Parameter Inference for Stochastic Kinetic Models of Bacterial Gene Regulation: A Bayesian Approach to Systems Biology

2011 ◽  
pp. 679-706 ◽  
Author(s):  
Darren J. Wilkinson
Keyword(s):  
Gene Regulation ◽  
Systems Biology ◽  
Bayesian Approach ◽  
Kinetic Models ◽  
Parameter Inference ◽  
Bacterial Gene

scholarly journals In planta bacterial multi-omics analysis illuminates regulatory principles underlying plant-pathogen interactions

2019 ◽  
Author(s):  
Tatsuya Nobori ◽  
Yiming Wang ◽  
Jingni Wu ◽  
Sara Christina Stolze ◽  
Yayoi Tsuda ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Pseudomonas Syringae ◽  
Protein Level ◽  
Plant Pathogens ◽  
Plant Immunity ◽  
Bacterial Pathogen ◽  
Bacterial Virulence ◽  
In Planta ◽  
Bacterial Gene ◽  
Global Food Security

AbstractUnderstanding how gene expression is regulated in plant pathogens is crucial for pest control and thus global food security. An integrated understanding of bacterial gene regulation in the host is dependent on multi-omic datasets, but these are largely lacking. Here, we simultaneously characterized the transcriptome and proteome of a foliar bacterial pathogen, Pseudomonas syringae, in Arabidopsis thaliana and identified a number of bacterial processes influenced by plant immunity at the mRNA and the protein level. We found instances of both concordant and discordant regulation of bacterial mRNAs and proteins. Notably, the tip component of bacterial type III secretion system was selectively suppressed by the plant salicylic acid pathway at the protein level, suggesting protein-level targeting of the bacterial virulence system by plant immunity. Furthermore, gene co-expression analysis illuminated previously unknown gene regulatory modules underlying bacterial virulence and their regulatory hierarchy. Collectively, the integrated in planta bacterial omics approach provides molecular insights into multiple layers of bacterial gene regulation that contribute to bacterial growth in planta and elucidate the role of plant immunity in controlling pathogens.

scholarly journals APPROXIMATE BAYESIAN PARAMETER INFERENCE FOR DYNAMICAL SYSTEMS IN SYSTEMS BIOLOGY

2017 ◽  
Vol 31 (1-2) ◽  
Author(s):  
Jovan Tanevski ◽  
Sašo Džeroski ◽  
Ljupčo Kocarev
Keyword(s):  
Systems Biology ◽  
Dynamical Systems ◽  
Stochastic Simulation ◽  
Deterministic Model ◽  
Mass Action ◽  
Mathematical Framework ◽  
Parameter Inference ◽  
Approximate Methods ◽  
Data Set ◽  
Approximate Bayesian

A b s t r a c t: This paper proposes to use approximate instead of exact stochastic simulation algorithms for approximate Bayesian parameter inference of dynamical systems in systems biology. It first presents the mathematical framework for the description of systems biology models, especially from the aspect of a stochastic formulation as opposed to deterministic model formulations based on the law of mass action. In contrast to maximum likelihood methods for parameter inference, approximate inference methodsare presented which are based on sampling parameters from a known prior probability distribution, which gradually evolves tward a posterior distribution, through the comparison of simulated data from the model to a given data set of measurements. The paper then discusses the simulation process, where an overview is given of the different exact and approximate methods for stochastic simulation and their improvements that we propose. The exact and approximate simulators are implemented and used within approximate Bayesian parameter inference methods. Our evaluation of these methods on two tasks of parameter estimation in two different models shows that equally good results are obtained much faster when using approximate simulation as compared to using exact simulation.

Biophysical Approaches to Bacterial Gene Regulation by Riboswitches

2016 ◽  
pp. 157-191 ◽  
Author(s):  
Cibran Perez-Gonzalez ◽  
Jonathan P. Grondin ◽  
Daniel A. Lafontaine ◽  
J. Carlos Penedo
Keyword(s):  
Gene Regulation ◽  
Bacterial Gene

Lies and deception in bacterial gene regulation: the roles of nucleic acid decoys

2014 ◽  
Vol 92 (4) ◽  
pp. 641-647 ◽  
Author(s):  
Yvonne Göpel ◽  
Boris Görke
Keyword(s):  
Gene Regulation ◽  
Nucleic Acid ◽  
Bacterial Gene

Moment closure based parameter inference of stochastic kinetic models

2012 ◽  
Vol 23 (2) ◽  
pp. 287-295 ◽  
Author(s):  
Peter Milner ◽  
Colin S. Gillespie ◽  
Darren J. Wilkinson
Keyword(s):  
Kinetic Models ◽  
Moment Closure ◽  
Parameter Inference

scholarly journals Who turned on the lights?: What the regulation of bacterial bioluminescence tells us about this and other bacterial group behaviours

2013 ◽  
Vol 35 (5) ◽  
pp. 18-23 ◽  
Author(s):  
Eric V. Stabb ◽  
Zomary Flores-Cruz
Keyword(s):  
Gene Regulation ◽  
Group Activity ◽  
Bacterial Group ◽  
Bacterial Gene ◽  
Bioluminescent Bacteria ◽  
Regulatory Decision ◽  
Bacterial Bioluminescence ◽  
Regulatory Circuits ◽  
Pheromone Signalling

Luminescence produced by organisms, or ‘bioluminescence’, holds a distinct fascination for humankind, and the study of bacterial bioluminescence has a long history in the field of microbiology. Advances in our understanding of bacterial bioluminescence have in many ways paralleled advances in the field as a whole. Intriguingly, studies of bioluminescent bacteria led to a seminal discovery in bacterial gene regulation and behaviour, because for bacteria, bioluminescence is a group activity. Bioluminescent bacteria communicate using pheromones, and as a result the regulatory decision to induce bioluminescence is only made if a group of bacteria has achieved a dense enough population to allow the build-up of pheromone. More recently, it has become clear that there are complex regulatory circuits governing not only luminescence, but also pheromone signalling itself. These additional layers of regulation pose new questions such as what are bacteria really saying to each other? Understanding regulation may also help answer ancient questions including, what use is luminescence?

Sign in / Sign up

Export Citation Format

Share Document