<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Week 7: Genetic Circuits 2 :: 2026a-asaf-balaga</title><link>https://pages.htgaa.org/2026a/asaf-balaga/homework/week-07/----title-week-5-protein-design-2-weight-10----_index/index.html</link><description>Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? The main advvantage I see of an IANN over a traditional genetic circuit (Boolean in nature) is that the IANN seems better suited for biological situations that require a more sophisticated estimation than a sharp TRUE/FALSE. We would need more ‘sensitive tools’ if we are to deal with many inputs, gradients, dynamic thresholds and intermidiate states - for all mentioned, binary logic is insufficient. I’m interested in cultivated meat, and in this course I will try to demonstrate control over marbeling of fat and meat tissues. This concept matters here because fat distribution is not a binary problem but a morphogentic spatial multi-input issue. IANN’s can seem more relevant when the goal is to account for several factors and generate a site-specific nuanced output.</description><generator>Hugo</generator><language>en</language><atom:link href="https://pages.htgaa.org/2026a/asaf-balaga/homework/week-07/----title-week-5-protein-design-2-weight-10----_index/index.xml" rel="self" type="application/rss+xml"/></channel></rss>