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Wolf Sheep Predation
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From Fig 12.2 p317 Pigliucci M and Muller GB (2010) Evolution: The Extended Synthesis
Development dynamic interactions
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Launchpad about reorganisation based on Bogdanov's Tektology general theory of organization, perceptual control theory, personal history and current concerns, linked to the modern (or historical) organization of biology and political economy. 
Personalised Reorganization Bogdanov Biology and Political Economy
4 months ago
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Questo modello usa un'altra ipotesi per la natura (e durata della fase lag). La popolazione N è composta da una frazione di cellule che non crescono NG e una di cellule che crescono immediatamente alla massima velocità, G. Il rapporto fra le due frazioni determina la durata della fase lag
Clone of Heterogeneous Population Model
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Trey's Food Chain
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Components of behaviour organised into domains, from NIMH Research Domain Criteria website and BMC paper and 2013 series on current controversies in psychiatry.
Functional dimensions of behavior
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BIO 110 Food Web
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Disease Dynamics
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Small Intestine example from Progress in Biophysics & Molecular Biology Special Issue 2016 From the century of the genome to the century of the organism: New theoretical approaches paper on organization. Compare with Bogdanov (click tag)
Biology Principles of Organization and Variation 1
12 months ago
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Description:

A simple model for breeding plants from generation to generation in 3 different locations, with one "yield" variable (e.g. height) and 4 combinations of plants from the parents. Simulation tracks the frequencies of each combination in each generation as well as the overall average height by generation.

The slider will select from 1 of 5 presets that changes the characteristics of each location's plants.

The graph of A1A2 Proportion represents both A1A2 and A2A1 since they are interchangeable.

Clone of Plant Breeding Simulation
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FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

FORCED GROWTH INTO TURBULENCE
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Evolutionary Accretion Model of Human Memory mostly from Murray 2016/7 Book and 2019 Ferbinteanu Memory Theory article See also Brain systems modelling 2021 article
The evolution of human memory
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From the Plausibility of life book esp p 220-227  Constraints that deconstrain

Facilitated Variation and Evolvability
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Killed People by Made-up virus
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A model of the exponential growth and collapse of E. coli.
Clone of E coli life cycle model
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From Fig 16.3  p429 Pigliucci M and Muller GB (2010) Evolution: The Extended Synthesis

Evolutionary theory structure
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OVERSHOOT GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

OVERSHOOT GROWTH INTO TURBULENCE
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Clone of Food Web on Autumn Olive
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A model of the exponential growth and collapse of E. coli.
Clone of E coli life cycle model
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Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Bio103 Predator-Prey Model ("Lotka'Volterra")
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Food Web of Garden Transect
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WIP Summary of Cell Systems 2024 article Versatile system cores as a conceptual basis for generality in cell and developmental biology
Core and periphery hypothesis for cells and developmental systems
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Simple Feedback of Insulin: Model 2 (dampening oscillation)
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Long Reef Aquatic Reserve Food Web