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A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (1 January 2024) | Viewed by 35561

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Dr. Milan Toma

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Guest Editor

Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury Campus, Northern Boulevard, Old Westbury, NY 11568-8000, USA
Interests: image processing; high-performance computing; computational mechanics/biomechanics; biomechanical/biomedical engineering
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Dr. Chi Wei Ong

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Guest Editor

Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Interests: cardiovascular mechanics; fluid structure interaction; computational fluid dynamics; finite element analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational methods play an important role in biology research. With the increasing use of computation in biology, the field of computational biology has grown rapidly in the past decade. Computational methods are used to analyze and interpret data from experiments and simulations. There are a variety of computational methods that can be used to analyze data. The three most common methods of analyzing data are statistical methods, machine learning methods, and mathematical modeling. Data generated by these methods can be analyzed to draw conclusions about a biological system. There are several factors that affect the success of computational methods in a biological system. These include the quality of the experimental data used, the complexity of the model being used, and the algorithm used to analyze the data. The types of data that can be analyzed by computational methods include biochemical data, gene expression data, genomic data, and structural data. Different types of computational methods can be used to analyze different types of data. Statistical methods are used to analyze data that is relatively easy to interpret and are often used to analyze data that is produced by measurements. Machine learning methods are used to analyze data that is produced by simulations. Mathematical modeling is used to analyze data that is produced by mathematical descriptions of the system being studied. In this special issue, we will examine the latest developments in computational methods that are being used to investigate biological systems.

Dr. Milan Toma
Dr. Chi Wei Ong
Guest Editors

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Keywords

biology
computational
statistics
machine learning
numerical
modeling

Published Papers (12 papers)

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Research

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20 pages, 3510 KiB

Open AccessArticle

Protein–Protein Interaction Network Extraction Using Text Mining Methods Adds Insight into Autism Spectrum Disorder

by Leena Nezamuldeen and Mohsin Saleet Jafri

Biology 2023, 12(10), 1344; https://doi.org/10.3390/biology12101344 - 18 Oct 2023

Viewed by 1534

Abstract

Text mining methods are being developed to assimilate the volume of biomedical textual materials that are continually expanding. Understanding protein–protein interaction (PPI) deficits would assist in explaining the genesis of diseases. In this study, we designed an automated system to extract PPIs from the biomedical literature that uses a deep learning sentence classification model, a pretrained word embedding, and a BiLSTM recurrent neural network with additional layers, a conditional random field (CRF) named entity recognition (NER) model, and shortest-dependency path (SDP) model using the SpaCy library in Python. The automated system ensures that it targets sentences that contain PPIs and not just these proteins mentioned in the framework of disease discovery or other context. Our first model achieved 13% greater precision on the Aimed/BioInfr benchmark corpus than the previous state-of-the-art BiLSTM neural network models. The NER model presented in this study achieved 98% precision on the Aimed/BioInfr corpus over previous models. In order to facilitate the production of an accurate representation of the PPI network, the processes were developed to systematically map the protein interactions in the texts. Overall, evaluating our system through the use of 6027 abstracts pertaining to seven proteins associated with Autism Spectrum Disorder completed the manually curated PPI network for these proteins. When it comes to complicated diseases, these networks would assist in understanding how PPI deficits contribute to disease development while also emphasizing the influence of interactions on protein function and biological processes. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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19 pages, 2422 KiB

Open AccessArticle

Bioinformatic Analysis Reveals the Role of Translation Elongation Efficiency Optimisation in the Evolution of Ralstonia Genus

by Aleksandra Y. Korenskaia, Yury G. Matushkin, Zakhar S. Mustafin, Sergey A. Lashin and Alexandra I. Klimenko

Biology 2023, 12(10), 1338; https://doi.org/10.3390/biology12101338 - 16 Oct 2023

Viewed by 998

Abstract

Translation efficiency modulates gene expression in prokaryotes. The comparative analysis of translation elongation efficiency characteristics of Ralstonia genus bacteria genomes revealed that these characteristics diverge in accordance with the phylogeny of Ralstonia. The first branch of this genus is a group of bacteria commonly found in moist environments such as soil and water that includes the species R. mannitolilytica, R. insidiosa, and R. pickettii, which are also described as nosocomial infection pathogens. In contrast, the second branch is plant pathogenic bacteria consisting of R. solanacearum, R. pseudosolanacearum, and R. syzygii. We found that the soil Ralstonia have a significantly lower number and energy of potential secondary structures in mRNA and an increased role of codon usage bias in the optimization of highly expressed genes’ translation elongation efficiency, not only compared to phytopathogenic Ralstonia but also to Cupriavidus necator, which is closely related to the Ralstonia genus. The observed alterations in translation elongation efficiency of orthologous genes are also reflected in the difference of potentially highly expressed gene’ sets’ content among Ralstonia branches with different lifestyles. Analysis of translation elongation efficiency characteristics can be considered a promising approach for studying complex mechanisms that determine the evolution and adaptation of bacteria in various environments. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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23 pages, 4315 KiB

Open AccessArticle

MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning

by Soyul Han, Woongsun Jeon, Wuming Gong and Il-Youp Kwak

Biology 2023, 12(10), 1291; https://doi.org/10.3390/biology12101291 - 27 Sep 2023

Cited by 1 | Viewed by 1517

Abstract

In this study, we constructed a model to predict abnormal cardiac sounds using a diverse set of auscultation data collected from various auscultation positions. Abnormal heart sounds were identified by extracting features such as peak intervals and noise characteristics during systole and diastole. Instead of using raw signal data, we transformed them into log-mel 2D spectrograms, which were employed as input variables for the CNN model. The advancement of our model involves integrating a deep learning architecture with feature extraction techniques based on existing knowledge of cardiac data. Specifically, we propose a multi-channel-based heart signal processing (MCHeart) scheme, which incorporates our proposed features into the deep learning model. Additionally, we introduce the ReLCNN model by applying residual blocks and MHA mechanisms to the LCNN architecture. By adding murmur features with a smoothing function and training the ReLCNN model, the weighted accuracy of the model increased from 79.6% to 83.6%, showing a performance improvement of approximately 4% point compared to the LCNN baseline model. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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14 pages, 9078 KiB

Open AccessArticle

Computationally Modelling Cholesterol Metabolism and Atherosclerosis

by Callum Davies, Amy E. Morgan and Mark T. Mc Auley

Biology 2023, 12(8), 1133; https://doi.org/10.3390/biology12081133 - 14 Aug 2023

Viewed by 1270

Abstract

Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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22 pages, 9037 KiB

Open AccessArticle

Molecular Modeling Insights into Metal-Organic Frameworks (MOFs) as a Potential Matrix for Immobilization of Lipase: An In Silico Study

by Prasanna J. Patil, Subodh A. Kamble, Maruti J. Dhanavade, Xin Liang, Chengnan Zhang and Xiuting Li

Biology 2023, 12(8), 1051; https://doi.org/10.3390/biology12081051 - 26 Jul 2023

Cited by 4 | Viewed by 1415

Abstract

CRL is a highly versatile enzyme that finds extensive utility in numerous industries, which is attributed to its selectivity and catalytic efficiency, which have been impeded by the impracticality of its implementation, leading to a loss of native catalytic activity and non-reusability. Enzyme immobilization is a necessary step for enabling its reuse, and it provides methods for regulating the biocatalyst’s functional efficacy in a synthetic setting. MOFs represent a novel category of porous materials possessing distinct superlative features that make MOFs an optimal host matrix for developing enzyme-MOF composites. In this study, we employed molecular modeling approaches, for instance, molecular docking and MD simulation, to explore the interactions between CRL and a specific MOF, ZIF-8. The present study involved conducting secondary structural analysis and homology modeling of CRL, followed by docking ZIF-8 with CRL. The results of the molecular docking analysis indicate that ZIF-8 was situated within the active site pocket of CRL, where it formed hydrogen bonds with Val-81, Phe-87, Ser-91, Asp-231, Thr-132, Lue-297, Phe-296, Phe-344, Thr-347, and Ser-450. The MD simulation analysis revealed that the CRL and ZIF-8 docked complex exhibited stability over the entire simulation period, and all interactions presented in the initial docked complex were maintained throughout the simulation. The findings derived from this investigation could promote comprehension of the molecular mechanisms underlying the interaction between CRL and ZIF-8 as well as the development of immobilized CRL for diverse industrial purposes. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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20 pages, 3820 KiB

Open AccessArticle

Understanding Snail Mucus Biosynthesis and Shell Biomineralisation through Genomic Data Mining of the Reconstructed Carbohydrate and Glycan Metabolic Pathways of the Giant African Snail (Achatina fulica)

by Pornpavee Nualnisachol, Pramote Chumnanpuen and Teerasak E-kobon

Biology 2023, 12(6), 836; https://doi.org/10.3390/biology12060836 - 9 Jun 2023

Cited by 2 | Viewed by 2831

Abstract

The giant African snail (Order Stylommatophora: Family Achatinidae), Achatina fulica (Bowdich, 1822), is the most significant and invasive land snail pest. The ecological adaptability of this snail involves high growth rate, reproductive capacity, and shell and mucus production, driven by several biochemical processes and metabolism. The available genomic information for A. fulica provides excellent opportunities to hinder the underlying processes of adaptation, mainly carbohydrate and glycan metabolic pathways toward the shell and mucus formation. The authors analysed the 1.78 Gb draft genomic contigs of A. fulica to identify enzyme-coding genes and reconstruct biochemical pathways related to the carbohydrate and glycan metabolism using a designed bioinformatic workflow. Three hundred and seventy-seven enzymes involved in the carbohydrate and glycan metabolic pathways were identified based on the KEGG pathway reference in combination with protein sequence comparison, structural analysis, and manual curation. Fourteen complete pathways of carbohydrate metabolism and seven complete pathways of glycan metabolism supported the nutrient acquisition and production of the mucus proteoglycans. Increased copy numbers of amylases, cellulases, and chitinases highlighted the snail advantage in food consumption and fast growth rate. The ascorbate biosynthesis pathway identified from the carbohydrate metabolic pathways of A. fulica was involved in the shell biomineralisation process in association with the collagen protein network, carbonic anhydrases, tyrosinases, and several ion transporters. Thus, our bioinformatic workflow was able to reconstruct carbohydrate metabolism, mucus biosynthesis, and shell biomineralisation pathways from the A. fulica genome and transcriptome data. These findings could reveal several evolutionary advantages of the A. fulica snail, and will benefit the discovery of valuable enzymes for industrial and medical applications. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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Review

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34 pages, 1412 KiB

Open AccessReview

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

by Danuta Cembrowska-Lech, Adrianna Krzemińska, Tymoteusz Miller, Anna Nowakowska, Cezary Adamski, Martyna Radaczyńska, Grzegorz Mikiciuk and Małgorzata Mikiciuk

Biology 2023, 12(10), 1298; https://doi.org/10.3390/biology12101298 - 30 Sep 2023

Cited by 4 | Viewed by 3065

Abstract

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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14 pages, 2198 KiB

Open AccessReview

Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

by Honoka Aida and Bei-Wen Ying

Biology 2023, 12(9), 1170; https://doi.org/10.3390/biology12091170 - 25 Aug 2023

Cited by 2 | Viewed by 1512

Abstract

Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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29 pages, 1590 KiB

Open AccessReview

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

by Sanghyuk Roy Choi and Minhyeok Lee

Biology 2023, 12(7), 1033; https://doi.org/10.3390/biology12071033 - 22 Jul 2023

Cited by 10 | Viewed by 8146

Abstract

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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23 pages, 847 KiB

Open AccessReview

Modeling Dynamics of the Cardiovascular System Using Fluid-Structure Interaction Methods

by Faiz Syed, Sahar Khan and Milan Toma

Biology 2023, 12(7), 1026; https://doi.org/10.3390/biology12071026 - 21 Jul 2023

Cited by 3 | Viewed by 9340

Abstract

Using fluid-structure interaction algorithms to simulate the human circulatory system is an innovative approach that can provide valuable insights into cardiovascular dynamics. Fluid-structure interaction algorithms enable us to couple simulations of blood flow and mechanical responses of the blood vessels while taking into account interactions between fluid dynamics and structural behaviors of vessel walls, heart walls, or valves. In the context of the human circulatory system, these algorithms offer a more comprehensive representation by considering the complex interplay between blood flow and the elasticity of blood vessels. Algorithms that simulate fluid flow dynamics and the resulting forces exerted on vessel walls can capture phenomena such as wall deformation, arterial compliance, and the propagation of pressure waves throughout the cardiovascular system. These models enhance the understanding of vasculature properties in human anatomy. The utilization of fluid-structure interaction methods in combination with medical imaging can generate patient-specific models for individual patients to facilitate the process of devising treatment plans. This review evaluates current applications and implications of fluid-structure interaction algorithms with respect to the vasculature, while considering their potential role as a guidance tool for intervention procedures. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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Other

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11 pages, 2310 KiB

Open AccessTechnical Note

The Significance of Cross-Sectional Shape Accuracy and Non-Linear Elasticity on the Numerical Modelling of Cerebral Veins under Tensile Loading

by Fábio A. O. Fernandes and Clara I. C. Silveira

Biology 2024, 13(1), 16; https://doi.org/10.3390/biology13010016 - 27 Dec 2023

Viewed by 1013

Abstract

Traumatic brain injury (TBI) is a serious global health issue, leading to serious disabilities. One type of TBI is acute subdural haematoma (ASDH), which occurs when a bridging vein ruptures. Many numerical models of these structures, mainly based on the finite element method, have been developed. However, most rely on linear elasticity (without validation) and others on simplifications at the geometrical level. An example of the latter is the assumption of a regular cylinder with a constant radius, or the geometry of the vein acquired from medical images. Unfortunately, these do not replicate the real conditions of a mechanical tensile test. In this work, the main goal is to evaluate the influence of the vein’s geometry in its mechanical behaviour under tensile loading, simulating the real conditions of experimental tests. The second goal is to implement a hyperelastic model of the bridging veins where it would be possible to observe its non-linear elastic behaviour. The results of the developed finite element models were compared to experimental data available in the literature and other models. It was possible to conclude that the geometry of the vein structure influences the tensile stress–strain curve, which means that flattened specimens should be modelled when validating constitutive models for bridging veins. Additionally, the implementation of hyperelastic material models has been verified, highlighting the potential application of the Marlow and reduced polynomial (of fourth and sixth orders) constitutive models. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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7 pages, 613 KiB

Open AccessBrief Report

Benchmarking State-of-the-Art Approaches for Norovirus Genome Assembly in Metagenome Sample

by Dmitry Meleshko and Anton Korobeynikov

Biology 2023, 12(8), 1066; https://doi.org/10.3390/biology12081066 - 29 Jul 2023

Viewed by 1084

Abstract

A recently published article in

B M C G e n o m i c s

by Fuentes-Trillo et al. contains a comparison of assembly approaches of several noroviral samples via different tools and preprocessing strategies. It turned out that the study used [...] Read more.

A recently published article in

B M C G e n o m i c s

by Fuentes-Trillo et al. contains a comparison of assembly approaches of several noroviral samples via different tools and preprocessing strategies. It turned out that the study used outdated versions of tools as well as tools that were not designed for the viral assembly task. In order to improve the suboptimal assemblies, authors suggested different sophisticated preprocessing strategies that seem to make only minor contributions to the results. We have reproduced the analysis using state-of-the-art tools designed for viral assembly, and we demonstrate that tools from the SPAdes toolkit (rnaviralSPAdes and coronaSPAdes) allow one to assemble the samples from the original study into a single contig without any additional preprocessing. Full article

(This article belongs to the Special Issue Computational Methods in Biology Research)

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