Special Issue "Computational Biology"

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. Milan Toma
E-Mail Website
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
Dr. Riccardo Concu
E-Mail Website
Guest Editor
Requimte/Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, s/n, 4169-007 Porto, Portugal
Interests: QSAR; machine learning; nanotechnology; nano-QSAR; nanotoxicology; computational biology

Special Issue Information

Not long ago, biologists did not have access to very large amounts of data. Recent advancements in technology are enabling us to generate and store an incredible amount of data. However, our ability to decipher data is slower than our ability to generate them, and thus, an increasing amount of unknown data is stacking up in databases such as the protein data bank. Due to this, by framing biomedical problems as computational problems, using tools adapted from computer science, mathematics, statistics, physics, chemistry, and other quantitative disciplines, scientists use those data to develop analytical methods, algorithms, and/or models for interpreting biological information.

This Special Issue welcomes submissions of original research and review manuscripts focusing on the application of computational techniques to problems in molecular biology, genomics, and biophysics. The goal is to achieve a realistic overview of this exciting and interdisciplinary field of biomedical research.

Dr. Milan Toma
Dr. Riccardo Concu
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. Biology 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 1800 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

  • computational biology 
  • mathematical modeling 
  • computational simulation 
  • statistics 
  • biochemistry 
  • biophysics 
  • molecular biology 
  • computer science 
  • anatomy 
  • biomodeling 
  • genomics 
  • genetics 
  • neuroscience 
  • pharmacology 
  • evolutionary biology 
  • cancer 
  • neuropsychiatry

Published Papers (15 papers)

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Editorial

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Editorial
Computational Biology: A New Frontier in Applied Biology
Biology 2021, 10(5), 374; https://doi.org/10.3390/biology10050374 - 27 Apr 2021
Viewed by 473
Abstract
All living things are related to one another [...] Full article
(This article belongs to the Special Issue Computational Biology)
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Research

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Article
New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches
Biology 2021, 10(3), 171; https://doi.org/10.3390/biology10030171 - 25 Feb 2021
Cited by 1 | Viewed by 634
Abstract
Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques [...] Read more.
Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = −9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = −6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = −5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection
Biology 2020, 9(12), 431; https://doi.org/10.3390/biology9120431 - 30 Nov 2020
Cited by 1 | Viewed by 1204
Abstract
The complex interplay between pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism Yersinia entercolitica (Ye) is one of the five top frequent causes of bacterial gastroenteritis based on [...] Read more.
The complex interplay between pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism Yersinia entercolitica (Ye) is one of the five top frequent causes of bacterial gastroenteritis based on the Epidemiological Bulletin of the Robert Koch Institute (RKI), 10 September 2020. A fundamental challenge in predicting the course of an infection is to understand whether co-infection with two Yersinia strains, differing only in their capacity to resist killing by the host immune system, may decrease the overall virulence by competitive exclusion or increase it by acting cooperatively. Herein, we study the primary interactions among Ye, the host immune system and the microbiota, and their influence on Yersinia population dynamics. The employed model considers commensal bacterial in two host compartments (the intestinal mucosa the and lumen), the co-existence of wt and mut Yersinia strains, and the host immune responses. We determine four possible equilibria: disease-free, wt-free, mut-free, and co-existence of wt and mut in equilibrium. We also calculate the reproduction number for each strain as a threshold parameter to determine if the population may be eradicated or persist within the host. We conclude that the infection should disappear if the reproduction numbers for each strain fall below one, and the commensal bacteria growth rate exceeds the pathogen’s growth rate. These findings will help inform medical control strategies. The supplement includes the MATLAB source script, Maple workbook, and figures. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Fluid-Structure Interaction Simulation of an Intra-Atrial Fontan Connection
Biology 2020, 9(12), 412; https://doi.org/10.3390/biology9120412 - 24 Nov 2020
Cited by 3 | Viewed by 572
Abstract
Total cavopulmonary connection (TCPC) hemodynamics has been hypothesized to be associated with long-term complications in single ventricle heart defect patients. Rigid wall assumption has been commonly used when evaluating TCPC hemodynamics using computational fluid dynamics (CFD) simulation. Previous study has evaluated impact of [...] Read more.
Total cavopulmonary connection (TCPC) hemodynamics has been hypothesized to be associated with long-term complications in single ventricle heart defect patients. Rigid wall assumption has been commonly used when evaluating TCPC hemodynamics using computational fluid dynamics (CFD) simulation. Previous study has evaluated impact of wall compliance on extra-cardiac TCPC hemodynamics using fluid-structure interaction (FSI) simulation. However, the impact of ignoring wall compliance on the presumably more compliant intra-atrial TCPC hemodynamics is not fully understood. To narrow this knowledge gap, this study aims to investigate impact of wall compliance on an intra-atrial TCPC hemodynamics. A patient-specific model of an intra-atrial TCPC is simulated with an FSI model. Patient-specific 3D TCPC anatomies were reconstructed from transverse cardiovascular magnetic resonance images. Patient-specific vessel flow rate from phase-contrast magnetic resonance imaging (MRI) at the Fontan pathway and the superior vena cava under resting condition were prescribed at the inlets. From the FSI simulation, the degree of wall deformation was compared with in vivo wall deformation from phase-contrast MRI data as validation of the FSI model. Then, TCPC flow structure, power loss and hepatic flow distribution (HFD) were compared between rigid wall and FSI simulation. There were differences in instantaneous pressure drop, power loss and HFD between rigid wall and FSI simulations, but no difference in the time-averaged quantities. The findings of this study support the use of a rigid wall assumption on evaluation of time-averaged intra-atrial TCPC hemodynamic metric under resting breath-held condition. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Integration of Real-Time Image Fusion in the Robotic-Assisted Treatment of Hepatocellular Carcinoma
Biology 2020, 9(11), 397; https://doi.org/10.3390/biology9110397 - 12 Nov 2020
Cited by 2 | Viewed by 623
Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a [...] Read more.
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide, with its mortality rate correlated with the tumor staging; i.e., early detection and treatment are important factors for the survival rate of patients. This paper presents the development of a novel visualization and detection system for HCC, which is a composing module of a robotic system for the targeted treatment of HCC. The system has two modules, one for the tumor visualization that uses image fusion (IF) between computerized tomography (CT) obtained preoperatively and real-time ultrasound (US), and the second module for HCC automatic detection from CT images. Convolutional neural networks (CNN) are used for the tumor segmentation which were trained using 152 contrast-enhanced CT images. Probabilistic maps are shown as well as 3D representation of HCC within the liver tissue. The development of the visualization and detection system represents a milestone in testing the feasibility of a novel robotic system in the targeted treatment of HCC. Further optimizations are planned for the tumor visualization and detection system with the aim of introducing more relevant functions and increase its accuracy. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Global Picture of Genetic Relatedness and the Evolution of Humankind
Biology 2020, 9(11), 392; https://doi.org/10.3390/biology9110392 - 10 Nov 2020
Cited by 1 | Viewed by 906
Abstract
We performed an exhaustive pairwise comparison of whole-genome sequences of 3120 individuals, representing 232 populations from all continents and seven prehistoric people including archaic and modern humans. In order to reveal an intricate picture of worldwide human genetic relatedness, 65 million very rare [...] Read more.
We performed an exhaustive pairwise comparison of whole-genome sequences of 3120 individuals, representing 232 populations from all continents and seven prehistoric people including archaic and modern humans. In order to reveal an intricate picture of worldwide human genetic relatedness, 65 million very rare single nucleotide polymorphic (SNP) alleles have been bioinformatically processed. The number and size of shared identical-by-descent (IBD) genomic fragments for every pair of 3127 individuals have been revealed. Over 17 million shared IBD fragments have been described. Our approach allowed detection of very short IBD fragments (<20 kb) that trace common ancestors who lived up to 200,000 years ago. We detected nine distinct geographical regions within which individuals had strong genetic relatedness, but with negligible relatedness between the populations of these regions. The regions, comprising nine unique genetic components for mankind, are the following: East and West Africa, Northern Europe, Arctica, East Asia, Oceania, South Asia, Middle East, and South America. The level of admixture in every studied population has been apportioned among these nine genetic components. Genetically, long-term neighboring populations are strikingly similar to each other in spite of any political, religious, and cultural differences. The topmost admixture has been observed at the center of Eurasia. These admixed populations (including Uyghurs, Azerbaijanis, Uzbeks, and Iranians) have roughly equal genetic contributions from the Middle East, Europe, China, and India, with additional significant traces from Africa and Arctic. The entire picture of relatedness of all the studied populations unfolds and presents itself in the form of shared number/size of IBDs. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights
Biology 2020, 9(11), 365; https://doi.org/10.3390/biology9110365 - 28 Oct 2020
Cited by 4 | Viewed by 1027
Abstract
Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction [...] Read more.
Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Signaling Mechanism of Transcriptional Bursting: A Technical Resolution-Independent Study
Biology 2020, 9(10), 339; https://doi.org/10.3390/biology9100339 - 19 Oct 2020
Cited by 3 | Viewed by 685
Abstract
Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass [...] Read more.
Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass technical constraints, we addressed this issue by introducing two independent theoretical methods including analytical research based on the classic two-model and information entropy research based on the architecture of transcription apparatus. Both methods connect the signaling mechanism of transcriptional bursting to the characteristics of transcriptional uncertainty (i.e., the differences in transcriptional levels of the same genes that are equally activated). By comparing the theoretical predictions with abundant experimental data collected from published papers, the results exclusively support frequency modulation. To further validate this conclusion, we showed that the data that appeared to support size modulation essentially supported frequency modulation taking into account the existence of burst clusters. This work provides a unified scheme that reconciles the debate on burst signaling. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Machine Learning Model for Identifying Antioxidant Proteins Using Features Calculated from Primary Sequences
Biology 2020, 9(10), 325; https://doi.org/10.3390/biology9100325 - 06 Oct 2020
Cited by 17 | Viewed by 1134
Abstract
Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses [...] Read more.
Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Computational Modeling of Skull Bone Structures and Simulation of Skull Fractures Using the YEAHM Head Model
Biology 2020, 9(9), 267; https://doi.org/10.3390/biology9090267 - 04 Sep 2020
Cited by 2 | Viewed by 1182
Abstract
The human head is a complex multi-layered structure of hard and soft tissues, governed by complex materials laws and interactions. Computational models of the human head have been developed over the years, reaching high levels of detail, complexity, and precision. However, most of [...] Read more.
The human head is a complex multi-layered structure of hard and soft tissues, governed by complex materials laws and interactions. Computational models of the human head have been developed over the years, reaching high levels of detail, complexity, and precision. However, most of the attention has been devoted to the brain and other intracranial structures. The skull, despite playing a major role in direct head impacts, is often overlooked and simplified. In this work, a new skull model is developed for the authors’ head model, the YEAHM, based on the original outer geometry, but segmenting it with sutures, diploë, and cortical bone, having variable thickness across different head sections and based on medical craniometric data. These structures are modeled with constitutive models that consider the non-linear behavior of skull bones and also the nature of their failure. Several validations are performed, comparing the simulation results with experimental results available in the literature at several levels: (i) local material validation; (ii) blunt trauma from direct impact against stationary skull; (iii) three impacts at different velocities simulating falls; (iv) blunt ballistic temporoparietal head impacts. Accelerations, impact forces, and fracture patterns are used to validate the skull model. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models
Biology 2020, 9(8), 198; https://doi.org/10.3390/biology9080198 - 30 Jul 2020
Cited by 1 | Viewed by 1016
Abstract
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental [...] Read more.
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository. Full article
(This article belongs to the Special Issue Computational Biology)
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Article
Effect of Edge-to-Edge Mitral Valve Repair on Chordal Strain: Fluid-Structure Interaction Simulations
Biology 2020, 9(7), 173; https://doi.org/10.3390/biology9070173 - 18 Jul 2020
Cited by 5 | Viewed by 1008
Abstract
Edge-to-edge repair for mitral valve regurgitation is being increasingly performed in high-surgical risk patients using minimally invasive mitral clipping devices. Known procedural complications include chordal rupture and mitral leaflet perforation. Hence, it is important to quantitatively evaluate the effect of edge-to-edge repair on [...] Read more.
Edge-to-edge repair for mitral valve regurgitation is being increasingly performed in high-surgical risk patients using minimally invasive mitral clipping devices. Known procedural complications include chordal rupture and mitral leaflet perforation. Hence, it is important to quantitatively evaluate the effect of edge-to-edge repair on chordal integrity. in this study, we employ a computational mitral valve model to simulate functional mitral regurgitation (FMR) by creating papillary muscle displacement. Edge-to-edge repair is then modeled by simulated coaptation of the mid portion of the mitral leaflets. in the setting of simulated FMR, edge-to-edge repair was shown to sustain low regurgitant orifice area, until a two fold increase in the inter-papillary muscle distance as compared to the normal mitral valve. Strain in the chordae was evaluated near the papillary muscles and the leaflets. Following edge-to-edge repair, strain near the papillary muscles did not significantly change relative to the unrepaired valve, while strain near the leaflets increased significantly relative to the unrepaired valve. These data demonstrate the potential for computational simulations to aid in the pre-procedural evaluation of possible complications such as chordal rupture and leaflet perforation following percutaneous edge-to-edge repair. Full article
(This article belongs to the Special Issue Computational Biology)
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Review

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Review
Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics
Biology 2021, 10(3), 185; https://doi.org/10.3390/biology10030185 - 02 Mar 2021
Cited by 2 | Viewed by 784
Abstract
Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid–structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. [...] Read more.
Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid–structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid–structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid–structure interactions in complex biological systems. Full article
(This article belongs to the Special Issue Computational Biology)
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Review
Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing
Biology 2020, 9(9), 295; https://doi.org/10.3390/biology9090295 - 18 Sep 2020
Cited by 5 | Viewed by 1315
Abstract
Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide [...] Read more.
Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation. Full article
(This article belongs to the Special Issue Computational Biology)
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Other

Technical Note
Multipath: An R Package to Generate Integrated Reproducible Pathway Models
Biology 2020, 9(12), 483; https://doi.org/10.3390/biology9120483 - 21 Dec 2020
Cited by 1 | Viewed by 709
Abstract
Biological pathway data integration has become a topic of interest in the past years. This interest originates essentially from the continuously increasing size of existing prior knowledge as well as from the many challenges scientists face when studying biological pathways. Multipath is a [...] Read more.
Biological pathway data integration has become a topic of interest in the past years. This interest originates essentially from the continuously increasing size of existing prior knowledge as well as from the many challenges scientists face when studying biological pathways. Multipath is a framework that aims at helping re-trace the use of specific pathway knowledge in specific publications, and easing the data integration of multiple pathway types and further influencing knowledge sources. Multipath thus helps scientists to increase the reproducibility of their code and analysis by allowing the integration of numerous data sources and documentation of their integration steps while doing so. In this paper, we present the package Multipath, and we describe how it can be used for data integration and tracking pathway modifications. We present a multilayer model built from the Wnt Pathway as a demonstration. Full article
(This article belongs to the Special Issue Computational Biology)
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