scholarly journals An Effective Method for Evolving Reaction Networks in Synthetic Biochemical Systems

2015 ◽  
Vol 19 (3) ◽  
pp. 374-386 ◽  
Author(s):  
Huy Q. Dinh ◽  
Nathanael Aubert ◽  
Nasimul Noman ◽  
Teruo Fujii ◽  
Yannick Rondelez ◽  
...  
Keyword(s):  
Reaction Networks ◽  
Biochemical Systems

Reaction networks and kinetics of biochemical systems

2017 ◽  
Vol 283 ◽  
pp. 13-29 ◽  
Author(s):  
Carlene Perpetua P. Arceo ◽  
Editha C. Jose ◽  
Angelyn R. Lao ◽  
Eduardo R. Mendoza
Keyword(s):  
Reaction Networks ◽  
Biochemical Systems ◽  
Kinetics Of

scholarly journals The chemical Langevin equation for biochemical systems in dynamic environments

2021 ◽  
Author(s):  
Lucy Ham ◽  
Megan Coomer ◽  
Michael P.H. Stumpf
Keyword(s):  
Langevin Equation ◽  
Stochastic Dynamics ◽  
Computational Simulation ◽  
Intrinsic Noise ◽  
Simulation Method ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Extrinsic Noise ◽  
Chemical Langevin Equation ◽  
Biochemical Systems

Modelling and simulation of complex biochemical reaction networks form cornerstones of modern biophysics. Many of the approaches developed so far capture temporal fluctuations due to the inherent stochasticity of the biophysical processes, referred to as intrinsic noise. Stochastic fluctuations, however, predominantly stem from the interplay of the network with many other - and mostly unknown - fluctuating processes, as well as with various random signals arising from the extracellular world; these sources contribute extrinsic noise. Here we provide a computational simulation method to probe the stochastic dynamics of biochemical systems subject to both intrinsic and extrinsic noise. We develop an extrinsic chemical Langevin equation - a physically motivated extension of the chemical Langevin equation - to model intrinsically noisy reaction networks embedded in a stochastically fluctuating environment. The extrinsic CLE is a continuous approximation to the Chemical Master Equation (CME) with time-varying propensities. In our approach, noise is incorporated at the level of the CME, and can account for the full dynamics of the exogenous noise process, irrespective of timescales and their mismatches. We show that our method accurately captures the first two moments of the stationary probability density when compared with exact stochastic simulation methods, while reducing the computational runtime by several orders of magnitude. Our approach provides a method that is practical, computationally efficient and physically accurate to study systems that are simultaneously subject to a variety of noise sources.

Comparison of four commercialized biochemical systems for clinical yeast identification by colour-producing reactions

1999 ◽  
Vol 37 (1) ◽  
pp. 11-17 ◽  
Author(s):  
A. PAUGAM ◽  
M. BENCHETRIT ◽  
A. FIACRE ◽  
C. TOURTE-SCHAEFER ◽  
J. DUPOUY-CAMET
Keyword(s):  
Yeast Identification ◽  
Biochemical Systems

Сочетанные генотипы генов, кодирующих белки холецистокининергической системы и ферменты фолатного цикла в значительной степени связаны с мигренью

2020 ◽  
Author(s):  
J.E. Azimova ◽  
E.A. Klimov ◽  
E.A. Naumova ◽  
Z.G. Kokaeva ◽  
A.I. Zaitseva ◽  
...  
Keyword(s):  
Population Control ◽  
Statistical Significance ◽  
Episodic Migraine ◽  
Protective Role ◽  
Control Group ◽  
Maoa Gene ◽  
Specific Pcr ◽  
Genotype Frequencies ◽  
Biochemical Systems ◽  
Combined Genotypes

Перспективным в изучении биомаркеров мигрени может быть многолокусный анализ, в частности, анализ частот сочетанных генотипов. Цель исследования - поиск составных генетических биомаркеров индивидуальной предрасположенности к мигрени, полученных на основе полиморфизмов генов, уже показавших статистическую значимость при однолокусном ассоциативном анализе. Методика. Обследовано 155 пациентов с мигренью (104 пациента с эпизодической мигренью, 51 - с хронической мигренью), наблюдавшихся в Университетской клинике головной боли (Москва). Все пациенты - представители белой расы, жители Московского региона. Возраст пациентов - 30-50 лет. Контроль составили 365 необследованных лиц (популяционный контроль). Выявление исследуемых 22 генов (всего 31 SNP) осуществляли методом ПЦР, ПЦР-ПДРФ, аллель-специфичной ПЦР и ПЦР в реальном времени. Выявление ассоциированных с мигренью сочетанных генотипов проводили с использованием программы анализа полигенных данных APSampler v3.6. Результаты. Выявлено 8 сочетанных генотипов с высокой статистически значимой ассоциацией с мигренью (ОШ>20,0). В состав сочетанных генотипов вошли гены: CCKAR, CCKBR, COMT, MTHFR, MTR, MTRR. Так же выявлено 4 защитных сочетанных генотипа (ОШ<0,02), основным в которых является ген MAOA. Заключение. Полученные данные об ассоциированных с мигренью сочетанных генотипах указывают на значимую роль в патогенезе заболевания 2 биохимических систем: 1) холецистокининергической системы, регулирующей выброс и обратный захват дофамина, и 2) фолатного цикла, в ходе работы которого гомоцистеин метаболизируется в метионин. Результаты, полученные в данном исследовании, позволяют говорить о защитной роли аллеля VNT:R4 гена MAOA.Multilocus analysis, specifically, analysis of combined genotype frequencies may be promising in studying migraine biomarkers. The aim of the study was to search for composite genetic biomarkers, which would predict individual predisposition to migraine, obtained on the basis of gene polymorphisms that have already shown a statistical significance in a single-locus associative analysis. Methods. 155 patients with migraine aging 41.7 ± 12.5 who had been followed up at the University Clinic of Headache, Moscow, were evaluated (104 patients with episodic migraine and 51 with chronic migraine). All patients were white and residents of the Moscow region. The control group included 365 unexamined individuals (population control). Identification of The 22 genes under study (total, 31 SNPs) were identified by PCR, PCR-RFLP, allele-specific PCR, and real-time PCR. Combined genotypes associated with migraine were identified using the APSampler v3.6 software for polygenic data analysis. Results. Eight combined genotypes were identified with a highly significant association with migraine (OR> 20.0). The combined genotypes included the CCKAR, CCKBR, COMT, MTHFR, MTR, and MTRR genes. Four protective combined genotypes were also identified (OS <0.02) with the MAOA gene as the major one. Conclusion. Our data on migraine-associated combined genotypes indicate a significant role in the migraine pathogenesis of two biochemical systems, i) the cholecystokininergic system that regulates the release and reuptake of dopamine, and ii) the folate cycle, where homocysteine is metabolized to methionine. The results obtained in this study suggest a protective role of the VNT: R4 allele of the MAOA gene.

An Observer for Mass-action Chemical Reaction Networks

2009 ◽  
Vol 15 (5) ◽  
pp. 578-597
Author(s):  
Marcello Farina ◽  
Sergio Bittanti
Keyword(s):  
Chemical Reaction ◽  
Chemical Reaction Networks ◽  
Mass Action ◽  
Reaction Networks

scholarly journals On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 99
Author(s):  
Cristian Privat ◽  
Sergio Madurga ◽  
Francesc Mas ◽  
Jaime Rubio-Martínez
Keyword(s):  
Molecular Dynamics ◽  
Amino Acids ◽  
Md Simulations ◽  
Point Of View ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Protonation State ◽  
Constant Ph ◽  
Biochemical Systems ◽  
Constant Ph Molecular Dynamics

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

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