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James E Ferrell - One of the best experts on this subject based on the ideXlab platform.
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Ultrasensitivity part III: cascades, bistable switches, and oscillators
Trends in biochemical sciences, 2014Co-Authors: James E FerrellAbstract:Switch-like, ultrasensitive responses – responses that resemble those of cooperative enzymes but are not necessarily generated by cooperativity – are widespread in signal transduction. In the previous installments in this series, we reviewed several mechanisms for generating Ultrasensitivity: zero-order Ultrasensitivity; multistep Ultrasensitivity; inhibitor Ultrasensitivity; and positive feedback (or double negative feedback) loops. In this review, we focus on how ultrasensitive components can be important for the functioning of more complex signaling circuits. Ultrasensitivity can allow the effective transmission of signals down a signaling cascade, can contribute to the generation of bistability by positive feedback, and can promote the production of biochemical oscillations in negative feedback loops. This makes Ultrasensitivity a key building block in systems biology and synthetic biology.
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Ultrasensitivity Part II: Multisite phosphorylation, stoichiometric inhibitors, and positive feedback
Trends in biochemical sciences, 2014Co-Authors: James E FerrellAbstract:In this series of reviews, we are examining ultrasensitive responses, the switch-like input–output relationships that contribute to signal processing in a wide variety of signaling contexts. In the first part of this series, we explored one mechanism for generating Ultrasensitivity, zero-order Ultrasensitivity, where the saturation of two converting enzymes allows the output to switch from low to high over a tight range of input levels. In this second installment, we focus on three conceptually distinct mechanisms for Ultrasensitivity: multisite phosphorylation, stoichiometric inhibitors, and positive feedback. We also examine several related mechanisms and concepts, including cooperativity, reciprocal regulation, coherent feed-forward regulation, and substrate competition, and provide several examples of signaling processes where these mechanisms are known or are suspected to be applicable.
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Ultrasensitivity part I: Michaelian responses and zero-order Ultrasensitivity
Trends in biochemical sciences, 2014Co-Authors: James E FerrellAbstract:Quantitative studies of signal transduction systems have shown that ultrasensitive responses – switch-like, sigmoidal input/output relationships – are commonplace in cell signaling. Ultrasensitivity is important for various complex signaling systems, including signaling cascades, bistable switches, and oscillators. In this first installment of a series on Ultrasensitivity we survey the occurrence of ultrasensitive responses in signaling systems. We review why the simplest mass action systems exhibit Michaelian responses, and then move on to zero-order Ultrasensitivity, a phenomenon that occurs when signaling proteins are operating near saturation. We also discuss the physiological relevance of zero-order Ultrasensitivity to cellular regulation.
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Ultrasensitivity in the Regulation of Cdc25C by Cdk1.
Molecular cell, 2011Co-Authors: Nicole B. Trunnell, Andy C. Poon, Sun Young Kim, James E FerrellAbstract:Cdc25C is a critical component of the interlinked positive and double-negative feedback loops that constitute the bistable mitotic trigger. Computational studies have indicated that the trigger's bistability should be more robust if the individual legs of the loops exhibit ultrasensitive responses. Here, we show that in Xenopus extracts two measures of Cdc25C activation (hyperphosphorylation and Ser 287 dephosphorylation) are highly ultrasensitive functions of the Cdk1 activity; estimated Hill coefficients were 11 to 32. Some of Cdc25C's Ultrasensitivity can be reconstituted in vitro with purified components, and the reconstituted Ultrasensitivity depends upon multisite phosphorylation. The response functions determined here for Cdc25C and previously for Wee1A allow us to formulate a simple mathematical model of the transition between interphase and mitosis. The model shows how the continuously variable regulators of mitosis work collectively to generate a switch-like, hysteretic response.
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substrate competition as a source of Ultrasensitivity in the inactivation of wee1
Cell, 2007Co-Authors: James E FerrellAbstract:Summary The mitotic regulators Wee1 and Cdk1 can inactivate each other through inhibitory phosphorylations. This double-negative feedback loop is part of a bistable trigger that makes the transition into mitosis abrupt and decisive. To generate a bistable response, some component of a double-negative feedback loop must exhibit an ultrasensitive response to its upstream regulator. Here, we experimentally demonstrate that Wee1 exhibits a highly ultrasensitive response to Cdk1. Several mechanisms can, in principle, give rise to Ultrasensitivity, including zero-order effects, multisite phosphorylation, and competition mechanisms. We found that the Ultrasensitivity in the inactivation of Wee1 arises mainly through two competition mechanisms: competition between two sets of phosphorylation sites in Wee1 and between Wee1 and other high-affinity Cdk1 targets. Based on these findings, we were able to reconstitute a highly ultrasensitive Wee1 response with purified components. Competition provides a simple way of generating the equivalent of a highly cooperative allosteric response.
Ariel Chernomoretz - One of the best experts on this subject based on the ideXlab platform.
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Ultrasensitivity in signaling cascades revisited: Linking local and global Ultrasensitivity estimations.
PloS one, 2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Ultrasensitive response motifs, capable of converting graded stimuli into binary responses, are well-conserved in signal transduction networks. Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can enhance the system’s Ultrasensitivity, how a given combination of layers affects a cascade’s Ultrasensitivity remains an open question for the general case. Here, we introduce a methodology that allows us to determine the presence of sequestration effects and to quantify the relative contribution of each module to the overall cascade’s Ultrasensitivity. The proposed analysis framework provides a natural link between global and local Ultrasensitivity descriptors and it is particularly well-suited to characterize and understand mathematical models used to study real biological systems. As a case study, we have considered three mathematical models introduced by O’Shaughnessy et al. to study a tunable synthetic MAPK cascade, and we show how our methodology can help modelers better understand alternative models.
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Ultrasensitivity in signaling cascades revisited: Linking local and global Ultrasensitivity estimations - Fig 4
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Schematic representations of Goldbeter-Koshland dose-response curves with K1 ≳ 1 and K2 ≪ 1 (see equation in S1 Text) shown in log-linear scale (A) and in log-log scale (B). The corresponding response coefficient (C) shows no local Ultrasensitivity for low input values (i.e. R ∼ 1), but displays high local Ultrasensitivity, even larger than the module’s Hill coefficient nH, for intermediate input regions.
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Fitting by a Hill function may obscure relevant behaviors.
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Dose-response curve of active MEK in O’Shaughnessy model compared with its fit by a Hill function (A). Respective response coefficient (B). Even though the dose-responses of active MEK and the Hill function appear to be similar (A), there are strong differences in their local Ultrasensitivity.
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Schematic response function diagrams for two different compositions of two GK ultrasensitive modules are shown in panels (A) and (B).
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Axes were arranged as explained in Fig 2’s caption. In panel (A) O1,max ≫ EC502, and module-1’s HWR covers the input region below EC501, a region in which the curve shows no local Ultrasensitivity (R1 = 1). In panel (B) we show a special scenario where the O2,max/EC502 ratio was tuned in order to set module-1’s HWR in its most ultrasensitive region.
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Hill function dose-response.
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Schematic representation of Hill-type dose-response curves, in log-linear (A) and log-log scale (B). The EC10 and EC90 are the inputs needed to produce an output of 10% and 90% of the maximal response (Omax), respectively. The Hill working range, HWR, is the input range relevant for the calculation of the system’s nH. For isolated modules, the HWR = [EC10, EC90]. Panel (C) displays the local Ultrasensitivity (the response coefficient R) as a function of input. Note that for Hill functions, inputs much smaller than the EC50 have Rs around the Hill coefficient.
Chunbo Lou - One of the best experts on this subject based on the ideXlab platform.
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engineering the ultrasensitive transcription factors by fusing a modular oligomerization domain
ACS Synthetic Biology, 2018Co-Authors: Junran Hou, Weiqian Zeng, Yeqing Zong, Zehua Chen, Chensi Miao, Baojun Wang, Chunbo LouAbstract:The dimerization and high-order oligomerization of transcription factors has endowed them with cooperative regulatory capabilities that play important roles in many cellular functions. However, such advanced regulatory capabilities have not been fully exploited in synthetic biology and genetic engineering. Here, we engineered a C-terminally fused oligomerization domain to improve the cooperativity of transcription factors. First, we found that two of three designed oligomerization domains significantly increased the cooperativity and Ultrasensitivity of a transcription factor for the regulated promoter. Then, seven additional transcription factors were used to assess the modularity of the oligomerization domains, and their Ultrasensitivity was generally improved, as assessed by their Hill coefficients. Moreover, we also demonstrated that the allosteric capability of the ligand-responsive domain remained intact when fusing with the designed oligomerization domain. As an example application, we showed that ...
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Engineering the Ultrasensitive Transcription Factors by Fusing a Modular Oligomerization Domain
2018Co-Authors: Junran Hou, Weiqian Zeng, Yeqing Zong, Zehua Chen, Chensi Miao, Baojun Wang, Chunbo LouAbstract:The dimerization and high-order oligomerization of transcription factors has endowed them with cooperative regulatory capabilities that play important roles in many cellular functions. However, such advanced regulatory capabilities have not been fully exploited in synthetic biology and genetic engineering. Here, we engineered a C-terminally fused oligomerization domain to improve the cooperativity of transcription factors. First, we found that two of three designed oligomerization domains significantly increased the cooperativity and Ultrasensitivity of a transcription factor for the regulated promoter. Then, seven additional transcription factors were used to assess the modularity of the oligomerization domains, and their Ultrasensitivity was generally improved, as assessed by their Hill coefficients. Moreover, we also demonstrated that the allosteric capability of the ligand-responsive domain remained intact when fusing with the designed oligomerization domain. As an example application, we showed that the engineered ultrasensitive transcription factor could be used to significantly improve the performance of a “stripe-forming” gene circuit. We envision that the oligomerization modules engineered in this study could act as a powerful tool to rapidly tune the underlying response profiles of synthetic gene circuits and metabolic pathway controllers
Molina Franck - One of the best experts on this subject based on the ideXlab platform.
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Biochemical Threshold Function Implementation with Zero-Order Ultrasensitivity
IEEE, 2019Co-Authors: Huang Wei-chih, Jiang Jie-hong, Fages François, Molina FranckAbstract:International audienceEngineering biochemical reactions is a key task in synthetic biology to enable biomedical and other applications. The biochemical threshold function is a crucial component in the biosensor circuits to be deployed in living cells or synthetic vesicles for disease diagnosis. In this work, based on the zero-order Ultrasensitivity, we propose an economic biochemical implementation of threshold functions with reconfigurable threshold values. We show that the so-constructed threshold function module well approximates the unit step function and allows robust composition with other function modules for complex computation tasks
Edgar Altszyler - One of the best experts on this subject based on the ideXlab platform.
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Ultrasensitivity in signaling cascades revisited: Linking local and global Ultrasensitivity estimations.
PloS one, 2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Ultrasensitive response motifs, capable of converting graded stimuli into binary responses, are well-conserved in signal transduction networks. Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can enhance the system’s Ultrasensitivity, how a given combination of layers affects a cascade’s Ultrasensitivity remains an open question for the general case. Here, we introduce a methodology that allows us to determine the presence of sequestration effects and to quantify the relative contribution of each module to the overall cascade’s Ultrasensitivity. The proposed analysis framework provides a natural link between global and local Ultrasensitivity descriptors and it is particularly well-suited to characterize and understand mathematical models used to study real biological systems. As a case study, we have considered three mathematical models introduced by O’Shaughnessy et al. to study a tunable synthetic MAPK cascade, and we show how our methodology can help modelers better understand alternative models.
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Ultrasensitivity in signaling cascades revisited: Linking local and global Ultrasensitivity estimations - Fig 4
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Schematic representations of Goldbeter-Koshland dose-response curves with K1 ≳ 1 and K2 ≪ 1 (see equation in S1 Text) shown in log-linear scale (A) and in log-log scale (B). The corresponding response coefficient (C) shows no local Ultrasensitivity for low input values (i.e. R ∼ 1), but displays high local Ultrasensitivity, even larger than the module’s Hill coefficient nH, for intermediate input regions.
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Fitting by a Hill function may obscure relevant behaviors.
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Dose-response curve of active MEK in O’Shaughnessy model compared with its fit by a Hill function (A). Respective response coefficient (B). Even though the dose-responses of active MEK and the Hill function appear to be similar (A), there are strong differences in their local Ultrasensitivity.
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Schematic response function diagrams for two different compositions of two GK ultrasensitive modules are shown in panels (A) and (B).
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Axes were arranged as explained in Fig 2’s caption. In panel (A) O1,max ≫ EC502, and module-1’s HWR covers the input region below EC501, a region in which the curve shows no local Ultrasensitivity (R1 = 1). In panel (B) we show a special scenario where the O2,max/EC502 ratio was tuned in order to set module-1’s HWR in its most ultrasensitive region.
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Hill function dose-response.
2017Co-Authors: Edgar Altszyler, Alejandra C. Ventura, Alejandro Colman-lerner, Ariel ChernomoretzAbstract:Schematic representation of Hill-type dose-response curves, in log-linear (A) and log-log scale (B). The EC10 and EC90 are the inputs needed to produce an output of 10% and 90% of the maximal response (Omax), respectively. The Hill working range, HWR, is the input range relevant for the calculation of the system’s nH. For isolated modules, the HWR = [EC10, EC90]. Panel (C) displays the local Ultrasensitivity (the response coefficient R) as a function of input. Note that for Hill functions, inputs much smaller than the EC50 have Rs around the Hill coefficient.
