iDEA: Drexel E-repository and Archives >
Drexel Academic Community >
School of Biomedical Engineering, Science & Health Systems >
Faculty Research and Publications (Biomed Eng) >
Accelerated search for biomolecular network models to interpret high-throughput experimental data
Please use this identifier to cite or link to this item:
|Title: ||Accelerated search for biomolecular network models to interpret high-throughput experimental data|
|Authors: ||Datta, Suman|
Sokhansanj, Bahrad A.
|Issue Date: ||18-Jul-2007 |
|Publisher: ||BioMed Central|
|Citation: ||BMC Bioinformatics, 8(258)|
|Abstract: ||Background: The functions of human cells are carried out by biomolecular networks, which
include proteins, genes, and regulatory sites within DNA that encode and control protein
expression. Models of biomolecular network structure and dynamics can be inferred from highthroughput
measurements of gene and protein expression. We build on our previously developed
fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges
of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays.
We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy
biomolecular network models consistent with a biological data set. We also develop a method to
estimate the probability of a potential network model fitting a set of data by chance. The resulting
metric provides an estimate of both model quality and dataset quality, identifying data that are too
noisy to identify meaningful correlations between the measured variables.
Results: Optimal parameters for the evolutionary search were identified based on artificial data,
and the algorithm showed scalable and consistent performance for as many as 150 variables. The
method was tested on previously published human cell cycle gene expression microarray data sets.
The evolutionary search method was found to converge to the results of exhaustive search. The
randomized evolutionary search was able to converge on a set of similar best-fitting network
models on different training data sets after 30 generations running 30 models per generation.
Consistent results were found regardless of which of the published data sets were used to train or
verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.
Conclusion: Our results demonstrate the capability of scalable evolutionary search for fuzzy
network models to address the problem of inferring models based on complex, noisy biomolecular
data sets. This approach yields multiple alternative models that are consistent with the data, yielding
a constrained set of hypotheses that can be used to optimally design subsequent experiments.|
|Appears in Collections:||Faculty Research and Publications (Biomed Eng)|
Items in iDEA are protected by copyright, with all rights reserved, unless otherwise indicated.