Evidence mapPaperPMID 42589495Full record

ReviewInternational journal of molecular sciences2026

Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.

Grzegorz Matyszczak

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Grzegorz MatyszczakFaculty of Chemistry, Warsaw University of Technology, Noakowski Street 3, 00-664 Warsaw, Poland.ORCID 0000-0001-7521-2284

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell signaling, and the biochemical reactions underlying it, are complex phenomena fundamental for control of cellular behavior in response to the incentives present in the cell's direct environment. It is crucial for coordination of cell activities such as growth, differentiation, metabolism, and death. The aim of this review is to present mathematical and computational models of biochemical reactions and cell signaling pathways in an educational and comprehensive way, outlining broad aspects of modeling such as differential equations, and artificial intelligence and machine learning approaches. This review also discusses potential applications of mathematical and computational models of cell signaling and biochemical reactions in fields such as systems biology, personalized medicine (i.e., cancer treatment, neurodegenerative disease treatment), and identification of drug targets.

Indexed as

Computer SimulationMachine LearningModels, BiologicalSignal TransductionAnimalsHumansSoft ComputingSystems Biologybiochemical reactionscell signalingenzyme kineticsmachine learningmathematical modelingordinary differential equationssystems biology

Identifiers

PMID42589495
PMCPMC13467184

What Socratic holds

Textmetadata
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 Socratic graph.