Late-Arriving Signals Contribute Less to Cell-Fate Decisions
| Autor: | Michael G. Cortes, Jimmy T. Trinh, Lanying Zeng, Gábor Balázsi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Stochastic Processes Threshold crossing Stochastic process Biophysics Gene regulatory network Cell fate determination Biology Bacteriophage lambda Models Biological Arrival time 03 medical and health sciences Variable (computer science) 030104 developmental biology Cell Biophysics Escherichia coli Gene Regulatory Networks Lysogeny Neuroscience Signal Transduction |
| Zdroj: | Biophysical Journal. 113:2110-2120 |
| ISSN: | 0006-3495 |
| DOI: | 10.1016/j.bpj.2017.09.012 |
| Popis: | Gene regulatory networks are largely responsible for cellular decision-making. These networks sense diverse external signals and respond by adjusting gene expression, enabling cells to reach environment-dependent decisions crucial for their survival or reproduction. However, information-carrying signals may arrive at variable times. Besides the intrinsic strength of these signals, their arrival time (timing) may also carry information about the environment and can influence cellular decision-making in ways that are poorly understood. For example, it is unclear how the timing of individual phage infections affects the lysis-lysogeny decision of bacteriophage λ despite variable infection times being likely in the wild and even in laboratory conditions. In this work, we combine mathematical modeling with experimentation to address this question. We develop an experimentally testable theory, which reveals that late-infecting phages contribute less to cellular decision-making. This implies that infection delays lower the probability of lysogeny compared to simultaneous infections. Furthermore, we show that infection delays reduce lysogenization by providing insufficient CII for threshold crossing during the critical decision-making period. We find evidence for a cutoff time after which subsequent infections cannot influence the cellular decision. We derive an intuitive formula that approximates the probability of lysogeny for variable infection times by a time-weighted average of probabilities for simultaneous infections. We validate these theoretical predictions experimentally. Similar concepts and simplifying modeling approaches may help elucidate the mechanisms underlying other cellular decisions. |
| Databáze: | OpenAIRE |
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