WIP Related to Systems and Complexity in general and Systems Science for Health in particular

Questo modello ripercorre le dinamiche nella fiaba di  Esopo "Al lupo al lupo!"

Concept maps help us to organize and integrate concepts from one or several sources or fields. 

The calorie reduction by Gene, copied so I can include stress, high intensity exercise, sleep and carb restriction.

From NIMH Research Domain Criteria website and BMC paper and 2013 series on current controversies in psychiatry.

​From Fig 1.1 p11  Pigliucci M and Muller GB (2010) Evolution: The Extended Synthesis. This is a shift in emphasis from statistical correlation to mechanistic causation (p12), including the conditions for the origin and innovation of traits (p13). It overcomes the gradualism, externalism and gene centrism of the Modern Synthesis. Non-gradual change is a property of complex dynamical systems. EvoDevo processes generate particular forms of change rather than others.Genes are followers in the evolutionary process that capture the emergent interactions among environment, development and inheritance into genetic-epigenetic circuits, which are passed to and elaborated on in subsequent generations (p14).

Navigation to aspects of systems relevant to applying the methods to health care; adapted from John Barton's representation of a system slide

Template file of Emergy Symbols 

Overview of theory 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. See example1 insight and example 2 insight Compare with Bogdanov

If I could understand the logic in how '+' and '-' arrows work in this diagram, I would be thrilled! This would solve a lot of confusion I experience with many other diagrams. If I understand it here, I can understand it there.

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.