A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data

Ali Emadi, Tomasz Lipniacki, Andre Levchenko, Ali Abdi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF-NF-κB cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF-NF-κB system case study in this paper and given the high involvement of the transcription factor NF-κB in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes.

Original languageEnglish (US)
Title of host publication2023 57th Annual Conference on Information Sciences and Systems, CISS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451819
DOIs
StatePublished - 2023
Event57th Annual Conference on Information Sciences and Systems, CISS 2023 - Baltimore, United States
Duration: Mar 22 2023Mar 24 2023

Publication series

Name2023 57th Annual Conference on Information Sciences and Systems, CISS 2023

Conference

Conference57th Annual Conference on Information Sciences and Systems, CISS 2023
Country/TerritoryUnited States
CityBaltimore
Period3/22/233/24/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Artificial Intelligence
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Keywords

  • A20 deficiency
  • Neyman-Pearson detector
  • biochemical signals
  • cancer
  • cell decision making
  • detection theory

Fingerprint

Dive into the research topics of 'A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data'. Together they form a unique fingerprint.

Cite this