| NSF Org: |
IOS Division Of Integrative Organismal Systems |
| Recipient: |
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| Initial Amendment Date: | May 11, 2021 |
| Latest Amendment Date: | May 11, 2021 |
| Award Number: | 2109790 |
| Award Instrument: | Fellowship Award |
| Program Manager: |
Gerald Schoenknecht
gschoenk@nsf.gov (703)292-5076 IOS Division Of Integrative Organismal Systems BIO Directorate for Biological Sciences |
| Start Date: | July 1, 2021 |
| End Date: | June 30, 2024 (Estimated) |
| Total Intended Award Amount: | $216,000.00 |
| Total Awarded Amount to Date: | $216,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
Saint louis MO US 63146-4536 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Olivette MO US 63132-0340 |
| 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): | NPGI PostDoc Rsrch Fellowship |
| Primary Program Source: |
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| 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): | 47.074 |
ABSTRACT
This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2021. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Renee Dale is "Establishing parametric variability as a driver of plasticity across genotypes with diverse life histories through a novel mathematical modeling framework". The host institution for the fellowship is the Donald Danforth Plant Science Center and the sponsoring scientists are Dr. Ivan Baxter and Dr. Shankar Mukherji.
Changes in climate increase the frequency and severity of extreme environmental conditions. Organisms with the ability to be flexible (?plasticity?) can rapidly adapt to these changes. Plants need to be especially flexible since they cannot move if their environment suddenly changes. Identifying the mechanisms that allow flexibility is increasingly important. Due to the amount of information needed to understand plasticity throughout the plant life cycle, models and computational approaches are needed. A new method is proposed to identify ways that plants are flexible using hundreds of plants coming from diverse environments. It is hypothesized that plants from extreme environments will be more flexible, with more modes of growth. Modeling will be used to identify different modes of growth and understand the underlying mechanisms. The results could be used in plant breeding programs to improve crop resilience and the approach can be used to understand the growth of other species. The proposed research will provide the PI with training and development opportunities in mathematics (biological stochasticity), biology (quantitative genetics, ecology, and plant physiology), and computation (Python coding and high-performance computing). The impact of this work will be broadened through the development and dissemination of an educational video game, intended to improve diversity and inclusion of under-represented individuals. The video game will introduce these concepts to high school students in a low-stress way, circumventing the negative social perceptions of math.
A novel mathematical modeling framework will be developed to identify processes driving plasticity across hundreds of plant genotypes with diverse eco-evolutionary backgrounds. A family of mathematical models of plant physiology and growth processes will be applied. Model parameters will be estimated using Bayesian parameter estimation, providing parametric distributions within and between genotypes. This will provide a conceptual framework to understand plasticity by integrating mathematical, computational, and biological theory. Processes driving plasticity have agricultural significance, as the ability of crops to adapt and respond to increasing environmental stress is paramount. Model parameters will be mapped to genomic loci, which could be used in breeding programs to test model predictions that such loci will confer plasticity, and to improve crop plants.
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.
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.
Understanding how plants are able to be resilient and flexible when they are faced with harsh conditions like drought and heat is important to safeguard our future. However, this flexibility can be complex, and we need quantitative models to understand it better.
To address this, I developed a simple mathematical model with few parameters that can be applied to many different plant species. I applied the model to Setaria, a model organism for some of our most important crops, such as corn. The model describes resource allocation and growth. The model predicts that competition for resources between leaves that grow at the same time drives size patterns of plant stems and leaves. I performed some experiments to validate the model.
To understand plant adaptation, we will apply the model to a large dataset of a diversity panel, of Setaria from around the world. To analyze the high-throughput imaging data, we developed an image analysis pipeline that extracts information about plant stem and leaf sizes. We will use parameter distributions of the model applied to this data set to acquire model-based predictions of flexible growth patterns and water use efficiency.
We are continuing to work on applying the model to this dataset going into the future. The model and the image analysis pipeline will enable researchers to quickly analyze large datasets and generate predictions about plant resilience and size patterns. The model design is highly generalizable and able to describe diverse plant species. The model design considers allocation of resources across developmental time, and in the future can be applied to non-plant systems to describe dynamic allocation problems across biology.
During my fellowship, I also worked on two educational video games that integrate mathematical modeling concepts with plant biology. First there is a simulator-like game, where you place proteins to complete the signaling pathway to defend your cell from drought: https://rdale.itch.io/plants. We playtested this game with local middle school classrooms. Secondly, we made a more traditional, tower defense style game with the same concepts here: https://chaoticformula.itch.io/droughtdefense.
Last Modified: 11/01/2024
Modified by: Renee Dale
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