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      Transient hysteresis and inherent stochasticity in gene regulatory networks

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          Abstract

          Cell fate determination, the process through which cells commit to differentiated states is commonly mediated by gene regulatory motifs with mutually exclusive expression states. The classical deterministic picture for cell fate determination includes bistability and hysteresis, which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. We provide a quantitative description of a transient hysteresis phenomenon reconciling experimental evidence of hysteretic behaviour in gene regulatory networks with inherent stochasticity: under sufficiently slow dynamics hysteresis is transient. We quantify this with an estimate of the convergence rate to the equilibrium and introduce a natural landscape capturing system’s evolution that, unlike traditional cell fate potential landscapes, is compatible with coexistence at the microscopic level.

          Abstract

          Cell fate commitment is understood in terms of bistable regulatory circuits with hysteresis, but inherent stochasticity in gene expression is incompatible with hysteresis. Here, the authors quantify how, under slow dynamics, the dependency of the non-stationary solutions on the initial state of the cells can lead to transient hysteresis.

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          Stochasticity and cell fate.

          Fundamental to living cells is the capacity to differentiate into subtypes with specialized attributes. Understanding the way cells acquire their fates is a major challenge in developmental biology. How cells adopt a particular fate is usually thought of as being deterministic, and in the large majority of cases it is. That is, cells acquire their fate by virtue of their lineage or their proximity to an inductive signal from another cell. In some cases, however, and in organisms ranging from bacteria to humans, cells choose one or another pathway of differentiation stochastically, without apparent regard to environment or history. Stochasticity has important mechanistic requirements. We speculate on why stochasticity is advantageous-and even critical in some circumstances-to the individual, the colony, or the species.
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            DIVERSITY-BASED, MODEL-GUIDED CONSTRUCTION OF SYNTHETIC GENE NETWORKS WITH PREDICTED FUNCTIONS

            SUMMARY Engineering artificial gene networks from modular components is one of the major goals of synthetic biology. However, the construction of gene networks with predictable functions remains hampered by a lack of suitable components and the fact that assembled networks often require extensive, iterative retrofitting to work as intended. Here we present an approach that couples libraries of diversified components (synthesized with randomized non-essential sequence) with in silico modeling to guide predictable gene network construction without the need for post-hoc tweaking. We demonstrate our approach in S. cerevisiae by synthesizing regulatory promoter libraries and using them to construct feedforward loop networks with different predicted input-output characteristics. We then expand our method to produce a synthetic gene network acting as a predictable timer, modifiable by component choice. We utilize this network to control the timing of yeast sedimentation, illustrating how the plug-and-play nature of our design can be readily applied to biotechnology.
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              Linking Stochastic Dynamics to Population Distribution: An Analytical Framework of Gene Expression

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                Author and article information

                Contributors
                antonio@iim.csic.es
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                8 October 2019
                8 October 2019
                2019
                : 10
                : 4581
                Affiliations
                [1 ]ISNI 0000 0001 2183 4846, GRID grid.4711.3, BioProcess Engineering Group, , IIM-CSIC. Spanish National Research Council, ; Eduardo Cabello 6, 36208 Vigo, Spain
                [2 ]ISNI 0000 0001 2176 8535, GRID grid.8073.c, Department of Mathematics, , University of A Coruña, ; Campus Elviña s/n, 15071 A Coruña, Spain
                Author information
                http://orcid.org/0000-0001-7340-9737
                http://orcid.org/0000-0003-2895-997X
                http://orcid.org/0000-0001-6591-2252
                http://orcid.org/0000-0001-8203-1799
                Article
                12344
                10.1038/s41467-019-12344-w
                6783536
                31594925
                a3069c8f-1ad2-484f-8aee-d732ea51b176
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 December 2018
                : 30 August 2019
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                © The Author(s) 2019

                Uncategorized
                cellular noise,computer modelling,regulatory networks,stochastic modelling
                Uncategorized
                cellular noise, computer modelling, regulatory networks, stochastic modelling

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