Description:
Princeton Docket #
10-2599
To
date, analysis of chemosensory array (¿artificial nose¿) data has been generally
limited to ¿fingerprinting¿ or ¿pattern recognition¿ methods of data analysis.
These methods do not factor in the physical nature of mixed, cross-inhibitory
signals from multiple ligands. This means that chemosensory arrays based on
non-specific receptors are not optimized to account for ¿crosstalk¿ among
analytes, which significantly limits their utility. It also means that sensory
arrays must frequently rely on highly specific ligand-receptor interactions if
they are to be specific for a given ligand, which limits the number of ligands
recognized by the array.
Researchers
in the Departments of Molecular Biology, Princeton University and the Department
of Physics and Astronomy, Rutgers University have developed a method for
statistical analysis of chemosensory array data based on a physical model that
accounts for cross-inhibitory signals in mixtures of ligands. This model
facilitates analysis of sensory data from chemical sensory arrays, making it
easier to design new assays, deconvolute chemical ¿signatures¿, make
quantitative predictions of the concentration of each component in complex
mixtures, and validate results.
The computational algorithm utilizes a Bayesian inference technique,
applied to a pharmacological model for receptor-analyte interactions.
Sensory data is independently compiled for each receptor-analyte pair; sensory
data from unknown mixtures can then be interpreted in terms of the known
response of the sensory array system to individual ligands. This methodology can be employed with both biological systems and
artificial receptor arrays (¿electronic noses¿) designed for various industrial
needs. Currently, the researchers are developing arrays of electrochemical
sensors for real-time detection of harmful gases such as NO, NO2, and CO in
complex mixtures such as diesel engine
exhaust.
Applications
·
Diagnostics
·
Chem/bio
weapons detection
·
Drug
tests
·
Food
monitoring
·
Environmental
monitoring
·
Quality/process
control
Advantages
·
Accounts
for combinatorial mixtures of chemical analytes
·
Quantitative
or semi-quantitative
·
Lower
error rates
·
Better
quality control
·
Better
discriminatory power
Publication
Tsitron
J, Ault AD, Broach JR, Morozov AV. ¿Decoding complex chemical
mixtures with a physical model of a sensor array.¿ PLoS
Comput Biol. 2011
Oct;7(10):e1002224.
The Inventors
James
Broach is
Professor and Associate Chair of Department of Molecular Biology. The research
in Professor Broach¿s laboratory is directed toward understanding cellular
regulation at the molecular level, using the yeast Saccharomyces cerevisiae as a model
system. Dr. Broach was Co-Founder
and Director of Research of Cadus Pharmaceuticals Corporation (Cadus), a
biotechnology firm with drug discovery programs that focused on G-protein
coupled receptors and utilized a core yeast technology for developing drug
discovery assays. Dr. Broach also serves as a member of the Science Advisory
Board for the US Food and Drug
Administration.
Addison
Ault is
post-doctoral fellow in Prof. Broach¿s
laboratory.
Alexandre
V. Morozov
is Assistant Professor in the Department of Physics & Astronomy at Rutgers
University. Professor Morozov¿s
research is focused on applications of statistical physics to biology, including
prediction of gene regulation on a whole-genome scale and its variation
influenced by cell type, environmental signals, developmental stage, and disease
state.
Julia
Tsitron is
a Ph.D. student in Professor Morozov¿s
laboratory.
Intellectual Property
status
Patent protection is pending.