
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | April 29, 2015 |
Latest Amendment Date: | August 31, 2017 |
Award Number: | 1446607 |
Award Instrument: | Continuing Grant |
Program Manager: |
Ralph Wachter
rwachter@nsf.gov (703)292-8950 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | May 1, 2015 |
End Date: | April 30, 2021 (Estimated) |
Total Intended Award Amount: | $1,882,852.00 |
Total Awarded Amount to Date: | $1,882,852.00 |
Funds Obligated to Date: |
FY 2016 = $912,457.00 FY 2017 = $483,121.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
1 SILBER WAY BOSTON MA US 02215-1703 (617)353-4365 |
Sponsor Congressional District: |
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Primary Place of Performance: |
110 Cummington St Boston MA US 02215-1300 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | CPS-Cyber-Physical Systems |
Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT 01001718DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem.
This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells.
The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
In this project, which was a collaboration among Boston University, University of Pennsylvania, and Massachusetts Institute of Technology, we synthesized desired behaviors in populations of bacterial and mammalian cells. The enabling technologies were synthetic biology and micron-scale mobile robotics. Synthetic gene circuits for decision making and local communication among the cells were automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which was designed and built as part of this project, was used to generate desired patterns in engineered cells. In RA, the engineered cells interacted with a set of microrobots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior.
By drawing inspiration from temporal logics used in formal methods, we defined and developed a framework allowing to specify a desired circuit behavior (function) in a high level, expressive language (e.g., “if the concentration of the (input) inducer molecule is under a given threshold, then the concentration of an (output, fluorescent) protein will exceed a given threshold within a desired deadline and will stay at such high values for a desired period of time”) and to automatically generate candidate synthetic circuits.
We defined a formal language for pattern specification and algorithms for pattern detection. The pattern specifiers were formulas in a novel spatial-superposition logic and the pattern detection algorithms were adapted model checkers in this logic. By using machine learning, we constructed specifiers for various patterns. We developed algorithms to map a given (global) pattern specified as a spatial-superposition logic formula to (local) specifications to be satisfied by each cell, i.e., by the time-evolving synthetic circuit inserted in each cell. We have also developed optimization methods to tune the circuits and to determine robot intervention strategies.
In order to implement the robot intervention strategy, we developed a technology for delivery of inducer molecules and chemical payloads (e.g., proteins for interaction with the cell membrane) on a subcellular scale. This was achieved with a magnetically actuated robotic system. We used auto-fluorescent robotic transporters and fluorescently labeled microbeads to aid tracking and control.
Experimentally, we controlled human pluripotent stem cell self organization by knock down of genes previously shown to affect stem cell colony organization. Our results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, and optimization can be used for spatial control of multicellular patterning to engineer human organoids and tissues.
Last Modified: 10/14/2021
Modified by: Calin A Belta
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