gabrys, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB UK

Foreword

Foreword
Content Type Journal ArticleDOI 10.1007/s11047-010-9181-5Authors
Friedrich Simmel, Technische Universität München Garching GermanyAshish Goel, Stanford University Stanford CA USA

Journal Natural ComputingOnline ISSN 1572-9796Print …

Foreword

  • Content Type Journal Article
  • DOI 10.1007/s11047-010-9181-5
  • Authors
    • Friedrich Simmel, Technische Universität München Garching Germany
    • Ashish Goel, Stanford University Stanford CA USA

On stoichiometry for the assembly of flexible tile DNA complexes

Abstract  Given a set of flexible branched junction DNA molecules with sticky-ends (building blocks), called here “tiles”, we consider
the problem of determining the proper stoichiometry such that all sticky-ends could end up connected. …

Abstract  

Given a set of flexible branched junction DNA molecules with sticky-ends (building blocks), called here “tiles”, we consider
the problem of determining the proper stoichiometry such that all sticky-ends could end up connected. In general, the stoichiometry
is not uniform, and the goal is to determine the proper proportion (spectrum) of each type of molecule within a test tube
to allow for complete assembly. According to possible components that assemble in complete complexes we partition multisets
of tiles, called here “pots”, into classes: unsatisfiable, weakly satisfiable, satisfiable and strongly satisfiable. This
classification is characterized through the spectrum of the pot, and it can be computed in PTIME using the standard Gauss-Jordan
elimination method. We also give a geometric description of the spectrum as a convex hull within the unit cube.

  • Content Type Journal Article
  • Pages 1121-1141
  • DOI 10.1007/s11047-009-9169-1
  • Authors
    • N. Jonoska, Department of Mathematics & Statistics, University of South Florida, Tampa, FL 33620, USA
    • G. L. McColm, Department of Mathematics & Statistics, University of South Florida, Tampa, FL 33620, USA
    • A. Staninska, Institute of Biomathematics and Biometry Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Abstract  Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems
since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimizat…

Abstract  

Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems
since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based
memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework
of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator
and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random
immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks
in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic
algorithm is robust and adaptable in dynamic environments.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9176-2
  • Authors
    • Hongfeng Wang, School of Information Science and Engineering, Northeastern University, Shenyang, 110004 People’s Republic of China
    • Shengxiang Yang, Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH UK
    • W. H. Ip, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China
    • Dingwei Wang, School of Information Science and Engineering, Northeastern University, Shenyang, 110004 People’s Republic of China

Swarm intelligence: the state of the art special issue of natural computing

Swarm intelligence: the state of the art special issue of natural computing
Content Type Journal ArticleDOI 10.1007/s11047-009-9172-6Authors
Eric Bonabeau, Icosystem, 10 Fawcett Street, Cambridge, MA 02138, USADavid Corne, Heriot-Watt University, Ed…

Swarm intelligence: the state of the art special issue of natural computing

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9172-6
  • Authors
    • Eric Bonabeau, Icosystem, 10 Fawcett Street, Cambridge, MA 02138, USA
    • David Corne, Heriot-Watt University, Edinburgh, EH14 4AS UK
    • Riccardo Poli, School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ UK

Multiobjective particle swarm optimization with nondominated local and global sets

Abstract  In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle
of the population has a great impact on the convergence and diversity of solutions, especially when optim…

Abstract  

In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle
of the population has a great impact on the convergence and diversity of solutions, especially when optimizing problems with
high number of objectives. This paper presents an approach using two sets of nondominated solutions. The ability of the proposed
approach to detect the true Pareto optimal solutions and capture the shape of the Pareto front is evaluated through experiments
on well-known non-trivial multiobjective test problems as well as the real-life electric power dispatch problem. The diversity
of the nondominated solutions obtained is demonstrated through different measures. The proposed approach has been assessed
through a comparative study with the reported results in the literature.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9171-7
  • Authors
    • M. A. Abido, Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia