Evidence map›Paper›PMID 42117514›Full record

ReviewBioEssays : news and reviews in molecular, cellular and developmental biology2026

Central Dogma Cycle and Network: A Model for Cell Memory.

Martin R Schiller

Abstract readReview
In one paragraph

Review in BioEssays : news and reviews in molecular, cellular and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Martin R SchillerNevada Institute of Personalized Medicine and School of Life Sciences, 4505 S. Maryland Pkwy, University of Nevada, Las Vegas, Nevada, USA.

Funding

The Genome Analysis and Sequencing Pipeline (GASP)P20GM121325 · NIGMS · UNIVERSITY OF NEVADA LAS VEGAS · PI CHEN, JINGCHUN · 2018 to 2022
$11.5M
High Content Screening for Bifunctional Chimera BiologicsR44AI188872 · NIAID · HELIGENICS INC · PI LIZ VALENTE · 2025 to 2026
$1.6M
A novel high-throughput functional screen based upon chimeric minimotif decoysR21AI116411 · NIAID · UNIVERSITY OF NEVADA LAS VEGAS · PI SCHILLER, MARTIN R · 2015 to 2016
$407k
NIH HHS P20GM121325NIH HHS R21AI116411NIH HHS R44AI188872
6 · The paper itself

Abstract

This paper proposes an extension of the traditional Central Dogma of molecular biology to a more dynamic model termed the Central Dogma cycle (CDC) and a broader network called the Central Dogma cyclic network (CDCN). While the Central Dogma is necessary for genetic information flow, it is insufficient to fully explain cellular memory, decision-making, and information management. The CDC incorporates additional well-established steps such as protein folding and networking, highlighting the cyclical nature of biological information flow. I propose that this cyclic architecture functions as a key mechanism for cellular memory, drawing analogies to memory functions in computers, such as input, read, write, execute, and erase. Within the CDCN, interconnected metabolic and signaling pathways act as logic-enabled processors that bridge DNA mutations to phenotypes. This model reframes heredity beyond nucleic acid sequences and evolution as the optimization of memory-bearing networks. It is extensible to broader biological systems such as physiological feedback loops. Understanding cellular memory through this cyclic network model offers a unified perspective on heredity, adaptation to the environment, cell processes, and the disruptions of information flow in disease pathology.

Indexed as

Models, BiologicalAnimalsHumansMetabolic Networks and PathwaysSignal TransductionCentral Dogmacycledigital logicmemorynetwork

Identifiers

PMID42117514
PMCPMC13162752

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.