| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | April 26, 2018 |
| Latest Amendment Date: | April 26, 2018 |
| Award Number: | 1832985 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Mitra Basu
mbasu@nsf.gov (703)292-8649 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | January 1, 2018 |
| End Date: | August 31, 2020 (Estimated) |
| Total Intended Award Amount: | $396,056.00 |
| Total Awarded Amount to Date: | $396,056.00 |
| Funds Obligated to Date: |
FY 2017 = $80,001.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
615 W 131ST ST NEW YORK NY US 10027-7922 (212)854-6851 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
630 West 168th Street New York NY US 10027-6902 |
| 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): |
Algorithmic Foundations, Software & Hardware Foundation |
| Primary Program Source: |
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
Molecular computing is a promising computational paradigm in which computational functions are evaluated at the nanoscale, with potential applications in smart molecular diagnostics and therapeutics. A molecular computing system comprises biomolecules, such as DNA strands, which have been designed to detect certain input molecules by binding to them and subsequently to undergo programmed sequences of chemical reactions that serve to compute a logical function based on the observed pattern of input molecules. For example, a molecular system that requires both of its two inputs to be present simultaneously in order to generate an output signal would be referred to as computing a logical "AND" function on the two inputs. However, despite recent advances in the field, prospects for direct application of these techniques to solve real-world problems are limited by the lack of robust interfaces between molecular computers and biological and chemical systems. This project will address this limitation by targeting two specific application domains: wide-spectrum chemical sensing and cell surface analysis using molecular logic cascades. The state of the art in molecular computer design, modeling, and implementation will be advanced by an interdisciplinary combination of research by computer scientists, bioengineers, chemists, and computer engineers, and successful completion of the proposed activity will be a significant step towards routine deployment of molecular computers to address real-world problems in chemical and biological sensing.
In this project, molecular circuit architectures that process sensor inputs from chemical sensors and cell-surface analysis reactions will be designed, modeled, and implemented in the laboratory. This will require specific advances in the isolation of aptamers (DNA sequences that exhibit particular binding affinity to one or more target non-nucleic acid molecules) and in their integration into molecular computing systems. In this context, the aptamer will serve as an interface that allows a rationally-designed DNA-based molecular computing system to use small molecules as input signals. Furthermore, computational modeling and simulation will be used to predict and optimize interactions between DNA aptamers and a range of binding targets, and to choose optimal aptamer combinations to produce cross-reactive multi-sensor arrays capable of discriminating between target ligands by effectively projecting the signal into a multi-dimensional aptamer response space. Furthermore, advanced molecular circuit architectures capable of adaptive, bio-inspired behavior, such as dynamic learning and adaptation, will be designed, with a view to future experimental implementations of these features in large-scale molecular computers. This will include research on highly recurrent, bio-inspired information processing networks to extract meaningful responses from potentially non-specific aptamer-based sensors.
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, the teams from Columbia University, University of New Mexico and Portland State University joined to transition the field of molecular computing with nucleic acid elements closer to practical applications. The team at Columbia University Irving Medical Center (PIs: Rudchenko and Stojanovic) focused on the following two projects, for which we explain the key practical and conceptual breakthroughs:
1. Molecular computing cascades on cell surfaces: Molecular computing cascades made of antibody-oligonucleotide conjugates were developed to perform simple Boolean algebra operations on cell surfaces with cell surface markers as inputs and tags, fluorescent or pull-down, as outputs. As a part of ‘molecular computing in the real world’ project, we expanded scope of elementary steps to include, for example, the amplify command. This new command allowed us to target pairs of cell surface markers that had widely discordant number of copies on targeted cells, which hindered expansion of cascades beyond most abundant surface markers. As the result our work, we have now moved this project substantially closer to its applications. Namely, the goal of engineering of cell mixtures for allogeneic stem cell transplantations is to pick-and-choose subsets of cells to minimize potential for side effects and, at the same time, to maximize restoration of helpful components of immune system. The molecular computing cascades that the Columbia team is developing are uniquely suitable for such applications because they can be used to remove very narrow subpopulation of cells.
2. Systematic development of aptamers as sensors for molecular computing: Molecular computing elements based on nucleic acids, as developed at University of New Mexico and Portland State University, require inputs in order to be able to interface with the real world. Aptamers, being themselves made of nucleic acids, while also being able to recognize metabolites in living organisms, are natural elements to serve as sensors for practical computing devices. However, we face a fundamental limitation in our ability to search (select) for aptamers that bind well to small molecule metabolites, with searches being based on very sparse sampling of vast oligonucleotide spaces with >1040possible species. Because of the resulting extremely low probabilities of finding useful aptamers for very simple molecules, aptamers reported in the literature as having practical potential are typically either selected for “easy” targets or have insufficient affinity or selectivity to be implemented in molecular devices. To overcome this limitation, we develop an original method that was inspired by a traditional organic synthesis: In organic synthesis, the concept of functional groups and their reactivity guides us through transformations involving relationships between nuclei and electron clouds, while in our new method, a structure-guided selection of aptamers, similar analysis helps us direct random searches through enormous spaces of complementary interactions with these same relationships. The large number of aptamers isolated during this study will also enable us to improve training sets for computational designs of oligonucleotide receptors.
Broader impact: The Columbia team focused on providing undergraduate students from the tri-state area with research experiences over summers. These experiences were largely focused on wet lab, except during COVID closures, when we moved to zoom presentations.
Last Modified: 06/30/2022
Modified by: Sergei Rudchenko
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