title: A strategy to establish Food Safety Model Repositories.
abstract: Transferring the knowledge of predictive microbiology into real world food manufacturing applications is still a major challenge for the whole food safety modelling community. To facilitate this process, a strategy for creating open, community driven and web-based predictive microbial model repositories is proposed. These collaborative model resources could significantly improve the transfer of knowledge from research into commercial and governmental applications and also increase efficiency, transparency and usability of predictive models. To demonstrate the feasibility, predictive models of Salmonella in beef previously published in the scientific literature were re-implemented using an open source software tool called PMM-Lab. The models were made publicly available in a Food Safety Model Repository within the OpenML for Predictive Modelling in Food community project. Three different approaches were used to create new models in the model repositories: (1) all information relevant for model re-implementation is available in a scientific publication, (2) model parameters can be imported from tabular parameter collections and (3) models have to be generated from experimental data or primary model parameters. All three approaches were demonstrated in the paper. The sample Food Safety Model Repository is available via: http://sourceforge.net/projects/microbialmodelingexchange/files/models and the PMM-Lab software can be downloaded from http://sourceforge.net/projects/pmmlab/. This work also illustrates that a standardized information exchange format for predictive microbial models, as the key component of this strategy, could be established by adoption of resources from the Systems Biology domain.
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
authors: C
authors: C
authors: Plaza-Rodríguez
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
authors: C
authors: C
authors: Thoens
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
authors: A
authors: A
authors: Falenski
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
authors: A A
authors: AA
authors: Weiser
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
authors: B
authors: B
authors: Appel
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany.
authors: A
authors: A
authors: Kaesbohrer
authors: Federal Institute for Risk Assessment, Department Biological Safety, Unit Epidemiology, Zoonoses and Antimicrobial Resistance, Max-Dohrn-Straße 8-10, 10589 Berlin, Germany. Electronic address: Matthias.Filter@bfr.bund.de.
authors: M
authors: M
authors: Filter
mesh_terms: D000818:Animals; D002417:Cattle; D016208:Databases, Factual; D004196:Disease Outbreaks / Q000517:prevention & control; D005506:Food Contamination; D059022:Food Safety / Q000379:methods*; D005544:Forecasting / Q000379:methods*; D020407:Internet; D008460:Meat / Q000382:microbiology; D008954:Models, Biological; D012475:Salmonella / Q000302:isolation & purification; D012480:Salmonella Infections / Q000382:microbiology; D012984:Software; D049490:Systems Biology / Q000379:methods*
keywords: Food safety modelling; Model database; PMF-ML; PMM-Lab; Predictive microbiology; SBML