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SUMMARY:Modelling cellular gene expression via neural networks and biparti
 te graphs - Dr Alessia Annibale (King's College London)
DTSTART:20190222T190000Z
DTEND:20190222T200000Z
UID:TALK118918@talks.cam.ac.uk
CONTACT:Valentin Hübner
DESCRIPTION:Cell differentiation is one of the most fascinating areas of d
 evelopmental biology. This was long thought to be an irreversible process\
 , however it has been shown recently that it is possible to reprogramme fu
 lly differentiated cells into a state of induced pluripotency\, which stro
 ngly resembles embryonic stem cells\, via the introduction of a few transc
 ription factors. This opens up exciting perspectives in the field of regen
 erative medicine\, however\, no universally accepted theory exists that ex
 plains the phenomena. The purpose of this work is to drive forward our und
 erstanding of cell reprogramming by introducing an analytical model for tr
 ansitions between cell types. Inspired by neural networks theory\, we mode
 l cell types as hierarchically organized dynamical attractors correspondin
 g to cell cycles. Stages of the cell cycle are fully characterised by the 
 configuration of gene expression levels\, and reprogramming corresponds to
  triggering transitions between such configurations. Two mechanisms were f
 ound for reprogramming: cycle-state specific perturbations and a noise-ind
 uced switching. The former corresponds to a directed perturbation that ind
 uces a transition into a cycle-state of a different cell type in the poten
 cy hierarchy (e.g. a stem cell) whilst the latter is a priori undirected a
 nd could be induced\, e.g. by a (stochastic) change in the cellular enviro
 nment.\nIn addition\, the mechanism for the effective interactions arising
  between genes\, is studied by means of a bipartite graph model\, that int
 egrates the genome and transcriptome into a single regulatory network. Wit
 h this perspective\, we are able to deduce important features of the regul
 atory network that exists in every cell type.
LOCATION:MR2\, Centre for Mathematical Sciences
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