Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Preprint . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2025
License: CC BY
Data sources: Datacite
versions View all 1 versions
addClaim

A Foundational Model for Biological Logic and Disease

Authors: Sinha, Saptarshi; Ghosh, Pradipta;

A Foundational Model for Biological Logic and Disease

Abstract

Biology and medicine mistake complexity for understanding. We build black-box AI models and stockpile terabytes of omics data, yet the logic of life remains hidden in plain sight. The cell, nature’s smallest decision-maker computes not by probability, but by logic: “on” or “off,” “commit” or “retract.” Boolean mathematics decodes this digital decision-making, transforming analog molecular noise into invariant “if–then” rules that persist across tissues, species, and diseases. Simultaneously, by quantifying how populations shift between binary states, Boolean frameworks map disease as an analog continuum—dynamic, graded, reversible and objectively measurable. By deciphering both the binary decisions and the graded population shifts that shape disease, this approach replaces static biomarkers with mechanistic rules—and advances a new premise: if life computes in logic, medicine should too.

Related Organizations
Keywords

StepMiner, Invariants, Systems Biology, Boolean Implication Networks, Function-Agnostic Modeling, Cellular Intelligence, Dose–Response Alignment

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green