Special Issue "Biological Networks"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 August 2016).

Special Issue Editors

Dr. Tatsuya Akutsu
E-Mail Website
Guest Editor
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
Interests: computational biology; string and tree algorithms; complex networks
Special Issues and Collections in MDPI journals
Dr. Takeyuki Tamura
E-Mail Website
Guest Editor
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji Kyoto 611-0011, Japan
Interests: algorithms and mathematical modeling for bioinformatics

Special Issue Information

Dear Colleagues,

In bioinformatics, the behaviors of cells are often mathematically modeled by many kinds of biological networks, including gene regulatory networks, metabolic networks, protein-protein interaction networks, signal networks, transcription networks, phylogenetic networks, etc.

The main themes of this Special Issue (though not an exhaustive list) are algorithms for biological networks, which include discrete algorithms, statistical algorithms, heuristic algorithms, probabilistic algorithms, randomized algorithms, and machine-learning methods to solve problems on biological networks that are computationally efficient. We accept both review papers and research papers.

Dr. Tatsuya Akutsu,
Dr. Takeyuki Tamura
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Protein–protein interaction networks
  • Genetic networks
  • Metabolic networks
  • Boolean networks
  • Bayesian networks
  • Petri nets
  • Discrete algorithms
  • Machine learning methods

Published Papers (5 papers)

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Research

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Open AccessArticle
Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data
Algorithms 2017, 10(1), 8; https://doi.org/10.3390/a10010008 - 09 Jan 2017
Cited by 3
Abstract
Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our [...] Read more.
Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems’ dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method. Full article
(This article belongs to the Special Issue Biological Networks)
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Open AccessArticle
Dependent Shrink of Transitions for Calculating Firing Frequencies in Signaling Pathway Petri Net Model
Algorithms 2017, 10(1), 4; https://doi.org/10.3390/a10010004 - 31 Dec 2016
Cited by 1
Abstract
Despite the recent rapid progress in high throughput measurements of biological data, it is still difficult to gather all of the reaction speed data in biological pathways. This paper presents a Petri net-based algorithm that can derive estimated values for non-valid reaction speeds [...] Read more.
Despite the recent rapid progress in high throughput measurements of biological data, it is still difficult to gather all of the reaction speed data in biological pathways. This paper presents a Petri net-based algorithm that can derive estimated values for non-valid reaction speeds in a signaling pathway from biologically-valid data. In fact, these reaction speeds are reflected based on the delay times in the timed Petri net model of the signaling pathway. We introduce the concept of a “dependency relation” over a transition set of a Petri net and derive the properties of the dependency relation through a structural analysis. Based on the theoretical results, the proposed algorithm can efficiently shrink the transitions with two elementary structures into a single transition repeatedly to reduce the Petri net size in order to eventually discover all transition sets with a dependency relation. Finally, to show the usefulness of our algorithm, we apply our algorithm to the IL-3 Petri net model. Full article
(This article belongs to the Special Issue Biological Networks)
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Open AccessArticle
Community Structure Detection for Directed Networks through Modularity Optimisation
Algorithms 2016, 9(4), 73; https://doi.org/10.3390/a9040073 - 01 Nov 2016
Cited by 4
Abstract
Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is [...] Read more.
Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is much higher than across different subgraphs. Each of these subgraphs forms a community (or module). In literature, a metric called modularity is defined that measures the quality of a partition of nodes into different mutually exclusive communities. One means of deriving community structure is modularity maximisation. In this paper, a novel mathematical programming-based model, DiMod, is proposed that tackles the problem of maximising modularity for directed networks. Full article
(This article belongs to the Special Issue Biological Networks)
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Review

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Open AccessReview
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Algorithms 2017, 10(1), 31; https://doi.org/10.3390/a10010031 - 22 Feb 2017
Cited by 4
Abstract
Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, [...] Read more.
Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained. Full article
(This article belongs to the Special Issue Biological Networks)
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Open AccessReview
Algorithms for Drug Sensitivity Prediction
Algorithms 2016, 9(4), 77; https://doi.org/10.3390/a9040077 - 17 Nov 2016
Cited by 8
Abstract
Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed [...] Read more.
Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice. Full article
(This article belongs to the Special Issue Biological Networks)
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