Brownian Dynamics Studies on DNA Gel Electrophoresis. II. `Defect' Dynamics in the Elongation-Contraction Motion

By means of the Brownian dynamics (BD) method of simulations we have developed, we study dynamics of individual DNA undergoing constant field gel electrophoresis (CFGE), focusing on the relevance of the `defect' concept due to de Gennes in CFGE. The corresponding embodiment, which we call {\it slack beads} (s-beads) is explicitly introduced in our BD model. In equilibrium under a vanishing field the distance between s-beads and their hopping range are found to be randomly distributed following a Poisson distribution. In the strong field range, where a chain undergoes the elongation-contraction motion, s-beads are observed to be alternatively annihilated in elongation and created in contraction of the chain. On the other hand, the distribution of hopping range of s-beads does not differ much from that in equilibrium. The results indicate that the motion of the chain elongated consists of a huge number of random movements of s-beads. We have also confirmed that these features of s-beads agree qualitatively with those of s-monomers in the extended bond fluctuation model (EBFM) which we recently proposed. The coincidence of the two simulations strongly supports the stochastic semi-local movement of s-monomers which we {\it a priori} introduced into the EBFM.Comment: 14 pages, 11 figure

Brownian Dynamics Studies on DNA Gel Electrophoresis. I. Numerical Method and Quasi-Periodic Behavior of Elongation-Contraction Motions

Dynamics of individual DNA undergoing constant field gel electrophoresis (CFGE) is studied by a Brownian dynamics (BD) simulation method which we have developed. The method simulates electrophoresis of DNA in a 3 dimensional (3D) space by a chain of electrolyte beads of hard spheres. Under the constraint that the separation of each pair of bonded beads is restricted to be less than a certain fixed value, as well as with the excluded volume effect, the Langevin equation of motion for the beads is solved by means of the Lagrangian multiplier method. The resultant mobilities, $\mu$, as a function of the electric field coincide satisfactorily with the corresponding experimental results, once the time, the length and the field of the simulation are properly scaled. In relatively strong fields quasi-periodic behavior is found in the chain dynamics, and is examined through the time evolution of the radius of the longer principal axis, $R_l(t)$. It is found that the mean width of a peak in $R_l(t)$, or a period of one elongation-contraction process of the chain, is proportional to the number of beads in the chain, $M$, while the mean period between two such adjacent peaks is proportional to $M^0$ for large $M$. These results, combined with the observation that the chain moves to the field direction by the distance proportional to $M$ in each elongation-contraction motion, yield $\mu \propto M^0$. This explains why CFGE cannot separate DNA according to their size $L (\propto M)$ for large $L$.Comment: 20 pages, 11 figure

Diffusion of single long polymers in fixed and low density matrix of obstacles confined to two dimensions

Diffusion properties of a self-avoiding polymer embedded in regularly distributed obstacles with spacing a=20 and confined in two dimensions is studied numerically using the extended bond fluctuation method which we have developed recently. We have observed for the first time to our knowledge, that the mean square displacement of a center monomer $\phi_{M/2}(t)$ exhibits four dynamical regimes, i.e., $\phi_{M/2}(t) \sim t^{\nu_m}$ with $\nu_m\sim 0.6$, 3/8, 3/4, and 1 from the shortest to longest time regimes. The exponents in the second and third regimes are well described by segmental diffusion in the ``self-avoiding tube''. In the fourth (free diffusion) regime, we have numerically confirmed the relation between the reptation time $\tau_d$ and the number of segments $M, \tau_d\propto M^3$.Comment: 7 pages, 11 figure

Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation

<p>Abstract</p> <p>Background</p> <p>In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of <it>in silico </it>prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL).</p> <p>Results</p> <p>The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%.</p> <p>Conclusion</p> <p>These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for <it>in silico </it>prediction of drug interactions. The following future researches for the <it>in silico </it>prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.</p

A new bond fluctuation method for a polymer undergoing gel electrophoresis

We present a new computational methodology for the investigation of gel electrophoresis of polyelectrolytes. We have developed the method initially to incorporate sliding motion of tight parts of a polymer pulled by an electric field into the bond fluctuation method (BFM). Such motion due to tensile force over distances much larger than the persistent length is realized by non-local movement of a slack monomer at an either end of the tight part. The latter movement is introduced stochastically. This new BFM overcomes the well-known difficulty in the conventional BFM that polymers are trapped by gel fibers in relatively large fields. At the same time it also reproduces properly equilibrium properties of a polymer in a vanishing filed limit. The new BFM thus turns out an efficient computational method to study gel electrophoresis in a wide range of the electric field strength.Comment: 15 pages, 11 figure

Expression of excess receptors and negative feedback control of signal pathways are required for rapid activation and prompt cessation of signal transduction

Cellular signal transduction is initiated by the binding of extracellular ligands to membrane receptors. Receptors are often expressed in excess, and cells are activated when a small number of receptors bind ligands. Intracellular signal proteins are act

Particle simulation approach for subcellular dynamics and interactions of biological molecules

BACKGROUND: Spatio-temporal dynamics within cells can now be visualized at appropriate resolution, due to the advances in molecular imaging technologies. Even single-particle tracking (SPT) and single fluorophore video imaging (SFVI) are now being applied to observation of molecular-level dynamics. However, little is known concerning how molecular-level dynamics affect properties at the cellular level. RESULTS: We propose an algorithm designed for three-dimensional simulation of the reaction-diffusion dynamics of molecules, based on a particle model. Chemical reactions proceed through the interactions of particles in space, with activation energies determining the rates of these chemical reactions at each interaction. This energy-based model can include the cellular membrane, membranes of other organelles, and cytoskeleton. The simulation algorithm was tested for a reversible enzyme reaction model and its validity was confirmed. Snapshot images taken from simulated molecular interactions on the cell-surface revealed clustering domains (size ~0.2 μm) associated with rafts. Sample trajectories of raft constructs exhibited "hop diffusion". These domains corralled the diffusive motion of membrane proteins. CONCLUSION: These findings demonstrate that our approach is promising for modelling the localization properties of biological phenomena

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection