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      Molecular circuits for associative learning in single-celled organisms

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          Abstract

          We demonstrate how a single-celled organism could undertake associative learning. Although to date only one previous study has found experimental evidence for such learning, there is no reason in principle why it should not occur. We propose a gene regulatory network that is capable of associative learning between any pre-specified set of chemical signals, in a Hebbian manner, within a single cell. A mathematical model is developed, and simulations show a clear learned response. A preliminary design for implementing this model using plasmids within Escherichia coli is presented, along with an alternative approach, based on double-phosphorylated protein kinases.

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          Foundations for engineering biology.

          Drew Endy (2005)
          Engineered biological systems have been used to manipulate information, construct materials, process chemicals, produce energy, provide food, and help maintain or enhance human health and our environment. Unfortunately, our ability to quickly and reliably engineer biological systems that behave as expected remains quite limited. Foundational technologies that make routine the engineering of biology are needed. Vibrant, open research communities and strategic leadership are necessary to ensure that the development and application of biological technologies remains overwhelmingly constructive.
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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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              The Ecological Approach To Visual Perception

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                Author and article information

                Journal
                J R Soc Interface
                RSIF
                Journal of the Royal Society Interface
                The Royal Society (London )
                1742-5689
                1742-5662
                3 October 2008
                6 May 2009
                : 6
                : 34
                : 463-469
                Affiliations
                [1 ]Systems Biology Centre, University of Birmingham Birmingham B15 2TT, UK
                [2 ]MRC National Institute for Medical Research Mill Hill, London NW7 1AA, UK
                [3 ]TU/e Techniche Universiteit Eindhoven 5600 MB Eindhoven, The Netherlands
                [4 ]Bio Systems Analysis Group, Friedrich Schiller University Jena Jena 07743, Germany
                [5 ]School of Computer Science, University of Birmingham Birmingham B15 2TT, UK
                Author notes
                [* ]Author and address for correspondence: Mathematical Biology, National Institute for Medical Research, Mill Hill, London NW7 1AA, UK ( ctf20@ 123456sussex.ac.uk )
                Article
                rsif20080344
                10.1098/rsif.2008.0344
                2582189
                18835803
                6fa73b5f-694f-4314-bfa1-1fbf99a99350
                Copyright © 2008 The Royal Society

                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 work is properly cited.

                History
                : 16 May 2008
                : 4 September 2008
                Categories
                Research Article

                Life sciences
                hebbian learning,single-celled organism,associative learning,synthetic biology,plasmid

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