Research Overview Current research gap. Classical gene regulatory networks (GRNs) rest on prior knowledge or on simple co-expression, which leaves the network at the level of correlation — associations without cause or direction. To move past this, I set out to build a regulatory network from real experimental perturbation data that carries genuine causality; then, from that causal network, to run a network analysis of how perturbation effects propagate through its topology, aggregate the gathered information into an embedding, and use that embedding to search for novel drug targets by guilt-by-association with known targets. Research questions. Proposed novel algorithm to read the propagation structure is a quantum walk — a quantum analogue of network diffusion that explores a graph by amplitude interference rather than by probability — which frames the two questions at the centre of this work: Does a quantum walk preserve biological information better than a classical random walk? Does this representation nominate credible, novel drug targets for autoimmune disease? The approach. The gene regulatory network (GRN) of a cell is the wiring of transcriptional control that governs its behaviour. The convergence of CRISPR and single-cell technology lets me curate a genuinely causal map: Perturb-seq couples genome-scale single-gene perturbation with single-cell readout at single-cell-type resolution. Here I curate such a causal network from a genome-scale Primary Human CD4⁺ T-Cell Perturb-seq dataset, read it with a quantum-walk algorithm that assigns every gene a quantum interference fingerprint of its position in the topology, and use the resulting embedding to nominate novel targets — testing both questions above. I also provide the causal perturb-map and its network analysis as an interactive tool so other researchers can explore the results directly.
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