PFLPhysiological Fitness Landscape

About the project: the problem, the idea and the people

The problem

A lab report compares each result with a fixed normal range. But risk of death changes smoothly across that range, and for 17 of the 146 markers with a curve it rises at both ends. Reading one marker at a time, at one moment, also misses how markers combine and which way a person is moving.

The idea

Brian Fertig and Jack Tuszynski developed the landscape concept. Fertig set it out in Metabolism and Medicine, Volume 2: The Metabolic Landscape of Health and Disease (CRC Press, 2022).

The PFL comes from fitness-landscape ideas in physics, which have since been applied to physiology. This project builds an open, measured version and tests it against study data.

Metabolic fitness

“Metabolic fitness is a fundamental determinant of physiological health, reflecting the body's capacity to sustain efficient energy production and biochemical balance; its progressive deterioration contributes to vulnerability, chronic disease, and biological aging.”

Brian Fertig

Metabolic fitness has many dimensions, and no single measure of it has yet been shown to predict how an individual's disease will unfold. Closing that gap is the central opportunity for the PFL.

The main open question: is falling mitochondrial efficiency just another sign of accumulated wear? Or is it a measurable mechanism that drives the loss of stability and the move into disease? If it is the mechanism, the PFL can predict when adaptation is close to failing. The problem in full.

Source: Fertig B. Metabolism and Medicine, Volume 2. CRC Press, 2022.

What we do with it

None of these is in clinical use. Each depends on the tests in the evidence register. The models behind them: Models and open problems.

Who

  • Brian FertigAuthor of Metabolism and Medicine; co-developer of the landscape concept
  • Jack TuszynskiCo-developer of the landscape concept
  • Matthew HalmaProject contact, [email protected]

How we handle study data

Analyses run under each study holder's agreement, on a local machine. Only aggregate results are shared, and small groups are left out. AI tools help write and review code and never process individual records.