Computational Approaches in Bioengineering, Volume 1: Computational Approaches in Biotechnology and Bioinformatics

✍ Scribed by Pranav Deepak Pathak, Roshani Raut, Sebastián Jaramillo-Isaza, Pradnya Borkar, Rutvij H. Jhaveri

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 404
• Category: Library

✦ Synopsis

Volume 1 of Computational Approaches in Bioengineering—Computational Approaches in Biotechnology and Bioinformatics—explores many significant topics of biomedical engineering and bioinformatics in an easily understandable format. It explores recent developments and applications in bioinformatics, biomechanics, artificial intelligence (AI), signal processing, wearable sensors, biomaterials, cell biology, synthetic biology, biostatistics, prosthetics, big data, and algorithms. From applications of biomaterials in advanced drug delivery systems to the role of big data, AI, and machine learning in disease diagnosis and treatment, the book will help readers understand how these technologies are being applied across the areas of biomedical engineering, bioinformatics, and healthcare. The chapters also include case studies on the role of medical robots in surgery and the determination of protein structure using genetic algorithms. The contributors are all leading experts across multiple disciplines and provide chapters that truly represent a complete view of these state-of-the-art technologies. FeaturesCovers a wide range of subjects from biomedical engineering like wearable devices, biomaterials, synthetic biology, phytochemical extraction, and prosthetics Explores AI, machine learning, big data analysis, and algorithms in biomedical engineering and bioinformatics in an easily understandable format Includes case studies on the role of medical robots in surgery and the determination of protein structure using genetic algorithms Discusses genetic diagnosis, classification, and risk prediction in cancer using next-generation sequencing in oncology This book is ideally designed for biomedical professionals, biomedical engineers, healthcare professionals, data engineers, clinicians, physicians, medical students, hospital directors, clinical researchers, and others who work in the field of artificial intelligence, bioinformatics, and computational biology.

✦ Table of Contents

Cover
Half Title
Series
Title
Copyright
Contents
Preface
About the Editors
List of Contributors
Chapter 1 Computational Approaches for the Discovery of New Drugs for Inflammatory and Infectious Diseases
1.1 Introduction
1.2 Drug Discovery Methods
1.2.1 Traditional Drug Discovery
1.2.2 Modern Drug Discovery Method
1.3 Computer-Aided Drug Design
1.3.1 Target Determination
1.3.2 Homology Modeling
1.3.3 Active Site Prediction/Identification
1.3.4 Ligand Preparation
1.3.5 Virtual Screening
1.3.6 Molecular Docking
1.3.7 Pharmacophore Modeling
1.3.8 Molecular Dynamics Simulation
1.3.9 Binding Free Energy
1.4 Inflammatory Diseases
1.4.1 Rheumatoid Arthritis
1.5 Infectious Diseases
1.6 Future Prospective and Limitations
1.7 Conclusion
References
Chapter 2 A Bioinformatics Approach Towards Plant-Based Anticancer Drug Discovery
2.1 Introduction
2.2 Bioinformatics Approaches in Drug Design
2.3 Absorption, Distribution, Metabolism, Excretion and Toxicity Prediction
2.4 Molecular Docking
2.5 Molecular Dynamics Simulations
2.6 Quantitative Structure-Activity Relationship
2.7 Artificial Intelligence in Drug Discovery
2.8 Future Prospects and Limitations
2.9 Conclusion
References
Chapter 3 Recent Advances in Anticancer Activity and Bioinformatics Approach from Potential Plants
3.1 Introduction
3.2 Development of Cancer and Phytochemical Pathways of Action
3.3 Steps Involved in the Development of Phytochemical Drugs from the Medicinal Plants
3.4 Major Phytochemical Constituents with Anticancer Properties
3.4.1 Flavonoids
3.4.2 Lectins
3.4.3 Saponins
3.4.4 Alkaloids
3.4.5 Carotenoids
3.4.6 Phenolic Acids
3.5 Selected Medicinal Plants with Anticancer Activities
3.5.1 Actaea racemosa
3.5.2 Allium sativum
3.5.3 Artemisia annua
3.5.4 Boswellia serrate
3.5.5 Catharanthus roseus
3.5.6 Centella asiatica
3.5.7 Curcuma longa
3.5.8 Indigofera tinctoria
3.5.9 Mangifera indica
3.5.10 Morinda citrifolia
3.5.11 Newbouldia laevis
3.5.12 Nigella sativa
3.5.13 Solanum incanum
3.6 Bioinformatics Approaches
3.6.1 Systems Pharmacology
3.6.2 Cheminformatics
3.7 Recent Trends in Indigenous Medicinal Plant Informatics and Avenues to Combat Cancer
3.8 Regulatory Aspects of Herbal Anticancer Drugs
3.9 Conclusion
References
Chapter 4 Extraction of Phenolic Compounds from Some Ayurvedic Botanicals (Nigella sativa, Andrographis paniculata, and Phyllanthus amarus) and Evaluation of Their Antibacterial and Antiviral Properties Using Bioinformatics Approaches
4.1 Introduction
4.1.1 Bioinformatics in Drug Discovery
4.1.2 Bioinformatics in Vaccine Development for Bacterial and Viral Diseases
4.2 Ayurvedic Medicine
4.3 Antibacterial and Antiviral Potencies of Extracts of Selected Ayurvedic Botanicals
4.3.1 Andrographis paniculata
4.3.2 Phyllanthus amarus
4.3.3 Nigella sativa
4.4 Extraction of Phenolic Compounds from Selected Ayurvedic Botanicals
4.5 Evaluation of Antibacterial Activities of Ayurvedic Botanicals Using Bioinformatics Approaches
4.5.1 Andrographis paniculata
4.5.2 Phyllanthus amarus
4.5.3 Nigella sativa
4.6 Antibiotic Resistance by Metallo-Β-Lactamases and Inhibitory Interactions of Some Phenolic Compounds from the Selected Botanicals Using Bioinformatics Approaches
4.7 Antiviral Activities Using Bioinformatics Approaches
4.7.1 Andrographis paniculata
4.7.2 Phyllanthus amarus
4.7.3 Nigella sativa
4.8 Conclusion and Future Prospects
4.9 Acknowledgement
References
Chapter 5 Phenolic Compounds: A Systematic Review of Extraction Methods and a Bioinformatics Approach for Their Antibacterial and Antiviral Properties
5.1 Phenolics: The Most Abundant Secondary Metabolites
5.2 Preparation of Extracts to Extract the Phenolic Compounds
5.2.1 Conventional Extraction Methods for Phenolics
5.2.2 Alternative Extraction Methods
5.3 Quantification and Characterization of Phenolics
5.4 Bioinformatics: An Effective Approach for the Bioprospecting of Phenolics
5.4.1 A Bioinformatics Approach for Antibacterial and Antiviral Drug Discovery
5.4.2 Workflow for Investigation of the Antibacterial and Antiviral Potential of Phenolic Compounds
5.5 Applications of This Study
5.6 Future Prospects and Limitations
5.7 Conclusion
References
Chapter 6 The Role of Tissue Engineering in the Treatment of Degenerative Diseases
6.1 Introduction
6.2 Components of Tissue-Engineered Products
6.3 Tissue Engineering’s Significance in Osteoarthritis Treatment
6.3.1 Pathogenesis of OA
6.3.2 Current Strategies for OA Treatment
6.4 Treatment of Articular Cartilage Defects by Tissue Engineering
6.5 Future Prospects of Ossein Tissue Engineering
6.6 Retinal Tissue Engineering
6.7 Cytokines and Bone Tissue Engineering
6.8 Advancements in Tissue Engineering
6.8.1 Using 3D Bioprinting Technology to Regenerate Tissue
6.8.2 Scaffolds
6.8.3 Peripheral Nerve Injury
6.8.4 Hydrogels
6.8.5 3D Scaffold
6.8.6 Nano-Enabled Systems
6.8.7 Neurology
6.8.8 Otolaryngology
6.8.9 Ophthalmology
6.9 Conclusion
References
Chapter 7 An Algorithmic Soft Computing Technique for Identifying Lipase-Producing Yeast Using Its Gene Expression Data
7.1 Introduction
7.2 Materials and Methods
7.2.1 Collection of Yeast Gene Expression Data
7.2.2 Data Pre-processing
7.2.3 Python Code Scripting
7.2.4 Model Development and Validation
7.2.5 Optimization of MLFFA Parameters
7.3 Results and Discussion
7.3.1 Effect of Hidden Layers in MLFFA for Classifying LPY and NLPY
7.3.2 Effect of Neurons per Layer in MLFFA for Classifying LPY and NLPY
7.3.3 Effect of Learning Rate in MLFFA for Classifying LPY and NLPY
7.3.4 Effect of Epochs in MLFFA for Classifying LPY and NLPY
7.3.5 Optimized Condition for Higher Classification Accuracy
7.3.6 Learning Characteristics at the Optimized Condition
7.3.7 Efficiency of MLFFA in Classification
7.4 Future Prospectives and Limitations
7.5 Conclusion
7.6 Acknowledgement
References
Chapter 8 Plant Phenolic Compound Isolation and Its Bioinformatics Approaches to Molecular Mechanisms in Antimicrobial Activities and Resistance
8.1 Introduction
8.2 Phenolic Compounds
8.3 Classification of Phenolic Compounds
8.4 Role of Phenolic Compounds in Human Health
8.5 Methods Used for Bioactive Compound Extraction, Isolation, and Purification
8.5.1 Extraction of Phenolic Compounds Using Solvents
8.5.2 Liquid-Liquid Extraction
8.5.3 Ultrasound-Assisted Extraction
8.5.4 Microwave-Assisted Extraction
8.6 Techniques of Isolation and Purification of Bioactive Molecules from Plants
8.6.1 Purification of the Bioactive Molecule
8.6.2 Structural Clarification of the Bioactive Molecules
8.6.3 UV-Visible Spectroscopy
8.6.4 Infrared Spectroscopy
8.6.5 Nuclear Magnetic Resonance Spectroscopy
8.7 Antibacterial Activity of Polyphenols
8.7.1 Antibacterial Activity of Flavonols
8.7.2 Antibacterial Activity of Flavan-3-Ols
8.7.3 Antibacterial Activity of Flavanones
8.7.4 Antibacterial Activity of Isoflavones
8.7.5 Antibacterial Activity of Phenolic Acids
8.7.6 Antibacterial Activity of Tannins
8.7.8 Antibacterial Activity of Stilbenes
8.8 Synergistic Antibacterial Activity
8.9 Antiviral Activity of Flavonoid and Non-Flavonoid Compounds
8.9.1 Antiviral Activity of Flavonols
8.9.2 Antiviral Activity of Flavones
8.9.3 Antiviral Activity of Flavan-3-Ols
8.9.4 Antiviral Activity of Flavanones
8.10 Antifungal Activity of Phenolic Compounds
8.11 Bioinformatics Approaches to Molecular Mechanisms in Antimicrobial Resistance
8.11.1 Approach 1: Identification of Known Genomic Signatures of AMR from WGS Data
8.11.2 Approach 2: Identification of AMR Signatures from Gene Expression Data
8.11.3 Approach 3: ARG Agnostic Identification of AMR Mechanisms via Pan-Genome Analysis
8.11.4 Approach 4: Identification of AMR Mechanisms from Metabolomics Data
8.12 Future Prospectives and Limitations
8.13 Conclusion
References
Chapter 9 Computational Evaluation of Peanut Skin Bioactive Compounds for Cancer Treatment
9.1 Introduction
9.1.1 Taxonomic Information
9.2 Review of the Literature
9.3 Materials and Methods
9.3.1 GC-MS Analysis
9.3.2 Preparation of the Target Protein
9.3.3 Ligand Preparation
9.3.4 Molecular Docking by Arguslab
9.3.5 Molecular Docking Visualization
9.4 Results
9.4.1 Description of the Compounds
9.5 Future Perspectives and Limitations
9.6 Conclusion
9.7 Acknowledgement
9.8 Conflict of Interest
References
Chapter 10 Bioinformatics Tools for the Discovery of Potential Anti-Diabetic Drugs from Lichens
10.1 Introduction
10.2 Materials and Methods
10.2.1 Selection and Preparation of Protein Targets
10.2.2 Preparation of Ligands
10.2.3 Active Site Prediction
10.2.4 Molecular Docking Analysis
10.2.5 Drug Likeness Prediction
10.2.6 ADMET Analysis
10.3 Results
10.3.1 Molecular Docking
10.3.2 Drug-Likeness Prediction
10.3.3 ADMET
10.4 Discussion
10.5 Future Prospects and Limitations
10.6 Conclusion
References
Chapter 11 In Silico Analysis of Lapachol and Nickel Lapachol Against Tumor Proteins: Insights into the Molecular Interactions and Drug Likeliness
11.1 Introduction
11.2 Materials and Methods
11.2.1 Preparation of Receptors
11.2.2 Preparation of Ligands
11.2.3 Molecular Docking
11.2.4 Drug Likeness Prediction
11.2.5 Absorption, Distribution, Metabolism, Excretion and Toxicity Analysis
11.3 Results and Discussion
11.3.1 Molecular Docking Analysis and Molecular Interactions
11.3.2 Drug-Likeness Prediction
11.3.3 ADMET
11.4 Future Scope
11.5 Conclusion
References
Chapter 12 Transcriptome Analysis Identifies Genes Involved in Vitamin B Biosynthesis in Solanum virginianum Whole Fruit
12.1 Introduction
12.2 Materials and Methods
12.2.1 Plant Sample Collection
12.2.2 Total RNA Isolation and Library Preparation
12.2.3 Transcriptome Sequencing, De Novo Assembly
12.2.4 Functional Annotation of CDS
12.2.5 Gene Ontology of CDS
12.2.6 Pathway Enrichment Analysis of CDS
12.3 Results
12.3.1 Transcriptome Sequencing, De Novo Assembly
12.3.2 Gene Ontology of CDS
12.3.3 Functional Annotation of CDS
12.3.4 Pathway Enrichment Analysis of CDS
12.3.5 Analysis of Biosynthetic Pathway Genes
12.4 Conclusion
12.5 Competing Interests
References
Chapter 13 Identification of Compounds Present in the Ethanolic Extract of Cecropia pachyatachya Trécul Leaves by CG-MS and In Silico Studies with the Enzymes 5-LOX and α-1-Antitrypsin
13.1 Introduction
13.2 Methodology
13.2.1 Botanical Material
13.2.2 Extract Production
13.2.3 Preliminary Phytochemical Prospection
13.2.4 Gas Chromatography Coupled with Mass Spectrometry of the CPF Extract
13.2.5 ADMET Prediction Analysis
13.2.6 Molecular Docking
13.3 Results and Discussion
13.3.1 Preliminary Phytochemical Prospection
13.3.2 GC-MS Analysis of the Ethanolic Extract
13.3.3 ADMET Prediction Analysis of Compounds Identified in the Ethanolic Extract
13.3.4 Molecular Docking Analysis of Complexes Formed with the Compounds Identified in the Ethanolic Extract
13.3.5 Future Perspectives
13.4 Conclusion
13.5 Acknowledgments
13.6 Disclosure Statement
13.7 Financing
References
Chapter 14 Computational Biology Approach in Viticulture
14.1 Introduction
14.2 Grapevine
14.2.1 Physiology
14.2.2 Chemical Composition
14.2.3 Disease and Pests
14.3 Development of Computational Biology Approach in Viticulture
14.3.1 Genomics
14.3.2 Transcriptomics
14.3.3 Proteomics
14.3.4 Metabolomics
14.4 Future Scope and Limitations
14.5 Conclusion
References
Chapter 15 Screenings of the Inhibitory Ability of Vietnamese Medicinal Plant-Based Substances and Protein-Related Structures: Computation-Pharmacological Experiment Correlations
15.1 Introduction
15.1.1 The Biodiversity of Vietnamese Medicinal Plants
15.1.2 Medicinal Plants Used in Vietnamese Traditional Medicine for Antibacterial, Antiviral and Antidiabetic Activities
15.2 Methodology of Molecular Docking Simulation
15.3 Methodology of In Vitro Pharmacological Studies of Vietnamese Medicinal Plants
15.3.1 Inhibitory Activity In Vitro Against Bacteria of Vietnamese Medicinal Plants
15.3.2 In Vitro α-Glucosidase Inhibitory Assay of the Extracts from Vietnamese Medicinal Plants
15.3.3 Statistical Analysis
15.4 Results and Discussion of Molecular Docking Analysis Inhibitability
15.4.1 Docking Simulation of Natural Compounds in Garlic Essential Oil and Cajeput Essential Oil into PBD-6LU7 Protein of SARS-CoV-2
15.4.2 Docking Simulation on Inhibitability of Some Alkaloids Against Influenza Virus Hemagglutinin
15.4.3 α-Glucosidase Inhibitability of Some Natural Compounds of the EtOAc Extract of Distichochlamys citrea Rhizomes Using Molecular Docking Analysis
15.5 Results and Discussion of In Vitro Studies
15.5.1 The Inhibitory Activity of n-Hexane Extract from Distichochlamys citrea M.F. Newman Rhizome Against Streptococcus pyogenes
15.5.2 The α-Glucosidase Inhibitory Activity of the Sub-Fraction from Distichochlamys citrea M.F. Newman Rhizome
15.6 Bioassay-Guided Isolation Led to the Discovery of New Plant Agents for Infectious and Diabetic Diseases
15.6.1 Isolation Techniques Used in the Discovery of New Promising Plant-Derived Substances for Infectious and Diabetic Diseases
15.7 Future Prospects and Limitations
15.8 Conclusions
References
Index