WorldCat Identities

Legrand, Jonathan

Works: 2 works in 2 publications in 1 language and 3 library holdings
Roles: Author
Publication Timeline
Most widely held works by Jonathan Legrand
Modelling the influence of dimerisation sequence dissimilarities on the auxin signalling network by Jonathan Legrand( )

1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide

Toward a multi-scale understanding of flower development - from auxin networks to dynamic cellular patterns by Jonathan Legrand( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

A striking aspect of flowering plants is that, although they seem to display a great diversity of size and shape, they are made of the same basics constituents, that is the cells. The major challenge is then to understand how multicellular tissues, originally undifferentiated, can give rise to such complex shapes. We first investigated the uncharacterised signalling network of auxin since it is a major phytohormone involved in flower organogenesis.We started by determining the potential binary network, then applied model-based graph clustering methods relying on connectivity profiles. We demonstrated that it could be summarise in three groups, closely related to putative biological groups. The characterisation of the network function was made using ordinary differential equation modelling, which was later confirmed by experimental observations.In a second time, we modelled the influence of the protein dimerisation sequences on the auxin interactome structure using mixture of linear models for random graphs. This model lead us to conclude that these groups behave differently, depending on their dimerisation sequence similarities, and that each dimerisation domains might play different roles.Finally, we changed scale to represent the observed early stages of A. thaliana flower development as a spatio-temporal property graph. Using recent improvements in imaging techniques, we could extract 3D+t cellular features, and demonstrated the possibility of identifying and characterising cellular identity on this basis. In that respect, hierarchical clustering methods and hidden Markov tree have proven successful in grouping cell depending on their feature similarities
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Audience level: 0.00 (from 0.00 for Modelling ... to 0.00 for Modelling ...)