https://pages.htgaa.org/2026a/paul-thiong-o/homework/week-07-hw-genetic-circuits-part-ii/index.htmlWeek # 7 Genetic Circuits Part II GENETIC CIRCUITS PART II To learn neuromorphic genetic circuits, showing how engineered gene networks can implement neural-network “perceptron”-like computation and learning Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? To understand the advantages of IANNs (In silico Artificial Neural Networks / Integrated Artificial Neural Networks in synthetic biology) over traditional Boolean genetic circuits, it helps to look at how biological computing is evolving. Traditional genetic circuits act like classic computer chips: they take inputs (like the presence of a specific molecule) and use logic gates (AND, OR, NOT) to produce a definitive, binary ON/OFF response. IANNs, however, mimic the brain’s neural networks using biological components. Here is why IANNs are a massive step up from traditional Boolean genetic circuits:Hugoen