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1. FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

   https://doi.org/10.1093/bioinformatics/btaf471  

   Czajka, Jeffrey J; Kim, Joonhoon; Tang, Yinjie J; Pomraning, Kyle R; Mukhopadhyay, Aindrila; Garcia_Martin, Hector (September 2025, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract MotivationMetabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. ResultsHere, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. Availability and implementationFluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP. 

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2. IcpCarb: A Matlab package for processing element data from ICP analysis

   https://doi.org/10.5281/zenodo.13929589  

   Guo, Weifu (October 2024, Zenodo)

   IcpCarb is a Matlab package that processes raw counts data from the iCAP (or other ICP instruments) and converts them to element ratios based on the determined standard curves and matrix curves.  License: Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) References: Please cite the following references when using the IcpCarb package: Guo, W. (2024) IcpCarb: A Matlab package for processing element data from ICP analysis, http://doi.org/10.5281/zenodo.13929590. Lu, W., Guo, W., Oppo, D. (2024) Assessing the precision and accuracy of foraminifera elemental analysis at low ratios, Geochem. Geophys. Geosyst. Funding: Development of this package was supported by the U.S. National Science Foundation [NSF-OCE-1811305] and the Investment in Science Fund at Woods Hole Oceanographic Institution. 

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3. IcpCarb: A Matlab package for processing element data from ICP analysis

   https://doi.org/10.5281/zenodo.13929590  

   Guo, Weifu (January 2024, Zenodo)

   IcpCarb is a Matlab package that processes raw counts data from the iCAP (or other ICP instruments) and converts them to element ratios based on the determined standard curves and matrix curves.  License: Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) References: Please cite the following references when using the IcpCarb package: Guo, W. (2024) IcpCarb: A Matlab package for processing element data from ICP analysis, Zenodo. http://doi.org/10.5281/zenodo.13929590 Lu, W., Guo, W., Oppo, D. W. (2024) Assessing the precision and accuracy of foraminifera elemental analysis at low ratios, Geochem. Geophys. Geosyst., 25, e2024GC011560. https:// doi.org/10.1029/2024GC011560 Funding: Development of this package was supported by the U.S. National Science Foundation [NSF-OCE-1811305] and the Investment in Science Fund at Woods Hole Oceanographic Institution. 

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4. Challenges in Engineering Machine Learning (Software) Systems

   Khatchadourian, Raffi (December 2025, CUNY Academic Works.)

   Lecture slides on the software engineering challenges unique to machine learning systems, for an undergraduate software engineering course. After contrasting traditional programming with machine learning, the deck examines why the usual tools for managing complexity—abstraction, reuse, and composition—are harder to apply to ML, given the lack of clear specifications and modularity. It covers concept drift, feedback loops (illustrated with crime-prediction and recommendation examples), and the accumulation of technical debt in ML systems, including the role and pitfalls of notebooks in moving from experimentation to production. Based on "Machine Learning in Production/AI Engineering" by Christian Kaestner and Eunsuk Kang (Carnegie Mellon University). 

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5. Curing “GFP-itis” in Bacteria with Base Editors: Development of a Genome Editing Science Program Implemented with High School Biology Students

   https://doi.org/10.1089/crispr.2023.0002  

   Vasquez, Carlos A.; Evanoff, Mallory; Ranzau, Brodie L.; Gu, Sifeng; Deters, Emma; Komor, Alexis C. (June 2023, The CRISPR Journal)

   The flexibility and precision of CRISPR-Cas9 and related technologies have made these genome editing tools increasingly popular in agriculture, medicine, and basic science research for the past decade. Genome editing will continue to be relevant and utilized across diverse scientific fields in the future. Given this, students should be introduced to genome editing technologies and encouraged to consider their ethical implications early on in precollege biology curricula. Furthermore, instruction on this topic presents an opportunity to create partnerships between researchers and educators at the K-12 levels that can strengthen student engagement in science, technology, engineering, and mathematics. To this end, we present a 3-day student-centered learning program to introduce high school students to genome editing technologies through a hands-on base editing experiment in Escherichia coli, accompanied by a relevant background lecture and facilitated ethics discussion. This unique partnership aims to educate students and provides a framework for research institutions to implement genome editing outreach programs at local high schools. We have included all requisite materials, including lecture slides, worksheets, experimental protocols, and suggestions on active learning strategies for others to reproduce our program with their local communities. 

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