[PM] Cell Modeling and Computational Analysis of Biolog PM Data
|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
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 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
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 evaluate
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.