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      Combinatorial Gene Regulation Using Auto-Regulation

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          As many as 59% of the transcription factors in Escherichia coli regulate the transcription rate of their own genes. This suggests that auto-regulation has one or more important functions. Here, one possible function is studied. Often the transcription rate of an auto-regulator is also controlled by additional transcription factors. In these cases, the way the expression of the auto-regulator responds to changes in the concentrations of the “input” regulators (the response function) is obviously affected by the auto-regulation. We suggest that, conversely, auto-regulation may be used to optimize this response function. To test this hypothesis, we use an evolutionary algorithm and a chemical–physical model of transcription regulation to design model cis-regulatory constructs with predefined response functions. In these simulations, auto-regulation can evolve if this provides a functional benefit. When selecting for a series of elementary response functions—Boolean logic gates and linear responses—the cis-regulatory regions resulting from the simulations indeed often exploit auto-regulation. Surprisingly, the resulting constructs use auto-activation rather than auto-repression. Several design principles show up repeatedly in the simulation results. They demonstrate how auto-activation can be used to generate sharp, switch-like activation and repression circuits and how linearly decreasing response functions can be obtained. Auto-repression, on the other hand, resulted only when a high response speed or a suppression of intrinsic noise was also selected for. The results suggest that, while auto-repression may primarily be valuable to improve the dynamical properties of regulatory circuits, auto-activation is likely to evolve even when selection acts on the shape of response function only.

          Author Summary

          Bacteria adjust which proteins they make, and how many copies of each kind, depending on their environment. The production rate of each regulated protein is controlled by a special class of proteins called transcription factors. The rate at which a certain protein is produced usually depends on the cellular concentrations of a few such transcription factors. When circumstances change, the concentrations of these transcription factors alter too and consequently the production rates of all proteins regulated by them are adjusted. Interestingly, many transcription factors also regulate their own synthesis rate. This suggests that this self-regulation must have one or more important functions. In this article we study one possible function. In order for cells to function properly each protein concentration has to respond in a particular way to changes in transcription factor concentrations. We have studied how bacteria can optimize and fine-tune these responses. To this end, we formulated a physical model of the regulation by transcription factors and performed computer simulations. These simulations show that self-regulation—and in particular self-activation—is often a useful tool to achieve the prescribed response. Therefore we conclude that natural selection on the regulation of protein levels could naturally lead to self-regulation.

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          Most cited references20

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          Network motifs in the transcriptional regulation network of Escherichia coli

          Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.
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            Phenotypic diversity, population growth, and information in fluctuating environments.

            Organisms in fluctuating environments must constantly adapt their behavior to survive. In clonal populations, this may be achieved through sensing followed by response or through the generation of diversity by stochastic phenotype switching. Here we show that stochastic switching can be favored over sensing when the environment changes infrequently. The optimal switching rates then mimic the statistics of environmental changes. We derive a relation between the long-term growth rate of the organism and the information available about its fluctuating environment.
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              On schemes of combinatorial transcription logic.

              Cells receive a wide variety of cellular and environmental signals, which are often processed combinatorially to generate specific genetic responses. Here we explore theoretically the potentials and limitations of combinatorial signal integration at the level of cis-regulatory transcription control. Our analysis suggests that many complex transcription-control functions of the type encountered in higher eukaryotes are already implementable within the much simpler bacterial transcription system. Using a quantitative model of bacterial transcription and invoking only specific protein-DNA interaction and weak glue-like interaction between regulatory proteins, we show explicit schemes to implement regulatory logic functions of increasing complexity by appropriately selecting the strengths and arranging the relative positions of the relevant protein-binding DNA sequences in the cis-regulatory region. The architectures that emerge are naturally modular and evolvable. Our results suggest that the transcription regulatory apparatus is a "programmable" computing machine, belonging formally to the class of Boltzmann machines. Crucial to our results is the ability to regulate gene expression at a distance. In bacteria, this can be achieved for isolated genes via DNA looping controlled by the dimerization of DNA-bound proteins. However, if adopted extensively in the genome, long-distance interaction can cause unintentional intergenic cross talk, a detrimental side effect difficult to overcome by the known bacterial transcription-regulation systems. This may be a key factor limiting the genome-wide adoption of complex transcription control in bacteria. Implications of our findings for combinatorial transcription control in eukaryotes are discussed.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                June 2010
                June 2010
                10 June 2010
                : 6
                : 6
                : e1000813
                Affiliations
                [1 ]Center for Theoretical Biological Physics, University of California, San Diego, California, United States of America
                [2 ]FOM Institute AMOLF, Amsterdam, The Netherlands
                University of Tokyo, Japan
                Author notes

                Conceived and designed the experiments: RH BU PRtW. Performed the experiments: RH BU. Analyzed the data: RH BU PRtW. Wrote the paper: RH.

                Article
                09-PLCB-RA-1298R2
                10.1371/journal.pcbi.1000813
                2883594
                20548950
                fcbe4434-93c0-4b19-b0b7-fd23a7302295
                Hermsen et al. 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.
                History
                : 26 October 2009
                : 7 May 2010
                Page count
                Pages: 13
                Categories
                Research Article
                Biophysics/Transcription and Translation
                Computational Biology/Transcriptional Regulation
                Genetics and Genomics/Gene Expression

                Quantitative & Systems biology
                Quantitative & Systems biology

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