Award Abstract # 1828149
NRT-HDR: Intersecting computational and data science to address grand challenges in plant biology

NSF Org: DGE
Division Of Graduate Education
Recipient: MICHIGAN STATE UNIVERSITY
Initial Amendment Date: August 22, 2018
Latest Amendment Date: July 25, 2024
Award Number: 1828149
Award Instrument: Standard Grant
Program Manager: Liz Webber
ewebber@nsf.gov
 (703)292-4316
DGE
 Division Of Graduate Education
EDU
 Directorate for STEM Education
Start Date: September 1, 2018
End Date: August 31, 2024 (Estimated)
Total Intended Award Amount: $2,999,967.00
Total Awarded Amount to Date: $2,999,967.00
Funds Obligated to Date: FY 2018 = $2,999,967.00
History of Investigator:
  • Tammy Long (Principal Investigator)
    longta@msu.edu
  • Erich Grotewold (Co-Principal Investigator)
  • Karen Cichy (Co-Principal Investigator)
  • Brian O'Shea (Co-Principal Investigator)
  • Shin-Han Shiu (Former Principal Investigator)
  • Carol Buell (Former Co-Principal Investigator)
  • Tammy Long (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Michigan State University
426 AUDITORIUM RD RM 2
EAST LANSING
MI  US  48824-2600
(517)355-5040
Sponsor Congressional District: 07
Primary Place of Performance: Michigan State University
East Lansing
MI  US  48824-2600
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): R28EKN92ZTZ9
Parent UEI: VJKZC4D1JN36
NSF Program(s): NSF Research Traineeship (NRT),
Project & Program Evaluation
Primary Program Source: 04001819DB NSF Education & Human Resource
01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 062Z, 9179, 7361, SMET
Program Element Code(s): 199700, 726100
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

Plants are indispensable for life on earth, providing food, energy, and oxygen, as well as the basis for many man-made products. A better understanding of plant science will lead to more secure plant resources, which is even more important given the rapidly increasing global population. Genomics research has significantly advanced our understanding about how plants function, with the application of genomics yielding datasets that could revolutionize plant science and lead to safe, reliable, and sustainable production of food and biofuels. To achieve these outcomes, there is a critical need for scientists with both an understanding of plant biology and computational skills. This National Science Foundation Research Traineeship (NRT) award to Michigan State University will address this demand by training doctoral students who can employ advanced computational and data science approaches to address grand challenges in plant biology. The project anticipates training approximately seventy (70) PhD students, including thirty-eight (38) funded trainees from plant biology and computational data science programs.

Trainees will engage in research and coursework that emphasize tackling "grand challenge" questions in plant biology by leveraging computational approaches. Training will go beyond the traditional genomics and bioinformatics approaches in plant biology to include the advanced training in computation and modeling required to handle increasingly heterogeneous, multi-scale data from the molecular to ecosystem levels. This type of training will allow students to tackle complex questions such as investigating genotype-phenotype relationships across the Plant Tree of Life or machine learning for high-dimensional plant data. In addition, the traineeship features professional development opportunities, outreach activities, and industry/governmental internships that serve to broaden trainees' career options while also improving their ability to communicate with a wide range of audiences. Upon completion of the training program, trainees will have a core understanding of plant and computational sciences, excel in interdisciplinary biological and computational research, and possess effective communication, leadership, management, teaching, and mentoring skills. Trainees will be co-advised by experts in plant science and computational/data science. To accomplish the training goals, trainees will participate in a program consisting of: (1) curricular and research activities that will create a cohort of trainees with dual expertise in computational sciences and plant biology, (2) a biweekly forum to encourage scientific interactions, (3) a trainee-led annual symposium that engages a wider scientific audience and builds organizational and leadership skills, (4) internship opportunities in industry and government agencies, (5) professional development activities tailored to individual career goals, including entrepreneurship, and (6) public engagement through outreach activities, further bolstering the ability of trainees to communicate with a wide range of audiences.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

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

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(Showing: 1 - 10 of 29)
Bird, Kevin A. and Jacobs, MacKenzie and Sebolt, Audrey and Rhoades, Kathleen and Alger, Elizabeth I. and Colle, Marivi and Alekman, Mitchell L. and Bies, Paulina K. and Cario, Adare J. and Chigurupati, Ramya S. and Collazo, Delaney R. and Finley, Savanna "Parental origins of the cultivated tetraploid sour cherry ( Prunus cerasus L.)" PLANTS, PEOPLE, PLANET , 2022 https://doi.org/10.1002/ppp3.10267 Citation Details
Bornowski, Nolan and Hart, John P. and Palacios, Ana Vargas and Ogg, Barry and Brick, Mark A. and Hamilton, John P. and Beaver, James S. and Buell, C. Robin and Porch, Timothy "Genetic variation in a tepary bean ( Phaseolus acutifolius A. Gray) diversity panel reveals loci associated with biotic stress resistance" The Plant Genome , v.16 , 2023 https://doi.org/10.1002/tpg2.20363 Citation Details
Bryson, Abigail E. and Lanier, Emily R. and Lau, Kin H. and Hamilton, John P. and Vaillancourt, Brieanne and Mathieu, Davis and Yocca, Alan E. and Miller, Garret P. and Edger, Patrick P. and Buell, C. Robin and Hamberger, Björn "Uncovering a miltiradiene biosynthetic gene cluster in the Lamiaceae reveals a dynamic evolutionary trajectory" Nature Communications , v.14 , 2023 https://doi.org/10.1038/s41467-023-35845-1 Citation Details
Bryson, Abigail E. and Wilson Brown, Maya and Mullins, Joey and Dong, Wei and Bahmani, Keivan and Bornowski, Nolan and Chiu, Christina and Engelgau, Philip and Gettings, Bethany and Gomezcano, Fabio and Gregory, Luke M. and Haber, Anna C. and Hoh, Donghee "Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves" Applications in Plant Sciences , v.8 , 2020 https://doi.org/10.1002/aps3.11404 Citation Details
Ford, Kathryne C. and Kaste, Joshua A. M. and Shachar-Hill, Yair and TerAvest, Michaela A. "Flux-Balance Analysis and Mobile CRISPRi-Guided Deletion of a Conditionally Essential Gene in Shewanella oneidensis MR-1" ACS Synthetic Biology , v.11 , 2022 https://doi.org/10.1021/acssynbio.2c00323 Citation Details
Gomez-Cano, Lina and Gomez-Cano, Fabio and Dillon, Francisco M. and Alers-Velazquez, Roberto and Doseff, Andrea I. and Grotewold, Erich and Gray, John "Discovery of modules involved in the biosynthesis and regulation of maize phenolic compounds" Plant Science , v.291 , 2020 https://doi.org/10.1016/j.plantsci.2019.110364 Citation Details
Hoopes, Genevieve and Meng, Xiaoxi and Hamilton, John P. and Achakkagari, Sai Reddy and de Alves Freitas Guesdes, Fernanda and Bolger, Marie E. and Coombs, Joseph J. and Esselink, Danny and Kaiser, Natalie R. and Kodde, Linda and Kyriakidou, Maria and Lav "Phased, chromosome-scale genome assemblies of tetraploid potato reveal a complex genome, transcriptome, and predicted proteome landscape underpinning genetic diversity" Molecular Plant , v.15 , 2022 https://doi.org/10.1016/j.molp.2022.01.003 Citation Details
Izquierdo, Paulo and Kelly, James D. and Beebe, Stephen E. and Cichy, Karen "Combination of metaanalysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean" The Plant Genome , v.16 , 2023 https://doi.org/10.1002/tpg2.20328 Citation Details
Izquierdo, Paulo and Sadohara, Rie and Wiesinger, Jason and Glahn, Raymond and Urrea, Carlos and Cichy, Karen "Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans" Frontiers in Genetics , v.15 , 2024 https://doi.org/10.3389/fgene.2024.1330361 Citation Details
Jacobs, MacKenzie and Thompson, Samantha and Platts, Adrian E and Body, Melanie_J A and Kelsey, Alexys and Saad, Amanda and Abeli, Patrick and Teresi, Scott J and Schilmiller, Anthony and Beaudry, Randolph and Feldmann, Mitchell J and Knapp, Steven J and "Uncovering genetic and metabolite markers associated with resistance against anthracnose fruit rot in northern highbush blueberry" Horticulture Research , v.10 , 2023 https://doi.org/10.1093/hr/uhad169 Citation Details
Jayakody, Thilani B and Hamilton, John P and Jensen, Jacob and Sikora, Samantha and Wood, Joshua C and Douches, David S and Buell, C Robin "Genome Report: Genome sequence of 1S1, a transformable and highly regenerable diploid potato for use as a model for gene editing and genetic engineering" G3 Genes|Genomes|Genetics , v.13 , 2023 https://doi.org/10.1093/g3journal/jkad036 Citation Details
(Showing: 1 - 10 of 29)

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.

Plant science in the 21st Century explores complex problems such as improving food security through crop resilience, restoring biodiversity in degraded landscapes, and understanding and predicting relationships between molecular-level processes and patterns in natural and human-designed plant systems. Solving such problems requires scientists trained in both fundamental plant biology and computational and data science approaches to analyze and interpret heterogenous, multi-scale measurement data to predict plant responses in variable environments at local to global scales.

The Integrated Training Model in Plant and Computational Sciences (aka, IMPACTS) is an interdisciplinary graduate training program that bridges computational and plant sciences.  Goals of the program include: (1) Proficiency in core knowledge in computational and plant sciences, (2) Expertise in interdisciplinary research in computational plant biology, (3) Development of skills to lead, manage, and communicate research to diverse stakeholders, including policy-makers and the public, and (4) Development of skills to effectively teach and mentor students, colleagues, and peers in diverse workplace contexts.

The IMPACTS program embraced a multifaceted training approach that aimed to integrate deep disciplinary learning with practical experiences that promote development of transferable and practical skills necessary for a wide range of careers in science, technology, engineering, and mathematics. Key components and outcomes of the training program include:

(1) A three-course curriculum that engaged trainees in cooperative and team-based learning to develop conceptual knowledge and frameworks that integrate plant and computational sciences. Trainees from plant sciences and from computational and data sciences and engineering collaborated in interdisciplinary teams to learn and synthesize core disciplinary principles and apply them to real-world problems posed by leading researchers from academia and industry. Coursework also targeted professional development training in science communication, interdisciplinary collaboration, and teaching and mentoring.  From 2019-2024, 217 students enrolled in IMPACTS courses, including 38 funded and 14 unfunded trainees. One of the courses focused on foundations of computational plant science engaged an international collaboration with Universidad Nacional Autónoma de México with an additional 68 international students participating from 2021-2024.

(2) Interdisciplinary research experiences that developed trainees’ research expertise within and across disciplines. Trainees wrote research proposals and conducted research projects supervised by mentors from at least two distinct disciplines. In addition to developing trainees’ interdisciplinary skills, these projects forged new collaborations among supervising researchers and promoted broader awareness of the work of colleagues from across the university and with industry professionals. From 2019-2024, trainees (funded and unfunded) produced 167 publications emanating from their research efforts.

(3) Internships in industry and/or governmental agencies allowed trainees to experience the practices, ways of thinking, decision-making, and cultures that distinguish them from academic settings. Trainees reported that these experiences were powerful in informing their career choices and significantly influenced their selection of training experiences within their degree programs. Over 27 trainees completed internships in organizations and industries including ConAgra Foods, Lawrence Berkeley National Laboratories, Heinrich Heine Universität, Bayer CropSciences, Corteva, Benson Hill, Google, Inari and NASA Space Crop Production.

(4) Trainee-led symposia, workshops, retreats, and outreach events afforded trainees opportunities to apply their leadership and management skills to real-world contexts. Trainees managed all aspects of organizing events and symposia, including determining themes, inviting guest speakers, and arranging logistics. Trainees also conducted outreach events that engaged the public in learning about plant science and technologies that support data collection in lab and field contexts.  Trainee-led workshops and panels provided opportunities for trainees to teach more than 285 non-trainee graduate students from across the university about data management and analysis, interdisciplinary collaborations, and diverse job readiness skills.

Overall, the IMPACTS program supported 38 funded and 14 non-funded trainees.  To date, 11 trainees have completed their degree programs with six continuing in academic appointments, four being placed in industry positions, and one pursuing a consulting career as a data analyst. The program has led to changes in computational training requirements across multiple departments and programs at Michigan State University and is currently sustained as a Graduate Certification Program in Computational Plant Science. Thus far, 18 graduate students have been conferred the certificate. IMPACTS serves as a model of interdisciplinary graduate training.  A focus on fundamental principles from plant and computational sciences will prepare students for the interdisciplinary work of computational plant biology while practical experiences and professional development training in transferable skills will prepare them for work across a broad range of careers in STEM.

 

 

 

 

 


Last Modified: 01/06/2025
Modified by: Tammy M Long

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