Evidence map›Paper›PMID 34892204›Full record

ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2021

Inferring initial state of the ancestral network of cellular fate decision: a case study of phage lambda.

Yiyu Pang, Jie Liang

Abstract read
In one paragraph

Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021. 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

2 authors.

Yiyu Pang
Jie Liang

Funding

Models and Algorithms for Biological Networks and Polymers: Stochastic Probability Landscape and Chromatin EnsemblesR35GM127084 · NIGMS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI LIANG, JIE · 2018 to 2025
$3.7M
NIGMS NIH HHS R35 GM127084
6 · The paper itself

Abstract

Gene regulatory networks (GRNs) describe how gene expression is controlled by interactions among DNA and proteins. The decision network controlling prophage induction in phage lambda has served as a paradigm for studying decision control of cellular fate, which has broad implications for understanding phenomena such as embryo development, tissue regeneration, and tumorigenesis. The phage-lambda GRN dictates whether the phage enters the lytic mode or the lysogenic mode. In this work, we study the evolutionary origin of this GRN and explore the initial architecture of the proto-GRN, from which the modern GRN is evolved. Specifically, we examined the model of proto-GRN of phage-lambda containing one operator, from which the modern GRN with three operators evolved. We constructed 9 network architectures of the proto-GRNs by different combinations of the three operators O

Indexed as

Bacteriophage lambdaLysogenyGene Regulatory Networks

Identifiers

PMID34892204
PMCPMC8957294

What OpenQuestion holds

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Registered trials

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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.