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Mathematics
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Bioinspired Computation: Recent Advances in Theory and Applications
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Bioinspired Computation: Recent Advances in Theory and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 21005

Special Issue Editor

Prof. Dr. Victor Mitrana

E-Mail Website
Guest Editor
Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei 14, RO-010014 Bucharest, Romania
Interests: theory of computation; algorithms and data structures; combinatorics on words; natural language processing

Special Issue Information

Dear Colleagues,

Bioinspired computation is a relatively new area of science which heavily relies on biology, computer science, and mathematics. It is mainly focused on the design of high-performance computational models inspired by phenomena in biochemistry, biology, and genetics. These models inspired by the way in which nature “computes” are powerful tools to solve both hard theoretical problems as well as real-life problems.

Along the same lines, this issue is intended to be an interdisciplinary study that links biological data with techniques from information processing, algorithms, and statistics. The main goals are the development of efficient algorithms for measuring sequence similarity, for information retrieval from biological databases, and for extending experimental data by predictions.

Topics of either theoretical or applied interest include but are not limited to:

  • Bioinspired computational models based on “in vitro” or “in vivo” molecular biology techniques;
  • Theoretical properties of bio-operations and applications;
  • DNA, molecular, and membrane computing;
  • Modeling, designing, and analysis of synthetic self-assembled systems;
  • Systems biology;
  • Identifying gene structures in the genome and recognizing regulatory motifs;
  • Computational methods in medicine and nanotechnology;
  • Network controllability algorithms in biology and medicine;
  • Algorithms for genomic distances by rearrangements;
  • Exact and approximate sequence analysis.

Prof. Dr. Victor Mitrana
Guest Editor

Manuscript Submission Information

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Published Papers (7 papers)

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Research

21 pages, 517 KiB  
Article
Simulations between Three Types of Networks of Splicing Processors
by José Ramón Sánchez Couso, José Angel Sanchez Martín, Victor Mitrana and Mihaela Păun
Mathematics 2021, 9(13), 1511; https://doi.org/10.3390/math9131511 - 28 Jun 2021
Viewed by 1798
Abstract
Networks of splicing processors (NSP for short) embody a subcategory among the new computational models inspired by natural phenomena with theoretical potential to handle unsolvable problems efficiently. Current literature considers three variants in the context of networks managed by random-context filters. Despite the [...] Read more.
Networks of splicing processors (NSP for short) embody a subcategory among the new computational models inspired by natural phenomena with theoretical potential to handle unsolvable problems efficiently. Current literature considers three variants in the context of networks managed by random-context filters. Despite the divergences on system complexity and control degree over the filters, the three variants were proved to hold the same computational power through the simulations of two computationally complete systems: Turing machines and 2-tag systems. However, the conversion between the three models by means of a Turing machine is unattainable because of the huge computational costs incurred. This research paper addresses this issue with the proposal of direct and efficient simulations between the aforementioned paradigms. The information about the nodes and edges (i.e., splicing rules, random-context filters, and connections between nodes) composing any network of splicing processors belonging to one of the three categories is used to design equivalent networks working under the other two models. We demonstrate that these new networks are able to replicate any computational step performed by the original network in a constant number of computational steps and, consequently, we prove that any outcome achieved by the original architecture can be accomplished by the constructed architectures without worsening the time complexity. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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12 pages, 342 KiB  
Article
On the Languages Accepted by Watson-Crick Finite Automata with Delays
by José M. Sempere
Mathematics 2021, 9(8), 813; https://doi.org/10.3390/math9080813 - 9 Apr 2021
Viewed by 1670
Abstract
In this work, we analyze the computational power of Watson-Crick finite automata (WKFA) if some restrictions over the transition function in the model are imposed. We consider that the restrictions imposed refer to the maximum length difference between the two input strands which [...] Read more.
In this work, we analyze the computational power of Watson-Crick finite automata (WKFA) if some restrictions over the transition function in the model are imposed. We consider that the restrictions imposed refer to the maximum length difference between the two input strands which is called the delay. We prove that the language class accepted by WKFA with such restrictions is a proper subclass of the languages accepted by arbitrary WKFA in general. In addition, we initiate the study of the language classes characterized by WKFAs with bounded delays. We prove some of the results by means of various relationships between WKFA and sticker systems. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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20 pages, 7624 KiB  
Article
Mexican Axolotl Optimization: A Novel Bioinspired Heuristic
by Yenny Villuendas-Rey, José L. Velázquez-Rodríguez, Mariana Dayanara Alanis-Tamez, Marco-Antonio Moreno-Ibarra and Cornelio Yáñez-Márquez
Mathematics 2021, 9(7), 781; https://doi.org/10.3390/math9070781 - 3 Apr 2021
Cited by 29 | Viewed by 5610
Abstract
When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to [...] Read more.
When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to the great computational complexity of the problems. As an alternative to exact optimization, there are approximate optimization algorithms, whose purpose is to reduce computational complexity by pruning some areas of the problem search space. To achieve this, researchers have been inspired by nature, because animals and plants tend to optimize many of their life processes. The purpose of this research is to design a novel bioinspired algorithm for numeric optimization: the Mexican Axolotl Optimization algorithm. The effectiveness of our proposal was compared against nine optimization algorithms (artificial bee colony, cuckoo search, dragonfly algorithm, differential evolution, firefly algorithm, fitness dependent optimizer, whale optimization algorithm, monarch butterfly optimization, and slime mould algorithm) when applied over four sets of benchmark functions (unimodal, multimodal, composite and competition functions). The statistical analysis shows the ability of Mexican Axolotl Optimization algorithm of obtained very good optimization results in all experiments, except for composite functions, where the Mexican Axolotl Optimization algorithm exhibits an average performance. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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17 pages, 2657 KiB  
Article
DNA-Guided Assembly for Fibril Proteins
by Alexandru Amărioarei, Frankie Spencer, Gefry Barad, Ana-Maria Gheorghe, Corina Iţcuş, Iris Tuşa, Ana-Maria Prelipcean, Andrei Păun, Mihaela Păun, Alfonso Rodriguez-Paton, Romică Trandafir and Eugen Czeizler
Mathematics 2021, 9(4), 404; https://doi.org/10.3390/math9040404 - 19 Feb 2021
Cited by 2 | Viewed by 2764
Abstract
Current advances in computational modelling and simulation have led to the inclusion of computer scientists as partners in the process of engineering of new nanomaterials and nanodevices. This trend is now, more than ever, visible in the field of deoxyribonucleic acid (DNA)-based nanotechnology, [...] Read more.
Current advances in computational modelling and simulation have led to the inclusion of computer scientists as partners in the process of engineering of new nanomaterials and nanodevices. This trend is now, more than ever, visible in the field of deoxyribonucleic acid (DNA)-based nanotechnology, as DNA’s intrinsic principle of self-assembly has been proven to be highly algorithmic and programmable. As a raw material, DNA is a rather unremarkable fabric. However, as a way to achieve patterns, dynamic behavior, or nano-shape reconstruction, DNA has been proven to be one of the most functional nanomaterials. It would thus be of great potential to pair up DNA’s highly functional assembly characteristics with the mechanic properties of other well-known bio-nanomaterials, such as graphene, cellulos, or fibroin. In the current study, we perform projections regarding the structural properties of a fibril mesh (or filter) for which assembly would be guided by the controlled aggregation of DNA scaffold subunits. The formation of such a 2D fibril mesh structure is ensured by the mechanistic assembly properties borrowed from the DNA assembly apparatus. For generating inexpensive pre-experimental assessments regarding the efficiency of various assembly strategies, we introduced in this study a computational model for the simulation of fibril mesh assembly dynamical systems. Our approach was based on providing solutions towards two main circumstances. First, we created a functional computational model that is restrictive enough to be able to numerically simulate the controlled aggregation of up to 1000s of elementary fibril elements yet rich enough to provide actionable insides on the structural characteristics for the generated assembly. Second, we used the provided numerical model in order to generate projections regarding effective ways of manipulating one of the the key structural properties of such generated filters, namely the average size of the openings (gaps) within these meshes, also known as the filter’s aperture. This work is a continuation of Amarioarei et al., 2018, where a preliminary version of this research was discussed. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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28 pages, 4386 KiB  
Article
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
by Ángel González-Prieto, Alberto Mozo, Edgar Talavera and Sandra Gómez-Canaval
Mathematics 2021, 9(4), 325; https://doi.org/10.3390/math9040325 - 6 Feb 2021
Cited by 5 | Viewed by 2400
Abstract
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several [...] Read more.
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several accessory heuristics to the networks to reach acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of generative adversarial networks. For this purpose, we propose to decompose the objective function of the adversary min–max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous alternating gradient descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of GAN. This approach is confirmed empirically by studying the training flow in a 2-parametric GAN, aiming to generate an unknown exponential distribution. As a by-product, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GANs. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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21 pages, 653 KiB  
Article
Networks of Picture Processors with Filtering Based on Evaluation Sets as Solvers for Cryptographic Puzzles Based on Random Multivariate Quadratic Equations
by Karina Paola Jiménez, Sandra Gómez-Canaval, Ricardo Villanueva-Polanco and Silvia Martín Suazo
Mathematics 2020, 8(12), 2160; https://doi.org/10.3390/math8122160 - 4 Dec 2020
Viewed by 1588
Abstract
Networks of picture processors is a massively distributed and parallel computational model inspired by the evolutionary cellular processes, which offers efficient solutions for NP-complete problems. This bio-inspired model computes two-dimensional strings (pictures) using simple rewriting rules (evolutionary operations). The functioning of this model [...] Read more.
Networks of picture processors is a massively distributed and parallel computational model inspired by the evolutionary cellular processes, which offers efficient solutions for NP-complete problems. This bio-inspired model computes two-dimensional strings (pictures) using simple rewriting rules (evolutionary operations). The functioning of this model mimics a community of cells (pictures) that are evolving according to these bio-operations via a selection process that filters valid surviving cells. In this paper, we propose an extension of this model that empowers it with a flexible method that selects the processed pictures based on a quantitative evaluation of its content. In order to show the versatility of this extension, we introduce a solver for a cryptographic proof-of-work based on the hardness of finding a solution to a set of random quadratic equations over the finite field F2. This problem is demonstrated to be NP-hard, even with quadratic polynomials over the field F2, when the number of equations and the number of variables are of roughly the same size. The proposed solution runs in O(n2) computational steps for any size (n,m) of the input pictures. In this context, this paper opens up a wide field of research that looks for theoretical and practical solutions of cryptographic problems via software/hardware implementations based on bio-inspired computational models. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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16 pages, 868 KiB  
Article
Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals
by Samed Jukic, Muzafer Saracevic, Abdulhamit Subasi and Jasmin Kevric
Mathematics 2020, 8(9), 1481; https://doi.org/10.3390/math8091481 - 2 Sep 2020
Cited by 42 | Viewed by 4071
Abstract
This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm [...] Read more.
This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy. Full article
(This article belongs to the Special Issue Bioinspired Computation: Recent Advances in Theory and Applications)
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