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[PM] Cell Modeling and Computational Analysis of Biolog PM Data
인성크로마텍(주)
Date : 2020.02.11
분류 : Bio & Medical Products > BiOLOG

 

Cell Modeling and Computational Analysis of Biolog PM Data


Many research groups are interested in cell modeling, and models have begun to gain acceptance and proliferate. But are these 

models accurate and capable of making useful predictions of cell function? The last several years have seen significant 

improvements in computational methods for performing phenotypic predictions from metabolic network models. The speed with

which such models can be assembled has also advanced. Similarly, experimental methods for high-throughput phenotypic 

characterization of cells have advanced during this time, and in particular, the use of Phenotype MicroArrays in challenging and 

improving models has become more widespread. A three-day conference will bring together researchers in these and related 

areas to define current gaps and explore potential synergies between these computational and experimental approaches. 

  

Biolog customers have developed software systems and computational methods for the storage, display, and statistical analysis of Biolog Phenotype MicroArray data. This newsletter provides a brief description of storage/display software, published methods for analyzing PM data, and examples of biological discovery. 


 

EcoCyc: Fusing Model Organism Databases With Systems Biology

Peter Karp and colleagues present an updated model organism database integrating genome sequence, experimental literature,

and Phenotype MicroArrays. Using new tools, the impact of EcoCyc knowledge is demonstrated.

 


Visualization and Curve-Parameter Estimation Strategies for Detailed Exploration of Phenotype MicroArray Kinetics

The DSMZ authors were motivated to extract as much information as possible from the high throughput phenotyping provided 

by Biolog Phenotype MicroArrays. They hypothesized that multiple kinetic parameter estimation is necessary to capture the 

diversity of the biological response recorded over time. Therefore they adapted R functions and existing statistical methods for

modeling kinetic curves into a package that is customized to work within the biological context of Biolog PM kinetic redox dye 

reduction curves.

 


Statistical Methods for Comparative Phenomics using High-Throughput Phenotype MicroArrays

New statistical methods are proposed for the analysis of PM data. Distance is quantified between mean and median curves 

followed by a permutation test. A second approach involved a permutation test on mean area under the curve. These methods

were applied to both synthetic and real data.



High-throughput Generation, Optimization, and Analysis of Genome-scale Metabolic Models

Genome-scale metabolic models were created and validated against Biolog data where available. Biolog data facilitated 

genome annotation by showing positive unpredicted reactions that were due to poorly annotated transporters. 

Biolog validation significantly improved metabolic models in all cases.

 


PheMaDB: A Web-based Database Management System for Storage, Retrieval, Visualization, and Analysis of 

OmniLog Phenotypic MicroArray Data

PheMaDB is a web-based relational database management system that is standardized for OmniLog phenotypic microarrays (PM)

data. It is used to store, visualize, and analyze large collections of time-series PM data for bio-pathogens. PheMaDB includes 

seven analytical modules: outlier analysis, negative control analysis, phenotype barplot, correlation matrix, phenotype profile 

search, k-means clustering, and heatmap analysis. The system was developed by the US Naval Medical Research Center and the 

MITRE Corporation.

 

 

Visualization of Growth Curve Data from Phenotype MicroArray Experiments

LBNL developed software to produce and display color images representing growth curve data. Using pseuodocolor, the authors

have turned the kinetic OmniLog response into a linear graphic called a PMColorMap. This software was used to compare 

replicates and identify phenotypic differences. 

 

 

 

Phenotype MicroArray Technology

 

Biolog’s Phentoype MicroArray technology enables researchers to evaluateMAP of PM Plates.PNG

nearly 2000 phenotypes of a microbial cell in a single experiment. This 

integrated system of cellular assays, instrumentation and bioinformatics 

software provides cellular knowledge that complements molecular 

information, helping you interpret and find the relevant aspects in massive 

amounts of gene expression or proteomics data. Through comprehensive 

and precise quantitation of phenotypes, researchers are able to obtain an 

unbiased perspective of the effect on cells of genetic differences, 

environmental change, exposure to chemicals or drugs, and more.