Project-based learning course on metabolic network modelling in computational systems biology

Sauter, Thomas; Bintener, Tamara Jean Rita; Kishk, Ali; Presta, Luana; Prohaska, Tessy; Guignard, D; Zeng, Ni; Cipriani, Claudia; Arshad, S. Hasan; Pfau, Thomas; Conde, Patricia Martins; Pacheco, Maria Pires

doi:10.1371/journal.pcbi.1009711

Cited by 7 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, bioinformatics is an inherently interdisciplinary field and can be well approached from a computer science perspective. Skills in programming in Python or R, familiarity with databases, open access to information, and knowledge of data processing, visualization, and machine learning, provide not only an excellent entry point into bioinformatics ( LeBlanc and Dyer 2004 ) but also wonderful opportunities for hands-on, project-based learning ( Emery and Morgan 2017 , Sauter et al 2022 ). The role and benefits of project-based learning are well documented in the literature ( Blumenfeld et al 1991 ).…”

Section: Introductionmentioning

confidence: 99%

Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students

Poličar,

Špendl,

Curk

et al. 2024

Bioinformatics

View full text Add to dashboard Cite

Motivation We learn more effectively through experience and reflection than through passive reception of information. Bioinformatics offers an excellent opportunity for project-based learning. Molecular data are abundant and accessible in open repositories, and important concepts in biology can be rediscovered by reanalyzing the data. Results In the manuscript, we report on five hands-on assignments we designed for master’s computer science students to train them in bioinformatics for genomics. These assignments are the cornerstones of our introductory bioinformatics course and are centered around the study of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). They assume no prior knowledge of molecular biology but do require programming skills. Through these assignments, students learn about genomes and genes, discover their composition and function, relate SARS-CoV-2 to other viruses, and learn about the body’s response to infection. Student evaluation of the assignments confirms their usefulness and value, their appropriate mastery-level difficulty, and their interesting and motivating storyline. Availability and Implementation The course materials are freely available on GitHub at https://github.com/IB-ULFRI.

show abstract

Section: Introductionmentioning

confidence: 99%

Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students

Poličar,

Špendl,

Curk

et al. 2024

Bioinformatics

View full text Add to dashboard Cite

show abstract

“…nwo.nl/en/projects/022004006-0 and https://gow.epsrc.ukri.org/ NGBOViewGrant.aspx?GrantRef=EP/L016494/1) have supported the creation of training centers for early career researchers, where students can follow courses covering fundamental topics in experimental and theoretical spheres of Systems and Synthetic Biology. At the academic level, educators have also described how problem-based learning and flipped classrooms can be used effectively to simulate computational biology research assuming that experimental data has already been obtained (Robeva and Laubenbacher, 2009;Eager, Peirce and Barlow, 2014;Sauter et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Research-driven education: An introductory course to systems and synthetic biology

Smith¹,

Garcia-Morales²,

Santos³

et al. 2022

Front. Syst. Biol.

View full text Add to dashboard Cite

Systems and Synthetic Biology are complementary fields emerging side-by-side into mainstream scientific research. Whilst systems biologists focus on understanding natural systems, synthetic biologists wish to modify, adapt and re-purpose biological systems towards certain desired goals, for example enhancing efficiency and robustness of desired biological traits. In both fields, data analysis, predictive mathematical modelling, experimental design, and controlled experimentation are crucial to obtain reproducible results and understand how applications can be scaled to larger systems and processes. As such, students from Life Sciences, Engineering, and Mathematics backgrounds must be taught fundamentals in biological systems, experimental techniques, mathematics, and data analysis/statistics. In addition, students must be trained for future multidisciplinary careers, where the interaction and communication between experimental and modelling researchers is fundamental. With the acceleration of technological developments (both computational and experimental) continuing unabated, educators need to bridge the increasing gap between fundamentally-required knowledge and skills that students need to pursue future academic or industrial research projects. In this paper, we will discuss how we have re-designed an introductory course in Systems and Synthetic Biology at Wageningen University and Research (Netherlands) that is targeted simultaneously to mathematical/computational students with an interest in biology and experimental methods, and to Life Science students interested in learning how biological systems can be mathematically analysed and modelled. The course highlights the links between fundamental methodologies and recently developed technologies within the Systems and Synthetic Biology fields. The course was re-designed for the 2021/22 academic year, we report that students from biology and biotechnology programmes graded their satisfaction of the course as 4.4 out of 5. We discuss how the course can act as a gateway to advanced courses in Systems Biology-oriented curricula (comprising: data infrastructure, modelling, and experimental synthetic biology), and towards future research projects.

show abstract

Integration of online and offline teaching mode in biochemistry and molecular biology courses

Yang,

Zhu

et al. 2024

Biochem Molecular Bio Educ

View full text Add to dashboard Cite

To enhance the effectiveness of integrating online and offline teaching, 1545 clinical and preventive medicine students from 2019 to 2021 were randomly allocated to two groups, A and B. The curriculum was divided into two segments. Initially, two groups were established for the first segment, covering an introduction to Biomolecular and Material Metabolism. The group A adopted a teaching strategy incorporating “massive open online course + a Social Media platform (WeChat) + Project/Problem‐Based Learning + Flipped classroom”, integrating online and offline methods. The group B followed conventional teaching practices. In the second course segment, which included molecular biology and clinical biochemistry, the two groups had their instructional format switched. Comparative analysis of student satisfaction, learning attitudes, and academic performance between the groups was conducted. The satisfaction survey indicated that the group which adopted the online and offline mode outperformed the conventional teaching group in satisfaction rate, satisfaction scores, excellence rate, and total scores. While both groups exhibited an improvement in learning attitudes, the teaching reform group showed a significantly higher level of enhancement. Furthermore, the reform group achieved superior overall average scores, basic average scores, comprehensive average scores, and an increased rate of excellence compared to the conventional group. The results demonstrate that adopting a blended teaching model significantly improved instructional quality and positively influenced students' engagement and attitudes in biochemistry and molecular biology studies.

show abstract

Project-based learning course on metabolic network modelling in computational systems biology

Cited by 7 publications

References 32 publications

Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students

Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students

Research-driven education: An introductory course to systems and synthetic biology

Integration of online and offline teaching mode in biochemistry and molecular biology courses

Contact Info

Product

Resources

About