Project Mentor
Dr. Pedro Mendes
Department of Center for Cell Analysis and Modeling
Undergraduate Research Opportunity Description
| Project Description | Recently several methods have been published that take protein and metabolite structural properties to predict values of kinetic parameters. These methods, which are based on classical machine learning or deep learning, have only been compared against each other by their authors with different data sets. This project is intended to create an independent comparison of those methods following a two-approach: 1) first establishing a test set to form the basis of the comparisons, and then 2) creating and running a workflow to generate performance metrics that can be used to form an objective comparison. |
| Project Direction | This project arises because our group is actively creating kinetic models of biochemical networks, where the lack of kinetic parameter values is always a limiting factor. We are interested in using machine learning to predict parameter values that we can then use in our models. The results of this project would be of interest to a wide audience and are potentially publishable in a peer-reviewed journal, of which the student would be a co-author. |
| Mentorship and Supervision | I will have daily meetings with the student to discuss plans and progress, and weekly lab meetings where the project would also be discussed with a PhD student that is a potential user of the methodologies being tested. Technical support will also be available when necessary from myself, and by software developers when needed (our Center employs several software developers who work on scientific software development). |
| Student Qualifications | Students need know how to program in Python, experience with scikit-learn would be advantageous. The project would be adequate for: 1) students with a background in biology with skills in computational methods, or 2) students with a background in computer science, physics or mathematics, with an interest in biological applications. |
| Summer Schedule Options | Research Dates: May 18 to July 24, 2026 Schedule: Monday-Friday 9am through 5pm. Schedule could be made flexible as long as same amount of time is kept. This work is amenable to a substantial amount of remote work, although in-person meetings will still be needed regularly. |
| Project Continuation | Fall 2026, Spring 2027 |
| Academic Year Time Commitment | 3, 6 hours/week |
| Possible Thesis Project | Yes |
Application
Submit an online application for this research opportunity at https://quest.uconn.edu/prog/HRPSU26-24. The application deadline is Monday, February 16, 2026.
This application requires a resume or CV, an unofficial transcript, and a brief statement of research interests.