Mathematical Problems in the Evolution of Senescence

on YouTube ↗49m

" summary

Develops two concrete models with proofs and computations: a measure-valued model of mutation accumulation that allows age-spread effects, concave (negative) epistasis and recombination, yields an explicit series solution, a convergence theorem, and shows the naive point-mass/linear extension produces a pathological “unraveling” or wall-of-death whereas concave selection costs prevent it; and a stochastic life‑history model of rare diapause/migration that numerically and analytically shows a tiny dormancy rate gives a large benefit scaling like 1/|log ε| growth gain under realistic variance, overturning the intuition that mixing always hurts.

AI-written summary · about

" chapters (5)

1. 0:00Evolutionary aging theory
2. 2:51Mutation accumulation
3. 7:39Modeling epistasis
4. 32:40Optimality and diapause
5. 41:54Discussion and concluding

" related talks

• 01h 10mFunctional Law of Large Numbers and PDEs for Spatial Epidemic Models with...Etienne Pardoux · Epidemics and Information Diffusion (Simons Institute, 2022)
• 42mMachine learning against biological agingAnton Kulaga · Øredev 2023
• 52mAn Evolutionary Model of Economic Behavior, Bounded Rationality, and IntelligenceAndrew Lo · Algorithmic Game Theory and Practice 2015
• 19mA Metacommunity Model of Interacting Hosts with Microbe ExchangeMason Porter · Smale@95: A Conference in Honor of Steve Smale
• 57mDarwinian Machine Learning: Principles of Machine Learning in Evo-devo, Evo-eco and Evolutionary...Richard Watson · Computational Theories of Evolution
• 53mDesigning Robust LearnersJacob Steinhardt · Emerging Challenges in Deep Learning (Simons Institute workshop, 2019)
• 01h 01mRobustness, Stochastics, Uncertainty 2Sasha Rakhlin, Ben Recht, Laurent El Ghaoui · Bridging Continuous and Discrete Optimization Boot Camp 2017
• 56mIntroduction to Cancer Bioinformatics III: Cancer Progression NetworksNiko Beerenwinkel · Algorithmic Challenges in Genomics Boot Camp