Abstract
Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.
| Original language | American English |
|---|---|
| Article number | e1007642 |
| Journal | PLoS Computational Biology |
| Volume | 16 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2020 |
Bibliographical note
Funding Information:RG is a recipient of a DOC Fellowship (Doctoral Fellowship Programme of the Austrian Academy of Sciences, https://stipendien.oeaw.ac. at/en/stipendien/doc/) of the Austrian Academy of Sciences at the Institute of Science and Technology Austria (grant number 25006). TF acknowledges financial support from the Hebrew University of Jerusalem. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Uri Alon and Tzachi Pilpel for raising the question that triggered this study. We thank Nick Barton, Yonatan Friedman, Avi Mayo, Tiago Paixão, Gašper Tkačik and Marcin Zagorski for comments on the manuscript. R. G. thanks Kirti Jain for discussions.
Publisher Copyright:
© 2020 Grah, Friedlander. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.