Tag: Natural Computing

Beyond evolutionary trees

Abstract  In Computational Biology, the notion of phylogeny has become synonymous with tree-like evolution. Recent advances in the Life
Sciences have suggested that evolution has a much more diverse course. In this paper we will survey some …

Abstract  

In Computational Biology, the notion of phylogeny has become synonymous with tree-like evolution. Recent advances in the Life
Sciences have suggested that evolution has a much more diverse course. In this paper we will survey some of the models that
have been proposed to overcome the limitations of using phylogenies to represent evolutionary histories.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9156-6
  • Authors
    • Gianluca Della Vedova, Università degli Studi di Milano-Bicocca Dipartimento di Statistica Milano Italy
    • Riccardo Dondi, Università degli Studi di Bergamo Dipartimento di Scienze dei Linguaggi, della Comunicazione e degli Studi Culturali Bergamo Italy
    • Tao Jiang, University of California at Riverside Department of Computer Science Riverside CA USA
    • Giulio Pavesi, Università degli Studi di Milano Dipartimento di Informatica e Comunicazione Milano Italy
    • Yuri Pirola, Università degli Studi di Milano-Bicocca Dipartimento di Informatica, Sistemistica e Comunicazione Milano Italy
    • Lusheng Wang, City University of Hong Kong Department of Computer Science Kowloon Hong Kong

Petri nets as a framework for the reconstruction and analysis of signal transduction pathways and regulatory networks

Abstract  Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic
and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) proce…

Abstract  

Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic
and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) processes. Here we
exemplify how molecular mechanisms, biochemical or genetic, can be consistently respresented in the form of place/transition
Petri nets. We then describe the application of Petri nets to the reconstruction of molecular and genetic networks from experimental
data and their power to represent biological processes with arbitrary degree of resolution of the subprocesses at the cellular
and the molecular level. Petri nets are executable formal language models that permit the unambiguous visualization of regulatory
mechanisms, and they can be used to encode the results of mathematical algorithms for the reconstruction of causal interaction
networks from experimental time series data.

  • Content Type Journal Article
  • Pages 639-654
  • DOI 10.1007/s11047-009-9152-x
  • Authors
    • Wolfgang Marwan, Magdeburg Centre for Systems Biology (MaCS), Otto-von-Guericke-Universität, Magdeburg, Germany
    • Annegret Wagler, Magdeburg Centre for Systems Biology (MaCS), Otto-von-Guericke-Universität, Magdeburg, Germany
    • Robert Weismantel, Magdeburg Centre for Systems Biology (MaCS), Otto-von-Guericke-Universität, Magdeburg, Germany

A discrete Petri net model for cephalostatin-induced apoptosis in leukemic cells

Abstract  Understanding the mechanisms involved in apoptosis has been an area of extensive study due to its critical role in the development
and homeostasis of multi-cellular organisms. Our special interest lies in understanding the apoptosi…

Abstract  

Understanding the mechanisms involved in apoptosis has been an area of extensive study due to its critical role in the development
and homeostasis of multi-cellular organisms. Our special interest lies in understanding the apoptosis of tumor cells which
is mediated by novel potential drugs. Cephalostatin 1 is a marine compound that can induce apoptosis in leukemic cells in
a dose- and time-dependent manner even at nano-molar concentrations using a recently discovered pathway that excludes the
receptor-mediated pathway and which includes both the mitochondrial and endoplasmic reticulum pathways (Dirsch et al., Cancer
Res 63:8869–8876, 2003; López-Antón et al., J Biol Chem 28:33078–33086, 2006). In this paper, the methods and tools of Petri net theory are used to construct, analyze, and validate a discrete Petri
net model for cephalostatin 1-induced apoptosis. Based on experimental results and literature search, we constructed a discrete
Petri net consisting of 43 places and 59 transitions. Standard Petri net analysis techniques such as structural and invariant
analyses and a recently developed modularity analysis technique using maximal abstract dependent transition sets (ADT sets)
were employed. Results of these analyses revealed model consistency with known biological behavior. The sub-modules represented
by the ADT sets were compared with the functional modules of apoptosis identified by Alberghina and Colangelo (BMC Neurosci
7(Suppl 1):S2, 2006).

  • Content Type Journal Article
  • Pages 993-1015
  • DOI 10.1007/s11047-009-9153-9
  • Authors
    • Eva M. Rodriguez, Department of Mathematics, University of Asia and the Pacific, Pasig City, Philippines
    • Anita Rudy, Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians University, Munich, Germany
    • Ricardo C. H. del Rosario, Institute of Mathematics, University of the Philippines Diliman, Quezon City, Philippines
    • Angelika M. Vollmar, Department of Pharmacy, Center for Drug Research, Ludwig-Maximilians University, Munich, Germany
    • Eduardo R. Mendoza, Department of Computer Science, University of the Philippines Diliman, Quezon City, Philippines

Petri net models for the semi-automatic construction of large scale biological networks

Abstract  For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks.
During the last 15 years, Petri nets have attracted more and more attention to help to solve this k…

Abstract  

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks.
During the last 15 years, Petri nets have attracted more and more attention to help to solve this key problem. Regarding the
published papers, it seems clear that hybrid functional Petri nets are the adequate method to model complex biological networks.
Today, a Petri net model of biological networks is built manually by drawing places, transitions and arcs with mouse events.
Therefore, based on relevant molecular database and information systems biological data integration is an essential step in
constructing biological networks. In this paper, we will motivate the application of Petri nets for modeling and simulation
of biological networks. Furthermore, we will present a type of access to relevant metabolic databases such as KEGG, BRENDA,
etc. Based on this integration process, the system supports semi-automatic generation of the correlated hybrid Petri net model.
A case study of the cardio-disease related gene-regulated biological network is also presented. MoVisPP is available at http://agbi.techfak.uni-bielefeld.de/movispp/.

  • Content Type Journal Article
  • Pages 1077-1097
  • DOI 10.1007/s11047-009-9151-y
  • Authors
    • Ming Chen, Bioinformatics Department, College of Life Sciences, Zhejiang University, Zijingang Campus, Hangzhou, 310058 China
    • Sridhar Hariharaputran, Bioinformatics Department, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany
    • Ralf Hofestädt, Bioinformatics Department, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany
    • Benjamin Kormeier, Bioinformatics Department, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany
    • Sarah Spangardt, Bioinformatics Department, Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany

Error-correcting Petri nets

Abstract  The paper introduces error-correcting Petri nets, an algebraic methodology for designing synthetic biologic systems with monitoring
capabilities. Linear error-correcting codes are used to extend the net’s structure in a way that …

Abstract  

The paper introduces error-correcting Petri nets, an algebraic methodology for designing synthetic biologic systems with monitoring
capabilities. Linear error-correcting codes are used to extend the net’s structure in a way that allows for the algebraic
detection and correction of non-reachable net markings. The presented methodology is based on modulo-p Hamming codes—which are optimal for the modulo-p correction of single errors—but also works with any other linear error-correcting code.

  • Content Type Journal Article
  • Pages 711-725
  • DOI 10.1007/s11047-009-9150-z
  • Authors
    • Anastasia Pagnoni, Dipartimento di Informatica e Comunicazione, Università degli Studi di Milano, Milano, Italy

Artificial Immune Systems: structure, function, diversity and an application to biclustering

Artificial Immune Systems: structure, function, diversity and an application to biclustering
Content Type Journal ArticleDOI 10.1007/s11047-009-9145-9Authors
Leandro N. de Castro, Mackenzie University Rua da Consolação 896, Consolação Sao Paulo …

Artificial Immune Systems: structure, function, diversity and an application to biclustering

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9145-9
  • Authors
    • Leandro N. de Castro, Mackenzie University Rua da Consolação 896, Consolação Sao Paulo SP 01302-907 Brazil
    • Jon Timmis, University of York Department of Computer Science Heslington York YO10 5DD UK
    • Helder Knidel, NatComp—From Nature to Business R. do Comércio, 44, Center Santos SP 11010-140 Brazil
    • Fernando Von Zuben, University of Campinas (Unicamp) Laboratory of Bioinformatics and Bio-inspired Computing (LBiC), School of Electrical and Computer Engineering (FEEC) P.O. Box 6101 13083-970 Campinas SP Brazil

A Petri net representation of Bayesian message flows: importance of Bayesian networks for biological applications

Abstract  This article combines Bayes’ theorem with flows of probabilities, flows of evidences (likelihoods), and fundamental concepts
for learning Bayesian networks as biological models from data. There is a huge amount of biological appl…

Abstract  

This article combines Bayes’ theorem with flows of probabilities, flows of evidences (likelihoods), and fundamental concepts
for learning Bayesian networks as biological models from data. There is a huge amount of biological applications of Bayesian
networks. For example in the fields of protein modeling, pathway modeling, gene expression analysis, DNA sequence analysis,
protein–protein interaction, or protein–DNA interaction. Usually, the Bayesian networks have to be learned (statistically
constructed) from array data. Then they are considered as an executable and analyzable model of the data source. To improve
that, this work introduces a Petri net representation for the propagation of probabilities and likelihoods in Bayesian networks.
The reason for doing so is to exploit the structural and dynamic properties of Petri nets for increasing the transparency
of propagation processes. Consequently the novel Petri nets are called “probability propagation nets”. By means of examples
it is shown that the understanding of the Bayesian propagation algorithm is improved. This is of particular importance for
an exact visualization of biological systems by Bayesian networks.

  • Content Type Journal Article
  • Pages 683-709
  • DOI 10.1007/s11047-009-9142-z
  • Authors
    • Kurt Lautenbach, Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070 Koblenz, Germany
    • Alexander Pinl, Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070 Koblenz, Germany

Stochastic Petri net models of Ca2+ signaling complexes and their analysis

Abstract  Mathematical models of Ca2+ release sites derived from Markov chain models of intracellular Ca2+ channels exhibit collective gating reminiscent of the experimentally observed phenomenon of stochastic Ca2+ excitability (i.e., puffs a…

Abstract  

Mathematical models of Ca2+ release sites derived from Markov chain models of intracellular Ca2+ channels exhibit collective gating reminiscent of the experimentally observed phenomenon of stochastic Ca2+ excitability (i.e., puffs and sparks). Ca2+ release site models are composed of a number of individual channel models whose dynamic behavior depends on the local Ca2+ concentration which is influenced by the state of all channels. We consider this application area to illustrate how stochastic
Petri nets and in particular stochastic activity networks can be used to model dynamical phenomena in cell biology. We highlight
how state-sharing composition operations as supported by the Möbius framework can represent both mean-field and spatial coupling
assumptions in a natural manner. We investigate how state-of-the-art techniques for the numerical and simulative analysis
of Markov chains associated with stochastic Petri nets scale when modeling Ca2+ signaling complexes of physiological size and complexity.

  • Content Type Journal Article
  • Pages 1045-1075
  • DOI 10.1007/s11047-009-9143-y
  • Authors
    • Ruth Lamprecht, Department of Computer Science, College of William and Mary, Williamsburg, VA 23187, USA
    • Gregory D. Smith, Department of Applied Sciences, College of William and Mary, Williamsburg, VA 23187, USA
    • Peter Kemper, Department of Computer Science, College of William and Mary, Williamsburg, VA 23187, USA

Structure versus function: a topological perspective on immune networks

Abstract  Many recent advances have been made in understanding the functional implications of the global topological properties of biological
networks through the application of complex network theory, particularly in the area of small-world…

Abstract  Many recent advances have been made in understanding the functional implications of the global topological properties of biological
networks through the application of complex network theory, particularly in the area of small-world and scale-free topologies.
Computational studies which attempt to understand the structure–function relationship usually proceed by defining a representation
of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the
networks that can arise—furthermore, we show that although simple topologies can be produced via representation and affinity
measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting
scale-free functionality. In this paper, we introduce the concept of the potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity
function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free
and regular topologies, finding that a key immunological property—tolerance—is promoted by bi-partite and heterogeneous topologies.
The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial
immune systems.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9138-8
  • Authors
    • Emma Hart, Edinburgh Napier University Edinburgh Scotland, UK
    • Hugues Bersini, IRIDIA, Universite de Bruxelles Bruxelles Belgium
    • Francisco Santos, IRIDIA, Universite de Bruxelles Bruxelles Belgium

A review of evolutionary and immune-inspired information filtering

Abstract  In recent years evolutionary and immune-inspired approaches have been applied to content-based and collaborative filtering.
These biologically inspired approaches are well suited to problems like profile adaptation in content-based…

Abstract  In recent years evolutionary and immune-inspired approaches have been applied to content-based and collaborative filtering.
These biologically inspired approaches are well suited to problems like profile adaptation in content-based filtering and
rating sparsity in collaborative filtering, due to their distributed and dynamic characteristics. In this paper we introduce
the relevant concepts and algorithms and review the state of the art in evolutionary and immune-inspired information filtering.
Our intention is to promote the interplay between information filtering and biologically inspired computing and boost developments
in this emerging interdisciplinary field.

  • Content Type Journal Article
  • DOI 10.1007/s11047-009-9126-z
  • Authors
    • Nikolaos Nanas, The Open University Computing Department Milton Keynes UK
    • Anne de Roeck, The Open University Computing Department Milton Keynes UK