Methods and Applications in Computational Physiology and Medicine

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Background

This Research Topic is part of the Methods and Applications in Physiology series, aiming to highlight the latest experimental techniques and methods used to understand the most fundamental questions in physiology research from molecular to organ function in living organisms. Other titles in this series are:

• Methods and Applications in Aquatic Physiology
• Methods and Applications in Clinical and Translational Physiology
• Methods and Applications in Environmental, Aviation and Space Physiology
• Methods and Applications in Exercise Physiology
• Methods and Applications in Fractal Physiology
• Methods and Applications in Integrative Physiology
• Methods and Applications in Invertebrate Physiology
• Methods and Applications in Metabolic Physiology
• Methods and Applications in Physio-logging
• Methods and Applications in Striated Muscle Physiology
• Methods and Applications in Respiratory Physiology
• Methods and Applications in Vascular Physiology
• New Methods for Red Blood Cell Research and Diagnosis, Volume II
• Combining Computational and Experimental Approaches to Characterize Ion Channels and Transporters

Please submit your article to the Research Topic that best suits the focus of your research.

Computational Physiology and Medicine guidelines
The scope of the Computational Physiology and Medicine specialty section explicitly underlines the fundamental role of modern methodologies for harnessing, processing and analysis of complex, potentially very large and heterogeneous biological and medical data: 'Our ability to collect quantitative data on the structure and function of living systems at many different spatio-temporal scales is expanding rapidly. Insights gained from these data are making it clear that physiological function arises from complex interactions both within (horizontal integration) and between (vertical integration) networks organized at different biological levels. Understanding how physiological function emerges from these network interactions is the next major frontier of biomedical science.’

As stated, data alone are not sufficient for reasoning about and modelling of complex biological phenomena. Multiscale and multiphysics methodologies have been proven to provide solid scaffolding for capturing functional richness and complexity of biological systems. A special class of such systems displays scaling property and is sometimes referred to as ‘fractal’. Yet, multiscale and multiphysics modelling approaches cover all classes of biological complexity and encompass both such multi-concept philosophy of data analysis approaches and approaches from first principles of physics through model formulation and validation on grounds of (computational) physiology and medicine.

In recent years, modern methodologies of capturing complex biological interactions in the formal language of networks (of networks) are beginning to form a new common ground facilitating cross-discipline understanding and modelling of complexity, benefitting all parties. It is now possible to obtain insights into the complex network of the biochemistry of life building blocks and relate it with phenomena and models at the level of population health and their corresponding networks of interactions.

Novel insights into the dynamics of functional processes are obtained using information theoretic methods, in particular evaluation of causality and entropy and information flow. Where data is abundant, but models are too difficult to build or are simply unknown, machine learning and artificial intelligence methods have been shown to be of advantage. Of particular interest to the readers of Methods and Applications in Computational Physiology and Medicine will be applications, approaches and methods making use of modern high performance computational facilities from inexpensive graphical cards to large scale supercomputer centers. In addition, this Research Topic welcomes methodological contributions, opinions, and review papers in the fields of causality, connectivity, information transfer, characterization of fractal structures and self-similar dynamics. It also encourages contributions to data and model management.

General guidelines
The contributions to this collection will undergo peer-review, but the criteria may be adjusted to fit the present Research Topic; for instance, while novelty is not necessarily decisive, the utility of a method or protocol must be evident. We welcome contributions covering all aspects of physiology and the submissions will be handled by the team of Topic Editors in the respective sections.

Frontiers in Physiology supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management and stewardship (Wilkinson et al., Sci. Data 3:160018, 2016).

This Research Topic welcomes:
• Methods: Include either existing methods that are significantly improved or adapted for specific purposes or new methods, which may also include primary (original) data.
• Protocols: Should provide a detailed description, with pitfalls and troubleshooting, and be of immediate use to the readers. The protocols must be proven to work.
• Perspective or General Commentaries on methods and protocols relevant for physiology research.
• Reviews and mini-reviews of current methods and protocols highlighting the important future directions of the field.

For more information on the description and formats of the different article types please see here.

Keywords: Physiological system models, Methods, Protocols, Data management, Model management

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic editors

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