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- All Subjects: Python
- Creators: Computing and Informatics Program
- Status: Published
The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.
The process of learning a new skill can be time consuming and difficult for both the teacher and the student, especially when it comes to computer modeling. With so many terms and functionalities to familiarize oneself with, this task can be overwhelming to even the most knowledgeable student. The purpose of this paper is to describe the methodology used in the creation of a new set of curricula for those attempting to learn how to use the Dynamic Traffic Simulation Package with Multi-Resolution Modeling. The current DLSim curriculum currently relates information via high-concept terms and complicated graphics. The information in this paper aims to provide a streamlined set of curricula for new users of DLSim, including lesson plans and improved infographics.