检索结果-内蒙古大学图书馆

A Monte Carlo method for the simulating hydrotreating of bio-oil model compounds

CHEMICAL ENGINEERING JOURNAL 2019年 377卷 120144-000页

作者： da Cruz, A. R. Costa Verstraete, J. J. Charon, N. Joly, J. -F. IFP Energies Nouvelles F-69360 Solaize France

Lignocellulosic biomass can be used as a source of hydrocarbons after an initial transformation by one of three different routes: biochemical conversion, chemical conversion and thermochemical conversion. In the last class, one of the possible approaches is fast pyrolysis, a process in which the biomass is exposed for a short time at high temperature and in the absence oxygen. Through this conversion step, gas, bio-char and bio-oil are the effluents that are generated. Bio-oil can be subsequently be upgraded into hydrocarbons by means of hydroconversion and hydrotreating. Like heavy oil fractions, fast pyrolysis oils are complex mixtures and, consequently, react via a complex reaction network. In the present work, the reaction pathways of model molecules representative of bio-oil are studied under hydrotreating conditions over a reduced NiMo/Al2O3 catalyst. To simulate the various reactions, a general kinetic Monte Carlo method, called stochastic simulation algorithm, was developed and applied to a model molecule guaiacol. The simulations are compared to the experimental results.

关键词： Fast pyrolysis bio-oil Guaiacol Hydrotreating Kinetics Monte Carlo stochastic simulation algorithm

来源：评论

学校读者我要写书评

暂无评论

Regularized Maximum Likelihood Estimation of Sparse stochastic Monomolecular Biochemical Reaction Networks

引用

IFAC Proceedings Volumes 2014年第3期47卷 9551-9556页

作者： IFAC Fellow Hong Jang Kwang-Ki K. Kim Jay H. Lee Richard D. Braatz Department of Biomolecular and Chemical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea (Tel: +82-42-350-3966 School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA Massachusetts Institute of Technology Cambridge MA 02139 USA

A sparse parameter estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation, which is composed of several ordinary differential equations describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation is available. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized, whereas most of existing studies on sparse reaction network identification use deterministic models for regularized least-square estimation. A simulation result is provided to verify performance improvement of the presented regularized MLE over the least squares (LSE) based on a deterministic mass-average model in the case of a small population size. Improved reaction structure detection is achieved by adding a penalty term for ℓ 1 regularization to the exact maximum likelihood function.

关键词： Sparse parameter estimation Exact maximum likelihood estimation Monomolecular biochemical reaction network Chemical master equation stochastic simulation algorithm Regularized maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Effective Finite-Difference Techniques for Estimating Sensitivities for stochastic Biochemical Systems

引用

Applied Mathematics 2022年第11期13卷 878-895页

作者： Fauzia Jabeen Silvana Ilie Department of Mathematics Ryerson University Toronto Canada

Cellular environments are in essence stochastic, owing to the random character of the biochemical reaction events in a single cell. stochastic fluctuations may substantially contribute to the dynamics of systems with small copy numbers of some biochemical species. Then, stochastic models are indispensable for properly portraying the behaviour of the system. Sensitivity analysis is one of the central tools for studying stochastic models of cellular dynamics. Here, we propose some finite-difference strategies for estimating parametric sensitivities of higher-order moments of the system state for stochastic discrete biochemical kinetic models. To reduce the variance of the sensitivity estimator, we employ various coupling techniques. The advantages of the proposed methods are illustrated in several models of biochemical systems of practical relevance.

关键词： stochastic simulation algorithm stochastic Biochemical Kinetics Sensitivity Analysis

来源：评论

学校读者我要写书评

暂无评论

Reducing stochastic Discrete Models of Biochemical Networks

引用

Applied Mathematics 2021年第5期12卷 449-469页

作者： Samaneh Gholami Silvana Ilie Department of Mathematics Ryerson University Toronto Canada

Biochemical systems have numerous practical applications, in particular to the study of critical intracellular processes. Frequently, biochemical kinetic models depict cellular processes as systems of chemical reactions. Many biological processes in a cell are inherently stochastic, due to the existence of some low molecular amounts. These stochastic fluctuations may have a great effect on the biochemical system’s behaviour. In such cases, stochastic models are necessary to accurately describe the system’s dynamics. Biochemical systems at the cellular level may entail many species or reactions and their mathematical models may be non-linear and with multiple scales in time. In this work, we provide a numerical technique for simplifying stochastic discrete models of well-stirred biochemical systems, which ensures that the main properties of the original system are preserved. The proposed technique employs sensitivity analysis and requires solving an optimization problem. The numerical tests on several models of practical interest show that our model reduction strategy performs very well.

关键词： stochastic simulation algorithm stochastic Biochemical Kinetics Sensitivity Analysis Model Reduction Methods

来源：评论

学校读者我要写书评

暂无评论

stochastic simulation of Imperfect Mixing in Free Radical Polymerization

引用

Macromolecular Symposia 2008年第1期271卷

作者： Hugo F. Hernandez Klaus Tauer Max Planck Institute of Colloids and Interfaces Research Campus Golm Am Mühlenberg 1 14476 Golm Germany

A modified algorithm for the stochastic simulation of chemical reactions subject to mass transfer limitation (imperfect mixing) is presented. This algorithm takes into account the mixing by diffusion of the reacting species between two consecutive reactions. The method is used to simulate the effect of mass transfer limitation in free-radical polymerization. Since this is a stiff reaction network, a hybrid stochastic-deterministic approach is considered. The hybrid stochastic algorithm under imperfect mixing (HSSA-IM) is applied to the bulk polymerization of methyl methacrylate up to high conversions. The accuracy of the algorithm relies on the precise determination of diffusion coefficients during the reaction.

关键词： hybrid stochastic simulation imperfect mixing kinetic monte carlo simulation radical polymerization stochastic simulation algorithm trommsdorff effect

来源：评论

学校读者我要写书评

暂无评论

On stochastic models of chemical reactions

引用

CHEMICAL PHYSICS 2021年 549卷 111259-111259页

作者： Vadillo, Fernando Univ Basque Country UPV EHU Dept Math Leioa Spain

In this short communication we study stochastic simulation algorithms (SSA) for chemical reactions;more specifically, we consider d species with m possible random changes and two procedures: the Chemical Calgevin Method and the general method from E. Allen. We demonstrate that both stochastic systems are equivalent and we check it in an example of Michaelis-Menten.

关键词： Chemical master equation stochastic differential equations stochastic simulation algorithm

来源：评论

学校读者我要写书评

暂无评论

Biologically plausible statistics from a Markov model of spiking cortical networks

引用

BMC Neuroscience 2008年第1期9卷 1-2页

作者： Marc Benayoun Wim van Drongelen Edward Wallace Jack Cowan Tanya Baker Department of Pediatrics University of Chicago Chicago IL 60637 USA Department of Mathematics University of Chicago Chicago IL 60637 USA The Salk Institute for Biological Studies La Jolla CA 92037 USA

来源：评论

学校读者我要写书评

暂无评论

Efficient calculation of steady state probability distribution for stochastic biochemical reaction network

引用

BMC GENOMICS 2012年第Sup6期13卷 S10-S10页

作者： Karim, Shahriar Buzzard, Gregery T. Umulis, David M. Purdue Univ Dept Agr & Biol Engn W Lafayette IN 47907 USA Purdue Univ Dept Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ Dept Math W Lafayette IN 47907 USA

The Steady State (SS) probability distribution is an important quantity needed to characterize the steady state behavior of many stochastic biochemical networks. In this paper, we propose an efficient and accurate approach to calculating an approximate SS probability distribution from solution of the Chemical Master Equation (CME) under the assumption of the existence of a unique deterministic SS of the system. To find the approximate solution to the CME, a truncated state-space representation is used to reduce the state-space of the system and translate it to a finite dimension. The subsequent ill-posed eigenvalue problem of a linear system for the finite state-space can be converted to a well-posed system of linear equations and solved. The proposed strategy yields efficient and accurate estimation of noise in stochastic biochemical systems. To demonstrate the approach, we applied the method to characterize the noise behavior of a set of biochemical networks of ligand-receptor interactions for Bone Morphogenetic Protein (BMP) signaling. We found that recruitment of type II receptors during the receptor oligomerization by itself doesn't not tend to lower noise in receptor signaling, but regulation by a secreted co-factor may provide a substantial improvement in signaling relative to noise. The steady state probability approximation method shortened the time necessary to calculate the probability distributions compared to earlier approaches, such as Gillespie's stochastic simulation algorithm (SSA) while maintaining high accuracy.

关键词： Bone Morphogenetic Protein Bone Morphogenetic Protein Signaling Kinetic Rate Constant stochastic simulation algorithm Chemical Master Equation

来源：评论

学校读者我要写书评

暂无评论

Colored extrinsic fluctuations and stochastic gene expression

引用

MOLECULAR SYSTEMS BIOLOGY 2008年第1期4卷 196页

作者： Shahrezaei, Vahid Ollivier, Julien F. Swain, Peter S. McGill Univ Dept Physiol Ctr Nonlinear Dynam Montreal PQ Canada

stochasticity is both exploited and controlled by cells. Although the intrinsic stochasticity inherent in biochemistry is relatively well understood, cellular variation, or 'noise', is predominantly generated by interactions of the system of interest with other stochastic systems in the cell or its environment. Such extrinsic fluctuations are nonspecific, affecting many system components, and have a substantial lifetime, comparable to the cell cycle (they are 'colored'). Here, we extend the standard stochastic simulation algorithm to include extrinsic fluctuations. We show that these fluctuations affect mean protein numbers and intrinsic noise, can speed up typical network response times, and can explain trends in high-throughput measurements of variation. If extrinsic fluctuations in two components of the network are correlated, they may combine constructively (amplifying each other) or destructively (attenuating each other). Consequently, we predict that incoherent feedforward loops attenuate stochasticity, while coherent feedforwards amplify it. Our results demonstrate that both the timescales of extrinsic fluctuations and their nonspecificity substantially affect the function and performance of biochemical networks.

关键词： biochemical networks extrinsic noise intrinsic noise stochastic simulation algorithm

来源：评论

学校读者我要写书评

暂无评论

Applications of personalised signalling network models in precision oncology

引用

PHARMACOLOGY & THERAPEUTICS 2020年 212卷 107555-107555页

作者： Hastings, Jordan F. O'Donnell, Yolande E. I. Fey, Dirk Croucher, David R. Garvan Inst Med Res Kinghorn Canc Ctr Sydney NSW Australia Univ Coll Dublin Syst Biol Ireland Dublin 4 Ireland Univ Coll Dublin Sch Med & Med Sci Dublin 4 Ireland Univ New South Wales St Vincents Hosp Sch Clin Sydney NSW 2052 Australia

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches arc beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment. (C) 2020 Elsevier Inc. All rights reserved.

关键词： BNGL BioNetGen language CME chemical master equation EGFR epidermal growth factor receptor ER oestrogen receptor ERK extracellular signal-regulated kinase FcεRI high-affinity immunoglobulin receptor HER2 human epidermal growth factor receptor 2 MAPK mitogen-activated protein kinase MEK mitogen-activated protein kinase kinase ODE ordinary differential equation PCA principal component analysis PDE partial differential equation PLSR partial least squares regression SSA stochastic simulation algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：