A. González, Harold Castro, Mario Villamizar, N. Cuervo, Gabriel L. Lozano, S. Restrepo, S. Orduz
{"title":"Mesoscale Modeling of the Bacillus Thuringiensis Sporulation Network Based on Stochastic Kinetics and Its Application for in Silico Scale-Down","authors":"A. González, Harold Castro, Mario Villamizar, N. Cuervo, Gabriel L. Lozano, S. Restrepo, S. Orduz","doi":"10.1109/HIBI.2009.17","DOIUrl":"https://doi.org/10.1109/HIBI.2009.17","url":null,"abstract":"Bacillus thuringiensis is a gram positive bacterium that is capable of synthesizing entomotoxines during the sporulation process. This work proposes a mesoscale model capable of describing the evolution of Bacillus thuringiensis sporulation process. The model was developed using stochastic kinetics and solving the master equation with a virtual cluster composed of 35 computers from a student’s computer room. Virtualization is the key strategy to gain computer power in an opportunistic way, it allows to the local users have priority while accessing resources and allows to the virtual cluster exploit the idle time of such resources, providing new capacities for research at a very low cost (close to zero). The results confirm the presence of a mixture of two Gaussian populations for phosphorylated Spo0A. Finally, it was evaluated the effect of oxygen gradients on the production of spores by oscillating the effect of KinA on the reaction of Spo0A phosphorylation.","PeriodicalId":403061,"journal":{"name":"2009 International Workshop on High Performance Computational Systems Biology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121486199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Brim, J. Barnat, I. Cerná, Sven Drazan, J. Fabriková, David Šafránek
{"title":"Computational Analysis of Large-Scale Multi-affine ODE Models","authors":"L. Brim, J. Barnat, I. Cerná, Sven Drazan, J. Fabriková, David Šafránek","doi":"10.1109/HIBI.2009.14","DOIUrl":"https://doi.org/10.1109/HIBI.2009.14","url":null,"abstract":"A biological system as considered in systems biology is understood in the form of a network of interactions among individual biochemical species. Complexity of these networks is inherently enormous, even for simple (e.g., procaryotic) organisms. When modeling and analyzing dynamics of these networks, i.e., exploring how the species evolve in time, we have to fight even another level of complexity -- the enormous state space. In this paper we deal with a class of biological models that can be described in terms of multi-affine dynamic systems. First, we present a prototype tool for parallel (distributed) analysis of multi-affine systems discretized into rectangles that adapts the approach of Belta et.al. Secondly, we propose heuristics that significantly increase applicability of the approach to large biological models. Effects of different settings of the heuristics is firstly compared on a set of experiments performed on small models. Subsequently, experiments on large models are provided as well.","PeriodicalId":403061,"journal":{"name":"2009 International Workshop on High Performance Computational Systems Biology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Mosca, P. Cazzaniga, I. Merelli, D. Pescini, G. Mauri, L. Milanesi
{"title":"Stochastic Simulations on a Grid Framework for Parameter Sweep Applications in Biological Models","authors":"E. Mosca, P. Cazzaniga, I. Merelli, D. Pescini, G. Mauri, L. Milanesi","doi":"10.1109/HIBI.2009.19","DOIUrl":"https://doi.org/10.1109/HIBI.2009.19","url":null,"abstract":"Stochastic modelling and simulations play a major role in Systems Biology because, at molecular level, biological systems exhibit noise coming both from within the cell (intrinsic) and from the environment (extrinsic). Stochastic modelling takes into account the effects of noise over the system dynamics, that can strongly affect the behavior of the system in conditions of relatively low amounts of molecular species. Stochastic simulations provide an effective way to describe the system dynamics, and can be applied on systems where specified chemical species are processed by a set of biochemical reactions, each one characterized by a stochastic constant. In the context of stochastic modelling, Parameter Sweep Applications (PSAs) can be a useful way to explore the huge spaces generated by the combinations of variables and parameters values in order to test their effects on systems dynamics. PSAs are common in the scientific community and are structured as sets of instances, each one characterized by a distinct parametrisation. A PSA that aims to sample such large spaces must involve a large number of instances and hence the problem becomes very time consuming. However, the independence of each instance of a particular PSA makes the distributed computing paradigm a very useful solution for large scale PSAs. In this work we present a grid based version of a multi-volume stochastic simulator, tau-DPP, implemented on the EGEE project platform. The aim of the proposed work is to exploit this platform for testing the reliability of PSAs over the grid, pointing out critical factors, bottlenecks and scalability by providing data about our experience in this kind of biological modelling and simulations. As a case study, we present a number of PSAs for a stochastic model of bacterial chemotaxis composed of 59 reactions and 31 chemical species.","PeriodicalId":403061,"journal":{"name":"2009 International Workshop on High Performance Computational Systems Biology","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133999179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"DiVinE 2.0: High-Performance Model Checking","authors":"J. Barnat, L. Brim, Petr Ročkai","doi":"10.1109/HIBI.2009.10","DOIUrl":"https://doi.org/10.1109/HIBI.2009.10","url":null,"abstract":"We present a tool for parallel enumerative LTL model-checking and reachability analysis. The tool brings model checking to high-powered multi-core systems, as well as high-performance clusters. Boasting pluggable modelling language framework, it is possible to leverage the available parallel algorithms for multiple problem domains, by using suitable input language.","PeriodicalId":403061,"journal":{"name":"2009 International Workshop on High Performance Computational Systems Biology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115492025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Efficient GPU Implementation for Large Scale Individual-Based Simulation of Collective Behavior","authors":"U. Erra, Bernardino Frola, V. Scarano, I. Couzin","doi":"10.1109/HIBI.2009.11","DOIUrl":"https://doi.org/10.1109/HIBI.2009.11","url":null,"abstract":"In this work we describe a GPU implementation for an individual-based model for fish schooling. In this model each fish aligns its position and orientation with an appropriate average of its neighbors’ positions and orientations. This carries a very high computational cost in the so-called nearest neighbors search. By leveraging the GPU processing power and the new programming model called CUDA we implement an efficient framework which permits to simulate the collective motion of high-density individual groups. In particular we present as a case study a simulation of motion of millions of fishes. We describe our implementation and present extensive experiments which demonstrate the effectiveness of our GPU implementation.","PeriodicalId":403061,"journal":{"name":"2009 International Workshop on High Performance Computational Systems Biology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124716811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}