Systems Medicine and Systems Therapy: A Call to a Natural Collaboration

1985 ◽  
Vol 4 (2) ◽  
pp. 15-31 ◽  
Author(s):  
George Saba ◽  
Donald L. Fink
Keyword(s):  
Systems Medicine

Systems Medicine Derived Biomarkers to Assess How Canagliflozin Delays Progression of Diabetic Kidney Disease

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1126-P
Author(s):  
HIDDO LAMBERS. HEERSPINK ◽  
PAUL PERCO ◽  
JOHANNES LEIERER ◽  
MICHAEL K. HANSEN ◽  
ANDREAS HEINZEL ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Systems Medicine ◽  
Diabetic Kidney

scholarly journals CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations

2021 ◽  
Vol 22 (14) ◽  
pp. 7590
Author(s):  
Liza Vinhoven ◽  
Frauke Stanke ◽  
Sylvia Hafkemeyer ◽  
Manuel Manfred Nietert
Keyword(s):  
Large Scale ◽  
Synergistic Effects ◽  
Small Scale ◽  
Systems Medicine ◽  
Promising Candidate ◽  
Cftr Mutations ◽  
Machine Readable ◽  
High Throughput Screens ◽  
Readable Format ◽  
Machine Readable Format

Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics.

scholarly journals Multiple-Molecule Drug Design Based on Systems Biology Approaches and Deep Neural Network to Mitigate Human Skin Aging

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3178
Author(s):  
Shan-Ju Yeh ◽  
Jin-Fu Lin ◽  
Bor-Sen Chen
Keyword(s):  
Neural Network ◽  
Systems Biology ◽  
Signaling Pathways ◽  
Human Skin ◽  
Deep Neural Network ◽  
Middle Age ◽  
Skin Aging ◽  
Identification System ◽  
Potential Candidate ◽  
Systems Medicine

Human skin aging is affected by various biological signaling pathways, microenvironment factors and epigenetic regulations. With the increasing demand for cosmetics and pharmaceuticals to prevent or reverse skin aging year by year, designing multiple-molecule drugs for mitigating skin aging is indispensable. In this study, we developed strategies for systems medicine design based on systems biology methods and deep neural networks. We constructed the candidate genomewide genetic and epigenetic network (GWGEN) via big database mining. After doing systems modeling and applying system identification, system order detection and principle network projection methods with real time-profile microarray data, we could obtain core signaling pathways and identify essential biomarkers based on the skin aging molecular progression mechanisms. Afterwards, we trained a deep neural network of drug–target interaction in advance and applied it to predict the potential candidate drugs based on our identified biomarkers. To narrow down the candidate drugs, we designed two filters considering drug regulation ability and drug sensitivity. With the proposed systems medicine design procedure, we not only shed the light on the skin aging molecular progression mechanisms but also suggested two multiple-molecule drugs for mitigating human skin aging from young adulthood to middle age and middle age to old age, respectively.

Models and Data Sources Used in Systems Medicine

2016 ◽  
Vol 55 (02) ◽  
pp. 107-113 ◽  
Author(s):  
M. Löpprich ◽  
C. Karmen ◽  
M. Ganzinger ◽  
M. Gietzelt
Keyword(s):  
Treatment Strategies ◽  
Computer Applications ◽  
Data Sources ◽  
Search Term ◽  
Systems Medicine ◽  
Data Types ◽  
Research Activities ◽  
Meta Analyses ◽  
Modelling Methods ◽  
Modelling Approaches

SummaryBackground: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

scholarly journals The 9th Santorini Conference: Systems Medicine, Personalised Health and Therapy. “The Odyssey from Hope to Practice”, Santorini, Greece, 30 September–3 October 2018

2018 ◽  
Vol 8 (4) ◽  
pp. 43
Author(s):  
Sophie Visvikis-Siest ◽  
Vesna Gorenjak ◽  
Maria Stathopoulou ◽  
Alexandros Petrelis ◽  
Georges Weryha ◽  
...  
Keyword(s):  
Big Data ◽  
Clinical Practice ◽  
Metabolic Diseases ◽  
Personalised Medicine ◽  
Modern Medicine ◽  
Greek Mythology ◽  
Scientific Program ◽  
Systems Medicine ◽  
Gene Environment ◽  
Personalised Therapy

The 9th traditional biannual conference on Systems Medicine, Personalised Health & Therapy—“The Odyssey from Hope to Practice”, inspired by the Greek mythology, was a call to search for practical solutions in cardio-metabolic diseases and cancer, to resolve and overcome the obstacles in modern medicine by creating more interactions among disciplines, as well as between academic and industrial research, directed towards an effective ‘roadmap’ for personalised health and therapy. The 9th Santorini Conference, under the Presidency of Sofia Siest, the director of the INSERM U1122; IGE-PCV (www.u1122.inserm.fr), University of Lorraine, France, offered a rich and innovative scientific program. It gathered 34 worldwide distinguished speakers, who shared their passion for personalised medicine with 160 attendees in nine specific sessions on the following topics: First day: The Odyssey from hope to practice: Personalised medicine—landmarks and challenges Second day: Diseases to therapeutics—genotype to phenotype an “-OMICS” approach: focus on personalised therapy and precision medicine Third day: Gene-environment interactions and pharmacovigilance: a pharmacogenetics approach for deciphering disease “bench to clinic to reality” Fourth day: Pharmacogenomics to drug discovery: a big data approach and focus on clinical data and clinical practice. In this article we present the topics shared among the participants of the conference and we highlight the key messages.

scholarly journals Expert Panel Discusses the Importance of Systems Medicine

2018 ◽  
Vol 1 (1) ◽  
pp. 3-8
Author(s):  
Harald H.H.W. Schmidt ◽  
Jan Baumbach ◽  
Joseph Loscalzo ◽  
Alvar Agusti ◽  
Edwin K. Silverman ◽  
...  
Keyword(s):  
Expert Panel ◽  
Systems Medicine

scholarly journals Consistency, Inconsistency and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome Scale Metabolic Modelling

2018 ◽  
Author(s):  
Nhung Pham ◽  
Ruben Van Heck ◽  
Jesse van Dam ◽  
Peter Schaap ◽  
Edoardo Saccenti ◽  
...  
Keyword(s):  
Systems Medicine ◽  
Biochemical Reactions ◽  
Metabolic Modelling ◽  
Research Areas ◽  
Limit Model ◽  
Metabolic Models ◽  
Genome Scale ◽  
Manual Verification

Genome scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in eleven biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

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