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Computational Biology Platform Technology for Cell Conversion and Differentiation

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D6319C0020
Agency Tracking Number: D2-2211
Amount: $1,402,131.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: ST17C-001
Solicitation Number: 17.C
Timeline
Solicitation Year: 2017
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-01-27
Award End Date (Contract End Date): 2021-06-27
Small Business Information
1600 Huron Parkway Ncrc Building 520
Ann Arbor, MI 48109
United States
DUNS: 080887509
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Indika Rajapakse
 Associate Professor
 (206) 327-2674
 indikar@umich.edu
Business Contact
 Lindsey Muir
Phone: (206) 327-2603
Email: lindseymuir@ireprogram.com
Research Institution
 Regents of the University of Michigan
 Linda Peasley Linda Peasley
 
3003 S. State Street
Ann Arbor, MI 48109
United States

 (734) 615-4788
 Nonprofit college or university
Abstract

Methods for interconversion between cell types (cellular reprogramming) are currently discovered through resource intensive trial and error. Experiments may test a multitude of transcription factors to identify correct combinations that influence cell fate. In addition, reprogramming approaches commonly use stem cell intermediates such as induced pluripotent stem cells (iPSCs), which are generated with low efficiency and high cost. Conversion of fibroblasts into a variety of lineages, including muscle cells, neurons, and cardiomyocytes, has been successful without a pluripotent intermediate, supporting direct reprogramming as a viable strategy in many settings. Given the current methodological inefficiencies and deficiencies in discovering reprogramming methods, we propose to improve discovery of new direct reprogramming schemes by analyzing high-throughput sequencing data with control theory techniques. Our approach models the cell state as a dynamical system, using time series genomics data to determine the optimal transcription factor combinations and timing to achieve reprogramming. Our objective in this Phase II effort is to refine our Computational Toolbox and develop a Biological Toolbox for systematically optimizing and validating predictions from our Computational Toolbox, yielding a Bio-Computational Platform that operates as a template for improving any cellular reprogramming system.

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

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