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Casotti MC, Meira DD, Alves LNR, Bessa BGDO, Campanharo CV, Vicente CR, Aguiar CC, Duque DDA, Barbosa DG, dos Santos EDVW, Garcia FM, de Paula F, Santana GM, Pavan IP, Louro LS, Braga RFR, Trabach RSDR, Louro TS, de Carvalho EF, Louro ID. Translational Bioinformatics Applied to the Study of Complex Diseases. Genes (Basel) 2023; 14:419. [PMID: 36833346 PMCID: PMC9956936 DOI: 10.3390/genes14020419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
Abstract
Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.
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Affiliation(s)
- Matheus Correia Casotti
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Dummer Meira
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Lyvia Neves Rebello Alves
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Camilly Victória Campanharo
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Creuza Rachel Vicente
- Departamento de Medicina Social, Universidade Federal do Espírito Santo, Vitória 29040-090, Espírito Santo, Brazil
| | - Carla Carvalho Aguiar
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Daniel de Almeida Duque
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Débora Gonçalves Barbosa
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | | | - Fernanda Mariano Garcia
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Flávia de Paula
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Gabriel Mendonça Santana
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Isabele Pagani Pavan
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Luana Santos Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Furlani Rocon Braga
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Raquel Silva dos Reis Trabach
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
| | - Thomas Santos Louro
- Escola Superior de Ciências da Santa Casa de Misericórdia de Vitória (EMESCAM), Vitória 29027-502, Espírito Santo, Brazil
| | - Elizeu Fagundes de Carvalho
- Instituto de Biologia Roberto Alcantara Gomes (IBRAG), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20551-030, Rio de Janeiro, Brazil
| | - Iúri Drumond Louro
- Departamento de Ciências Biológicas, Universidade Federal do Espírito Santo, Vitória 29075-010, Espírito Santo, Brazil
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Prinz R. A simple measure for biocomplexity. Biosystems 2022; 217:104670. [DOI: 10.1016/j.biosystems.2022.104670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/23/2022] [Accepted: 03/26/2022] [Indexed: 11/02/2022]
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Vazquez P, Hirayama-Shoji K, Novik S, Krauss S, Rayner S. Globally Accessible Distributed Data Sharing (GADDS): a decentralized FAIR platform to facilitate data sharing in the life sciences. Bioinformatics 2022; 38:3812-3817. [PMID: 35639939 PMCID: PMC9344842 DOI: 10.1093/bioinformatics/btac362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/12/2022] [Accepted: 05/24/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Technical advances have revolutionized the life sciences and researchers commonly face challenges associated with handling large amounts of heterogeneous digital data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles provide a framework to support effective data management. However, implementing this framework is beyond the means of most researchers in terms of resources and expertise, requiring awareness of metadata, policies, community agreements, and other factors such as vocabularies and ontologies. RESULTS We have developed the Globally Accessible Distributed Data Sharing (GADDS) platform to facilitate FAIR-like data-sharing in cross-disciplinary research collaborations. The platform consists of (i) a blockchain based metadata quality control system, (ii) a private cloud-like storage system and (iii) a version control system. GADDS is built with containerized technologies, providing minimal hardware standards and easing scalability, and offers decentralized trust via transparency of metadata, facilitating data exchange and collaboration. As a use case, we provide an example implementation in engineered living material technology within the Hybrid Technology Hub at the University of Oslo. AVAILABILITY Demo version available at https://github.com/pavelvazquez/GADDS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pavel Vazquez
- Hybrid Technology Hub - Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1110 Blindern 0317, Oslo, Norway
| | - Kayoko Hirayama-Shoji
- Hybrid Technology Hub - Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1110 Blindern 0317, Oslo, Norway
| | - Steffen Novik
- Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, P.O. Box 1032 Blindern N-0315, Oslo, Norway
| | - Stefan Krauss
- Hybrid Technology Hub - Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1110 Blindern 0317, Oslo, Norway.,Department of Immunology and Transfusion Medicine, Oslo University Hospital, P.O. Box 4950 Nydalen, 0424, Oslo, Norway
| | - Simon Rayner
- Hybrid Technology Hub - Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1110 Blindern 0317, Oslo, Norway.,Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
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Kameda T, Awazu A, Togashi Y. Molecular dynamics analysis of biomolecular systems including nucleic acids. Biophys Physicobiol 2022; 19:e190027. [DOI: 10.2142/biophysico.bppb-v19.0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/18/2022] [Indexed: 12/01/2022] Open
Affiliation(s)
| | - Akinori Awazu
- Graduate School of Integrated Sciences for Life, Hiroshima University
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Djordjevic M, Rodic A, Salom I, Zigic D, Milicevic O, Ilic B, Djordjevic M. A systems biology approach to COVID-19 progression in population. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:291-314. [PMID: 34340771 PMCID: PMC8092812 DOI: 10.1016/bs.apcsb.2021.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.
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Affiliation(s)
| | - Andjela Rodic
- Computational Systems Biology Group, Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | - Igor Salom
- Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
| | - Dusan Zigic
- Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
| | - Ognjen Milicevic
- Department for Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Bojana Ilic
- Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
| | - Marko Djordjevic
- Computational Systems Biology Group, Faculty of Biology, University of Belgrade, Belgrade, Serbia.
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Horácio ECA, Hickson J, Murta SMF, Ruiz JC, Nahum LA. Perspectives From Systems Biology to Improve Knowledge of Leishmania Drug Resistance. Front Cell Infect Microbiol 2021; 11:653670. [PMID: 33996631 PMCID: PMC8120230 DOI: 10.3389/fcimb.2021.653670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/09/2021] [Indexed: 11/17/2022] Open
Abstract
Neglected Tropical Diseases include a broad range of pathogens, hosts, and vectors, which represent evolving complex systems. Leishmaniasis, caused by different Leishmania species and transmitted to humans by sandflies, are among such diseases. Leishmania and other Trypanosomatidae display some peculiar features, which make them a complex system to study. Leishmaniasis chemotherapy is limited due to high toxicity of available drugs, long-term treatment protocols, and occurrence of drug resistant parasite strains. Systems biology studies the interactions and behavior of complex biological processes and may improve knowledge of Leishmania drug resistance. System-level studies to understand Leishmania biology have been challenging mainly because of its unusual molecular features. Networks integrating the biochemical and biological pathways involved in drug resistance have been reported in literature. Antioxidant defense enzymes have been identified as potential drug targets against leishmaniasis. These and other biomarkers might be studied from the perspective of systems biology and systems parasitology opening new frontiers for drug development and treatment of leishmaniasis and other diseases. Our main goals include: 1) Summarize current advances in Leishmania research focused on chemotherapy and drug resistance. 2) Share our viewpoint on the application of systems biology to Leishmania studies. 3) Provide insights and directions for future investigation.
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Affiliation(s)
- Elvira Cynthia Alves Horácio
- René Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Brazil.,Department of Genetics, Ecology and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Jéssica Hickson
- René Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Brazil
| | | | | | - Laila Alves Nahum
- René Rachou Institute, Oswaldo Cruz Foundation, Belo Horizonte, Brazil.,Department of Genetics, Ecology and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil.,Promove College of Technology, Belo Horizonte, Brazil
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Hastings JF, O'Donnell YEI, Fey D, Croucher DR. Applications of personalised signalling network models in precision oncology. Pharmacol Ther 2020; 212:107555. [PMID: 32320730 DOI: 10.1016/j.pharmthera.2020.107555] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 04/07/2020] [Indexed: 02/07/2023]
Abstract
As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.
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Affiliation(s)
- Jordan F Hastings
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia
| | | | - Dirk Fey
- Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland; School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - David R Croucher
- The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, Australia; School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland; St Vincent's Hospital Clinical School, University of New South Wales, Sydney, NSW 2052, Australia.
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Gatta R, Depeursinge A, Ratib O, Michielin O, Leimgruber A. Integrating radiomics into holomics for personalised oncology: from algorithms to bedside. Eur Radiol Exp 2020; 4:11. [PMID: 32034573 PMCID: PMC7007467 DOI: 10.1186/s41747-019-0143-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 12/06/2019] [Indexed: 12/18/2022] Open
Abstract
Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other “omics” data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other “omics” into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called “holomics”, will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.
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Affiliation(s)
- Roberto Gatta
- Personalised Analytic Oncology, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Adrien Depeursinge
- Personalised Analytic Oncology, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.,University of Applied Sciences and Arts Western Switzerland (HES-SO), Sierre, Switzerland
| | - Osman Ratib
- Service of Medical Imaging, Riviera-Chablais Hospital, Rennaz, Switzerland.,Department of Medical Imaging, Lausanne University Hospital, Lausanne, Switzerland
| | - Olivier Michielin
- Personalised Analytic Oncology, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Antoine Leimgruber
- Personalised Analytic Oncology, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland. .,Service of Medical Imaging, Riviera-Chablais Hospital, Rennaz, Switzerland.
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Arsov Z, Koklič T. Regional Biophysics Conference: RBC2018. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2019; 48:405-406. [PMID: 31267213 DOI: 10.1007/s00249-019-01381-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Affiliation(s)
- Zoran Arsov
- Laboratory of Biophysics, Department of Condensed Matter Physics, Jozef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia.
| | - Tilen Koklič
- Laboratory of Biophysics, Department of Condensed Matter Physics, Jozef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia.
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