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Modeling gene networks : application to the study of cell transformation

Claude Gérard & Frédéric Lemaigre
Duve Institute, Université catholique de Louvain (UCL),
JPG - 18.9 ko

An increasing amount of evidence demonstrates a close relation between inflammation and cancer development, which reveals the importance of the tumour microenvironment for the development of cancers. Recently, a molecular pathway linking inflammation to cell transformation has been discovered [1]. This molecular pathway rests on a positive inflammatory feedback loop between NF-B, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-B, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-B. In turn, IL6 can promote NF-B activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation.

We developed a mathematical model to account for the dynamical behavior of this positive inflammatory feedback loop [2]. A deterministic version of the model is based on ordinary differential equations describing the time evolution of the transcription factor and miRNA concentration of the main components of the network. Using this deterministic model, we showed that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical network linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Simulations with a stochastic version of the model suggest that random fluctuations (due to molecular noise) are able to trigger cell transformation, highlighting possible links between stochasticity and cancer development. Finally, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards stochastic fluctuations.

Voir ce site : Institut de Duve

[1] Iliopoulos D, Hirsch HA, Struhl K (2009) An epigenetic switch involving NF-kappaB, Lin28, Let-7 MicroRNA, and IL6 links inflammation to cell transformation. Cell 139, 693-706.

[2] Gérard C., Gonze D., Lemaigre F., Novak B. (2014) A model for the epigenetic switch linking inflammation to cell transformation : deterministic and stochastic approaches. PLoS Comput. Biol. 10(1), e1003455.

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