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Convergence Research and Training in Computational Bioengineering: A Case Study on AI/ML-Driven Biofilm-Material Interaction Discovery

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Title: Convergence Research and Training in Computational Bioengineering: A Case Study on AI/ML-Driven Biofilm-Material Interaction Discovery
Language: English
Authors: Jessica L. S. Zylla, Alain B. Bomgni, Rajesh K. Sani, Mahadevan Subramaniam, Carol Lushbough, Robb Winter, Venkataramana R. Gadhamshetty, Parvathi Chundi, Etienne Z. Gnimpieba (ORCID 0000-0002-5338-084X)
Source: Biomedical Engineering Education. 2024 4(2):283-294.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 12
Publication Date: 2024
Sponsoring Agency: National Science Foundation (NSF), Office of Integrative Activities (OIA)
National Institutes of Health (NIH) (DHHS)
Contract Number: 1849206
1920954
5P20GM10344320
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Research Methodology, Interdisciplinary Approach, Active Learning, Student Projects, Learning Modules, Group Instruction, Convergent Thinking, Biotechnology, Engineering Education, Graduate Study
DOI: 10.1007/s43683-024-00146-6
ISSN: 2730-5937
2730-5945
Abstract: Historically, research disciplines have successfully operated independently. However, the emergence of transdisciplinary research has led to convergence methodologies, resulting in groundbreaking discoveries. Despite the benefits, graduate programs face challenges in implementing transdisciplinary research and preparing students for real-world collaboration across diverse disciplines and experience levels. We propose a convergence training framework integrating project-based learning, training modules, and collaborative teaming to address this. This approach, tested in a multi-institutional workshop, proved effective in bridging expertise gaps and fostering successful convergence learning experiences in computational biointerface (material-biology interface) research. Here, biointerface research focuses on control of biomolecular interactions with technologically relevant material surfaces, which is a critical component of biotechnology and engineering applications. Positive outcomes, including conference presentations and published models, endorse the framework's application in graduate curricula, particularly for students engaging in transdisciplinary collaboration.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1432509
Database: ERIC
FullText Text:
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PubType: Academic Journal
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  Data: Convergence Research and Training in Computational Bioengineering: A Case Study on AI/ML-Driven Biofilm-Material Interaction Discovery
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  Data: <searchLink fieldCode="SO" term="%22Biomedical+Engineering+Education%22"><i>Biomedical Engineering Education</i></searchLink>. 2024 4(2):283-294.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Historically, research disciplines have successfully operated independently. However, the emergence of transdisciplinary research has led to convergence methodologies, resulting in groundbreaking discoveries. Despite the benefits, graduate programs face challenges in implementing transdisciplinary research and preparing students for real-world collaboration across diverse disciplines and experience levels. We propose a convergence training framework integrating project-based learning, training modules, and collaborative teaming to address this. This approach, tested in a multi-institutional workshop, proved effective in bridging expertise gaps and fostering successful convergence learning experiences in computational biointerface (material-biology interface) research. Here, biointerface research focuses on control of biomolecular interactions with technologically relevant material surfaces, which is a critical component of biotechnology and engineering applications. Positive outcomes, including conference presentations and published models, endorse the framework's application in graduate curricula, particularly for students engaging in transdisciplinary collaboration.
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  Data: 2024
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  Data: EJ1432509
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