{"artifact_id":"L1-902","layer":"L1","title":"Hallmarks-of-Aging Network & Longevity","domain":"Longevity Science","sub_domain":"Mortality Dynamics","physics_fingerprint":{"intro":"Mortality rises exponentially with age (Gompertz). Longevity science asks whether we can bend that curve — slowing the rate of aging itself with geroprotectors (rapamycin, senolytics, caloric restriction) acting on the hallmarks of aging — to extend healthspan, not just lifespan.","title":"Hallmarks-of-Aging Network & Longevity","domain":"Longevity Science","chapter":"Ch.13 Life Sciences & Aging","why_hard":"Aging is a networked, multi-organ process; interventions interact, read out slowly, and human trials take decades. Surrogate aging clocks are noisy proxies for mortality.","agent_idea":"An agent that proposes and ranks combination interventions by predicted healthspan gain and safety, and designs the cheapest informative trial.","approaches":["Mechanistic hallmark-network models mapping interventions → aging-rate G","Multi-omic aging clocks validated against hard mortality endpoints","Active-learning design of combination geroprotector trials"],"sub_domain":"Mortality Dynamics","forward_model":"Mortality hazard μ(t)=A+B·e^{G·t} (Gompertz–Makeham); interventions act on the aging rate G through a network of the twelve hallmarks coupled to multi-omic state x(t).","benchmark_goal":"Predict the shift in the survival curve (mortality-rate doubling-time gain) from multi-omic trajectories under a geroprotector, within a few percent.","challenge_blurb":"Bend the Gompertz mortality curve — slow the rate of aging itself, extending healthspan, not just lifespan.","challenge_group":"life","challenge_short":"Longevity","grand_challenge":true,"governing_equation":"μ(t) = A + B·e^{G·t};   dG/dθ = −Σ_i w_i·intervention_i"},"observable_profile":{"unit":"× mortality-rate doubling-time gain","floor":0.15,"metric":"MRDT_gain","sota_reference":"Rapamycin / senolytics / caloric-restriction meta-analyses (ITP)"},"size_tiers":{"cohort_n":[1000,50000,500000],"omic_layers":[1,4,9]},"hardness_fn":{"type":"grand_challenge","metric":"MRDT_gain","baseline":"Cox proportional-hazards","delta_tier":50},"initiator_dataset":[{"name":"UK-Biobank multi-omic aging cohort","weight":0.5,"ipfs_cid":null,"license_hash":null},{"name":"Interventions Testing Program survival curves","weight":0.3,"ipfs_cid":null,"license_hash":null},{"name":"Proteomic aging-clock panel","weight":0.2,"ipfs_cid":null,"license_hash":null}],"status":"testnet","staked_pwm":5000.0,"chain_hash":null,"chain_tx_hash":null,"chain_block":null,"wp":{},"plain_intro":"Hallmarks-of-Aging Network & Longevity (Mortality Dynamics) is a problem in Longevity Science. The forward model maps the hidden the unknown quantity to a measurement. The inverse goal is to recover the the unknown quantity from the observed data."}