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Dane
Flannery

School of
Mathematics, Statistics and Applied Mathematics

We survey progress over the past few years in computing with finitely generated linear groups over an infinite domain. We indicate some important open questions and suggest avenues for continuing research.

Haixuan
Yang

School of Mathematics, Statistics and Applied Mathematics

School of Mathematics, Statistics and Applied Mathematics

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. This results in 622 putative protein complexes, depicted as constellation within a cell -- proteins as stars and a cell as a universe. Most of these complexes are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism.

Claas
Röver

School of Mathematics, Statistics and Applied Mathematics

School of Mathematics, Statistics and Applied Mathematics

One of the easiest ways to bring structure into a countable set is to introduce some order. I'll talk about the `pure' mathematics of a fixed number of shunting tracks that meet at a single point such that precisely two are connected there at any one time.

Jerry
Murphy

School of Mathematics, Statistics and Applied Mathematics, NUIG

and Department of Mechanical Engineering, DCU

Ligaments, muscles and tendons share the same physical characteristics: they are incompressible, non-linearly elastic, transversely isotropic materials. There is a well-developed mathematical model for materials with these characteristics. The difficulties encountered when matching experimental data with this theory are described.

Patrick
E. Farrell

Imperial College London

Imperial College London

The derivatives of PDE models are key ingredients in many important algorithms of computational mathematics. They find applications in diverse areas such as sensitivity analysis, PDE-constrained optimisation, continuation and bifurcation analysis, error estimation, and generalised stability theory.

These derivatives, computed using the so-called tangent linear and adjoint models, have made an enormous impact in certain scientific fields (such as aeronautics, meteorology, and oceanography). However, their use in other areas has been hampered by the great practical difficulty of the derivation and implementation of tangent linear and adjoint models. In his recent book, Naumann (2011) describes the problem of the robust automated derivation of parallel tangent linear and adjoint models as ``one of the great open problems in the field of high-performance scientific computing''.

In this talk, we present an elegant solution to this problem for the common case where the forward model may be written in variational form, and discuss some of its applications.

School of Mathematics, Statistics and Applied Mathematics

National University of Ireland, Galway, University Road, Galway, Ireland.

Phone: +353 (0)91 492332 (direct) , +353 (0)91 524411 x2332 (switchboard)

Fax: +353 (0)91 494542 Email: Mary.Kelly@nuigalway.ie

This page was last updated Saturday, May 04

National University of Ireland, Galway, University Road, Galway, Ireland.

Phone: +353 (0)91 492332 (direct) , +353 (0)91 524411 x2332 (switchboard)

Fax: +353 (0)91 494542 Email: Mary.Kelly@nuigalway.ie

This page was last updated Saturday, May 04