11 research outputs found

    Σχεδίαση Aρχιτεκτονικής SoC για τον FRM-SSA

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    Στην Ενότητα 2 παρουσιάζονται οι στοχαστικές μέθοδοι προσομοίωσης και αλγόριθμοι SSA και FRM-SSA του Gillespie. Στην Ενότητα 3 παρουσιάζονται αναλυτικά οι προδιαγραφές του συστήματος που υλοποιήθηκε, ο βαθμός παραμετροποίησης του καθώς και οι τρόποι λειτουργίας του. Στην Ενότητα 4 αναλύεται η αρχιτεκτονική FRM SoC σε επίπεδο συστήματος καθώς επίσης γίνεται και σύντομη αναφορά στο σύστημα επικοινωνίας υπολογιστή και συστήματος. Στην Ενότητα 5 παρουσιάζεται η αρχιτεκτονική της επεξεργαστικής μονάδας (FRM Processing Unit - FPU) ενός SSA Core. Δίνεται έμφαση στη δίοδο δεδομένων της FPU ενώ περιγράφονται αναλυτικά και οι υπόλοιπες μονάδες που πλαισιώνουν τη δίοδο δεδομένων της FPU. Επιπλέον παρουσιάζεται και η θεωρητική μελέτη των επιδόσεων που έγινε κατά το σχεδιασμό. Στην Ενότητα 6 παρουσιάζονται τα στατιστικά αποτελέσματα που προέκυψαν από τη σύνθεση του συστήματος για διάφορους τρόπους λειτουργίας. Στην 7 και τελευταία ενότητα παρουσιάζονται πραγματικά αποτέλεσμα από δοκιμές του συστήματος με σκοπό την επικύρωση της σχεδίασης. Για αυτό το λόγο γίνεται σύγκριση των αποτελεσμάτων με τα αποτελέσματα γνωστών πλατφόρμων προσομοίωσης

    Σχεδίαση και FPGA Υλοποίηση IP πυρήνων και SoCs για Παράλληλη Στοχαστική Προσομοίωση Βιολογικών Δικτύων

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    Η προσομοίωση ολόκληρου του κυττάρου αποτελεί μια από τις μεγαλύτερες προκλήσεις του 21ου αιώνα. Σε κυτταρικό επίπεδο ένα βιοχημικό σύστημα μπορεί να περιγράφεται από χιλιάδες αντιδράσεις όπου συμμετέχουν αλληλοεπιδρώντα μοριακά είδη. Ωστόσο τα σημερινά υπολογιστικά συστήματα αδυνατούν να διαχειριστούν αποδοτικά βιομοντέλα τέτοιας πολυπλοκότητας. Το αντικείμενο της παρούσας διπλωματικής εργασίας είναι η σχεδίαση και ανάπτυξη ενός ενσωματωμένου υπολογιστικού συστήματος το οποίο θα μπορεί να χρησιμοποιηθεί για τη στοχαστική προσομοίωση βιομοντέλων χιλιάδων αντιδράσεων. Μέσω VHDL περιγραφών υλοποιήσαμε με FPGAs μια ευέλικτη πολυπύρηνη αρχιτεκτονική που στόχο έχει την παράλληλη εκτέλεση στοχαστικών προσομοιώσεων χρησιμοποιώντας τον αλγόριθμο του Gillespie, First Reaction Method. Η αρχιτεκτονική που αναπτύχθηκε έχει την μορφή "μαλακού πυρήνα" (soft IP core) και είναι πλήρως παραμετρική ως προς τα χαρακτηριστικά του βιομοντέλου αλλά και ως προς τη δέσμευση πόρων υλικού. Χρησιμοποιεί έναν υπολογιστή γενικού σκοπού για την επικοινωνία του χρήστη και υποστηρίζει προσομοιώσεις βιομοντέλων με έως 4Κ αντιδράσεις σε ένα μετρίου μεγέθους FPGA. Τέλος αποδεικνύεται ότι το σύστημα που αναπτύχθηκε λειτουργώντας στη συχνότητα των 220 MHz μπορεί να επιταχύνει την διαδικασία της προσομοίωσης έως και 17.6 φορές σε σύγκριση με τη σειριακή εκτέλεση του αλγορίθμου από δημοφιλή προγράμματα προσομοίωσης βιοχημικών δικτύων όπως το COPASI.Whole cell simulation is one of the greatest challenges of the 21st century. A biochemical system at the cellular level may involve thousands of reaction channels and molecular species. However at present, computational tools are unable to handle efficiently the simulation of biomodels of such a high complexity. In this thesis we designed and developed a System-on-Chip to simulate efficiently and stochastically biomodels with practically any number of reaction channels given an appropriate size FPGA. Using parametric VHDL descriptions we realized a flexible multicore architecture to perform in parallel stochastic simulations based on Gillespie’s First Reaction Method. Our MPSoC architecture is captured as a soft IP core that is fully parametric in terms of biomodel’s characteristics and hardware resources and serves as an accelerator to a general purpose PC, the front-end for user interaction. It supports the stochastic simulation of biochemical reaction networks with up to 4K reaction channels and molecular species using a medium-size FPGA. It is shown that the system when operating at 220 MHz can accelerate simulation by a factor of 17.6 compared to well-known serial software simulator COPASI running on a very fast compute server

    An integrated approach to enhancing functional annotation of sequences for data analysis of a transcriptome

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    Given the ever increasing quantity of sequence data, functional annotation of new gene sequences persists as being a significant challenge for bioinformatics. This is a particular problem for transcriptomics studies in crop plants where large genomes and evolutionarily distant model organisms, means that identifying the function of a given gene used on a microarray, is often a non-trivial task. Information pertinent to gene annotations is spread across technically and semantically heterogeneous biological databases. Combining and exploiting these data in a consistent way has the potential to improve our ability to assign functions to new or uncharacterised genes. Methods: The Ondex data integration framework was further developed to integrate databases pertinent to plant gene annotation, and provide data inference tools. The CoPSA annotation pipeline was created to provide automated annotation of novel plant genes using this knowledgebase. CoPSA was used to derive annotations for Affymetrix GeneChips available for plant species. A conjoint approach was used to align GeneChip sequences to orthologous proteins, and identify protein domain regions. These proteins and domains were used together with multiple evidences to predict functional annotations for sequences on the GeneChip. Quality was assessed with reference to other annotation pipelines. These improved gene annotations were used in the analysis of a time-series transcriptomics study of the differential responses of durum wheat varieties to water stress. Results and Conclusions: The integration of plant databases using the Ondex showed that it was possible to increase the overall quantity and quality of information available, and thereby improve the resulting annotation. Direct data aggregation benefits were observed, as well as new information derived from inference across databases. The CoPSA pipeline was shown to improve coverage of the wheat microarray compared to the NetAffx and BLAST2GO pipelines. Leverage of these annotations during the analysis of data from a transcriptomics study of the durum wheat water stress responses, yielded new biological insights into water stress and highlighted potential candidate genes that could be used by breeders to improve drought response

    Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

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    The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

    An integrated approach to enhancing functional annotation of sequences for data analysis of a transcriptome

    Get PDF
    Given the ever increasing quantity of sequence data, functional annotation of new gene sequences persists as being a significant challenge for bioinformatics. This is a particular problem for transcriptomics studies in crop plants where large genomes and evolutionarily distant model organisms, means that identifying the function of a given gene used on a microarray, is often a non-trivial task. Information pertinent to gene annotations is spread across technically and semantically heterogeneous biological databases. Combining and exploiting these data in a consistent way has the potential to improve our ability to assign functions to new or uncharacterised genes. Methods: The Ondex data integration framework was further developed to integrate databases pertinent to plant gene annotation, and provide data inference tools. The CoPSA annotation pipeline was created to provide automated annotation of novel plant genes using this knowledgebase. CoPSA was used to derive annotations for Affymetrix GeneChips available for plant species. A conjoint approach was used to align GeneChip sequences to orthologous proteins, and identify protein domain regions. These proteins and domains were used together with multiple evidences to predict functional annotations for sequences on the GeneChip. Quality was assessed with reference to other annotation pipelines. These improved gene annotations were used in the analysis of a time-series transcriptomics study of the differential responses of durum wheat varieties to water stress. Results and Conclusions: The integration of plant databases using the Ondex showed that it was possible to increase the overall quantity and quality of information available, and thereby improve the resulting annotation. Direct data aggregation benefits were observed, as well as new information derived from inference across databases. The CoPSA pipeline was shown to improve coverage of the wheat microarray compared to the NetAffx and BLAST2GO pipelines. Leverage of these annotations during the analysis of data from a transcriptomics study of the durum wheat water stress responses, yielded new biological insights into water stress and highlighted potential candidate genes that could be used by breeders to improve drought response

    Using MapReduce Streaming for Distributed Life Simulation on the Cloud

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    Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

    Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

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    Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40
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