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About the Authors:
Iman Habibi
Affiliation: Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, United States of America
Raymond Cheong
Affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
Tomasz Lipniacki
Affiliation: Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, Warsaw, Poland
ORCID http://orcid.org/0000-0002-3488-2561
Andre Levchenko
Affiliation: Yale Systems Biology Institute and Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America
Effat S. Emamian
Affiliation: Advanced Technologies for Novel Therapeutics, Enterprise Development Center, New Jersey Institute of Technology, Newark, NJ, United States of America
Ali Abdi
* E-mail: [email protected]
Affiliations Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, United States of America, Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ, United States of AmericaAbstract
In this study a new computational method is developed to quantify decision making errors in cells, caused by noise and signaling failures. Analysis of tumor necrosis factor (TNF) signaling pathway which regulates the transcription factor Nuclear Factor KB (NF-KB) using this method identifies two types of incorrect cell decisions called false alarm and miss. These two events represent, respectively, declaring a signal which is not present and missing a signal that does exist. Using single cell experimental data and the developed method, we compute false alarm and miss error probabilities in wild-type cells and provide a formulation which shows how these metrics depend on the signal transduction noise level. We also show that in the presence of abnormalities in a cell, decision making processes can be significantly affected, compared to a wild-type cell, and the method is able to model and measure such effects. In the TNF-NF-KB pathway, the method computes and reveals changes in false alarm and miss probabilities in A20-deficient cells, caused by cell’s inability to inhibit TNF-induced NF-KB response. In biological terms, a higher false alarm metric in this abnormal TNF signaling system indicates perceiving more cytokine signals which in fact do not exist at the system input, whereas a higher miss metric indicates that it is highly likely to miss signals that actually exist. Overall, this study demonstrates the ability of the...