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Adhikari, U.

Publications and source records attributed to Adhikari, U..

2 recordsLinked to original sources

Enhancing CRISPR Education: A Plug-And-Play Framework for Teaching Gene Editing in Plant Biology

Modern molecular biology tools and technologies such as CRISPR have sped up scientific discovery. From an educational perspective, these advancements are both exciting and overwhelming. Educators shaping these future scientists face the ongoing challenge of staying compliant with the latest developments in molecular biology while finding effective ways to teach these discoveries. As the use of CRISPR gene editing technology continues to expand globally, there is an increasing need for a workforce that is both knowledgeable about its theoretical foundations and trained in its practical use. While advanced, technology-driven STEM courses have the potential to improve student retention, they are often lecture-heavy and lack intentional engagement strategies that support deeper learning. Moreover, agriculture is the second most impacted sector by this technology, yet there is a significant lack of teaching materials focused on CRISPR in plant biology. To address these gaps, we developed a framework for teaching gene editing that incorporates multiple engagement strategies beyond traditional lecture-based instruction. This framework was implemented over two semesters in an Introduction to Gene Editing course at Tennessee State University, offered to both undergraduate and graduate students enrolled in a degree in Agricultural Sciences. This manuscript outlines the various strategies used in the course including active learning, multimodal instructional approaches and experiential learning strategies that can be adopted in other classrooms to effectively teach gene editing. Survey-based results from the course indicate a measurable increase in student comfort with designing and executing CRISPR-Cas based experiments. Societal Impact StatementPlant biology lacks accessible teaching materials for CRISPR, a powerful gene-editing technology widely used to improve agriculture. We developed an engaging framework to teach CRISPR concepts to advanced undergraduates and graduate students in plant sciences, which can be readily adopted by other instructors. The approach increased self-reported confidence in students and comfort in explaining CRISPR. Since instructors often have limited time to design interactive lessons, this framework offers a ready-to-use, effective strategy that makes CRISPR more widely available in classrooms, ultimately strengthening CRISPR literacy in the future agricultural workforce.

scientific communication and education↗

Computational estimation of ms-sec atomistic folding times

Despite the development of massively parallel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeasible to simulate the folding of atomistic proteins at room temperature using conventional molecular dynamics (MD) beyond the s scale. Here we report the folding of atomistic, implicitly solvated protein systems with folding times {tau}f ranging from ~100 s to ~10s using the weighted ensemble (WE) strategy in combination with GPU computing. Starting from an initial structure or set of structures, WE organizes an ensemble of GPU-accelerated MD trajectory segments via intermittent pruning and replication events to generate statistically unbiased estimates of rate constants for rare events such as folding; no biasing forces are used. Although the variance among atomistic WE folding runs is significant, multiple independent runs are used to reduce and quantify statistical uncertainty. Three systems were examined: NTL9 at low solvent viscosity (yielding {tau}f ~ 5s), NTL9 at water-like viscosity ({tau}f ~ 40s), and Protein G at low viscosity ({tau}f ~ 10s). In all cases the folding time, uncertainty, and ensemble properties could be estimated from WE simulation; for protein G, this characterization required significantly less overall computing than would be required to observe a single folding event with conventional MD simulations. Our results suggest discrepancies with experimental folding times that should enable improvement of force fields and solvent models.

biophysics↗