Using multitype branching models to analyze bacterial pathogenicity

Daniah Tahir, et al. "Using multitype branching models to analyze bacterial pathogenicity." Mathematica Applicanda 48 (2020): null. <http://eudml.org/doc/295483>.

@article{DaniahTahir2020,
abstract = {We apply multitype, continuous time, Markov branching models to study pathogenicity in E. coli, a bacterium belonging to the genus Escherichia. First, we examine briefly, the properties of multitype branching processes and we also survey some fundamental limit theorems regarding the behavior of such models under various conditions. These theorems are then applied to discrete, state dependent models, in order to analyze pathogenicity in a published clinical data set consisting of 251 strains of E. coli. We use well established methods, incorporating maximum likelihood techniques, to estimate speciation rates as well as the rates of transition between different states of the models. From the analysis, we not only derive new results, but we also verify some preexisting notions about virulent behavior in bacterial strains.},
author = {Daniah Tahir, Ingemar Kaj, Krzysztof Bartoszek, Marta Majchrzak, Pawel Parniewski, Sebastian Sakowski},
journal = {Mathematica Applicanda},
keywords = {Markov models; branching processes; limit theorems; virulence factors; E. coli strains},
language = {eng},
pages = {null},
title = {Using multitype branching models to analyze bacterial pathogenicity},
url = {http://eudml.org/doc/295483},
volume = {48},
year = {2020},
}

TY - JOUR
AU - Daniah Tahir
AU - Ingemar Kaj
AU - Krzysztof Bartoszek
AU - Marta Majchrzak
AU - Pawel Parniewski
AU - Sebastian Sakowski
TI - Using multitype branching models to analyze bacterial pathogenicity
JO - Mathematica Applicanda
PY - 2020
VL - 48
SP - null
AB - We apply multitype, continuous time, Markov branching models to study pathogenicity in E. coli, a bacterium belonging to the genus Escherichia. First, we examine briefly, the properties of multitype branching processes and we also survey some fundamental limit theorems regarding the behavior of such models under various conditions. These theorems are then applied to discrete, state dependent models, in order to analyze pathogenicity in a published clinical data set consisting of 251 strains of E. coli. We use well established methods, incorporating maximum likelihood techniques, to estimate speciation rates as well as the rates of transition between different states of the models. From the analysis, we not only derive new results, but we also verify some preexisting notions about virulent behavior in bacterial strains.
LA - eng
KW - Markov models; branching processes; limit theorems; virulence factors; E. coli strains
UR - http://eudml.org/doc/295483
ER -