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Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with
Ali Emadi1, Tomasz Lipniacki2, Andre Levchenko3,4
1Center for Wireless Information Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, 323 King Blvd, Newark, NJ 07102, USA.
Biology
|December 22, 2023
Summary
Cells make decisions based on signals, but noise can cause errors. This study models cellular decision-making using two key protein outputs, NFκB and ATF-2, to understand and quantify these errors in biological systems.
Area of Science:
- Cellular biology
- Systems biology
- Signal transduction
Background:
- Cells respond to external signals to determine their fate.
- Signal transduction noise can lead to incorrect cellular decisions, such as false alarms or missed signals.
- The tumor necrosis factor (TNF) pathway involves key transcription factors like NFκB and ATF-2.
Purpose of the Study:
- To develop methods for modeling and computing cell decision-making parameters based on two signaling system outputs.
- To quantify decision thresholds and error probabilities (false alarm and miss) in cellular responses.
- To investigate the role of joint signaling outputs in complex cellular decisions and pathological conditions.
Main Methods:
- Modeling a two-output signaling system regulated by TNF.
- Developing computational methods to analyze single-cell concentration levels of NFκB and ATF-2.
- Defining and calculating decision error probabilities based on joint output analysis.
Main Results:
- Computed decision thresholds for cellular responses based on NFκB and ATF-2 levels.
- Quantified false alarm and miss probabilities for the signaling system.
- Demonstrated the utility of considering joint output responses for understanding cellular decision-making.
Conclusions:
- Analyzing the combined response of NFκB and ATF-2 provides insights into cellular decision-making processes.
- Quantifying decision errors is crucial for understanding cellular behavior and disease development.
- The developed methods can be applied to study various physiological and pathological conditions involving signal transduction noise.
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