https://pages.htgaa.org/2026a/ariadna-abigail-ruiz-castro/homework/week-07-hw-genetic-circuits-part-ii/index.htmlAssignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Traditional genetic circuits primarily rely on Boolean logic (AND, OR, NOT gates), which results in “all-or-nothing” digital responses. Intracellular Artificial Neural Networks (IANNs) offer several distinct advantages: Non-linear Signal Integration: Unlike Boolean gates that require strict thresholds, IANNs use activation functions (like Hill functions) to process analog chemical gradients, allowing for more nuanced environmental sensing. Weighted Inputs: IANNs allow for “tunable” inputs. By varying promoter strength or ribosome binding site (RBS) efficiency, the cell can assign different weights (w) to various biological signals, prioritizing one metabolite over another. Noise Filtering: Biological environments are inherently “noisy.” The summation and thresholding architecture of a perceptron acts as a natural buffer, preventing the circuit from misfiring due to minor stochastic fluctuations in gene expression. Computational Density: A single-layer IANN can perform complex classifications that would require a much larger and more metabolically taxing combination of traditional logic gates. Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal. Application: An engineered E. coli strain that acts as a therapeutic diagnostic tool within the human gut.Hugoen