Reference : Dynamical hybrid modeling of human metabolism
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Computer science
Human health sciences : Pharmacy, pharmacology & toxicology
Systems Biomedicine
http://hdl.handle.net/10993/35297
Dynamical hybrid modeling of human metabolism
English
Ben Guebila, Marouen mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
5-Mar-2018
University of Luxembourg, ​​Luxembourg
Docteur en Biologie
Thiele, Ines mailto
Balling, Rudi mailto
Krüger, Rejko mailto
Küpfer, Lars mailto
Moerland, Perry mailto
[en] metabolic networks ; drug metabolism ; dynamical modeling
[en] Human metabolism plays a key role in disease pathogenesis and drug action. Half a century of biochemical literature leveraged by the advent of genomics allowed the emergence of computational modeling techniques and the in silico analysis of complex biological systems. In particular, Constraint-Based Reconstruction and Analysis (COBRA) methods address the complexity of metabolism through building tissue-specific networks in their steady state. It is known that biological systems respond to perturbations induced by pathogens, drugs or malignant processes by shifting their activity to safeguard key metabolic functions. Extending the modeling framework to consider the dynamics of these complex systems will bring simulations closer to observable human phenotypes.
In this thesis, I combined physiologically-based pharmacokinetic (PBPK) models with genome-scale metabolic models (GSMMs) to form hybrid genome-scale dynamical models that provide a hypothesis-free framework to study the perturbations induced by one or more perturbagen on human tissues. On a first stage, these methodologies were applied to decipher the absorption of levodopa and amino acids by the intestinal epithelium and allowed to derive a model-based diet for Parkinson's Disease patients. In the next phase, we extended the study to 605 drugs in order to predict the occurrence of gastrointestinal side effects through a machine learning classifier, using a combination of gene expression and metabolic reactions set as features. Finally, the approach upscaled to several tissues, specifically to investigate the genesis of metabolic symptoms in type 1 diabetes and to suggest key metabolic players underlying within and between-individual variability to insulin action. Taken as whole, the integration of two modeling techniques constrained by expert biological knowledge and heterogeneous data types will be a step forward in achieving convergence in human biology.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/35297

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
BenGuebila_Thesis_18.pdfAuthor postprint13.84 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.