Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

Recent Trends in Modelling Spatio-Temporal Data

Il lavoro fornisce una disamina delle pi`u recenti metodologie proposte nell’ambito dei modelli spazio-temporali. Nel tentativo di proporre una visione unificata delle metodologie trattate, viene fornita prima una descrizione dei vari tipi di dati spazio-temporali. Successivamente, si procede con la discussione dei modelli per processi spazialmente continui. La modellistica spazio-temporale `e stata largamente utilizzata per affrontare problemi in ambito ambientale, geostatistico, idrologico e meteorologico. Questo articolo fornisce una analisi dei metodi correntemente applicati in molte di queste aree

Human Immunodeficiency virus type 1 subtype C external glycoproteins epitopes : in silico predictions

Subtype C human immunodeficiency vírus type 1 (HIV-1) is rapidly diversifying among populations, which display extensiva polymorphism of genes encoding class I human leukocyte antigen (HLA) proteins, as detected in different regions of the world. Broadly conserved HIV-1 cytotoxic T cell (CTL) epitopes considering 128 subtype C externai glycoprotein gp120 sequences selected from GenBank were identified to A*0201, A*0301, A*1101 and 8*07 HLA alleles using Epijen software. NetChop allowed to predict proteasome cleavage followed by prediction of binding to transport associated with antigen processing on TapPred. Glycosylation and positively selected sites within epitope sequences were also observed. Furthermore, three-dimensional structures of subtype C gp120 were predicted from consensus sequences in PHYRE and the PYMOL software was used to verify positions occupied by conserved epitopes. Finally, we predicted discontinuous B cell epitopes in DiscoTope 1.2. There is a recognized evolutionary force of HIV-1 to escape from B cells and CTL responses mutating sites that can negatively select the viral population. ' These types of analyses could be useful to understand HIV-1 epidemiology associated with polymorphisms in HLA alleles frequent in a determined region. 1t is expected that such knowledge may provide additional support for vaccine development

2016 - The Twenty-first Annual Symposium of Student Scholars

The full program book from the Twenty-first Annual Symposium of Student Scholars, held on April 21, 2016. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1015/thumbnail.jp

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.Siirretty Doriast

The Timescale of Emergence and Spread of Turnip Mosaic Potyvirus

Plant viruses have important global impacts on crops, and identifying their centre and date of emergence is important for planning control measures. Turnip mosaic virus (TuMV) is a member of the genus Potyvirus in the family Potyviridae and is a major worldwide pathogen of brassica crops. For two decades, we have collected TuMV isolates, mostly from brassicas, in Turkey and neighbouring countries. This region is thought to be the centre of emergence of this virus. We determined the genomic sequences of 179 of these isolates and used these to estimate the timescale of the spread of this virus. Our Bayesian coalescent analyses used synonymous sites from a total of 417 novel and published whole-genome sequences. We conclude that TuMV probably originated from a virus of wild orchids in Germany and, while adapting to wild and domestic brassicas, spread via Southern Europe to Asia Minor no more than 700 years ago. The population of basal-B group TuMVs in Asia Minor is older than all other populations of this virus, including a newly discovered population in Iran. The timescale of the spread of TuMV correlates well with the establishment of agriculture in these countries.This work was in part funded by Saga University and supported by JSPS KAKENHI Grant numbers 18405022, 24405026 and 16K14862 and Grant-in-Aid for JSPS Research Fellow Grant number 16J04390

Systemwide Review of Plant Breeding Methodologies in the CGIAR

Report of the systemwide review of plant breeding methodologies in the CGIAR conducted in 2000 by a panel chaired by Donald N. Duvick. The document includes an excerpt from the summary of CGIAR International Centers Week 2000, a transmittal letter from TAC Chair Emil Javier, TAC's commentary, and a transmittal letter from the panel chair.The study was based on sub-reports for nine centers, which were made available through the CGIAR website. There were six main findings:1. centers are using traditional techniques effectively and efficiently;2. new tools are used effectively, but will not replace traditional methods in the short term;3. biotechnology will increase efficiency and effectiveness but cost more;4. centers are outsourcing biotechnology effectively but should do it more;5. more financial support is needed for germplasm research and mechanisms that hinder intercenter collaboration should be changed;6. better intercenter collaboration, consolidation, and even centralization could increase effectiveness.The Group endorsed these recommendations.There are nine annexes covering among other things: breeding methods for CGIAR commodities, biotechnology methods used at centers, resource commitments, and CGIAR-NARS interactions in plant breeding and biotechnology

Development of genomic resources and tools for precision farming of pikeperch through high-throughput sequencing and computational genomics

This thesis provides the first genomic tools and resources to enhance pikeperch's innovative farming, optimal domestication, and adaption into modern intensive aquaculture systems, including a high-quality chromosome-level assembly, reference transcriptome, and gene expression atlas. The pikeperch genome was also used as a reference for comparative genomics analyses and population genetics analyses in domesticated individuals to establish the landscape of genetic variations. These findings lay the foundation for addressing critical issues in genomics-informed pikeperch farming.Diese Dissertation stellt die ersten genomischen Werkzeuge und Ressourcen zur Verfügung, um die innovative Zanderzucht, optimale Domestizierung und Anpassung an moderne intensive Aquakultursysteme zu verbessern, einschließlich einer hochwertigen Genom-Assemblierung auf Chromosomenebene, eines Referenztranskriptoms und eines Genexpressionsatlasses. Das Genom des Zanders wurde auch als Referenz für vergleichende Genomanalysen und populationsgenetische Analysen bei domestizierten Individuen verwendet, um die Landschaft der genetischen Variationen zu ermitteln