The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform

David Gilbert - One of the best experts on this subject based on the ideXlab platform.

  • coloured petri nets for multilevel multiscale and multidimensional Modelling of Biological Systems
    Briefings in Bioinformatics, 2019
    Co-Authors: Monika Heiner, David Gilbert
    Abstract:

    : Owing to the availability of data of one Biological phenomenon at different levels/scales, Modelling of Biological Systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional Modelling of some Biological Systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional Modelling of Biological Systems. This understanding of how to use ColPNs for Modelling Biological Systems will assist readers in selecting appropriate ColPN classes for specific Modelling circumstances.

  • A machine learning approach for generating temporal logic classifications of complex model behaviours
    Proceedings of the 2012 Winter Simulation Conference (WSC), 2012
    Co-Authors: Daniele Maccagnola, Enza Messina, David Gilbert
    Abstract:

    Systems biology aims to facilitate the understanding of complex interactions between components in Biological Systems. Petri nets (PN), and in particular Coloured Petri Nets (CPN) have been demonstrated to be a suitable formalism for Modelling Biological Systems and building computational models over multiple spatial and temporal scales. To explore the complex and high-dimensional solution space over the behaviours generated by such models, we propose a clustering methodology which combines principal component analysis (PCA), distance similarity and density factors through the application of DBScan. To facilitate the interpretation of clustering results and enable further analysis using model checking we apply a pattern mining approach aimed at generating high-level classificatory descriptions of the clusters' behaviour in temporal logic. We illustrate the power of our approach through the analysis of two case studies: multiple knockdown of the Mitogen-activated protein-kinase (MAPK) pathway, and selective knockout of Planar Cell Polarity (PCP) signalling in Drosophila wing.

Qixia Yuan - One of the best experts on this subject based on the ideXlab platform.

  • A new decomposition-based method for detecting attractors in synchronous Boolean networks
    Science of Computer Programming, 2019
    Co-Authors: Qixia Yuan, Andrzej Mizera, Jun Pang, Hongyang Qu
    Abstract:

    Abstract Boolean networks are a well-established formalism for Modelling Biological Systems. An important aspect of analysing a Boolean network is to identify all its attractors. This becomes challenging for large Boolean networks due to the infamous state-space explosion problem. In this paper, we propose a new strongly connected component (SCC) based decomposition method for attractor detection in large synchronous Boolean networks and prove its correctness. Experimental results show that our proposed method is significantly better in terms of performance when compared to existing methods in the literature.

  • Taming Asynchrony for Attractor Detection in Large Boolean Networks
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2019
    Co-Authors: Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
    Abstract:

    Boolean networks is a well-established formalism for Modelling Biological Systems. A vital challenge for analyzing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life Biological networks.

  • Reviving the Two-State Markov Chain Approach
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2018
    Co-Authors: Andrzej Mizera, Jun Pang, Qixia Yuan
    Abstract:

    Probabilistic Boolean networks (PBNs) is a well-established computational framework for Modelling Biological Systems. The steady-state dynamics of PBNs is of crucial importance in the study of such Systems. However, for large PBNs, which often arise in Systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall. Second, we conduct an extensive experimental comparison of the extended two-state Markov chain approach and another approach based on the Skart method. We analyze the results with machine learning techniques and we show that statistically the two-state Markov chain approach has a better performance. Finally, we demonstrate the potential of the extended two-state Markov chain approach on a case study of a large PBN model of apoptosis in hepatocytes.

  • SETTA - A New Decomposition Method for Attractor Detection in Large Synchronous Boolean Networks
    Dependable Software Engineering. Theories Tools and Applications, 2017
    Co-Authors: Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
    Abstract:

    Boolean networks is a well-established formalism for Modelling Biological Systems. An important challenge for analysing a Boolean network is to identify all its attractors. This becomes challenging for large Boolean networks due to the well-known state-space explosion problem. In this paper, we propose a new SCC-based decomposition method for attractor detection in large synchronous Boolean networks. Experimental results show that our proposed method is significantly better in terms of performance when compared to existing methods in the literature.

  • Taming Asynchrony for Attractor Detection in Large Boolean Networks (Technical Report).
    arXiv: Molecular Networks, 2017
    Co-Authors: Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
    Abstract:

    Boolean networks is a well-established formalism for Modelling Biological Systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life Biological networks.

Shiji Song - One of the best experts on this subject based on the ideXlab platform.

  • Application of lρ norm regularization methods for Modelling Biological Systems
    2009 International Conference on Machine Learning and Cybernetics, 2009
    Co-Authors: Kang Li, P. Gormley, Shiji Song
    Abstract:

    In Systems biology, molecular interactions are typically modelled using white-box differential equations based on mass action kinetics. Unfortunately, problems with dimensionality can arise when the number of molecular species in the system becomes very large, which make the transparent Modelling and behavior simulation extremely difficult or computationally too expensive. As an alternative, data-driven identification of molecular interaction pathways using a black-box approach has recently been investigated. One of the main objectives in building black-box models, which in many cases are linear-in-the-parameters ones, is to produce a sparse model to effectively represent the system behavior. A popular approach is to select model terms one by one from a pool of candidates (basis functions), and an information criterion is then used to stop the selection process. The advantage is the computational efficiency, the disadvantage is that the derived model is not necessarily sparse. Alternative approach is to introduce into the normal loss function a penalty term on the parameters, leading to improved sparseness and generalization performance of the derived model. Moreover, there is a positive probability that the model structure can be accurately picked up among a wide range of possibilities. Generally speaking, there are three lrho norm regularization methods, including the Lasso (rho = 1), Ridge (rho = 2) and Bridge (0 < rho < 1). In particular, Lasso has been introduced into computational biology in recent years. This paper investigates the effectiveness of the three (lrho) regularization methods on the model identification of the MAPK signal transduction pathway, and simulation results are compared and analyzed.

  • application of l ρ norm regularization methods for Modelling Biological Systems
    International Conference on Machine Learning and Cybernetics, 2009
    Co-Authors: Kang Li, P. Gormley, Shiji Song
    Abstract:

    In Systems biology, molecular interactions are typically modelled using white-box differential equations based on mass action kinetics. Unfortunately, problems with dimensionality can arise when the number of molecular species in the system becomes very large, which make the transparent Modelling and behavior simulation extremely difficult or computationally too expensive. As an alternative, data-driven identification of molecular interaction pathways using a black-box approach has recently been investigated. One of the main objectives in building black-box models, which in many cases are linear-in-the-parameters ones, is to produce a sparse model to effectively represent the system behavior. A popular approach is to select model terms one by one from a pool of candidates (basis functions), and an information criterion is then used to stop the selection process. The advantage is the computational efficiency, the disadvantage is that the derived model is not necessarily sparse. Alternative approach is to introduce into the normal loss function a penalty term on the parameters, leading to improved sparseness and generalization performance of the derived model. Moreover, there is a positive probability that the model structure can be accurately picked up among a wide range of possibilities. Generally speaking, there are three l ρ norm regularization methods, including the Lasso (ρ = 1), Ridge (ρ = 2) and Bridge (0 ≪ ρ ≪ 1). In particular, Lasso has been introduced into computational biology in recent years. This paper investigates the effectiveness of the three (l ρ ) regularization methods on the model identification of the MAPK signal transduction pathway, and simulation results are compared and analyzed.

  • Application of lρ norm regularization methods for Modelling Biological Systems
    Machine Learning and Cybernetics, 2009 International Conference on, 2009
    Co-Authors: Kang Li, P. Gormley, Shiji Song
    Abstract:

    In Systems biology, molecular interactions are typically modelled using white-box differential equations based on mass action kinetics. Unfortunately, problems with dimensionality can arise when the number of molecular species in the system becomes very large, which make the transparent Modelling and behavior simulation extremely difficult or computationally too expensive. As an alternative, data-driven identification of molecular interaction pathways using a black-box approach has recently been investigated. One of the main objectives in building black-box models, which in many cases are linear-in-the-parameters ones, is to produce a sparse model to effectively represent the system behavior. A popular approach is to select model terms one by one from a pool of candidates (basis functions), and an information criterion is then used to stop the selection process. The advantage is the computational efficiency, the disadvantage is that the derived model is not necessarily sparse. Alternative approach is to introduce into the normal loss function a penalty term on the parameters, leading to improved sparseness and generalization performance of the derived model. Moreover, there is a positive probability that the model structure can be accurately picked up among a wide range of possibilities. Generally speaking, there are three lrho norm regularization methods, including the Lasso (rho = 1), Ridge (rho = 2) and Bridge (0 < rho < 1). In particular, Lasso has been introduced into computational biology in recent years. This paper investigates the effectiveness of the three (lrho) regularization methods on the model identification of the MAPK signal transduction pathway, and simulation results are compared and analyzed.

Andrzej Mizera - One of the best experts on this subject based on the ideXlab platform.

  • A new decomposition-based method for detecting attractors in synchronous Boolean networks
    Science of Computer Programming, 2019
    Co-Authors: Qixia Yuan, Andrzej Mizera, Jun Pang, Hongyang Qu
    Abstract:

    Abstract Boolean networks are a well-established formalism for Modelling Biological Systems. An important aspect of analysing a Boolean network is to identify all its attractors. This becomes challenging for large Boolean networks due to the infamous state-space explosion problem. In this paper, we propose a new strongly connected component (SCC) based decomposition method for attractor detection in large synchronous Boolean networks and prove its correctness. Experimental results show that our proposed method is significantly better in terms of performance when compared to existing methods in the literature.

  • Taming Asynchrony for Attractor Detection in Large Boolean Networks
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2019
    Co-Authors: Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
    Abstract:

    Boolean networks is a well-established formalism for Modelling Biological Systems. A vital challenge for analyzing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life Biological networks.

  • Reviving the Two-State Markov Chain Approach
    IEEE ACM Transactions on Computational Biology and Bioinformatics, 2018
    Co-Authors: Andrzej Mizera, Jun Pang, Qixia Yuan
    Abstract:

    Probabilistic Boolean networks (PBNs) is a well-established computational framework for Modelling Biological Systems. The steady-state dynamics of PBNs is of crucial importance in the study of such Systems. However, for large PBNs, which often arise in Systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall. Second, we conduct an extensive experimental comparison of the extended two-state Markov chain approach and another approach based on the Skart method. We analyze the results with machine learning techniques and we show that statistically the two-state Markov chain approach has a better performance. Finally, we demonstrate the potential of the extended two-state Markov chain approach on a case study of a large PBN model of apoptosis in hepatocytes.

  • SETTA - A New Decomposition Method for Attractor Detection in Large Synchronous Boolean Networks
    Dependable Software Engineering. Theories Tools and Applications, 2017
    Co-Authors: Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
    Abstract:

    Boolean networks is a well-established formalism for Modelling Biological Systems. An important challenge for analysing a Boolean network is to identify all its attractors. This becomes challenging for large Boolean networks due to the well-known state-space explosion problem. In this paper, we propose a new SCC-based decomposition method for attractor detection in large synchronous Boolean networks. Experimental results show that our proposed method is significantly better in terms of performance when compared to existing methods in the literature.

  • Taming Asynchrony for Attractor Detection in Large Boolean Networks (Technical Report).
    arXiv: Molecular Networks, 2017
    Co-Authors: Andrzej Mizera, Jun Pang, Hongyang Qu, Qixia Yuan
    Abstract:

    Boolean networks is a well-established formalism for Modelling Biological Systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life Biological networks.

Gilles Bernot - One of the best experts on this subject based on the ideXlab platform.

  • Validation of Biological models with Temporal Logic and Timed Hybrid Petri Nets
    2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
    Co-Authors: Sylvie Troncale, Jean-paul Comet, Gilles Bernot
    Abstract:

    The Hybrid Functional Petri Nets (HFPN) formalism has shown its convenience for Modelling Biological Systems. This class of models has been fruitfully applied in biology but the remarkable expressiveness of HFPN often leads to incomplete validations. In this paper, we propose a logical framework for Timed Hybrid Petri Nets (THPN), a sub-class of HFPN. We propose an extension of Event Clock Logic dedicated to THPN and a procedure to convert a THPN into a real-time automaton. A small Biological model shows that our framework allows us to formally prove properties by a well suited model- checking procedure.