<?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 HW: Genetic Circuits Part 2 :: 2026a-sean-murphy</title><link>https://pages.htgaa.org/2026a/sean-murphy/homework/week-07-hw-genetic-circuits-part-2/index.html</link><description>Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? IANNs offer three advantages over Boolean genetic circuits. They operate on graded, continuous intracellular signals rather than discrete ON/OFF states, enabling weighted summation, nonlinear activation, and universal function approximation. Weiss-coauthored neuromorphic circuits demonstrated these capabilities through analog computation, soft majority voting, and ternary switching in living cells. IANNs also permit tunable decision boundaries without topological redesign because effective weights and biases can be adjusted by modifying stoichiometry, promoter strength, or recognition-site placement. The PERSIST endoRNase system illustrates this: the same RNase acts as a repressor or activator depending on 5′-UTR versus 3′-UTR target-site positioning. Finally, multilayer IANNs have greater expressive power per circuit, representing smooth classifiers and nonlinearly separable response surfaces that Boolean truth tables cannot efficiently encode.</description><generator>Hugo</generator><language>en</language><atom:link href="https://pages.htgaa.org/2026a/sean-murphy/homework/week-07-hw-genetic-circuits-part-2/index.xml" rel="self" type="application/rss+xml"/></channel></rss>