{"title":"Selection of a Primary Model to Describe the Growth Curve of Burkholderia glumae","authors":"R. Vergara, I. Arrocha, R. Herrera","doi":"10.1109/IESTEC46403.2019.00019","DOIUrl":null,"url":null,"abstract":"The gram-negative bacteriumBurkholderia Glumaeis the causal agent of the disease called bacterial panicle blight,which causes grain rot, in our case rotting of rice, which is themain crop in Panama. It was first reported in our country in2005 causing losses greater than 40 %. This study focuses onthe comparison of various nonlinear mathematical models oftenused to describe the growth dynamics of these kinds of bacteriaand seeks to determine the best fit. The state parameters ofthese models will be optimized via the metaheuristic ParticleSwarm algorithm, which is an iterative method that seeks tooptimize a function, in order to select the mathematical modelthat allows modeling the growth of the bacteria and analysis ofits characteristics, such as replication patterns, among others.","PeriodicalId":388062,"journal":{"name":"2019 7th International Engineering, Sciences and Technology Conference (IESTEC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 7th International Engineering, Sciences and Technology Conference (IESTEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IESTEC46403.2019.00019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The gram-negative bacteriumBurkholderia Glumaeis the causal agent of the disease called bacterial panicle blight,which causes grain rot, in our case rotting of rice, which is themain crop in Panama. It was first reported in our country in2005 causing losses greater than 40 %. This study focuses onthe comparison of various nonlinear mathematical models oftenused to describe the growth dynamics of these kinds of bacteriaand seeks to determine the best fit. The state parameters ofthese models will be optimized via the metaheuristic ParticleSwarm algorithm, which is an iterative method that seeks tooptimize a function, in order to select the mathematical modelthat allows modeling the growth of the bacteria and analysis ofits characteristics, such as replication patterns, among others.