A dissipative particle dynamics model of carbon nanotubes

A mesoscale dissipative particle dynamics model of single wall carbon nanotubes (CNTs) is designed and demonstrated. The coarse-grained model is produced by grouping together carbon atoms and by bonding the new lumped particles through pair and triplet forces. The mechanical properties of the simulated tube are determined by the bonding forces, which are derived by virtual experiments. Through the introduction of van der Waals interactions, tube–tube interactions were studied. Owing to the reduced number of particles, this model allows the simulation of relatively large systems. The applicability of the presented scheme to model CNT based mechanical devices is discussed.     

一种基于时间自动机的基因网络逻辑模型

最近几年来国外基因组（基因网络）系统逻辑行为的研究新进展——基于有限状态自动机模型的方法,针对该方法的局限性,提出了一种基于时间自动机的基因网络模型,以描述网络行为的时间约束。     

Mesoscopic dynamics of colloids simulated with dissipative particle dynamics and fluid particle model

We report results of numerical simulations of complex fluids, using a combination of discrete-particle methods. Our molecular modeling repertoire comprises three simulation techniques: molecular dynamics (MD), dissipative particle dynamics (DPD), and the fluid particle model (FPM). This type of model can depict multi-resolution molecular structures (see the Figure) found in complex fluids ranging from single micelle, colloidal crystals, large-scale colloidal aggregates up to the mesoscale processes of hydrodynamical instabilities in the bulk of colloidal suspensions. We can simulate different colloidal structures in which the colloidal beds are of comparable size to the solvent particles. This undertaking is accomplished with a two-level discrete particle model consisting of the MD paradigm with a Lennard-Jones (L-J) type potential for defining the colloidal particle system and DPD or FPM for modeling the solvent. We observe the spontaneous emergence of spherical or rod-like micelles and their crystallization in stable hexagonal or worm-like structures, respectively. The ordered arrays obtained by using the particle model are similar to the 2D colloidal crystals observed in laboratory experiments. The micelle shape and its hydrophobic or hydrophilic character depend on the ratio between the scaling factors of the interactions between colloid–colloid to colloid–solvent. Unlike the miscellar arrays, the colloidal aggregates involve the colloid–solvent interactions prescribed by the DPD forces. Different from the assumption of equilibrium growth, the two-level particle model can display much more realistic molecular physics, which allows for the simulation of aggregation for various types of colloids and solvent liquids over a very broad range of conditions. We discuss the potential prospects of combining MD, DPD, and FPM techniques in a single three-level model. Finally, we present results from large-scale simulation of the Rayleigh–Taylor instability and dispersion of colloidal slab in 2D and 3D. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s00894-001-0068-3.Electronic Supplementary Material available.     

A novel BLyS antagonist peptide designed based on the 3-D complex structure of BCMA and BLyS

B lymphocyte stimulator (BLyS) is a member of tumor necrosis factor (TNF) family. Because of its roles in autoimmune diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjogren syndrome (SS), BLyS antagonists have been tested to treat SLE- and RA-like symptoms in mice and obtained optimistic results. So far, reported BLyS antagonists were mostly decoyed BLyS receptors or anti-BLyS antibodies. In this study, a novel BLyS antagonist peptide, PT, was designed based on the modeling 3-D complex structure of BCMA and BLyS. The interaction mode of PT with BLyS was analyzed theoretically. The results of competitive ELISA demonstrated that PT could inhibit the binding of BCMA-Fc and anti-BLyS antibody to BLyS in vitro. In addition, PT could partly block the proliferating activity of BLyS on mice splenocytes. The BLyS antagonizing activity of PT was significant (p<0.05). This study highlights the possibility of using BLyS antagonist peptide to neutralize BLyS activity. Further optimization of PT with computer-guided molecular design method to enhance its biopotency may be useful in developing new BLyS antagonists to treat BLyS-related autoimmune diseases.     

A 3-D model of coronary vessel development

Coronary vascular disease is one of the leading causes of mortality and morbidity in the United States. Therefore, a mechanistic understanding of coronary vessel morphogenesis would aid in the innovation of new therapies targeting vascular disorders. Moreover, a functionally equivalent in vitro model system allows for the delineation of the molecular mechanisms that regulate coronary vessel development. In this study, we present a novel in vitro model system. This three-dimensional (3-D) model system consists of a tubular scaffold, which is engineered from type-I collagen and has been optimized to support the growth of embryonic cardiac tissues. In this report, proepicardial (PE) cells, the developmental precursors of coronary vessels, have been isolated from several model species and cultured on this scaffold. In this model system, the PE cells were able to recapitulate several aspects of coronary vessel morphogenesis including epicardial formation, the epicardial to mesenchymal transformation, and de novo coronary vessel development or vasculogenesis. The differentiation of PE cells was characterized using a variety of specific protein markers. The potential uses of this novel coronary developmental model are discussed.     

Evolutionary dynamics of tumor progression with random fitness values

Most human tumors result from the accumulation of multiple genetic and epigenetic alterations in a single cell. Mutations that confer a fitness advantage to the cell are known as driver mutations and are causally related to tumorigenesis. Other mutations, however, do not change the phenotype of the cell or even decrease cellular fitness. While much experimental effort is being devoted to the identification of the functional effects of individual mutations, mathematical modeling of tumor progression generally considers constant fitness increments as mutations are accumulated. In this paper we study a mathematical model of tumor progression with random fitness increments. We analyze a multi-type branching process in which cells accumulate mutations whose fitness effects are chosen from a distribution. We determine the effect of the fitness distribution on the growth kinetics of the tumor. This work contributes to a quantitative understanding of the accumulation of mutations leading to cancer.     

A mathematical model for progression and heterogeneity in colorectal cancer dynamics

This paper deals with the development of a mathematical model that describes cancer dynamics at the cellular scale.The selected case study concerns colon and rectum cancer, which originates in colorectal crypts. Cells inside the crypts are assumed to be organized according to a compartmental-like arrangement and to be homogeneously mixing. A mathematical model for cancer progression is proposed here. This model describes the generation of multiple clonal sub-populations of cells at different progression stages in a single crypt.Asymptotic analysis and simulations are developed with an exploratory aim. The obtained results offer some insights into the role played by mutation, proliferation and differentiation phenomena on cancer dynamics. In particular, the acquisition of an additional growing power and a reduction for cellular differentiation seem more likely to be the driving force behind carcinogenesis rather than an increase in the mutation rate. The mutation rate instead seems to affect progression dynamics and intra-tumor heterogeneity. The role played by cells, at different differentiation stages, in the onset and progression of colorectal cancer is highlighted. The results support the fact that stem cells play a key role in cancer development and the idea that transit-amplifying cells could also take on an active role in carcinogenesis.     

Collective dynamics of EcoRI-DNA complex by elastic network model and molecular dynamics simulations

Anisotropic network model (ANM) is used to analyze the collective motions of restriction enzyme EcoRI in free form and in complex with DNA. For comparison, three independent molecular dynamics (MD) simulations, each of 1.5 ns duration, are also performed for the EcoRI-DNA complex in explicit water. Although high mobility (equilibrium fluctuations) of inner and outer loops that surround the DNA is consistent in both methods and experiments, MD runs sample different conformational subspaces from which reliable collective dynamics cannot be extracted. However, ANM employed on different conformations from MD simulations indicates very similar collective motions. The stems of the inner loops are quite immobile even in the free enzyme and form a large, almost fixed, pocket for DNA binding. As a result, the residues that make specific and non-specific interactions with the DNA exhibit very low fluctuations in the free enzyme. The vibrational entropy difference between the EcoRI complex and free protein + unkinked DNA is positive (favorable), which may partially counteract the unfavorable enthalpy difference of DNA kink formation. Dynamic domains in EcoRI complex and cross-correlations between residue fluctuations indicate possible means of communication between the distal active sites.     

A heterologous 3-D coculture model of breast tumor cells and fibroblasts to study tumor-associated fibroblast differentiation

The objective of our study was to establish spheroid cocultures as a valid 3-D in vitro model mimicking tumor-fibroblast interactions in scirrhous breast tumors. The experimental setup was designed to verify if in cocultures (a) adherence and migration reflect the invasive potential of breast tumor cells, (b) breast tumor cells induce tumor-associated fibroblast differentiation, and (c) tumor-derived fibroblasts better reflect the in vivo situation than normal skin fibroblasts. Only one (SK-BR-3) out of five tumor cell types showed extensive fibroblast infiltration, MCF-7 cells frequently invaded fibroblast spheroids; BT474, T47D, and ZR-75-1 were noninvasive. While tumor cell invasion was independent of fibroblast origin, tumor-associated myofibroblast differentiation defined by alpha-SMA expression was demonstrated for tumor-derived but not normal skin fibroblasts in coculture indicating that (a) tumor cell invasion and myofibroblast differentiation are autonomous processes and (b) cocultures with tumor-derived fibroblasts resemble advanced stages of desmoplastic carcinomas while cocultures with normal skin fibroblasts rather reflect the early tumor development. The latter is also implied by fibroblast-associated alterations in tumor cell morphology and ECM distribution in the system. By using RNA arbitrarily primed PCR and cells isolated from cocultures by fluorescence-activated and magnetic cell separation, peripheral myelin protein PMP22/SR13 has been identified as a novel candidate with potential relevance in the interaction between tumor cell and normal fibroblast since PMP22 mRNA was significantly reduced in normal skin fibroblasts in coculture with BT474 cells.     

Glioma epileptiform activity and progression are driven by IGSF3-mediated potassium dysregulation

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基于元胞自动机的城市空间动态模拟

城市空间动态的模拟与预测可以为城市可持续发展规划与管理提供重要的参考依据。SLEUTH元胞自动机模型在城市空间模拟中较强的适用性和可移植性,该模型通过对历史数据的蒙特卡洛迭代自动寻找城市增长误差最小的参数组合,解决了传统元胞自动机模型中转换规则不易确定的问题。以武汉市为研究案例,运用SLEUTH模型进行了城市空间动态模拟与情景预测。2007年至2011年的城市空间模拟结果显示,模拟结果与实际历史数据可以获得良好的空间匹配度,Lee-Sallee形状指数均在0.6以上,显示SLEUTH元胞自动机模型经过本地化校正后具有较强的适用性和满意的模拟精度。进而,设置了现状趋势、基本保护、严格保护等3种情景对武汉2025年城市空间动态进行了预测,结果显示,各情景模式下城市居住用地均明显增长,农业用地、林地、水域等均有所减少;现状趋势情景和基本保护情景下农田、林地、水域减少的幅度较大,会加剧区域的生境破碎、耕地功能下降、水资源匮乏、湖滨湿地萎缩等生态问题,说明这两种情景不能有效满足城市生态系统健康和可持续发展的需要。严格保护情景下,城市居住用地扩张的程度得到了明显的控制,水域和林地得到了有效的保护,对于重要的自然生态系统组分保护及其服务能力维持可以起到显著作用。     

A multiscale 0-D/3-D approach to patient-specific adaptation of a cerebral autoregulation model for computational fluid dynamics studies of cardiopulmonary bypass

Neurological complication often occurs during cardiopulmonary bypass (CPB). One of the main causes is hypoperfusion of the cerebral tissue affected by the position of the cannula tip and diminished cerebral autoregulation (CA). Recently, a lumped parameter approach could describe the baroreflex, one of the main mechanisms of cerebral autoregulation, in a computational fluid dynamics (CFD) study of CPB. However, the cerebral blood flow (CBF) was overestimated and the physiological meaning of the variables and their impact on the model was unknown. In this study, we use a 0-D control circuit representation of the Baroreflex mechanism, to assess the parameters with respect to their physiological meaning and their influence on CBF. Afterwards the parameters are transferred to 3D-CFD and the static and dynamic behavior of cerebral autoregulation is investigated.     

A neural mass model based on single cell dynamics to model pathophysiology

Neural mass models are successful in modeling brain rhythms as observed in macroscopic measurements such as the electroencephalogram (EEG). While the synaptic current is explicitly modeled in current models, the single cell electrophysiology is not taken into account. To allow for investigations of the effects of channel pathologies, channel blockers and ion concentrations on macroscopic activity, we formulate neural mass equations explicitly incorporating the single cell dynamics by using a bottom-up approach. The mean and variance of the firing rate and synaptic input distributions are modeled. The firing rate curve (F(I)-curve) is used as link between the single cell and macroscopic dynamics. We show that this model accurately reproduces the behavior of two populations of synaptically connected Hodgkin-Huxley neurons, also in non-steady state.     

3-D particle tracking in a two-photon microscope: application to the study of molecular dynamics in cells

We developed a method for tracking particles in three dimensions designed for a two-photon microscope, which holds great promise to study cellular processes because of low photodamage, efficient background rejection, and improved depth discrimination. During a standard cycle of the tracking routine (32 ms), the laser beam traces four circular orbits surrounding the particle in two z planes above and below the particle. The radius of the orbits is half of the x,y-width of the point spread function, and the distance between the z planes is the z-width of the point spread function. The z-position is adjusted by moving the objective with a piezoelectric-nanopositioner. The particle position is calculated on the fly from the intensity profile obtained during the cycle, and these coordinates are used to set the scanning center for the next cycle. Applying this method, we were able to follow the motion of 500-nm diameter fluorescent polystyrene microspheres moved by a nanometric stage in either steps of 20-100 nm or sine waves of 0.1-10 microm amplitude with 20 nm precision. We also measured the diffusion coefficient of fluorospheres in glycerol solutions and recovered the values expected according to the Stokes-Einstein relationship for viscosities higher than 3.7 cP. The feasibility of this method for live cell measurements is demonstrated studying the phagocytosis of protein-coated fluorospheres by fibroblasts.     

A cellular automata model of enzyme kinetics

We have developed a cellular automata model of an enzyme reaction with a substrate in water. The model produces Michaelis-Menten kinetics with good Lineweaver-Burk plots. The variation in affinity parameters predicts that, in general, hydrophobic substrates are more reactive with enzymes, this attribute being more important than the relationship between enzyme and substrate. The ease of generation and the illustrative value of the model lead us to believe that cellular automata models have a useful role in the study of dynamic phenomena such as enzyme kinetics.     

A model of tumor growth based on cell cycle kinetics

This work describes a mathematical model of growth based on the kinetics of the cell cycle. A traditional model of the cell cycle has been used, with the addition of a resting (G₀) state from which cells could reenter the reproductive cycle. The model assumes that a growth regulatory substance regulates the transition of cells to and from the resting state. Other transitions between the phases of the cycle were modeled as a first order process. Cell loss is an important feature of growth kinetics, and has been represented by a general but tractable mathematical form. The resulting model forms a system of ordinary nonlinear differential equations. Analytic methods are employed first in the study of this system. Simplifying assumptions regarding cell loss give rise to special cases for which equilibrium solutions can be found. One special case, which assumes first order loss from all cell cycle phases at equal rates, is presented here. For small time values, approximations corresponding to exponential growth were developed. The equations describing an intrinsic growth rate were derived. Simulation methods were used to further characterize the behavior of this model. Parameter values were chosen based on animal tumor cell cycle kinetic data, resulting in a set of 45 model simulations. Several tumor treatment protocols were simulated which illustrated the importance of the intrinsic growth rate and cell loss concepts. Although the qualitative behavior regarding absolute and relative growth is reasonable, this model awaits data for model fitting, parameter estimation, or revision of the equations.     

A multiscale model of complex endothelial cell dynamics in early angiogenesis

We introduce a hybrid two-dimensional multiscale model of angiogenesis, the process by which endothelial cells (ECs) migrate from a pre-existing vascular bed in response to local environmental cues and cell-cell interactions, to create a new vascular network. Recent experimental studies have highlighted a central role of cell rearrangements in the formation of angiogenic networks. Our model accounts for this phenomenon via the heterogeneous response of ECs to their microenvironment. These cell rearrangements, in turn, dynamically remodel the local environment. The model reproduces characteristic features of angiogenic sprouting that include branching, chemotactic sensitivity, the brush border effect, and cell mixing. These properties, rather than being hardwired into the model, emerge naturally from the gene expression patterns of individual cells. After calibrating and validating our model against experimental data, we use it to predict how the structure of the vascular network changes as the baseline gene expression levels of the VEGF-Delta-Notch pathway, and the composition of the extracellular environment, vary. In order to investigate the impact of cell rearrangements on the vascular network structure, we introduce the mixing measure, a scalar metric that quantifies cell mixing as the vascular network grows. We calculate the mixing measure for the simulated vascular networks generated by ECs of different lineages (wild type cells and mutant cells with impaired expression of a specific receptor). Our results show that the time evolution of the mixing measure is directly correlated to the generic features of the vascular branching pattern, thus, supporting the hypothesis that cell rearrangements play an essential role in sprouting angiogenesis. Furthermore, we predict that lower cell rearrangement leads to an imbalance between branching and sprout elongation. Since the computation of this statistic requires only individual cell trajectories, it can be computed for networks generated in biological experiments, making it a potential biomarker for pathological angiogenesis.     

Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia

An urban cellular automata model has been designed, developed and tested for the city of Riyadh in Saudi Arabia as a research project. The model uses fuzzy set theory to capture the uncertainty associated with the transition rules and employs two automated methods of calibration: a genetic algorithm and simulated annealing. This paper describes the results of the calibration process for three time periods: 1987–1997, 1997–2005 and 1987–2005, which are characterised by different patterns of urban development. Nine different scenarios have been devised to capture the effect of different primary drivers to development including transport, urban agglomeration and topography and their interactions. The results showed that the genetic algorithm produces a better calibrated model than parallel simulated annealing. The model that contains all primary drivers and all interactions produced the best performing calibrated model overall.