This is the final version. Available on open access from Frontiers Media via the DOI in this recordEngineering and Physical Sciences Research Council (EPSRC

Diaz-Zuccarini, V

Tsaneva-Atanasova, K

Frontiers in Physiology

Open Research Exeter

EDITORIALpublished: 24 July 2018doi: 10.3389/fphys.2018.00985Frontiers in Physiology | www.frontiersin.org 1 July 2018 | Volume 9 | Article 985Edited by:Raimond L. Winslow,Johns Hopkins University,United StatesReviewed by:Jason Papin,University of Virginia, United StatesHenggui Zhang,University of Manchester,United Kingdom*Correspondence:Krasimira Tsaneva-Atanasovak.tsaneva-atanasova@exeter.ac.ukSpecialty section:This article was submitted toComputational Physiology andMedicine,a section of the journalFrontiers in PhysiologyReceived: 14 April 2018Accepted: 04 July 2018Published: 24 July 2018Citation:Tsaneva-Atanasova K andDiaz-Zuccarini V (2018) Editorial:Mathematics for Healthcare as Part ofComputational Medicine.Front. Physiol. 9:985.doi: 10.3389/fphys.2018.00985Editorial: Mathematics for Healthcareas Part of Computational MedicineKrasimira Tsaneva-Atanasova 1,2* and Vanessa Diaz-Zuccarini 31Department of Mathematics and Living Systems Institute, University of Exeter, Exeter, United Kingdom, 2 EPSRC Centre forPredictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom, 3Multiscale Cardiovascular EngineeringGroup, Department of Mechanical Engineering, University College London, London, United KingdomKeywords: mathematical modeling, computer simulation, precision medicine, patient specific modeling, digitalhealthEditorial on the Research TopicMathematics for Healthcare as Part of Computational MedicineAppropriate mathematical tools and methodologies are critical for ensuring robust and reliablecomputational model predictions based on medical and healthcare data in the era of the digitalhealth revolution (Duggal et al., 2018). Patient-specific approaches are being increasingly pursued,with simulations benchmarked by clinical data (e.g., brain activity recordings; Breakspear, 2017)obtained in non-invasive manner on individual level (e.g., resting state; Spetsieris et al., 2015).Precision Medicine, although not a new concept, is gaining momentum (Hodson, 2016) poweredby the ever increasing volume of patients data (Colijn et al.). Quantifying patient similarity is animportant challenge that is critical in predicting patients’ disease trajectories (Sharafoddini et al.,2017). In an opinion article (Brown) patient similarity concept has been introduced as a paradigmshift in optimizing personalisation of patient care.Applications of mathematics in healthcare are achieving unprecedented growth at vertiginousspeed in a vast number of areas. Mental health presents a formidable challenge in our modernsociety and computational psychiatry has recently emerged (Huys et al., 2016) as a fieldcombing computational models and patients’ data in an attempt to enhance the prognosis,diagnosis and treatment of mental health conditions. This special issue offers an example (Wonget al.) of statistical learning in the model space for Attention-Deficit Hyperactivity Disordermedication response prediction at individual patient level. Mental health is intimately related toneurological diseases and brain modeling for neurological disease treatments (Rubin, 2017) hasfound applications to setting deep-brain stimulation parameters in Parkinson’s disease treatment(Mandali et al.).Non-negligible effort is currently being devoted to capturing disease progression, a realchallenge in this field. Disease progression modeling involves simulations of disease evolutionbased on available biomarkers or other time-dependent measures of disease status. This isparticularly important in the case of chronic (non-communicable) diseases as demonstratedin this special issue in the case of hypertension (Wang et al.) as well as type 1 diabetes(Wedgwood et al.). Chronic diseases are often extremely complex leading to computational modelsformulated in high-dimensional space, which poses a challenge for characterizing the pathwaysTsaneva-Atanasova and Diaz-Zuccarini Editorial: Mathematics for Healthcareof disease progression or patient-specific disease progressiontrajectories as discussed in Colijn et al. Computational cardiologyencompasses mathematical modeling and computer simulationof dynamical processes in the heart and the cardiovascular systemin health and disease (Trayanova et al., 2012). An example ofan application of computational cardiology is the use of subject-specific computer models to predict neointimal hyperplasiain vein grafts (Donadoni et al.). Computational cardiologyapplications could help in improving clinical decision supportsystems in cardiac ablation therapy for example (Green et al.).Moreover, the use of collections of patient-specific models couldprovide a tool for pre-clinical and clinical assessment of diseasepathology such as atherosclerosis and associated calcification(Alimohammadi et al.).Computational oncology broadly refers to computationalmodeling and simulations of biological process underlyingtumors’ development and progression as well as cancer therapyincluding cancer biomarkers and drug effects (Barbolosi et al.,2016). An example of computational modeling of signalingpathways involved in multiple myeloma is presented inKendrick et al. whereas (Iuliano et al.) presents a network-based statistical methodology for cancer biomarker selection.Computational and mathematical pharmacology is becomingincreasingly relevant for drug development. Mechanistic modelshas become more and more widely used and our understandingof the models’ qualitative and quantitative behavior hasimproved (Krzyzanski and van Hasselt, 2018). There areoutstanding challenges, however, associated with parameteridentifiability of pharmacodynamics models (Janzén et al.) aswell as the estimation of drug absorption profiles in-vivo(Trägårdh et al.).Robust quantitative methods for identifyingbiologically/physiologically relevant computational modelparameters from experimental data are critical for thesuccessful applications of computational medicine in precisionhealthcare (Colijn et al.). This special issue presents severalexamples of such methodological developments in thecase of quantifying: the biomechanical properties of humangallbladder (Li et al.); the forces involved in abrasion damageto skin (Jayawardana et al.); and intracellular calcium signals(Mackay et al.).Infectious disease modeling including the underlyingmechanisms is becoming increasingly important in the faceof the anti-microbial resistance and its associated clinical andpublic health burden (de Kraker et al., 2016). Grasping thecomplexity of host-pathogen interactions remains a challengeand mathematical modeling and analysis could help designingappropriate disease management strategies at patient-specificlevel (Domínguez-Hüttinger et al.) that are necessary forimplementation of precision healthcare (Colijn et al.) as well asto inform public policies related to vaccination, for example see(Hamami et al.).By no means the topics included in this special issue areexhaustive. They are rather indicative of a wider range ofproblems specific to computational medicine that not only canbe tackled by available mathematical approaches but also inspirethe development of novel tools and techniques. Examples ofmethods that have not been included are machine learningand artificial intelligence for electronic health records analysisand usage (Callahan and Shah, 2018). We hope that futureFrontiers Research Topics will contain an increasing number ofcontributions within the scope of mathematics for healthcare aspart of computational medicine.AUTHOR CONTRIBUTIONSKT-A and VD-Z conceived and wrote the editorial.FUNDING01/01/2016–31/12/2019 EP/N014391/1 EPSRC Centrefor Predictive Modeling in Healthcare at the University ofExeter (£2,008,955).ACKNOWLEDGMENTSWe would like to thank all reviewers, particularly those ofinterdisciplinary background, for their constructive commentsin an attempt to initiate a dialogue between theoreticians andpractitioners in the articles of this special issue. KT-A gratefullyacknowledges the financial support of the EPSRC via grantEP/N014391/1.REFERENCESBarbolosi, D., Ciccolini, J., Lacarelle, B., Barlési, F., and Andr,é, N.(2016). Computational oncology—mathematical modelling of drugregimens for precision medicine. Nat. Rev. Clin. Oncol. 13, 242–254.doi: 10.1038/nrclinonc.2015.204Breakspear, M. (2017). Dynamic models of large-scale brain activity.Nat. Neurosci.20, 340–352. doi: 10.1038/nn.4497Callahan, A., and Shah, N. H. (2018). “Machine learning in healthcare,” in KeyAdvances in Clinical Informatics (Amsterdam: Elsevier), 279–291.de Kraker, M. E., Stewardson, A. J., and Harbarth, S. (2016). Will10 million people die a year due to antimicrobial resistanceby 2050? PLoS Med. 13:e1002184. doi: 10.1371/journal.pmed.1002184Duggal, R., Brindle, I., and Bagenal, J. (2018). Digital healthcare: regulating therevolution. Br. Med. J. 2018:360. doi: 10.1136/bmj.k6Hodson, R. (2016). Precision medicine. Nature 537:S49. doi: 10.1038/537S49aHuys, Q. J., Maia, T. V., and Frank, M. J. (2016). Computationalpsychiatry as a bridge from neuroscience to clinicalapplications. Nat. Neurosci. 19, 404–413. doi: 10.1038/nn.4238Krzyzanski, W., and van Hasselt, J. G. C. (2018). Special issue:mathematical pharmacology. J. Pharmacok. Pharmacodyn. 45, 1–1.doi: 10.1007/s10928-017-9566-5Rubin, J. E. (2017). Computational models of basal ganglia dysfunction:the dynamics is in the details. Curr. Opin. Neurobiol. 46, 127–135.doi: 10.1016/j.conb.2017.08.011Frontiers in Physiology | www.frontiersin.org 2 July 2018 | Volume 9 | Article 985Tsaneva-Atanasova and Diaz-Zuccarini Editorial: Mathematics for HealthcareSharafoddini, A., Dubin, J. A., and Lee, J. (2017). Patient similarity in predictionmodels based on health data: a scoping review. JMIR Med. Inform. 5:e7.doi: 10.2196/medinform.6730Spetsieris, P. G., Ko, J. H., Tang, C. C., Nazem, A., Sako, W., Peng,S., et al. (2015). Metabolic resting-state brain networks in health anddisease. Proc. Natl. Acad. Sci. U.S.A. 112, 2563–2568. doi: 10.1073/pnas.1411011112Trayanova, N. A., O’hara, T., Bayer, J. D., Boyle, P. M., McDowell, K. S.,Constantino, J., et al. (2012). Computational cardiology: how computersimulations could be used to develop new therapies and advance existing ones.Europace 14(Suppl. 5), v82–v89. doi: 10.1093/europace/eus277Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.Copyright © 2018 Tsaneva-Atanasova and Diaz-Zuccarini. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, providedthe original author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Physiology | www.frontiersin.org 3 July 2018 | Volume 9 | Article 985

Editorial: Mathematics for Healthcare as Part of Computational Medicine

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Editorial: Mathematics for Healthcare as Part of Computational Medicine

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