Logic and Linear programs to understand cancer response - LS2N - équipe COMBI ( Combinatoire et Bio-informatique ) Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2018

Logic and Linear programs to understand cancer response

Résumé

Understanding which are the key components of a system that distinguish a normal from a cancerous cell has been approached widely in the recent years using machine learning and statistical approaches to detect gene signatures and predict cell growth. Recently, the idea of using gene regulatory and signaling networks, in the form of logic programs, has been introduced in order to detect the mechanisms that control cells state change. Complementary to this, a large literature deals with constraint based methods for analyzing genome-scale metabolic networks. One of the major outcome of these methods concerns the quantitative prediction of growth rates under both given environmental conditions and the presence or absence of a given set of enzymes which catalyze biochemical reactions. It is of high importance to plug logic regulatory models into metabolic networks by using a gene-enzyme logical interaction rule. In this work, our aim is first to review previously proposed logic programs to discover key components in the graph based causal models that distinguish different variants of cell types. These variants represent either cancerous vs. healthy cell types, multiple cancer cell lines, or patients with different treatment response. With these approaches, we can handle experimental data coming from transcriptomic profiles, gene expression micro-arrays or RNAseq, as well as (multi-perturbation) phosphoproteomics measurements. In a second part, we deal with the problem of combining both, regulatory and signaling, logic models within metabolic networks. Such a combination allows us to obtain quantitative prediction of tumor cell growth. Our results point to logic program models built for 3 cancer types: Multiple Myeloma, Acute Myeloid Leukemia, and Breast Cancer. Experimental data for Misbah Razzaq · Pierre Le Jeune · Carito Guziolowski () LS2N, UMR 6004, ´ 1 2 Authors Suppressed Due to Excessive Length these studies was collected through DREAM challenges and in collaboration with biologists that produced them. The networks were built using several publicly available pathway databases, such as Pathways Interaction Database [37], KEGG [17], Reactome [10], and Trrust [13]. We show how these models allow us to identify the key mechanisms distinguishing a cancerous cell. In complement to this, we sketch a methodology, based on currently available frameworks and datasets, that relates both the linear component of the metabolic networks and the logical part of logic programing based methods.
Fichier principal
Vignette du fichier
preprint_ARSBM18.pdf (2.82 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01897036 , version 1 (16-10-2018)

Identifiants

  • HAL Id : hal-01897036 , version 1

Citer

Misbah Razzaq, Lokmane Chebouba, Pierre Le Jeune, Hanen Mhamdi, Carito Guziolowski, et al.. Logic and Linear programs to understand cancer response. 2018. ⟨hal-01897036⟩
102 Consultations
136 Téléchargements

Partager

Gmail Facebook X LinkedIn More