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Computer-Assisted Strain Construction And Development Engineering (CASCADE)

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: W911NF-06-C-0056
Agency Tracking Number: C061-107-0110
Amount: $99,847.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CBD06-107
Solicitation Number: 2006.1
Timeline
Solicitation Year: 2006
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-05-10
Award End Date (Contract End Date): 2006-11-06
Small Business Information
3375 Scott Blvd
Santa Clara, CA 95054
United States
DUNS: 058044111
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Ying Zhao
 (408) 980-0090
 ying.zhao@quantumii.com
Business Contact
 Charles Zhou
Phone: (408) 203-8325
Email: charles.zhou@quantumii.com
Research Institution
N/A
Abstract

This proposal aims at solving for a “Computer-Assisted Strain Construction and Development Engineering (CASCADE)” (SBIR CBD06-107). Our approach is to integrate BioSpice (a DOD product) and our statistical model developed by Quantum Intelligence together with publicly available data to develop a system for discovering the predictive relations between genomic fingerprints of an organism and its metabolic efficiency, and then use the relations to assess the potential for a biological system to produce specifically designed recombinant proteins. We will obtain the available genomic information, along with other properties, such as biochemistry, growth characteristics, and physiology of various organisms, and link them with the metabolic efficiencies of each organism. We will analyze these data and compare them among organisms, then use the data to build a predictive model. The model will be used to predict the metabolic efficiencies of other organisms which are not included in the pool of organisms to build the model. The genomic information of the tested organisms will be fed into our model, and the predictive results will be tested in laboratory to see how well the model predicts the protein production capabilities of the tested organisms compared to the real-life experiments.

* Information listed above is at the time of submission. *

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