1
|
de Bono B, Gillespie T, Surles-Zeigler MC, Kokash N, Grethe JS, Martone M. Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY. Front Physiol 2022; 13:795303. [PMID: 35547570 PMCID: PMC9083405 DOI: 10.3389/fphys.2022.795303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/07/2022] [Indexed: 01/04/2023] Open
Abstract
We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY's knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.
Collapse
Affiliation(s)
- Bernard de Bono
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Tom Gillespie
- Department of Neuroscience, University of California, San Diego, San Diego, CA, United States
| | | | - Natallia Kokash
- Faculty of Humanities, University of Amsterdam, Amsterdam, Netherlands
| | - Jeff S. Grethe
- Department of Neuroscience, University of California, San Diego, San Diego, CA, United States
| | - Maryann Martone
- Department of Neuroscience, University of California, San Diego, San Diego, CA, United States
| |
Collapse
|
2
|
Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M, Sheppard DW. Big Data in Drug Discovery. PROGRESS IN MEDICINAL CHEMISTRY 2018; 57:277-356. [PMID: 29680150 DOI: 10.1016/bs.pmch.2017.12.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Aaron Sim
- BenevolentAI, London, United Kingdom
| | | | - Magdalena Zwierzyna
- BenevolentAI, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
| | | |
Collapse
|
3
|
Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
Collapse
Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| |
Collapse
|
4
|
Omholt SW, Hunter PJ. The Human Physiome: a necessary key for the creative destruction of medicine. Interface Focus 2016. [DOI: 10.1098/rsfs.2016.0003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Stig W. Omholt
- Faculty of Medicine, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Peter J. Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| |
Collapse
|