Conventional silicon-based storage is approaching fundamental physical and economic limits under exponential data growth, motivating exploration of alternative archival substrates. This paper introduces Living Information Storage Systems (LISS), a framework that exploits self-replicating biological genomes as a simultaneous storage medium, replication engine, and error-repair substrate. Within LISS, the paper presents a formal, end-to-end, quantitative treatment of human somatic genomic storage comprising: (i) an information-theoretic capacity bound derived from the Shannon capacity of the genomic substitution channel; (ii) a closed-form effective capacity model ; (iii) the Adaptive Safe-Harbor Encoding (ASHE) algorithm, formulated as a constrained nucleotide sequence optimisation with a multi-objective placement scoring function, targeting \(\eta\) = 1.75 bits/nt; (iv) a hierarchical Genomic Addressing Layer (GAL) with a CHR:LOCUS:BLOCK:OFFSET address space and 16-nt barcode scheme; (v) the Genomic Redundant Distributed Placement (GRDP) algorithm, framed as a Maximum Distance Separable (MDS) code over safe-harbor loci; (vi) a stochastic clonal cell-population model for long-term data integrity; and (vii) a five-tier governance framework with regulatory citations. Monte Carlo simulation ( trials) over an injection-deletion-substitution error channel yields post-ECC recovery rates of 91.53%–99.17% (95% CI) and raw BER of 41.8%–46.7% across 128 B–1 KB payloads. A mutation-drift model projects 77.9% data integrity at 50 years, and analytical MDS bounds predict that GRDP with achieves end-to-end retrieval probability of 99.65% (dual-parity). A Gompertz-based clonal expansion model quantifies mosaicism degradation to 61.4% cell-fraction retention at 20 years, motivating ex vivo refreshal protocols. Four primary unsolved constraints for practical deployment are identified: prime-editing efficiency, innate immune response to CRISPR machinery, safe-harbor scarcity, and regulatory absence. To the author's knowledge, this constitutes among the first systematic, quantitative, end-to-end architectures for human somatic genomic storage in the literature.