Zinc in Biochemical Systems

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.

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A Two-Dimensional Model for Elf Electric Fields Interaction with Biochemical Systems

Electromagnetic Biology and Medicine ◽

10.3109/15368378409027708 ◽

1984 ◽

Vol 3 (3) ◽

pp. 469-482

Author(s):

S. T. Hsieh ◽

Y. J. Seto

Keyword(s):

Electric Fields ◽

Two Dimensional ◽

Dimensional Model ◽

Two Dimensional Model ◽

Biochemical Systems

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

Polymers ◽

10.3390/polym13010099 ◽

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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Epidemiological modeling in StochSS Live!

Bioinformatics ◽

10.1093/bioinformatics/btab061 ◽

2021 ◽

Author(s):

Richard Jiang ◽

Bruno Jacob ◽

Matthew Geiger ◽

Sean Matthew ◽

Bryan Rumsey ◽

...

Keyword(s):

Stochastic Model ◽

Epidemiological Model ◽

Supplementary Information ◽

Supplementary Data ◽

Web Based ◽

Epidemiological Modeling ◽

Modeling Simulation ◽

Wide Range ◽

Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

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Training an asymmetric signal perceptron through reinforcement in an artificial chemistry

Journal of The Royal Society Interface ◽

10.1098/rsif.2013.1100 ◽

2014 ◽

Vol 11 (93) ◽

pp. 20131100 ◽

Cited By ~ 14

Author(s):

Peter Banda ◽

Christof Teuscher ◽

Darko Stefanovic

Keyword(s):

State Of The Art ◽

Chemical Species ◽

Mass Action ◽

Mass Action Kinetics ◽

Dna Strand Displacement ◽

Autonomous Adaptation ◽

Biochemical Systems ◽

Dna Strand ◽

Logic Functions ◽

Application Specific

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.

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Novel domain expansion methods to improve the computational efficiency of the Chemical Master Equation solution for large biological networks

BMC Bioinformatics ◽

10.1186/s12859-020-03668-2 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Rahul Kosarwal ◽

Don Kulasiri ◽

Sandhya Samarasinghe

Keyword(s):

Master Equation ◽

State Space ◽

Performance Management ◽

Numerical Solutions ◽

De Novo ◽

Exact Analytical Solution ◽

Chemical Master Equation ◽

The State ◽

Biochemical System ◽

Biochemical Systems

Abstract Background Numerical solutions of the chemical master equation (CME) are important for understanding the stochasticity of biochemical systems. However, solving CMEs is a formidable task. This task is complicated due to the nonlinear nature of the reactions and the size of the networks which result in different realizations. Most importantly, the exponential growth of the size of the state-space, with respect to the number of different species in the system makes this a challenging assignment. When the biochemical system has a large number of variables, the CME solution becomes intractable. We introduce the intelligent state projection (ISP) method to use in the stochastic analysis of these systems. For any biochemical reaction network, it is important to capture more than one moment: this allows one to describe the system’s dynamic behaviour. ISP is based on a state-space search and the data structure standards of artificial intelligence (AI). It can be used to explore and update the states of a biochemical system. To support the expansion in ISP, we also develop a Bayesian likelihood node projection (BLNP) function to predict the likelihood of the states. Results To demonstrate the acceptability and effectiveness of our method, we apply the ISP method to several biological models discussed in prior literature. The results of our computational experiments reveal that the ISP method is effective both in terms of the speed and accuracy of the expansion, and the accuracy of the solution. This method also provides a better understanding of the state-space of the system in terms of blueprint patterns. Conclusions The ISP is the de-novo method which addresses both accuracy and performance problems for CME solutions. It systematically expands the projection space based on predefined inputs. This ensures accuracy in the approximation and an exact analytical solution for the time of interest. The ISP was more effective both in predicting the behavior of the state-space of the system and in performance management, which is a vital step towards modeling large biochemical systems.

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