https://pages.htgaa.org/2026a/jorge-alejandro-electo-oshiyama/homework/week-10-hw-imaging-and-measurement/index.htmlHomework: Final Project Figure 1 below presents some key aspects of my final project that require experimental testing and quantitative evaluation. These aspects refer to the expression of the protein binders generated using a Deep Learning Model and selected after in silico prediction of their therapeutic characteristics. Figure 1: Experimental aspects of the final project: AI-driven Antivenom: A Generative Pipeline for De novo Neutralizing Peptides Against Snake Toxins. Image generated using: Copilot AI Aspect 1: Cell-Free Expression System (CFS) Cell-Free Expression System (CFS) enables rapid expression of multiple protein variants in parallel. Since this project aims to predict and express different protein binders, CFS provides a scalable and automatable platform that avoids cell culture and allows preliminary functional testing without full purification (Cui et al., 2022) To ensure reproducibility, the CFS workflow must be standarized and quantitatively monitored. Expression efficiency will be measures by using detection tags like biotinylated lysine or His-tag into the peptide of interest, enabling detection through SDS-PAGE followed by Western Blotting. A colorimetric readout using biotin-binding secondary antibody and chromogenic substrate will aloow quantification of expression levels (Hunt et al., 2024)Hugoen