The Experts below are selected from a list of 14037 Experts worldwide ranked by ideXlab platform
Richard M Murray - One of the best experts on this subject based on the ideXlab platform.
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A geometric and structural approach to the analysis and design of Biological Circuit dynamics: a theory tailored for synthetic biology
2020Co-Authors: John P. Marken, Fangzhou Xiao, Richard M MurrayAbstract:Much of the progress in developing our ability to successfully design genetic Circuits with predictable dynamics has followed the strategy of molding Biological systems to fit into conceptual frameworks used in other disciplines, most notably the engineering sciences. Because Biological systems have fundamental differences from systems in these other disciplines, this approach is challenging and the insights obtained from such analyses are often not framed in a Biologically-intuitive way. Here, we present a new theoretical framework for analyzing the dynamics of genetic Circuits that is tailored towards the unique properties associated with Biological systems and experiments. Our framework approximates a complex Circuit as a set of simpler Circuits, which the system can transition between by saturating its various internal components. These approximations are connected to the intrinsic structure of the system, so this representation allows the analysis of dynamics which emerge solely from the system's structure. Using our framework, we analyze the presence of structural bistability in a leaky autoactivation motif and the presence of structural oscillations in the Repressilator.
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model reduction tools for phenomenological modeling of input controlled Biological Circuits
bioRxiv, 2020Co-Authors: Ayush Pandey, Richard M MurrayAbstract:We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic Biological Circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic Biological Circuit, it is easy to obtain a chemical reaction model of the Circuit under the assumptions of mass-action kinetics using various existing tools. However, using these models to guide design decisions during an experiment is difficult due to a large number of reaction rate parameters and species in the model. Hence, phenomenological models are often developed that describe the effective relationships among the Circuit inputs, outputs, and only the key states and parameters. In this paper, we present an algorithm to obtain these phenomenological models in an automated manner using a Python package for Circuits with inputs that control the desired outputs. This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species9 abundance to obtain the reduced models that can be used for design of synthetic Biological Circuits. We consider an example of a simple gene expression Circuit and another example of a layered genetic feedback control Circuit to demonstrate the use of the model reduction procedure.
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An automated model reduction tool to guide the design and analysis of synthetic Biological Circuits
2019Co-Authors: Ayush Pandey, Richard M MurrayAbstract:Abstract We present an automated model reduction algorithm that uses quasi-steady state approximation based reduction to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence of parametric uncertainties. We develop the theory for this model reduction algorithm and present the implementation of the algorithm that can be used to perform model reduction of given SBML models. To demonstrate the utility of this algorithm, we consider the design of a synthetic Biological Circuit to control the population density and composition of a consortium consisting of two different cell strains. We show how the model reduction algorithm can be used to guide the design and analysis of this Circuit.
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Implementation and System Identification of a Phosphorylation-Based Insulator in a Cell-Free Transcription-Translation System
2017Co-Authors: Shaobin Guo, Enoch Yeung, Richard M MurrayAbstract:An outstanding challenge in the design of synthetic bioCircuits is the development of a robust and efficient strategy for interconnecting functional modules. Recent work demonstrated that a phosphorylation-based insulator (PBI) implementing a dual strategy of high gain and strong negative feedback can be used as a device to attenuate retroactivity. This paper describes the implementation of such a Biological Circuit in a cell-free transcription-translation system and the structural identifiability of the PBI in the system. We first show that the retroactivity also exists in the cell-free system by testing a simple negative regulation Circuit. Then we demonstrate that the PBI Circuit helps attenuate the retroactivity significantly compared to the control. We consider a complex model that provides an intricate description of all chemical reactions and leveraging specific physiologically plausible assumptions. We derive a rigorous simplified model that captures the output dynamics of the PBI. We performed standard system identification analysis and determined that the model is globally identifiable with respect to three critical parameters. These three parameters are identifiable under specific experimental conditions and we performed these experiments to estimate the parameters. Our experimental results suggest that the functional form of our simplified model is sufficient to describe the reporter dynamics and enable parameter estimation. In general, this research illustrates the utility of the cell-free expression system as an alternate platform for bioCircuit implementation and system identification and it can provide interesting insights into future Biological Circuit designs.
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an analytical approach to bistable Biological Circuit discrimination using real algebraic geometry
Journal of the Royal Society Interface, 2015Co-Authors: Dan Siegalgaskins, Tiffany Zhou, Elisa Franco, Richard M MurrayAbstract:Biomolecular Circuits with two distinct and stable steady states have been identified as essential components in a wide range of Biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between Circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable Circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm's theorem—a tool from nineteenth-century real algebraic geometry—to comparing ‘functionally equivalent’ bistable Circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback Circuits, and show how specific topological properties present in each type of Circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely monomeric (and otherwise monostable) mutual repressor Circuit is sufficient for bistability. Finally, we compare our approach with the Routh–Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm's theorem demonstrated in this work suggest that algebraic geometric techniques may be underused in biomolecular Circuit analysis.
Eva Sciacca - One of the best experts on this subject based on the ideXlab platform.
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reliable Biological Circuit design including uncertain kinetic parameters
Fuzzy Optimization, 2010Co-Authors: Eva Sciacca, Salvatore SpinellaAbstract:In the context of possibilistic decision making, this work deals with Biological design problems particularly important in the near future when it will be possible to produce Biological entities and synthetic organisms for pharmacological and medical usage. The Biological systems is investigated in terms of performances or main key features of the system. The analysis of the Biological system is based on the idea that the set of parameters involved in the model can be classified into two different typologies: the uncertain kinetic parameters and the control design parameters. In order to design a robust and reliable Biological system with respect to a target performance, the design parameter values are set up to balance the uncertainty of the kinetic parameters. To take into account these uncertainties arising from the estimations of the kinetic parameters, the function representing the feedback of the system is fuzzified and a measure of failure of the designed Biological Circuit is minimized to reach the required performance. An application of this methodology is illustrated on a case study of an autonomously oscillatory system: the Drosophila Period Protein which is a central component of the Drosophila circadian clocks. Finally, the results of the fuzzy methodology are compared with a deterministic method.
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Fuzzy Optimization - Reliable Biological Circuit Design Including Uncertain Kinetic Parameters
Fuzzy Optimization, 2010Co-Authors: Eva Sciacca, Salvatore SpinellaAbstract:In the context of possibilistic decision making, this work deals with Biological design problems particularly important in the near future when it will be possible to produce Biological entities and synthetic organisms for pharmacological and medical usage. The Biological systems is investigated in terms of performances or main key features of the system. The analysis of the Biological system is based on the idea that the set of parameters involved in the model can be classified into two different typologies: the uncertain kinetic parameters and the control design parameters. In order to design a robust and reliable Biological system with respect to a target performance, the design parameter values are set up to balance the uncertainty of the kinetic parameters. To take into account these uncertainties arising from the estimations of the kinetic parameters, the function representing the feedback of the system is fuzzified and a measure of failure of the designed Biological Circuit is minimized to reach the required performance. An application of this methodology is illustrated on a case study of an autonomously oscillatory system: the Drosophila Period Protein which is a central component of the Drosophila circadian clocks. Finally, the results of the fuzzy methodology are compared with a deterministic method.
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robust parameter identification for Biological Circuit calibration
Bioinformatics and Bioengineering, 2008Co-Authors: Giuseppe Nicosia, Eva SciaccaAbstract:The aim of this work is to compare some deterministic optimization algorithms and evolutionary algorithms on parameter estimation in a Biological Circuit design problem: the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.
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BIBE - Robust parameter identification for Biological Circuit calibration
2008 8th IEEE International Conference on BioInformatics and BioEngineering, 2008Co-Authors: Giuseppe Nicosia, Eva SciaccaAbstract:The aim of this work is to compare some deterministic optimization algorithms and evolutionary algorithms on parameter estimation in a Biological Circuit design problem: the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.
Fei Xu - One of the best experts on this subject based on the ideXlab platform.
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sensitive detection of microrna in complex Biological samples by using two stages dsn assisted target recycling signal amplification method
Biosensors and Bioelectronics, 2017Co-Authors: Kai Zhang, Ke Wang, Fei XuAbstract:Abstract MicroRNA (miRNA) has become an important biomarker candidate for cancer diagnosis, prognosis, and therapy. In this study, we have developed a novel fluorescence method for sensitive and specific miRNA detection via duplex specific nuclease (DSN) signal amplification and demonstrated its practical application in Biological samples. Malachite green (MG) was employed as a “label-free" signal transducer since fluorescence of MG could be enhanced by 100-fold when MG were binding to a G-quadruplex structure formed within the d(G 2 T) 13 G sequence. The proposed signal amplification strategy is an integrated “Biological Circuit” designed to initiate a cascade of enzymatic reactions in order to detect, amplify, and measure a specific miRNA sequence by using the isothermal cleavage property of a DSN. The Circuit is composed of two molecular switches operating in series: the amplification reaction activated by a specific miRNA and the strand-displacement polymerization reaction designed to initiate molecular beacon-assisted amplification and signal transduction by using MG/G-quadruplex complex. The hsa-miR-141 (miR141) was chosen as a target miRNA because its level specifically abnormal in a wide range of common human cancers including breast, lung, colon, and prostate cancer. The proposed method allowed quantitative sequence-specific detection of miR141 (with a detection limit of 1.03 pM) in a dynamic range from 1 pM to 10 μM, with an excellent ability to discriminate differences in miRNAs. Moreover, the detection assay was applied to quantify miR141 in cancerous cell lysates. On the basis of these findings, we believe that this proposed sensitive and specific assay has great potential as a miRNA quantification method for use in biomedical research and clinical diagnosis.
Dan Siegalgaskins - One of the best experts on this subject based on the ideXlab platform.
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an analytical approach to bistable Biological Circuit discrimination using real algebraic geometry
Journal of the Royal Society Interface, 2015Co-Authors: Dan Siegalgaskins, Tiffany Zhou, Elisa Franco, Richard M MurrayAbstract:Biomolecular Circuits with two distinct and stable steady states have been identified as essential components in a wide range of Biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between Circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable Circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm's theorem—a tool from nineteenth-century real algebraic geometry—to comparing ‘functionally equivalent’ bistable Circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback Circuits, and show how specific topological properties present in each type of Circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely monomeric (and otherwise monostable) mutual repressor Circuit is sufficient for bistability. Finally, we compare our approach with the Routh–Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm's theorem demonstrated in this work suggest that algebraic geometric techniques may be underused in biomolecular Circuit analysis.
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an analytical approach to bistable Biological Circuit discrimination using real algebraic geometry
bioRxiv, 2015Co-Authors: Dan Siegalgaskins, Tiffany Zhou, Elisa Franco, Richard M MurrayAbstract:Biomolecular Circuits with two distinct and stable steady states have been identified as essential components in a wide range of Biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between Circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable Circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm?s theorem--a tool from 19th-century real algebraic geometry--to comparing ?functionally equivalent? bistable Circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback Circuits, and show how specific topological properties present in each type of Circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely-monomeric (and otherwise monostable) mutual repressor Circuit is sufficient for bistability. Finally, we compare our approach with the Routh-Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm?s theorem demonstrated in this work suggests that algebraic geometric techniques may be underutilized in biomolecular Circuit analysis.
Bangce Ye - One of the best experts on this subject based on the ideXlab platform.
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sensitive detection of microrna in complex Biological samples via enzymatic signal amplification using dna polymerase coupled with nicking endonuclease
Analytical Chemistry, 2013Co-Authors: Bangce YeAbstract:MicroRNA (miRNA) has become an ideal biomarker candidate for cancer diagnosis, prognosis, and therapy. In this study, we have developed a novel one-step method for sensitive and specific miRNA detection via enzymatic signal amplification and demonstrated its practical application in Biological samples. The proposed signal amplification strategy is an integrated “Biological Circuit” designed to initiate a cascade of enzymatic polymerization reactions in order to detect, amplify, and measure a specific miRNA sequence by using the isothermal strand-displacement property of a mesophilic DNA polymerase together with the nicking activity of a restriction endonuclease. The Circuit is composed of two molecular switches operating in series: the nicking endonuclease-assisted isothermal polymerization reaction activated by a specific miRNA and the strand-displacement polymerization reaction designed to initiate molecular beacon-assisted amplification and signal transduction. The hsa-miR-141 (miR-141) was chosen as a...