Evidence map›Paper›PMID 38132287›Full record

ArticleBiology2023

Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules.

Ali Emadi, Tomasz Lipniacki, Andre Levchenko, Ali Abdi

Open access · goldAbstract read
In one paragraph

Article in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 35% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
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

4 authors at 3 institutions in 2 countries.

Ali EmadiCenter for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 King Blvd, Newark, NJ 07102, USA.ORCID 0000-0002-2224-8854
Tomasz LipniackiInstitute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland.ORCID 0000-0002-3488-2561
Andre LevchenkoYale Systems Biology Institute, Yale University, New Haven, CT 06520, USA.ORCID 0000-0001-6262-1222
Ali AbdiCenter for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 King Blvd, Newark, NJ 07102, USA.ORCID 0000-0002-5694-6888
New Jersey Institute of Technology · USInstitute of Fundamental Technological Research · PLYale University · US

Funding

National Science Center 2019/35/B/NZ2/03898National Science Foundation 2231765 from the MCB division
6 · The paper itself

Abstract

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

Indexed as

cell decision makingcellular decision error probabilitiesdecision theorymolecular signaling systemssignal transduction noise

Identifiers

PMID38132287
PMCPMC10740708
OpenAlexW4388942048

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.