No abstract

450,000 European citizens are diagnosed every year with colorectal cancer (CRC) and more than 230,000 succumb to the disease annually. For this reason, significant resources are dedicated to the identification of more effective therapies for this disease. However, classical assessment techniques for these treatments are slow and costly. Consequently, systems biology researchers at the Royal College of Surgeons in Ireland (RCSI) are developing computational agent-based models simulating tumour growth and treatment responses with the objective of speeding up the therapeutic development process while, at the same time, producing a tool for adapting treatments to patient-specific characteristics. However, the model complexity and the high number of agents to be simulated require a thorough optimisation of the process in order to execute realistic simulations of tumour growth on currently available platforms. We propose to apply the most advanced HPC techniques to achieve the efficient and realistic simulation of a virtual tissue model that mimics tumour growth or regression in space and time. These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied other solid tumours.This work has been partially supported by MICINN-Spain under contract TIN2011- 28689-C02-01 and TIN2014-53234-C2-1-R and GenCat-DIUiE(GRR) 2014-SGR-576. This research was also funded by the European Community’s Framework Programme Seven (FP7) Programme under contract No. 278981\ud 680 AngioPredict and supported by the DJEI/DES/SFI/HEA Irish Centre for High-\ud End Computing (ICHEC).Peer ReviewedPostprint (author's final draft

No abstract

The List of luster LC is an eetive tehnique to index high dimension metri spaes. LC is an array-type struture for similarity searh based on lustering. Sparse Spatial Seletion SSS is a new struture based on pivots for similarity searh in metri spaes. This array-type struture has shown good performane during the searh as ompared to other methods. This work shows dierent onstrution strategies on LC ; for instane, the use of SSS as a general seletion method of pivots or enters, among others. The artile also shows the advantages of the use of other tehniques like keeping the distane between the ob jets and the luster enter, apart from revising the eets of reursive appliation of suh methods. Finally, the inuene of the use of Voronoi partitions for the distribution of the ob jets within the struture will be shown. Preliminary experimental results show that the new versions of LC have a better performane in terms of distane evaluation than the original data struture and other renowned strutures. Keywords: Databases, Data strutures, Algorithms, Metri Spaes, Similarity Searh.