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Diniz P, Grimm B, Garcia F, Fayad J, Ley C, Mouton C, Oeding JF, Hirschmann MT, Samuelsson K, Seil R. Digital twin systems for musculoskeletal applications: A current concepts review. Knee Surg Sports Traumatol Arthrosc 2025; 33:1892-1910. [PMID: 39989345 DOI: 10.1002/ksa.12627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 02/02/2025] [Accepted: 02/02/2025] [Indexed: 02/25/2025]
Abstract
Digital twin (DT) systems, which involve creating virtual replicas of physical objects or systems, have the potential to transform healthcare by offering personalised and predictive models that grant deeper insight into a patient's condition. This review explores current concepts in DT systems for musculoskeletal (MSK) applications through an overview of the key components, technologies, clinical uses, challenges, and future directions that define this rapidly growing field. DT systems leverage computational models such as multibody dynamics and finite element analysis to simulate the mechanical behaviour of MSK structures, while integration with wearable technologies allows real-time monitoring and feedback, facilitating preventive measures, and adaptive care strategies. Early applications of DT systems to MSK include optimising the monitoring of exercise and rehabilitation, analysing joint mechanics for personalised surgical techniques, and predicting post-operative outcomes. While still under development, these advancements promise to revolutionise MSK care by improving surgical planning, reducing complications, and personalising patient rehabilitation strategies. Integrating advanced machine learning algorithms can enhance the predictive abilities of DTs and provide a better understanding of disease processes through explainable artificial intelligence (AI). Despite their potential, DT systems face significant challenges. These include integrating multi-modal data, modelling ageing and damage, efficiently using computational resources and developing clinically accurate and impactful models. Addressing these challenges will require multidisciplinary collaboration. Furthermore, guaranteeing patient privacy and protection against bias is extremely important, as is navigating regulatory requirements for clinical adoption. DT systems present a significant opportunity to improve patient care, made possible by recent technological advancements in several fields, including wearable sensors, computational modelling of biological structures, and AI. As these technologies continue to mature and their integration is streamlined, DT systems may fast-track medical innovation, ushering in a new era of rapid improvement of treatment outcomes and broadening the scope of preventive medicine. Level of Evidence: Level V.
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Affiliation(s)
- Pedro Diniz
- Department of Orthopaedic Surgery, Centre Hospitalier de Luxembourg - Clinique d'Eich, Luxembourg, Luxembourg
- Luxembourg Institute of Research in Orthopaedics, Sports Medicine and Science (LIROMS), Luxembourg, Luxembourg
- Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Department of Bioengineering, iBB - Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Bernd Grimm
- Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
| | - Frederic Garcia
- Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
| | - Jennifer Fayad
- Luxembourg Institute of Research in Orthopaedics, Sports Medicine and Science (LIROMS), Luxembourg, Luxembourg
- Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
| | - Christophe Ley
- Department of Mathematics, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Caroline Mouton
- Department of Orthopaedic Surgery, Centre Hospitalier de Luxembourg - Clinique d'Eich, Luxembourg, Luxembourg
- Luxembourg Institute of Research in Orthopaedics, Sports Medicine and Science (LIROMS), Luxembourg, Luxembourg
| | - Jacob F Oeding
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michael T Hirschmann
- Department of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland, Bruderholz, Switzerland
| | - Kristian Samuelsson
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Romain Seil
- Department of Orthopaedic Surgery, Centre Hospitalier de Luxembourg - Clinique d'Eich, Luxembourg, Luxembourg
- Luxembourg Institute of Research in Orthopaedics, Sports Medicine and Science (LIROMS), Luxembourg, Luxembourg
- Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
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Ouzounis CA. Biology's transformation: from observation through experiment to computation. BIOINFORMATICS ADVANCES 2024; 4:vbae069. [PMID: 38799705 PMCID: PMC11127110 DOI: 10.1093/bioadv/vbae069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/24/2024] [Accepted: 05/07/2024] [Indexed: 05/29/2024]
Abstract
Summary We explore the nuanced temporal and epistemological distinctions among natural sciences, particularly the contrasting treatment of time and the interplay between theory and experimentation. Physics, an exemplar of mature science, relies on theoretical models for predictability and simulations. In contrast, biology, traditionally experimental, is witnessing a computational surge, with data analytics and simulations reshaping its research paradigms. Despite these strides, a unified theoretical framework in biology remains elusive. We propose that contemporary global challenges might usher in a renewed emphasis, presenting an opportunity for the establishment of a novel theoretical underpinning for the life sciences. Availability and implementation https://github.com/ouzounis/CLS-emerges Data in Json format, Images in PNG format.
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Affiliation(s)
- Christos A Ouzounis
- Biological Computation & Computational Biology Group, Artificial Intelligence & Information Analysis Laboratory, School of Informatics, Faculty of Sciences, Aristotle University of Thessalonica, Thessalonica GR-54124, Greece
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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Marangoni R, Bevilacqua V, Cannataro M, Mele BH, Mauri G, Marabotti A. An overview of bioinformatics courses delivered at the academic level in Italy: Reflections and recommendations from BITS. PLoS Comput Biol 2023; 19:e1010846. [PMID: 36780436 PMCID: PMC9924992 DOI: 10.1371/journal.pcbi.1010846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Abstract
In Italian universities, bioinformatics courses are increasingly being incorporated into different study paths. However, the content of bioinformatics courses is usually selected by the professor teaching the course, in the absence of national guidelines that identify the minimum indispensable knowledge in bioinformatics that undergraduate students from different scientific fields should achieve. The Training&Teaching group of the Bioinformatics Italian Society (BITS) proposed to university professors a survey aimed at portraying the current situation of bioinformatics courses within undergraduate curricula in Italy (i.e., bioinformatics courses activated within both bachelor's and master's degrees). Furthermore, the Training&Teaching group took a cue from the survey outcomes to develop recommendations for the design and the inclusion of bioinformatics courses in academic curricula. Here, we present the outcomes of the survey, as well as the BITS recommendations, with the hope that they may support BITS members in identifying learning outcomes and selecting content for their bioinformatics courses. As we share our effort with the broader international community involved in teaching bioinformatics at academic level, we seek feedback and thoughts on our proposal and hope to start a fruitful debate on the topic, including how to better fulfill the real bioinformatics knowledge needs of the research and the labor market at both the national and international level.
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Affiliation(s)
- Roberto Marangoni
- Bioinformatics Italian Society (BITS), Roma, Italy
- Department of Biology, University of Pisa, Pisa, Italy
- * E-mail: (RM); (AM)
| | - Vitoantonio Bevilacqua
- Bioinformatics Italian Society (BITS), Roma, Italy
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Bary, Italy
| | - Mario Cannataro
- Bioinformatics Italian Society (BITS), Roma, Italy
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Bruno Hay Mele
- Bioinformatics Italian Society (BITS), Roma, Italy
- Department of Biology, University of Naples “Federico II”, Naples, Italy
| | - Giancarlo Mauri
- Bioinformatics Italian Society (BITS), Roma, Italy
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Anna Marabotti
- Bioinformatics Italian Society (BITS), Roma, Italy
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, Italy
- * E-mail: (RM); (AM)
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Lim J, Park HT, Ko S, Park HE, Lee G, Kim S, Shin MK, Yoo HS, Kim D. Genomic diversity of Mycobacterium avium subsp. paratuberculosis: pangenomic approach for highlighting unique genomic features with newly constructed complete genomes. Vet Res 2021; 52:46. [PMID: 33736686 PMCID: PMC7977185 DOI: 10.1186/s13567-021-00905-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 01/26/2021] [Indexed: 01/25/2023] Open
Abstract
Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic granulomatous enteropathy in ruminants. Determining the genetic diversity of MAP is necessary to understand the epidemiology and biology of MAP, as well as establishing disease control strategies. In the present study, whole genome-based alignment and comparative analysis were performed using 40 publicly available MAP genomes, including newly sequenced Korean isolates. First, whole genome-based alignment was employed to identify new genomic structures in MAP genomes. Second, the genomic diversity of the MAP population was described by pangenome analysis. A phylogenetic tree based on the core genome and pangenome showed that the MAP was differentiated into two major types (C- and S-type), which was in keeping with the findings of previous studies. However, B-type strains were discriminated from C-type strains. Finally, functional analysis of the pangenome was performed using three virulence factor databases (i.e., PATRIC, VFDB, and Victors) to predict the phenotypic diversity of MAP in terms of pathogenicity. Based on the results of the pangenome analysis, we developed a real-time PCR technique to distinguish among S-, B- and C-type strains. In conclusion, the results of our study suggest that the phenotypic differences between MAP strains can be explained by their genetic polymorphisms. These results may help to elucidate the diversity of MAP, extending from genomic features to phenotypic traits.
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Affiliation(s)
- Jaewon Lim
- School of Life Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Hong-Tae Park
- Department of Infectious Disease, College of Veterinary Medicine, Seoul National University, Seoul, Korea
| | - Seyoung Ko
- School of Life Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Hyun-Eui Park
- Department of Microbiology, Research Institute of Life Science, College of Medicine, Gyeongsang National University, Jinju, Korea
| | - Gyumin Lee
- School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Suji Kim
- Department of Infectious Disease, College of Veterinary Medicine, Seoul National University, Seoul, Korea
| | - Min-Kyoung Shin
- Department of Microbiology, Research Institute of Life Science, College of Medicine, Gyeongsang National University, Jinju, Korea
| | - Han Sang Yoo
- Department of Infectious Disease, College of Veterinary Medicine, Seoul National University, Seoul, Korea. .,Bio-MAX/N-Bio Institute, Seoul National University, Seoul, 08826, Korea.
| | - Donghyuk Kim
- School of Life Sciences, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea. .,School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea.
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Borba JVVB, Silva AC, Lima MNN, Mendonca SS, Furnham N, Costa FTM, Andrade CH. Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 124:187-223. [PMID: 33632465 DOI: 10.1016/bs.apcsb.2020.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.
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Affiliation(s)
- Joyce Villa Verde Bastos Borba
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Arthur Carvalho Silva
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Marilia Nunes Nascimento Lima
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Sabrina Silva Mendonca
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fabio Trindade Maranhão Costa
- Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Carolina Horta Andrade
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
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Chasapi A, Promponas VJ, Ouzounis CA. The bioinformatics wealth of nations. Bioinformatics 2020; 36:2963-2965. [PMID: 32129821 PMCID: PMC7203752 DOI: 10.1093/bioinformatics/btaa132] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/16/2020] [Accepted: 02/24/2020] [Indexed: 11/12/2022] Open
Affiliation(s)
- Anastasia Chasapi
- Biological Computation & Process Lab (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), Thessalonica, GR-57001, Greece
| | - Vasilis J Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, CY-2109, Cyprus
| | - Christos A Ouzounis
- Biological Computation & Process Lab (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), Thessalonica, GR-57001, Greece
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8
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Guzzi AF, Oliveira FSL, Amaro MMS, Tavares-Filho PF, Gabriel JE. In silico prediction of the functional and structural consequences of the non-synonymous single nucleotide polymorphism A122V in bovine CXC chemokine receptor type 1. BRAZ J BIOL 2020; 80:39-46. [DOI: 10.1590/1519-6984.188655] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 07/17/2018] [Indexed: 02/04/2023] Open
Abstract
Abstract The current study aimed to assess whether the A122V causal polymorphism promotes alterations in the functional and structural proprieties of the CXC chemokine receptor type 1 protein (CXCR1) of cattle Bos taurus by in silico analyses. Two amino acid sequences of bovine CXCR1 was selected from database UniProtKB/Swiss-Prot: a) non-polymorphic sequence (A7KWG0) with alanine (A) at position 122, and b) polymorphic sequence harboring the A122V polymorphism, substituting alanine by valine (V) at same position. CXCR1 sequences were submitted as input to different Bioinformatics’ tools to examine the effects of this polymorphism on functional and structural stabilities, to predict eventual alterations in the 3-D structural modeling, and to estimate the quality and accuracy of the predictive models. The A122V polymorphism exerted tolerable and non-deleterious effects on the polymorphic CXCR1, and the predictive structural model for polymorphic CXCR1 revealed an alpha helix spatial structure typical of a receptor transmembrane polypeptide. Although higher variations in the distances between pairs of amino acid residues at target-positions are detected in the polymorphic CXCR1 protein, more than 97% of the amino acid residues in both models were located in favored and allowed conformational regions in Ramachandran plots. Evidences has supported that the A122V polymorphism in the CXCR1 protein is associated with increased clinical mastitis incidence in dairy cows. Thus, the findings described herein prove that the replacement of the alanine by valine amino acids provokes local conformational changes in the A122V-harboring CXCR1 protein, which could directly affect its post-translational folding mechanisms and biological functionality.
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Affiliation(s)
- A. F. Guzzi
- Universidade Federal do Vale do São Francisco, Brasil
| | | | | | | | - J. E. Gabriel
- Universidade Federal do Vale do São Francisco, Brasil
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Modlin IM, Kidd M, Drozdov IA, Bodei L. The Use of Deep Learning and Neural Networks in Imaging: Welcome to the New Mathematical Milieu of Medicine. Neuroendocrinology 2020; 110:322-327. [PMID: 31694034 DOI: 10.1159/000504605] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 11/19/2022]
Affiliation(s)
- Irvin M Modlin
- Yale University School of Medicine, New Haven, Connecticut, USA,
| | - Mark Kidd
- Wren Laboratories, Branford, Connecticut, USA
| | | | - Lisa Bodei
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Chasapi A, Aivaliotis M, Angelis L, Chanalaris A, Iliopoulos I, Kappas I, Karapiperis C, Kyrpides NC, Pafilis E, Panteris E, Topalis P, Tsiamis G, Vizirianakis IS, Vlassi M, Promponas VJ, Ouzounis CA. Establishment of computational biology in Greece and Cyprus: Past, present, and future. PLoS Comput Biol 2019; 15:e1007532. [PMID: 31856214 PMCID: PMC6922331 DOI: 10.1371/journal.pcbi.1007532] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Anastasia Chasapi
- Biological Computation & Process Lab, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
| | - Michalis Aivaliotis
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessalonica, Greece
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Lefteris Angelis
- School of Informatics, Aristotle University of Thessaloniki, Thessalonica, Greece
| | - Anastasios Chanalaris
- Botnar Research Centre, NDORMS, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - Ioannis Iliopoulos
- Division of Basic Sciences, School of Medicine, University of Crete, Heraklion, Greece
| | - Ilias Kappas
- School of Biology, Aristotle University of Thessaloniki, Thessalonica, Greece
| | - Christos Karapiperis
- School of Informatics, Aristotle University of Thessaloniki, Thessalonica, Greece
| | - Nikos C. Kyrpides
- Department of Energy, Joint Genome Institute, Walnut Creek, California, United States of America
| | - Evangelos Pafilis
- Institute of Marine Biology Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Greece
| | - Eleftherios Panteris
- First Psychiatric Clinic, Papageorgiou General Hospital, Aristotle University of Thessaloniki, Thessalonica, Greece
| | - Pantelis Topalis
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - George Tsiamis
- Department of Environmental Engineering, School of Engineering, University of Patras, Patras, Greece
| | - Ioannis S. Vizirianakis
- Department of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessalonica, Greece
| | - Metaxia Vlassi
- Institute of Biosciences & Applications, National Centre for Scientific Research Demokritos, Athens, Greece
| | - Vasilis J. Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
- * E-mail: (VJP); (CAO)
| | - Christos A. Ouzounis
- Biological Computation & Process Lab, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
- * E-mail: (VJP); (CAO)
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Addressing Interdisciplinary Difficulties in Developmental Biology/Mathematical Collaborations: A Neural Crest Example. Methods Mol Biol 2019. [PMID: 30977062 DOI: 10.1007/978-1-4939-9412-0_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Mathematical modeling can allow insight into the biological processes that can be difficult to access by conventional biological means alone. Such projects are becoming increasingly attractive with the appearance of faster and more powerful quantitative techniques in both biological data acquisition and data storage, manipulation, and presentation. However, as is frequent in interdisciplinary research, the main hurdles are not within the mindset and techniques of each discipline but are usually encountered in attempting to meld the different disciplines together. Based upon our experience in applying mathematical methods to investigate how neural crest cells interact to form the enteric nervous system, we present our views on how to pursue biomathematical modeling projects, what difficulties to expect, and how to overcome, or at least survive, these hurdles. The main advice being: persevere.
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Abstract
Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.
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Ouzounis CA. Developing computational biology at meridian 23° E, and a little eastwards. ACTA ACUST UNITED AC 2018; 25:18. [PMID: 30460210 PMCID: PMC6237004 DOI: 10.1186/s40709-018-0091-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 11/09/2018] [Indexed: 11/23/2022]
Abstract
Modern biology is experiencing a deep transformation by the expansion of molecular-level measurements at all scales, using omics technologies. A key element in this transformation is the field of bioinformatics, that has—in the meanwhile—permeated pretty much all of biological and biomedical research and is now emerging as a key inter-disciplinary area that connects the natural sciences, chemical and electrical engineering, science education and science policy, on a number of science and technology fronts. The strong tradition of open access for large volumes of raw data, collections of complex results and high-quality algorithm implementations in bioinformatics makes the field a unique, special case of open science. We report on our recent research activities, the development of training initiatives in the wider region during the past years, and the lessons learned regarding our efforts away from major epicenters, within the general context of open science.
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Affiliation(s)
- Christos A Ouzounis
- Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), PO Box 361, 57001 Thessaloníki, Greece
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14
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Hassan M, Namasivayam AA, DeBlasio D, Fatima N, Siranosian B, Parra RG, Cuypers B, Shome S, Monzon AM, Fumey J, Rahman F. Reflections on a journey: a retrospective of the ISCB Student Council symposium series. BMC Bioinformatics 2018; 19:347. [PMID: 30301451 PMCID: PMC6176502 DOI: 10.1186/s12859-018-2369-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
This article describes the motivation, origin and evolution of the student symposia series organised by the ISCB Student Council. The meeting series started thirteen years ago in Madrid and has spread to four continents. The article concludes with the highlights of the most recent edition of annual Student Council Symposium held in conjunction with the 25th Conference on Intelligent Systems for Molecular Biology and the 16th European Conference on Computational Biology, in Prague, in July 2017.
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Affiliation(s)
- Mehedi Hassan
- Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Cardiff, UK
| | | | - Dan DeBlasio
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania USA
| | - Nazeefa Fatima
- Department of Biology, Faculty of Science, Lund University, Lund, Sweden
| | | | | | - Bart Cuypers
- Institute of Tropical Medicine and the University of Antwerp, Antwerp, Belgium
| | - Sayane Shome
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa USA
| | - Alexander Miguel Monzon
- Structural Bioinformatics Group, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Buenos Aires, Argentina
| | - Julien Fumey
- Human Genetics and Cognitive Function, Institut Pasteur, Paris, France
| | - Farzana Rahman
- Genomics and Computational Biology Research Group, Faculty of Computing, Engineering and Science, University of South Wales, Cardiff, UK
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15
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Keshtvarz M, Salimian J, Yaseri M, Bathaie SZ, Rezaie E, Aliramezani A, Norouzbabaei Z, Amani J, Douraghi M. Bioinformatic prediction and experimental validation of a PE38-based recombinant immunotoxin targeting the Fn14 receptor in cancer cells. Immunotherapy 2017; 9:387-400. [PMID: 28357912 DOI: 10.2217/imt-2017-0008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
AIM AFn14R can serve as an ideal target for cancer immunotherapy. Here, a combined bioinformatic and experimental approach was applied to characterize an immunotoxin consisting of single-chain variable fragment antibody that targets Fn14 and a toxin fragment (PE38). METHODS & RESULTS Flow cytometry results showed that the rate of PE38-P4A8 binding to Fn14 was approximately 60 and 40% in HT-29 and A549 cells, respectively. Moreover, 1 ng/µl of immunotoxin was able to lyse approximately 53 and 41% of HT-29 and A549, respectively. PE38-P4A8 showed stability in mouse serum (∼90%) after 3-h incubation. Most importantly, using bioinformatics for determining the structure and function of fusion proteins can be very helpful in designing of experiments. CONCLUSION Coupled with bioinformatics, experimental approaches revealed that PE38-P4A8 could be used as a promising therapeutic agent for cancer cells expressing Fn14.
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Affiliation(s)
- Maryam Keshtvarz
- Division of Microbiology, Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Jafar Salimian
- Chemical Injuries Research Center, Baqiyatallah, University of Medical Sciences, Tehran, Iran
| | - Mehdi Yaseri
- Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Ehsan Rezaie
- Applied Microbiology Research Center, Baqiyatallah University of Medical Sciences, Vanak Sq. Molasadra St, Tehran, Iran
| | - Amir Aliramezani
- Division of Microbiology, Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Norouzbabaei
- Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Jafar Amani
- Applied Microbiology Research Center, Baqiyatallah University of Medical Sciences, Vanak Sq. Molasadra St, Tehran, Iran
| | - Masoumeh Douraghi
- Division of Microbiology, Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Food Microbiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
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16
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Baichoo S, Ouzounis CA. Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment. Biosystems 2017; 156-157:72-85. [PMID: 28392341 DOI: 10.1016/j.biosystems.2017.03.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 03/21/2017] [Accepted: 03/22/2017] [Indexed: 12/12/2022]
Abstract
A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality.
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Affiliation(s)
- Shakuntala Baichoo
- Department of Computer Science & Engineering, University of Mauritius, Réduit 80837, Mauritius.
| | - Christos A Ouzounis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica 57001, Greece.
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17
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Lewis J, Bartlett A, Atkinson P. Hidden in the Middle: Culture, Value and Reward in Bioinformatics. MINERVA 2016; 54:471-490. [PMID: 27942075 PMCID: PMC5124041 DOI: 10.1007/s11024-016-9304-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Bioinformatics - the so-called shotgun marriage between biology and computer science - is an interdiscipline. Despite interdisciplinarity being seen as a virtue, for having the capacity to solve complex problems and foster innovation, it has the potential to place projects and people in anomalous categories. For example, valorised 'outputs' in academia are often defined and rewarded by discipline. Bioinformatics, as an interdisciplinary bricolage, incorporates experts from various disciplinary cultures with their own distinct ways of working. Perceived problems of interdisciplinarity include difficulties of making explicit knowledge that is practical, theoretical, or cognitive. But successful interdisciplinary research also depends on an understanding of disciplinary cultures and value systems, often only tacitly understood by members of the communities in question. In bioinformatics, the 'parent' disciplines have different value systems; for example, what is considered worthwhile research by computer scientists can be thought of as trivial by biologists, and vice versa. This paper concentrates on the problems of reward and recognition described by scientists working in academic bioinformatics in the United Kingdom. We highlight problems that are a consequence of its cross-cultural make-up, recognising that the mismatches in knowledge in this borderland take place not just at the level of the practical, theoretical, or epistemological, but also at the cultural level too. The trend in big, interdisciplinary science is towards multiple authors on a single paper; in bioinformatics this has created hybrid or fractional scientists who find they are being positioned not just in-between established disciplines but also in-between as middle authors or, worse still, left off papers altogether.
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Affiliation(s)
- Jamie Lewis
- School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VIIth Avenue, Cardiff, CF10 3WT UK
| | - Andrew Bartlett
- School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VIIth Avenue, Cardiff, CF10 3WT UK
| | - Paul Atkinson
- School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VIIth Avenue, Cardiff, CF10 3WT UK
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18
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Wren JD. Bioinformatics programs are 31-fold over-represented among the highest impact scientific papers of the past two decades. Bioinformatics 2016; 32:2686-91. [PMID: 27153671 DOI: 10.1093/bioinformatics/btw284] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 04/21/2016] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION To analyze the relative proportion of bioinformatics papers and their non-bioinformatics counterparts in the top 20 most cited papers annually for the past two decades. RESULTS When defining bioinformatics papers as encompassing both those that provide software for data analysis or methods underlying data analysis software, we find that over the past two decades, more than a third (34%) of the most cited papers in science were bioinformatics papers, which is approximately a 31-fold enrichment relative to the total number of bioinformatics papers published. More than half of the most cited papers during this span were bioinformatics papers. Yet, the average 5-year JIF of top 20 bioinformatics papers was 7.7, whereas the average JIF for top 20 non-bioinformatics papers was 25.8, significantly higher (P < 4.5 × 10(-29)). The 20-year trend in the average JIF between the two groups suggests the gap does not appear to be significantly narrowing. For a sampling of the journals producing top papers, bioinformatics journals tended to have higher Gini coefficients, suggesting that development of novel bioinformatics resources may be somewhat 'hit or miss'. That is, relative to other fields, bioinformatics produces some programs that are extremely widely adopted and cited, yet there are fewer of intermediate success. CONTACT jdwren@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jonathan D Wren
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104-5005, USA, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, USA
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19
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Bartlett A, Lewis J, Williams ML. Generations of interdisciplinarity in bioinformatics. NEW GENETICS AND SOCIETY 2016; 35:186-209. [PMID: 27453689 PMCID: PMC4940887 DOI: 10.1080/14636778.2016.1184965] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 04/28/2016] [Indexed: 06/06/2023]
Abstract
Bioinformatics, a specialism propelled into relevance by the Human Genome Project and the subsequent -omic turn in the life science, is an interdisciplinary field of research. Qualitative work on the disciplinary identities of bioinformaticians has revealed the tensions involved in work in this "borderland." As part of our ongoing work on the emergence of bioinformatics, between 2010 and 2011, we conducted a survey of United Kingdom-based academic bioinformaticians. Building on insights drawn from our fieldwork over the past decade, we present results from this survey relevant to a discussion of disciplinary generation and stabilization. Not only is there evidence of an attitudinal divide between the different disciplinary cultures that make up bioinformatics, but there are distinctions between the forerunners, founders and the followers; as inter/disciplines mature, they face challenges that are both inter-disciplinary and inter-generational in nature.
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Affiliation(s)
- Andrew Bartlett
- School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VII, CardiffCF10 3WT, UK
| | - Jamie Lewis
- School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VII, CardiffCF10 3WT, UK
| | - Matthew L. Williams
- School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VII, CardiffCF10 3WT, UK
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20
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Sinha S, Song J, Weinshilboum R, Jongeneel V, Han J. KnowEnG: a knowledge engine for genomics. J Am Med Inform Assoc 2015; 22:1115-9. [PMID: 26205246 PMCID: PMC5009907 DOI: 10.1093/jamia/ocv090] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Revised: 05/27/2015] [Accepted: 06/02/2015] [Indexed: 12/26/2022] Open
Abstract
We describe here the vision, motivations, and research plans of the National Institutes of Health Center for Excellence in Big Data Computing at the University of Illinois, Urbana-Champaign. The Center is organized around the construction of "Knowledge Engine for Genomics" (KnowEnG), an E-science framework for genomics where biomedical scientists will have access to powerful methods of data mining, network mining, and machine learning to extract knowledge out of genomics data. The scientist will come to KnowEnG with their own data sets in the form of spreadsheets and ask KnowEnG to analyze those data sets in the light of a massive knowledge base of community data sets called the "Knowledge Network" that will be at the heart of the system. The Center is undertaking discovery projects aimed at testing the utility of KnowEnG for transforming big data to knowledge. These projects span a broad range of biological enquiry, from pharmacogenomics (in collaboration with Mayo Clinic) to transcriptomics of human behavior.
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Affiliation(s)
- Saurabh Sinha
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jun Song
- Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Victor Jongeneel
- Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jiawei Han
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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21
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Belmann P, Dröge J, Bremges A, McHardy AC, Sczyrba A, Barton MD. Bioboxes: standardised containers for interchangeable bioinformatics software. Gigascience 2015; 4:47. [PMID: 26473029 PMCID: PMC4607242 DOI: 10.1186/s13742-015-0087-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 09/29/2015] [Indexed: 11/29/2022] Open
Abstract
Software is now both central and essential to modern biology, yet lack of availability, difficult installations, and complex user interfaces make software hard to obtain and use. Containerisation, as exemplified by the Docker platform, has the potential to solve the problems associated with sharing software. We propose bioboxes: containers with standardised interfaces to make bioinformatics software interchangeable.
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Affiliation(s)
- Peter Belmann
- Faculty of Technology and Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany
| | - Johannes Dröge
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany
| | - Andreas Bremges
- Faculty of Technology and Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany ; Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany
| | - Alice C McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany
| | - Alexander Sczyrba
- Faculty of Technology and Center for Biotechnology, Bielefeld University, 33615 Bielefeld, Germany
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22
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Nunes R, Barbosa de Almeida Júnior E, Pessoa Pinto de Menezes I, Malafaia G. Learning nucleic acids solving by bioinformatics problems. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2015; 43:377-383. [PMID: 26251209 DOI: 10.1002/bmb.20886] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 04/14/2015] [Accepted: 05/25/2015] [Indexed: 06/04/2023]
Abstract
The article describes the development of a new approach to teach molecular biology to undergraduate biology students. The 34 students who participated in this research belonged to the first period of the Biological Sciences teaching course of the Instituto Federal Goiano at Urutaí Campus, Brazil. They were registered in Cell Biology in the first semester of 2013. They received four 55 min-long expository/dialogued lectures that covered the content of "structure and functions of nucleic acids". Later the students were invited to attend four meetings (in a computer laboratory) in which some concepts of Bioinformatics were presented and some problems of the Rosalind platform were solved. The observations we report here are very useful as a broad groundwork to development new research. An interesting possibility is research into the effects of bioinformatics interventions that improve molecular biology learning.
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Affiliation(s)
- Rhewter Nunes
- Programa de Pós-Graduação em Genética e Melhoramento de Plantas Universidade Federal de Goiás, Câmpus Samambaia, Goiânia, Goiás, Brazil
| | | | - Ivandilson Pessoa Pinto de Menezes
- Professor do Departamento de Ciências Biológicas, Instituto Federal Goiano, Laboratório de Genética e Biologia Molecular, Câmpus Urutaí, Goiás, Brazil
| | - Guilherme Malafaia
- Professor do Departamento de Ciências Biológicas, Instituto Federal Goiano, Laboratório de Pesquisas Biológicas, Câmpus Urutaí, Goiás, Brazil
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23
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Vincent AT, Charette SJ. Who qualifies to be a bioinformatician? Front Genet 2015; 6:164. [PMID: 25964799 PMCID: PMC4408859 DOI: 10.3389/fgene.2015.00164] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 04/12/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Antony T Vincent
- Institut de Biologie Intégrative et des Systèmes, Université Laval Quebec City, QC, Canada ; Département de Biochimie, de Microbiologie et de Bio-Informatique, Faculté des Sciences et de génie, Université Laval Quebec, QC, Canada ; Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec Quebec City, QC, Canada
| | - Steve J Charette
- Institut de Biologie Intégrative et des Systèmes, Université Laval Quebec City, QC, Canada ; Département de Biochimie, de Microbiologie et de Bio-Informatique, Faculté des Sciences et de génie, Université Laval Quebec, QC, Canada ; Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec Quebec City, QC, Canada
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24
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Abstract
In recent years, high-throughput technologies have brought big data to the life sciences. The march of progress has been rapid, leaving in its wake a demand for courses in data analysis, data stewardship, computing fundamentals, etc., a need that universities have not yet been able to satisfy—paradoxically, many are actually closing “niche” bioinformatics courses at a time of critical need. The impact of this is being felt across continents, as many students and early-stage researchers are being left without appropriate skills to manage, analyse, and interpret their data with confidence. This situation has galvanised a group of scientists to address the problems on an international scale. For the first time, bioinformatics educators and trainers across the globe have come together to address common needs, rising above institutional and international boundaries to cooperate in sharing bioinformatics training expertise, experience, and resources, aiming to put ad hoc training practices on a more professional footing for the benefit of all.
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25
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Nim HT, Boyd SE, Rosenthal NA. Systems approaches in integrative cardiac biology: illustrations from cardiac heterocellular signalling studies. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 117:69-77. [PMID: 25499442 DOI: 10.1016/j.pbiomolbio.2014.11.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Revised: 11/26/2014] [Accepted: 11/28/2014] [Indexed: 12/27/2022]
Abstract
Understanding the complexity of cardiac physiology requires system-level studies of multiple cardiac cell types. Frequently, however, the end result of published research lacks the detail of the collaborative and integrative experimental design process, and the underlying conceptual framework. We review the recent progress in systems modelling and omics analysis of the heterocellular heart environment through complementary forward and inverse approaches, illustrating these conceptual and experimental frameworks with case studies from our own research program. The forward approach begins by collecting curated information from the niche cardiac biology literature, and connecting the dots to form mechanistic network models that generate testable system-level predictions. The inverse approach starts from the vast pool of public omics data in recent cardiac biological research, and applies bioinformatics analysis to produce novel candidates for further investigation. We also discuss the possibility of combining these two approaches into a hybrid framework, together with the benefits and challenges. These interdisciplinary research frameworks illustrate the interplay between computational models, omics analysis, and wet lab experiments, which holds the key to making real progress in improving human cardiac wellbeing.
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Affiliation(s)
- Hieu T Nim
- Systems Biology Institute (SBI) Australia, Level 1, Building 75, Monash University, VIC 3800, Australia; Australian Regenerative Medicine Institute, Level 1, Building 75, Monash University, VIC 3800, Australia.
| | - Sarah E Boyd
- Systems Biology Institute (SBI) Australia, Level 1, Building 75, Monash University, VIC 3800, Australia; Australian Regenerative Medicine Institute, Level 1, Building 75, Monash University, VIC 3800, Australia
| | - Nadia A Rosenthal
- Australian Regenerative Medicine Institute, Level 1, Building 75, Monash University, VIC 3800, Australia
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26
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Baier H, Schultz J. ISAAC - InterSpecies Analysing Application using Containers. BMC Bioinformatics 2014; 15:18. [PMID: 24428905 PMCID: PMC3897929 DOI: 10.1186/1471-2105-15-18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Accepted: 01/10/2014] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Information about genes, transcripts and proteins is spread over a wide variety of databases. Different tools have been developed using these databases to identify biological signals in gene lists from large scale analysis. Mostly, they search for enrichments of specific features. But, these tools do not allow an explorative walk through different views and to change the gene lists according to newly upcoming stories. RESULTS To fill this niche, we have developed ISAAC, the InterSpecies Analysing Application using Containers. The central idea of this web based tool is to enable the analysis of sets of genes, transcripts and proteins under different biological viewpoints and to interactively modify these sets at any point of the analysis. Detailed history and snapshot information allows tracing each action. Furthermore, one can easily switch back to previous states and perform new analyses. Currently, sets can be viewed in the context of genomes, protein functions, protein interactions, pathways, regulation, diseases and drugs. Additionally, users can switch between species with an automatic, orthology based translation of existing gene sets. As todays research usually is performed in larger teams and consortia, ISAAC provides group based functionalities. Here, sets as well as results of analyses can be exchanged between members of groups. CONCLUSIONS ISAAC fills the gap between primary databases and tools for the analysis of large gene lists. With its highly modular, JavaEE based design, the implementation of new modules is straight forward. Furthermore, ISAAC comes with an extensive web-based administration interface including tools for the integration of third party data. Thus, a local installation is easily feasible. In summary, ISAAC is tailor made for highly explorative interactive analyses of gene, transcript and protein sets in a collaborative environment.
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Affiliation(s)
- Herbert Baier
- Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, Würzburg 97074, Germany
| | - Jörg Schultz
- Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, Würzburg 97074, Germany
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27
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Abstract
Computationally complex systems models are needed to advance research and implement policy in theoretical and applied population biology. Difference and differential equations used to build lumped dynamic models (LDMs) may have the advantage of clarity, but are limited in their inability to include fine-scale spatial information and individual-specific physical, physiological, immunological, neural and behavioral states. Current formulations of agent-based models (ABMs) are too idiosyncratic and freewheeling to provide a general, coherent framework for dynamically linking the inner and outer worlds of organisms. Here I propose principles for a general, modular, hierarchically scalable, framework for building computational population models (CPMs) designed to treat the inner world of individual agents as complex dynamical systems that take information from their spatially detailed outer worlds to drive the dynamic inner worlds of these agents, simulate their ecology and the evolutionary pathways of their progeny. All the modeling elements are in place, although improvements in software technology will be helpful; but most of all we need a cultural shift in the way population biologists communicate and share model components and the models themselves, fit, test, refute, and refine models, to make the progress needed to meet the ecosystems management challenges posed by global change biology.
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Affiliation(s)
- Wayne M Getz
- Department of Environmental Science, Policy and Management, 130 Mulford Hall, University of California, Berkeley, CA 94720-3114, School of Mathematical Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa
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