Network modeling approaches for metabolic diseases and diabetes

Diabetes; Metabolic diseases; Networks; Modeling and Simulation; Biochemistry, Genetics and Molecular Biology (all); Drug Discovery; Computer Science Applications; Applied Mathematics

Abstract :

[en] Metabolic diseases (MD) are amenable to network-based modeling frameworks, given the systemic perturbations induced by disrupted molecular mechanisms. We present here a brief overview of network modeling methods applied to inborn errors of metabolism (IEM), systemic metabolic conditions (mainly diabetes), and metabolism-related inflammation and autoimmune disorders. Clinical diagnosis and identification of causal agents in IEMs and uncovering the multifactorial mechanisms underlying the development of diabetes and other systemic metabolic diseases are the main challenges being addressed. The review also highlights some of the studies undertaken to investigate the role of the gut microbiome in MD, especially in diabetes. While the network frameworks employed in different modeling approaches have provided novel insights, some technique-specific limitations and overall gaps in general research trends need further attention.

Precision for document type :

Review article

Disciplines :

Life sciences: Multidisciplinary, general & others

Author, co-author :

BADKAS, Apurva ; University of Luxembourg > Faculty of Science, Technology and Medicine > Department of Life Sciences and Medicine > Team Thomas SAUTER

Pacheco, Maria Pires; Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

SAUTER, Thomas ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)

External co-authors :

Language :

English

Title :

Network modeling approaches for metabolic diseases and diabetes

Publication date :

December 2024

Journal title :

Current Opinion in Systems Biology

eISSN :

2452-3100

Publisher :

Elsevier Ltd

Volume :

Pages :

100530

Peer reviewed :

Editorial reviewed

Additional URL :

https://api.elsevier.com/content/article/PII:S245231002400026X?httpAccept=text/xml

Funders :

FNR

Funding text :

This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference [PRIDE15/10675146/CANBIO]

Available on ORBilu :

since 19 August 2024

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