Award Abstract # 1446607
CPS: Frontier: Collaborative Research: BioCPS for Engineering Living Cells

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: TRUSTEES OF BOSTON UNIVERSITY
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 2015 = $487,274.00
FY 2016 = $912,457.00

FY 2017 = $483,121.00
History of Investigator:
  • Calin Belta (Principal Investigator)
    calin.belta@gmail.com
  • Douglas Densmore (Co-Principal Investigator)
Recipient Sponsored Research Office: Trustees of Boston University
1 SILBER WAY
BOSTON
MA  US  02215-1703
(617)353-4365
Sponsor Congressional District: 07
Primary Place of Performance: Trustees of Boston University
110 Cummington St
Boston
MA  US  02215-1300
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): THL6A6JLE1S7
Parent UEI:
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01001516DB NSF RESEARCH & RELATED ACTIVIT
01001617DB NSF RESEARCH & RELATED ACTIVIT

01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7918, 8236
Program Element Code(s): 791800
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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(Showing: 1 - 10 of 18)
Ashley RG Libby, Demarcus Briers, Iman Haghighi, David A. Joy, Bruce R. Conklin, Calin Belta, Todd C. McDevitt "Automated Design of Pluripotent Stem Cell Self-Organization" Cell Systems , v.9 , 2019 , p.1
Curtis Madsen, Prashant Vaidyanathan, Sadra Sadraddini, Cristian-Ioan Vasile, Nicholas A. DeLateur, Ron Weiss, Douglas Densmore, and Calin Belta "Metrics for Signal Temporal Logic Formulae" IEEE International Conference on Design and Control (CDC), Miami, Florida, December 2018 , 2018
Demarcus Briers, Iman Haghighi, Douglas White, Melissa L. Kemp, Calin Belta "Pattern Synthesis in a 3D Agent-Based Model of Stem Cell Differentiation" IEEE International Conference on Decision and Control (CDC), Las Vegas, NV, 2016 , 2016 10.1109/CDC.2016.7798907
Giuseppe Bombara and Calin Belta "Offline and Online Learning of Signal Temporal Logic Formulae Using Decision Trees" ACM Transactions on Cyber-Physical Systems , v.5 , 2021 , p.1
Goksel Misirli, Tramy Nguyen, James Alastair McLaughlin, Prashant Vaidyanathan, Douglas Densmore, Chris Myers, and Anil Wipat "A Computational Workflow for the Automated Generation of Models of Genetic Designs" ACS Synthetic Biology , v.8 , 2019 , p.1548
Iman Haghighi, Kevin Leahy, Rachael Ivision, Calin Belta "Semi-supervised Pattern Synthesis in Spatially Distributed Dynamical Systems" American Control Conference (ACC), Seattle, WA, May 2017 , 2017
Junmin Wang and Calin Belta "Retroactivity Affects the Adaptive Robustness of Transcriptional Regulatory Networks" Proceedings of the 2019 American Control Conference (ACC), Philadelphia, PA , 2019
Junmin Wang, Calin Belta, and Samuel A. Isaacson "How Retroactivity Affects the Behavior of Incoherent Feed-Forward Loops" iScience , v.23 , 2020
Junmin Wang, Samuel A. Isaacson, and Calin Belta "Modeling Genetic Circuit Behavior in Transiently Transfected Mammalian Cells" ACS Synthetic Biology , v.8 , 2017 , p.697
Junmin Wang, Samuel A. Isaacson, and Calin Belta "Modeling Genetic Circuit Behavior in Transiently Transfected Mammalian Cells" ACS Synthetic Biology , v.8 , 2019 , p.697
Junmin Wang, Samuel A. Isaacson, and Calin Belta "Predictions of Genetic Circuit Behavior Based on Modular Composition in Transiently Transfected Mammalian Cells" Proceedings of the 2018 IEEE Life Sciences Conference (LSC) , 2018 , p.85 10.1109/LSC.2018.8572174
(Showing: 1 - 10 of 18)

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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