Mathematical modeling

Quasispecies model

The quasispecies model is a description of the process of the Darwinian evolution of certain self-replicating entities within the framework of physical chemistry. A quasispecies is a large group or "cloud" of related genotypes that exist in an environment of high mutation rate (at stationary state), where a large fraction of offspring are expected to contain one or more mutations relative to the parent. This is in contrast to a species, which from an evolutionary perspective is a more-or-less stable single genotype, most of the offspring of which will be genetically accurate copies. It is useful mainly in providing a qualitative understanding of the evolutionary processes of self-replicating macromolecules such as RNA or DNA or simple asexual organisms such as bacteria or viruses (see also viral quasispecies), and is helpful in explaining something of the early stages of the origin of life. Quantitative predictions based on this model are difficult because the parameters that serve as its input are impossible to obtain from actual biological systems. The quasispecies model was put forward by Manfred Eigen and Peter Schuster based on initial work done by Eigen. (Wikipedia).

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Numerical mathematics of quasicrystals – Pingwen Zhang – ICM2018

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From playlist Numerical Analysis and Scientific Computing

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Virology in a nutshell, quasispecies and experimental virus evolution by Santiago F Elena

The Third Bangalore School on Population Genetics and Evolution DATE:05 March 2018 to 17 March 2018 VENUE:Ramanujan Lecture Hall, ICTS Bangalore. No living organism escapes evolutionary change. Evolutionary biology thus connects all biological disciplines. To understand the processes dri

From playlist Third Bangalore School on Population Genetics and Evolution

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Practical aspects of quasispecies theory by Isabel Novella

Second Bangalore School on Population Genetics and Evolution URL: http://www.icts.res.in/program/popgen2016 DESCRIPTION: Just as evolution is central to our understanding of biology, population genetics theory provides the basic framework to comprehend evolutionary processes. Population

From playlist Second Bangalore School on Population Genetics and Evolution

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Some questions around quasi-periodic dynamics – Bassam Fayad & Raphaël Krikorian – ICM2018

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From playlist Dynamical Systems and ODE

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"AWESOME Antigravity double cone" (science experiments)

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From playlist MECHANICS

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Nathalie Priebe Frank : Introduction to hierarchical tiling dynamical systems

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From playlist Dynamical Systems and Ordinary Differential Equations

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Population Genetics and Evolution – II by Luca Peliti

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From playlist Winter School on Quantitative Systems Biology

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Jean Michel : Quasisemisimple classes

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From playlist Lie Theory and Generalizations

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Quasi-Fermi Levels Explained

https://www.patreon.com/edmundsj If you want to see more of these videos, or would like to say thanks for this one, the best way you can do that is by becoming a patron - see the link above :). And a huge thank you to all my existing patrons - you make these videos possible. Quasi-Fermi l

From playlist Electronics I: Semiconductor Physics and Devices

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A geometrical explanation of the law of cosines.

From playlist Trigonometry

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a quasi-Pythagorean identity

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From playlist Complex Analysis

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An attempt at growing a quasicrystal

Like the simulation https://youtu.be/YjerTwsRUp0 this one shows the motion of particles coupled to a thermostat, and interacting with an anisotropic Lennard-Jones type potential. Only this time, the potential has a pentagonal symmetry instead of a square symmetry. Two isolated particles ha

From playlist Molecular dynamics

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Nikolaus Kriegskorte - Controversial stimuli: experiments to adjudicate computational hypotheses

Recorded 13 January 2023. Nikolaus Kriegeskorte of Columbia University presents "Controversial stimuli: Optimizing experiments to adjudicate among computational hypotheses" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Learn more online at: http://www.ipam.ucl

From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights

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Using & Expanding the NLP Models Hub 1 | Webinar

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From playlist AI & NLP Webinars

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Creating HEV Plant Models

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From playlist Hybrid Electric Vehicles

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DDPS | Learning hierarchies of reduced-dimension and context-aware models for Monte Carlo sampling

In this DDPS Seminar Series talk from Sept. 2, 2021, University of Texas at Austin postdoctoral fellow Ionut-Gabriel Farcas discusses hierarchies of reduced-dimension and context-aware low-fidelity models for multi-fidelity Monte Carlo sampling. Description: In traditional model reduction

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

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Lecture 14 - Building Models

This is Lecture 14 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www

From playlist CSE519 - Data Science Fall 2016

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Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 15

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: http://cs330.stanford.edu/fall2021/index.html To view all online courses and programs offered by Stanford, visit: http:/

From playlist Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn

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Albert Einstein, Holograms and Quantum Gravity

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From playlist In Theory

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DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models

This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent

From playlist Learning resources

Related pages

Self-replication | DNA | System of linear equations | Expected value | Perron–Frobenius theorem | Diagonalizable matrix | Gard model | Irreducibility (mathematics) | Probability