A Novel Method for Signaling Pathway Analysis

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$146,256.00
Award Year:
2009
Program:
STTR
Phase:
Phase I
Contract:
1R41GM087013-01
Award Id:
93823
Agency Tracking Number:
GM087013
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
ADVAITA CORPORATION, 1777 KIRTS BLVD, APT 115, TROY, MI, 48084
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
198047529
Principal Investigator:
SORINDRAGHICI
(734) 922-0110
Business Contact:
SORINDRAGHICI
() -
Research Institute:
WAYNE STATE UNIVERSITY

WAYNE STATE UNIVERSITY
SPONSORED PROGRAM ADMINISTRATION
DETROIT, MI, 48202 7411

Nonprofit college or university
Abstract
DESCRIPTION (provided by applicant): A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions on various regulatory pathways. Currently, a statistical approach is unive rsally used to identify the most relevant pathways in a given experiment. This approach only considers the set of genes present on each pathway and completely ignores other important biological factors. Here we show that in spite of its general adoption, a nd independently of the particular model used, this statistical analysis is unsatisfactory, and can often provide incorrect results. Using a systems biology approach, we developed an impact analysis that includes the classical statistics, but also consider s other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. Our preliminary work shows that the classical analysis produces both false positives and false negatives while the impact analysis provides biologically meaningful results. In this Phase I application, we are proposing to develop a prototype that would demonstrate the feasibility of a commercial software analysis package based on this novel approach. Our tea m has a very strong track record as demonstrated by: a large number of citations to our previous publications, a large user-base for our previously developed software (over 5,000 scientists from all 5 continents), and very strong letters of support. 1 PUBL IC HEALTH RELEVANCE: The classical statistical approaches, which are universally used to identify the most relevant biological pathways in a given experiment, only consider the number of di(R)erentially expressed genes on each pathway and completely ignore s other important biological factors. However, in spite of its general adoption, these statistical approaches are unsatisfactory, and can often provide incorrect results. We propose a novel signaling pathway analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc.

* information listed above is at the time of submission.

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