Evidence map›Paper›PMID 29925605›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2018

Predicting perturbation patterns from the topology of biological networks.

Marc Santolini, Albert-László Barabási

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers.

0numbers the graph read from it
0cells of the map it votes in
113citing papers in PubMed
10.4field-weighted citation impact, top 1% of its field
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

113 citing papers in PubMed, 332 citations in OpenAlex.

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53 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 2 countries.

Marc SantoliniCenter for Complex Network Research, Department of Physics, Northeastern University, Boston, MA 02115.ORCID 0000-0003-1491-0120
Albert-László BarabásiCenter for Complex Network Research, Department of Physics, Northeastern University, Boston, MA 02115; alb@neu.edu.ORCID 0000-0002-4028-3522
Brigham and Women's Hospital · US

Funding

Genomic Analysis of Network Perturbations in Human DiseaseP50HG004233 · NHGRI · DANA-FARBER CANCER INST · PI VIDAL, MARC · 2007 to 2017
$31.6M
Systems Biology of Airway DiseaseP01HL132825 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMEO, DAWN L · 2016 to 2020
$12.6M
NHGRI NIH HHS P50 HG004233NHLBI NIH HHS P01 HL132825
6 · The paper itself

Abstract

High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

Indexed as

Models, BiologicalBacteriaChemotaxisGene Expression Regulation, Bacterialbiological networkschemotaxisperturbation patternstopological models

Identifiers

PMID29925605
PMCPMC6142275
OpenAlexW2807869290

What OpenQuestion 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 OpenQuestion graph.