Compound Race Pathology: A Friction-Theoretic Framework for Multi-Scale Disease Progression and Intervention

A Hypothesis & Theory synthesis and research programme on multi-scale, multi-mechanism ("compound") disease. It builds explicitly on established work — metabolic control analysis and polypharmacology (Moreno-Sánchez et al. 2010), network medicine (Barabási, Gulbahce & Loscalzo 2011), and the autoimmune HSCT/CAR-T literature on tissue reversibility (Sullivan et al. 2018) — and offers a candidate account of why combining interventions across rate-limiting nodes, and matching intervention to substrate reversibility, work as they do. Its predictions are invitations to test, to be read against that established literature. v4 (August 2026). This version sharpens the paper's positioning against the prior formal literature it builds on: the step from distributed metabolic control to multi-target intervention, and the boundedness of single-target intervention, are credited as established results (metabolic control analysis and polypharmacology; robustness and network topology), with the paper's own contribution narrowed to the cross-scale extension, the four-regime classification, and the hysteresis-fighting taxonomy. The multi-stage power-law age-incidence of carcinogenesis is credited to its original account and its exponent recast as a testable quantity; a network-medicine anchor for single-agent insufficiency is added. Editorial pass. Earlier versions remain in the version history.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-27
DOI
https://doi.org/10.5281/zenodo.22132991
Primary Topic
Mental Health Research Topics
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Compound Race Pathology: A Friction-Theoretic Framework for Multi-Scale Disease Progression and Intervention

Tomas Pødenphant Lund
Zenodo (CERN European Organization for Nuclear Research)
Mental Health Research Topics
preprint

Compound Race Pathology: A Friction-Theoretic Framework for Multi-Scale Disease Progression and Intervention

Tomas Pødenphant Lund
preprint en

Abstract

A Hypothesis & Theory synthesis and research programme on multi-scale, multi-mechanism ("compound") disease. It builds explicitly on established work — metabolic control analysis and polypharmacology (Moreno-Sánchez et al. 2010), network medicine (Barabási, Gulbahce & Loscalzo 2011), and the autoimmune HSCT/CAR-T literature on tissue reversibility (Sullivan et al. 2018) — and offers a candidate account of why combining interventions across rate-limiting nodes, and matching intervention to substrate reversibility, work as they do. Its predictions are invitations to test, to be read against that established literature. v4 (August 2026). This version sharpens the paper's positioning against the prior formal literature it builds on: the step from distributed metabolic control to multi-target intervention, and the boundedness of single-target intervention, are credited as established results (metabolic control analysis and polypharmacology; robustness and network topology), with the paper's own contribution narrowed to the cross-scale extension, the four-regime classification, and the hysteresis-fighting taxonomy. The multi-stage power-law age-incidence of carcinogenesis is credited to its original account and its exponent recast as a testable quantity; a network-medicine anchor for single-agent insufficiency is added. Editorial pass. Earlier versions remain in the version history.

Zenodo (CERN European Organization for Nuclear Research)
Mental Health Research Topics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.