The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform

Elazer R Edelman - One of the best experts on this subject based on the ideXlab platform.

  • the total quasi steady State Approximation is valid for reversible enzyme kinetics
    Journal of Theoretical Biology, 2004
    Co-Authors: Abraham R Tzafriri, Elazer R Edelman
    Abstract:

    The Briggs–Haldane Approximation of the irreversible Michaelis–Menten scheme of enzyme kinetics is cited in virtually every biochemistry textbook and is widely considered the classic example of a quasi-steady-State Approximation. Though of similar importance, the reversible Michaelis–Menten scheme is not as well characterized. This is a serious limitation since even enzymatic reactions that go to completion may be reversible. The current work derives a total quasi-steady-State Approximation (tQSSA) for the reversible Michaelis–Menten and delineates its validity domain. The tQSSA allows the derivation of uniformly valid Approximations for the limit of low enzyme concentrations, ET⪡ST+KM, and under certain more restrictive conditions also for high enzyme concentrations such that ST⪡ET+KM. Using these simple analytical Approximations, a sequential experimental–theoretical method is suggested for unambiguously estimating all the kinetic parameters of the reversible Michaelis–Menten scheme.

Craig Boutilier - One of the best experts on this subject based on the ideXlab platform.

  • value directed belief State Approximation for pomdps
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We consider the problem belief-State monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief State. Other schemes for belief-State Approximation (e.g., based on minimixing a measures such as KL-diveregence between the true and estimated State) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed Approximation schemes, where Approximation quality is determined by the expected error in utility rather than by the error in the belief State itself. We propose heuristic methods for finding good projection schemes for belief State estimation - exhibiting anytime characteristics - given a POMDP value fucntion. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief State Approximation.

  • vector space analysis of belief State Approximation for pomdps
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We propose a new approach to value-directed belief State Approximation for POMDPs. The value-directed model allows one to choose Approximation methods for belief State monitoring that have a small impact on decision quality. Using a vector space analysis of the problem, we devise two new search procedures for selecting an Approximation scheme that have much better computational properties than existing methods. Though these provide looser error bounds, we show empirically that they have a similar impact on decision quality in practice, and run up to two orders of magnitude more quickly.

  • UAI - Vector-space Analysis of Belief-State Approximation for POMDPs
    2001
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We propose a new approach to value-directed belief State Approximation for POMDPs. The value directed model allows one to choose Approximation methods for belief State monitoring that have a small impact on decision quality. Using a vector space analysis of the problem, we devise two new search procedures for selecting an Approximation scheme that have much better computational properties than existing methods, Though these provide looser error bounds, we show empirically that they have a similar impact on decision quality in practice, and run up to two orders of magnitude more quickly.

  • value directed belief State Approximation for pomdps
    Uncertainty in Artificial Intelligence, 2000
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We consider the problem belief-State monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief State. Other schemes for beliefState Approximation (e.g., based on minimizing a measure such as KL-divergence between the true and estimated State) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed Approximation schemes, where Approximation quality is determined by the expected error in utility rather than by the error in the belief State itself. We propose heuristic methods for finding good projection schemes for belief State estimation--exhibiting anytime characteristics--given a POMDP value function. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief State Approximation.

  • UAI - Value-directed belief State Approximation for POMDPs
    2000
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We consider the problem belief-State monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief State. Other schemes for beliefState Approximation (e.g., based on minimizing a measure such as KL-divergence between the true and estimated State) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed Approximation schemes, where Approximation quality is determined by the expected error in utility rather than by the error in the belief State itself. We propose heuristic methods for finding good projection schemes for belief State estimation--exhibiting anytime characteristics--given a POMDP value function. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief State Approximation.

Michel Pierre - One of the best experts on this subject based on the ideXlab platform.

  • quasi steady State Approximation for a reaction diffusion system with fast intermediate
    Journal of Mathematical Analysis and Applications, 2010
    Co-Authors: Dieter Bothe, Michel Pierre
    Abstract:

    Abstract We consider a prototype reaction–diffusion system which models a network of two consecutive reactions in which chemical components A and B form an intermediate C which decays into two products P and Q. Such a situation often occurs in applications and in the typical case when the intermediate is highly reactive, the species C is eliminated from the system by means of a quasi-steady-State Approximation. In this paper, we prove the convergence of the solutions in L 2 , as the decay rate of the intermediate tends to infinity, for all bounded initial data, even in the case of initial boundary layers. The limiting system is indeed the one which results from formal application of the QSSA. The proof combines the recent L 2 -approach to reaction–diffusion systems having at most quadratic reaction terms, with local L ∞ -bounds which are independent of the decay rate of the intermediate. We also prove existence of global classical solutions to the initial system.

  • Quasi-steady-State Approximation for a reaction–diffusion system with fast intermediate
    Journal of Mathematical Analysis and Applications, 2010
    Co-Authors: Dieter Bothe, Michel Pierre
    Abstract:

    Abstract We consider a prototype reaction–diffusion system which models a network of two consecutive reactions in which chemical components A and B form an intermediate C which decays into two products P and Q. Such a situation often occurs in applications and in the typical case when the intermediate is highly reactive, the species C is eliminated from the system by means of a quasi-steady-State Approximation. In this paper, we prove the convergence of the solutions in L 2 , as the decay rate of the intermediate tends to infinity, for all bounded initial data, even in the case of initial boundary layers. The limiting system is indeed the one which results from formal application of the QSSA. The proof combines the recent L 2 -approach to reaction–diffusion systems having at most quadratic reaction terms, with local L ∞ -bounds which are independent of the decay rate of the intermediate. We also prove existence of global classical solutions to the initial system.

Pascal Poupart - One of the best experts on this subject based on the ideXlab platform.

  • value directed belief State Approximation for pomdps
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We consider the problem belief-State monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief State. Other schemes for belief-State Approximation (e.g., based on minimixing a measures such as KL-diveregence between the true and estimated State) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed Approximation schemes, where Approximation quality is determined by the expected error in utility rather than by the error in the belief State itself. We propose heuristic methods for finding good projection schemes for belief State estimation - exhibiting anytime characteristics - given a POMDP value fucntion. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief State Approximation.

  • vector space analysis of belief State Approximation for pomdps
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We propose a new approach to value-directed belief State Approximation for POMDPs. The value-directed model allows one to choose Approximation methods for belief State monitoring that have a small impact on decision quality. Using a vector space analysis of the problem, we devise two new search procedures for selecting an Approximation scheme that have much better computational properties than existing methods. Though these provide looser error bounds, we show empirically that they have a similar impact on decision quality in practice, and run up to two orders of magnitude more quickly.

  • UAI - Vector-space Analysis of Belief-State Approximation for POMDPs
    2001
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We propose a new approach to value-directed belief State Approximation for POMDPs. The value directed model allows one to choose Approximation methods for belief State monitoring that have a small impact on decision quality. Using a vector space analysis of the problem, we devise two new search procedures for selecting an Approximation scheme that have much better computational properties than existing methods, Though these provide looser error bounds, we show empirically that they have a similar impact on decision quality in practice, and run up to two orders of magnitude more quickly.

  • value directed belief State Approximation for pomdps
    Uncertainty in Artificial Intelligence, 2000
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We consider the problem belief-State monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief State. Other schemes for beliefState Approximation (e.g., based on minimizing a measure such as KL-divergence between the true and estimated State) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed Approximation schemes, where Approximation quality is determined by the expected error in utility rather than by the error in the belief State itself. We propose heuristic methods for finding good projection schemes for belief State estimation--exhibiting anytime characteristics--given a POMDP value function. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief State Approximation.

  • UAI - Value-directed belief State Approximation for POMDPs
    2000
    Co-Authors: Pascal Poupart, Craig Boutilier
    Abstract:

    We consider the problem belief-State monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief State. Other schemes for beliefState Approximation (e.g., based on minimizing a measure such as KL-divergence between the true and estimated State) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed Approximation schemes, where Approximation quality is determined by the expected error in utility rather than by the error in the belief State itself. We propose heuristic methods for finding good projection schemes for belief State estimation--exhibiting anytime characteristics--given a POMDP value function. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief State Approximation.

Sharmistha Dhatt - One of the best experts on this subject based on the ideXlab platform.

  • Efficacy of quasi-steady-State Approximation in Michaelis–Menten kinetics: a stochastic signature
    Journal of Mathematical Chemistry, 2019
    Co-Authors: Sharmistha Dhatt, Kinshuk Banerjee
    Abstract:

    Michaelis–Menten (MM) scheme serves as the benchmark model to characterize enzyme kinetics. Standard theoretical analyses of product formation rate for such a scheme are based on the quasi-steady-State Approximation (QSSA). There exist well-established criteria, applicable to both deterministic and stochastic scenarios, to judge the validity of QSSA. These criteria are given in terms of initial concentrations and rate parameters. In this work, we present a complementary stochastic signature to investigate the legitimacy of QSSA in MM kinetics with a finite copy number of species. Our condition is formulated in terms of the time-evolution of the product of coefficients of variation of suitable pair of species population. It can be measured, in principle, from time-series data of molecular population, avoiding estimation of model kinetic parameters.

  • Enzyme Kinetics: A Critique of the Quasi-Steady-State Approximation
    arXiv: Chemical Physics, 2013
    Co-Authors: Kamal Bhattacharyya, Sharmistha Dhatt
    Abstract:

    The standard two-step model of homogeneous-catalyzed reactions had been theoretically analyzed at various levels of Approximations from time to time. The primary aim was to check the validity of the quasi-steady-State Approximation, and hence emergence of the Michaelis-Menten kinetics, with various substrate-enzyme ratios. But, conclusions vary. We solve here the desired set of coupled nonlinear differential equations by invoking a new set of dimensionless variables. Approximate solutions are obtained via the power-series method aided by Pade approximants. The scheme works very successfully in furnishing the initial dynamics at least up to the region where existence of any steady State can be checked. A few conditions for its validity are put forward and tested against the findings. Temporal profiles of the substrate and the product are analyzed in addition to that of the complex to gain further insights into legitimacy of the above Approximation. Some recent observations like the 'reactant stationary Approximation' and the notions of different timescales are revisited. Signatures of the quasi- steady-State Approximation are also nicely detected by following the various reduced concentration profiles in triangular plots. Conditions for the emergence of Michaelis-Menten kinetics are scrutinized and it is stressed how one can get the reaction constants even in the absence of any steady State.

  • Single-substrate enzyme kinetics: the quasi-steady-State Approximation and beyond
    Journal of Mathematical Chemistry, 2013
    Co-Authors: Sharmistha Dhatt, Kamal Bhattacharyya
    Abstract:

    We analyze the standard model of enzyme-catalyzed reactions at various substrate-enzyme ratios by adopting a different scaling scheme and computational procedure. The regions of validity of the quasi-steady-State Approximation are noted. Certain prevalent conditions are checked and compared against the actual findings. Efficacies of a few other measures, obtained from the present work, are highlighted. Some recent observations are rationalized, particularly at moderate and high enzyme concentrations.

  • Single-substrate Enzyme Kinetics: The Quasi-steady-State Approximation and Beyond
    arXiv: Chemical Physics, 2012
    Co-Authors: Sharmistha Dhatt, Kamal Bhattacharyya
    Abstract:

    We analyze the standard model of enzyme-catalyzed reactions at various substrate-enzyme ratios to identify the regions of validity of the quasi-steady-State Approximation. Certain prevalent conditions are checked and compared against the actual findings. Efficacies of a few other measures are highlighted. Some very recent observations are rationalized, particularly at moderate and high enzyme concentrations.