High altitude environments challenge the physiology of mammals mainly due to hypoxic conditions. As a response to this, many mammals show molecular adaptations e.g. by mutations in the genes encoding the hemoglobins. This can result in an increase O2 affinity (and thereby a leftshifted O2 dissociation curve) which will enhance the pulmonary loading of O2. However, the shift can also hamper the O2 unloading process to the metabolic tissue. To elucidate this trade-off between loading and unloading a numerical computer model was made in the program Insigt maker based on the organism, deer mouse (Peromyscus maniculatus). The numerical solution was calculated by the Runge-Kutta method. A time step (dt) of 0.00001 was chosen, as the results were independent of dt at this value. This was tested by investigating the residual values of an interval of dt around 0.00001. The model showed how the trade-off is expressed at different altitudes and how the degree of advantages/disadvantages varies. Based on the model it can be suggested that the trade-off is beneficial, when the hypoxia is severe at high altitude. This is consistent with previously findings. 

 

Gene delivery using ELP

This is a stock-flow consistent model, namely the simplest (SIM) model with government money. I have added imports and exports and an exchange rate which has an influence on consumption demand via real disposable income and real foreign demand.

Godley/Lavoie (2006), chapter 3

(I had to change the equation for consumption demand. The disposable income now enters with a time lag of one period. Otherwise the model would be recursive and would not work in Insight Maker. Thanks to Gene for support.)

New Learning tend to reduce Outdated Thinking, Communicating & Learning though our Outdated Thinking, Communicating & Learning inhibits new learning. The question is then how do we break this cycle. Adopted from "An Introduction to Systems Thinking with STELLA" by Barry Richmond.

Testingin...

Causal loop summary stimulated by Nate Osgood's 2026 Youtube Video, Design and Implementation of a Generalized Algebraic Theory for Dimensional Stock and Flow Models, based on my gemini interaction May 2026 using Gene Bellinger's AI prompts

Success to the successful archetype represents two reinforcing structures which may be in a delicate balance though as soon as one gains a small advantage the resource allocation favors the more successful and the result is then rapidly skewed in the direction of the more successful. See also Archetypes.

Unfolding WIP of Eric Wolstenholme's explanation of hospital congestion from March 2022 Youtube video and online stella presentation. Use of cascading interlinked archetypes. See Kumu version,  early discharge boundaries IM for an earlier version and Generic Archetypes IM from Gene and Simpler Version IM

New Learning tend to reduce Outdated Thinking, Communicating & Learning though our Outdated Thinking, Communicating & Learning inhibits new learning. The question is then how do we break this cycle. Adopted from "An Introduction to Systems Thinking with STELLA" by Barry Richmond.

Causal loop diagram provided by Gene Bellinger in Projects folder using his AI prompts for Gemini. See also World 2 simulation insight

New Learning tend to reduce Outdated Thinking, Communicating & Learning though our Outdated Thinking, Communicating & Learning inhibits new learning. The question is then how do we break this cycle. Adopted from "An Introduction to Systems Thinking with STELLA" by Barry Richmond.

From Jennifer Prah Ruger (2010) Health Capability Conceptualization and Operationalization Am J Public Health 100 p41-49 available from SSRN. Extends Insight 779 with Dahlgren and Whitehead's Sunrise Diagram of Social Determinants of Health added. See also wikipedia Capability Approach

Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses

New Learning tend to reduce Outdated Thinking, Communicating & Learning though our Outdated Thinking, Communicating & Learning inhibits new learning. The question is then how do we break this cycle. Adopted from "An Introduction to Systems Thinking with STELLA" by Barry Richmond.

WIP for SD representation of evolution ​From Mobus website and SJ Gould's big book

Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses See also Pattern Oriented Modelling IM-3834

Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses See also Pattern Oriented Modelling IM-3834

Our computer model details the change in allele frequency of resistant mosquitoes in Africa when the government began spraying DDT. The few mosquitoes that naturally survived the chemical sprays reproduced, and created a large population of resistant mosquitoes. When DDT was sprayed later to prevent the spread of malaria, the DDT was not as effective because of the large amount of DDT-resistant phenotypes in the population.

Our computer model details the change in allele frequency of resistant mosquitoes in Africa when the government began spraying DDT. The few mosquitoes that naturally survived the chemical sprays reproduced, and created a large population of resistant mosquitoes. When DDT was sprayed later to prevent the spread of malaria, the DDT was not as effective because of the large amount of DDT-resistant phenotypes in the population.

Our computer model details the change in allele frequency of resistant mosquitoes in Africa when the government began spraying DDT. The few mosquitoes that naturally survived the chemical sprays reproduced, and created a large population of resistant mosquitoes. When DDT was sprayed later to prevent the spread of malaria, the DDT was not as effective because of the large amount of DDT-resistant phenotypes in the population.

Our computer model details the change in allele frequency of resistant mosquitoes in Africa when the government began spraying DDT. The few mosquitoes that naturally survived the chemical sprays reproduced, and created a large population of resistant mosquitoes. When DDT was sprayed later to prevent the spread of malaria, the DDT was not as effective because of the large amount of DDT-resistant phenotypes in the population.