| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
|
| Initial Amendment Date: | June 16, 2022 |
| Latest Amendment Date: | June 16, 2022 |
| Award Number: | 2231150 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2022 |
| End Date: | August 31, 2024 (Estimated) |
| Total Intended Award Amount: | $498,458.00 |
| Total Awarded Amount to Date: | $319,008.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
500 E 9TH ST CLAREMONT CA US 91711-5929 (909)607-7085 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
CA US 91711-5929 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Info Integration & Informatics |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project will develop new techniques and software tools to aid biologists in studying the relationships between groups such as parasites and their host organisms. These methods and tools will allow life scientists to better understand the origins of viral and bacterial diseases, parasites that attack crops, and other pairs of co-evolving groups. The project will involve the design and analysis of new algorithms, systematic validation of these algorithms on large biological datasets, and the development of a software tool that will be broadly disseminated. This work will be conducted at an undergraduate college and will prepare approximately 25 students to engage in research. The work will also result in teaching materials and outreach activities for high school and college students.
The work under this award will explore the problem of reconciling pairs of phylogenetic trees representing taxa such as hosts and parasites or genes and species. Given a pair of phylogenetic trees and the associations between their leaves, maximum parsimony reconciliation seeks to map one tree (e.g., the parasite tree) onto the other (e.g., the host species tree) to minimize the number of biological events required to explain their discordance. In general, the number of such maximum parsimony reconciliations can grow exponentially with the size of the trees. Consequently, it can be difficult or impossible to identify one, or a small number, of best representative reconciliations. This work will develop efficient algorithms to find best representative reconciliations in order to make more robust conclusions about the evolutionary histories of the pairs of taxa. These algorithms will be implemented in a new software tool for life science researchers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
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.
Viruses and bacteria evolve rapidly in response to their environments. Understanding how genes evolve in different species allows scientists to understand the origins of diseases which can ultimately lead to interventions. This research developed new computational methods that help address these kinds of questions.
More generally, a major focus of this research was in studying the evolution of pairs of entities. These entities can be genes and the species in which they are found, hosts and parasites, and pairs of symbiotic species. Understanding how such pairs "co-evolve" is important both for advancing basic science and also for understanding specific systems such as the evolution of genes within viruses, parasites on agricultural crops, and pairs of species that depend on one another for their survival.
This work involved the development of new computational algorithms to address these types of problems. Those algorithms were tested and evaluated and, ultimately, implemented in software tools that are freely available to researchers. Some of those tools are being widely-used by researchers in the life sciences. For example, new discoveries on the evolution of coronaviruses and malaria viruses were made using our tools. New insights into viral disease effecting critically endangered species of porpoises were made using our tools. The study of viruses that are likely candidates for cross-species transmission were made using our tools as well.
Overall, this work has involved collaborations between computer scientists and biologists to help address critically important issues related to the health of our species and our environment. This work has also trained numerous undergraduates to engage in advance work in industry and academia.
Last Modified: 09/02/2024
Modified by: Ran Libeskind-Hadas
Please report errors in award information by writing to: awardsearch@nsf.gov.
