Computational Biology Platform Technology for Cellular Reprogramming

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D6318C0089
Agency Tracking Number: D17C-001-0014
Amount: $224,226.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST17C-001
Solicitation Number: 2017.0
Timeline
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-03-07
Award End Date (Contract End Date): 2018-12-28
Small Business Information
1600 Huron Parkway Ncrc Building 520 Rm 3392, Ann Arbor, MI, 48105
DUNS: 000265636
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Indika Rajapakse
 (206) 327-2674
 indikar@umich.edu
Business Contact
 Dr. Lindsey Muir
Phone: (206) 272-2603
Email: lindseymuir@gmail.com
Research Institution
 University of Michigan
 Linda Peasley
 3003 S. State Street
Ann Arbor, MI, 48109
 (734) 615-4788
 Domestic nonprofit research organization
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 genome form and function data (Hi-C and RNA-sequencing) to determine the optimal transcription factor combinations and timing to achieve reprogramming. Our goals are to provide a TRL4 compliant computational biology platform for identifying key determinants (genes, time windows) underlying cellular reprogramming, and to harness genomics data gathered in established settings of reprogramming to refine our existing data-guided cellular reprogramming algorithm.

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

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