title: [Mathematical dynamical models for personalized medicine].
abstract: One of the necessary conditions to perform any personalized medicine is to obtain good individual predictions. In addition to the numerous markers available (omics data), the methods used to analyze the data are very important too. We are presenting an example of mathematical dynamical mechanistic model that could be used for adapting the antiretroviral treatment in patients infected by the human immunodeficiency virus. The interest of this type of approach is to build a model based on biological knowledge about the interaction between markers and therefore to allow for a better predictive power.
authors.affiliation: Inserm U897, INRIA SISTM (statistics in systems biology and translational medicine), université de Bordeaux, ISPED (institut de santé publique, d'épidémiologie et de développement), CHU de Bordeaux, unité de soutien méthodologique à la recherche clinique et épidémiologique, Bordeaux, France; institut de recherche vaccinale (Labex) UPEC, Créteil, France.
authors.forename: Rodolphe
authors.initials: R
authors.lastname: Thiébaut
authors.affiliation: Inserm U897, INRIA SISTM (statistics in systems biology and translational medicine), université de Bordeaux, ISPED (institut de santé publique, d'épidémiologie et de développement), CHU de Bordeaux, unité de soutien méthodologique à la recherche clinique et épidémiologique, Bordeaux, France; institut de recherche vaccinale (Labex) UPEC, Créteil, France.
authors.forename: Mélanie
authors.initials: M
authors.lastname: Prague
authors.affiliation: Inserm U897, INRIA SISTM (statistics in systems biology and translational medicine), université de Bordeaux, ISPED (institut de santé publique, d'épidémiologie et de développement), CHU de Bordeaux, unité de soutien méthodologique à la recherche clinique et épidémiologique, Bordeaux, France; institut de recherche vaccinale (Labex) UPEC, Créteil, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: Commenges
mesh_terms: D019380:Anti-HIV Agents / Q000493:pharmacokinetics / Q000627:therapeutic use; D001374:Azacitidine / Q000493:pharmacokinetics / Q000627:therapeutic use; D018791:CD4 Lymphocyte Count; D015496:CD4-Positive T-Lymphocytes / Q000821:virology; D015224:Dideoxynucleosides / Q000493:pharmacokinetics / Q000627:therapeutic use; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology / Q000821:virology; D015497:HIV-1 / Q000187:drug effects / Q000201:enzymology / Q000502:physiology; D015235:HLA-B Antigens / Q000235:genetics; D006801:Humans; D019259:Lamivudine / Q000493:pharmacokinetics / Q000627:therapeutic use; D008962:Models, Theoretical; D057285:Precision Medicine; D019713:Receptors, CCR5 / Q000235:genetics; D018894:Reverse Transcriptase Inhibitors / Q000493:pharmacokinetics / Q000627:therapeutic use; D053585:Virus Attachment; D000099227:Abacavir
chemical_list: D019380:Anti-HIV Agents; D001374:Azacitidine; D015224:Dideoxynucleosides; D015235:HLA-B Antigens; D019259:Lamivudine; D019713:Receptors, CCR5; D018894:Reverse Transcriptase Inhibitors; D000099227:Abacavir; C584933:CCR5 protein, human; C484830:HLA-B*57:01 antigen