A Model for p38MAPK-Induced Astrocyte Senescence.

Experimental evidence indicates that aging leads to accumulation of senescent cells in tissues and they develop a secretory phenotype (also known as SASP, for senescence-associated secretory phenotype) that can contribute to chronic inflammation and diseases. Recent results have showed that markers of senescence in astrocytes from aged brains are increased in brains with Alzheimer's disease. These studies strongly involved the stress kinase p38MAPK in the regulation of the secretory phenotype of astrocytes, yet the molecular mechanisms underlying the onset of senescence and SASP activation remain unclear.

Modelling the onset of senescence at the G1/S cell cycle checkpoint.

Background: DNA damage (single or double-strand breaks) triggers adapted cellular responses. These responses are elicited through signalling pathways, which activate cell cycle checkpoints and basically lead to three cellular fates: cycle arrest promoting DNA repair, senescence (permanent arrest) or cell death. Cellular senescence is known for having a tumour-suppressive function and its regulation arouses a growing scientific interest.

Induced genome maintenance pathways in pre-cancer tissues describe an anti-cancer barrier in tumor development.

A recent model proposing that a barrier is raised against tumor evolution in pre-cancer tissues is investigated. For that we quantify expression alterations in genome maintenance pathways: DNA damage response, death pathways and cell cycle and also differentially expressed genes in transcriptomes of pre-cancerous and cancerous lesions deposited in the GEO database. We find that the main alterations in pre-cancer samples comprising the barrier are: (1) DNA double strand-breaks signaling and repair pathways induction, (2) upregulation of cyclin-dependent kinases, (3) p53 dependent (and independent) repair and apoptosis pathways induction and (4) replicative senescence induction early in tissue transformation.

A method to identify important dynamical states in Boolean models of regulatory networks: application to regulation of stomata closure by ABA in A. thaliana.

Background: We introduce a method to analyze the states of regulatory Boolean models that identifies important network states and their biological influence on the global network dynamics. It consists in (1) finding the states of the network that are most frequently visited and (2) the identification of variable and frozen nodes of the network. The method, along with a simulation that includes random features, is applied to the study of stomata closure by abscisic acid (ABA) in A.