{"artifact_id":"L1-905","layer":"L1","title":"Proteostasis Collapse & Neurodegeneration","domain":"Neurodegeneration","sub_domain":"Protein Aggregation","physics_fingerprint":{"intro":"Alzheimer's and Parkinson's track with the aggregation of proteins (Aβ, tau, α-synuclein) as the proteostasis network fails with age. Predicting and reversing aggregation kinetics could prevent or slow neurodegeneration.","title":"Proteostasis Collapse & Neurodegeneration","domain":"Neurodegeneration","chapter":"Ch.13 Life Sciences & Aging","why_hard":"Aggregation is governed by coupled nucleation/elongation rates that are hard to measure in vivo; biomarkers lag pathology; clearance interventions have narrow therapeutic windows.","agent_idea":"An agent that fits a patient's aggregation kinetics and proposes clearance-boosting regimens with predicted load reduction.","approaches":["Master-equation (Knowles) inversion to infer rate constants from biomarker trajectories","Neural-ODE emulators of aggregation / clearance","Design of proteostasis-boosting interventions"],"sub_domain":"Protein Aggregation","forward_model":"Nucleation–elongation aggregation master equation (Knowles): aggregate mass M(t) from primary/secondary nucleation and elongation rate constants; clearance by proteostasis network.","benchmark_goal":"Predict the fractional aggregate-load reduction over time under a proteostasis-boosting intervention, within ~10%.","challenge_blurb":"Predict and reverse age-driven protein aggregation — the proteostasis collapse behind Alzheimer's and Parkinson's.","challenge_group":"life","challenge_short":"Proteostasis","grand_challenge":true,"governing_equation":"dM/dt = 2 k_+ m(t) P(t);  dP/dt = k_n m^{nc} + k_2 m^{n2} M − k_clear·P"},"observable_profile":{"unit":"fractional aggregate-load reduction","floor":0.3,"metric":"load_reduction","sota_reference":"Knowles aggregation kinetics + anti-amyloid antibody trials"},"size_tiers":{"species":[1,3,12],"time_pts":[10,50,500]},"hardness_fn":{"type":"grand_challenge","metric":"load_reduction","baseline":"Exponential-decay fit","delta_tier":50},"initiator_dataset":[{"name":"CSF amyloid/tau longitudinal (ADNI-like)","weight":0.5,"ipfs_cid":null,"license_hash":null},{"name":"In-vitro aggregation kinetics panel","weight":0.3,"ipfs_cid":null,"license_hash":null},{"name":"Proteostasis-network perturbation screen","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":"Proteostasis Collapse & Neurodegeneration (Protein Aggregation) is a problem in Neurodegeneration. 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."}