Calculating spatial statistics for velocity jump processes with experimentally observed reorientation parameters

Mathematical modelling of the directed movement of animals, microorganisms and cells is of great relevance in the fields of biology and medicine. Simple diffusive models of movement assume a random walk in the position, while more realistic models include the direction of movement by assuming a random walk in the velocity. These velocity jump processes, although more realistic, are much harder to analyse and an equation that describes the underlying spatial distribution only exists in one dimension. In this communication we set up a realistic reorientation model in two dimensions, where the mean turning angle is dependent on the previous direction of movement and bias is implicitly introduced in the probability distribution for the direction of movement. This model, and the associated reorientation parameters, is based on data from experiments on swimming microorganisms. Assuming a transport equation to describe the motion of a population of random walkers using a velocity jump process, together with this realistic reorientation model, we use a moment closure method to derive and solve a system of equations for the spatial statistics. These asymptotic equations are a very good match to simulated random walks for realistic parameter values.     

Cancer stem cells: A guide for skeptics

Cancer stem cells (CSC) were postulated to exist many years ago as cells within a tumor that regenerate the tumor following treatment. A stochastic clonal evolution model was used to explain observed tumor heterogeneity. Recently, xenotransplantation studies have demonstrated that prospectively identifiable subpopulations from human cancers can initiate tumors in immune deficient mice, and these results along with recent advances in stem cell biology have generated much excitement in the cancer field. The modern CSC theory posits a hierarchy of cells analogous to normal stem cell development. Some controversy remains, however, as to whether these tumor initiating cells truly represent CSC, and whether the modern CSC field can live up to the promise of providing improved cancer treatments based on a novel model of cancer biology. Recent data from CSC investigators are discussed critically. J. Cell. Biochem. 106: 745–749, 2009. © 2009 Wiley‐Liss, Inc.     

A semiparametric Bayesian approach for structural equation models

In the development of structural equation models (SEMs), observed variables are usually assumed to be normally distributed. However, this assumption is likely to be violated in many practical researches. As the non‐normality of observed variables in an SEM can be obtained from either non‐normal latent variables or non‐normal residuals or both, semiparametric modeling with unknown distribution of latent variables or unknown distribution of residuals is needed. In this article, we find that an SEM becomes nonidentifiable when both the latent variable distribution and the residual distribution are unknown. Hence, it is impossible to estimate reliably both the latent variable distribution and the residual distribution without parametric assumptions on one or the other. We also find that the residuals in the measurement equation are more sensitive to the normality assumption than the latent variables, and the negative impact on the estimation of parameters and distributions due to the non‐normality of residuals is more serious. Therefore, when there is no prior knowledge about parametric distributions for either the latent variables or the residuals, we recommend making parametric assumption on latent variables, and modeling residuals nonparametrically. We propose a semiparametric Bayesian approach using the truncated Dirichlet process with a stick breaking prior to tackle the non‐normality of residuals in the measurement equation. Simulation studies and a real data analysis demonstrate our findings, and reveal the empirical performance of the proposed methodology. A free WinBUGS code to perform the analysis is available in Supporting Information.     

SDF-1alpha-induced intracellular calcium transient involves Rho GTPase signalling and is required for migration of hematopoietic progenitor cells

Signalling through the chemokine stromal derived factor (SDF)-1alpha and its receptor CXCR4 has been recognized as a key event in the migratory response of hematopoietic stem and progenitor cells (HPC). Small GTPases of the Rho/Rac family might be involved in SDF-1alpha signalling at several different levels. In the present study we report that two toxins from Clostridium species which inhibit the small GTPase Rho suppressed SDF-1alpha-induced generation of intracellular calcium transients in HPC. Chelation of intracellular Ca(2+) with BAPTA or depletion of intracellular Ca(2+) stores with thapsigargin demonstrated that calcium transients are essential for SDF-1alpha-induced chemotactic migration of HPC. Furthermore, transplantation of HPC pretreated with Ca(2+) flux inhibitors into mice revealed a suppression of HPC homing to the bone marrow and increased levels of cells remaining in the bloodstream or circulating to the spleen. Our data indicate that the small GTPase Rho is required for the induction of Ca(2+) transients in HPC, which in turn are necessary for the coordinated migratory response of HPC both in vitro and in vivo.     

Pluripotent Stem Cells Models for Huntington＇s Disease： Prospects and Challenges

Pluripotent cellular models have shown great promise in the study of a number of neurological disorders.Several advantages of using a stem cell model include the potential for cells to derive disease relevant neuronal cell types,providing a system for researchers to monitor disease progression during neurogenesis,along with serving as a platform for drug discovery.A number of stem cell derived models have been employed to establish in vitro research models of Huntington’s disease that can be used to investigate cellular pathology and screen for drug and cell-based therapies.Although some progress has been made,there are a number of challenges and limitations that must be overcome before the true potential of this research strategy is achieved.In this article we review current stem cell models that have been reported,as well as discuss the issues that impair these studies.We also highlight the prospective application of Huntington’s disease stem cell models in the development of novel therapeutic strategies and advancement of personalized medicine.     

Neurotrophic factor NT-3 displays a non-canonical cell guidance signaling function for cephalic neural crest cells

Chemotactic cell migration is triggered by extracellular concentration gradients of molecules segregated by target fields. Neural crest cells (NCCs), paradigmatic as an accurately moving cell population, undergo wide dispersion along multiple pathways, invading with precision defined sites of the embryo to differentiate into many derivatives. This report addresses the involvement of NT-3 in early colonization by cephalic NCCs invading the optic vesicle region. The results of in vitro and in vivo approaches showed that NCCs migrate directionally up an NT-3 concentration gradient. We also demonstrated the expression of NT-3 in the ocular region as well as their functional TrkB, TrkC and p75 receptors on cephalic NCCs. On whole-mount embryo, a perturbed distribution of NCCs colonizing the optic vesicle target field was shown after morpholino cancelation of cephalic NT-3 or TrkC receptor on NCCs, as well as in situ blocking of TrkC receptor of mesencephalic NCCs by specific antibody released from inserted microbeads. The present results strongly suggest that, among other complementary cell guidance factor(s), the chemotactic response of NCCs toward the ocular region NT-3 gradient is essential for spatiotemporal cell orientation, amplifying the functional scope of this neurotrophic factor as a molecular guide for the embryo cells, besides its well-known canonical functions.     

Human stem cells for modeling neurological disorders: accelerating the drug discovery pipeline

The availability of human stem cells heralds a new era for modeling normal and pathologic tissues and developing therapeutics. For example, the in vitro recapitulation of normal and aberrant neurogenesis holds significant promise as a tool for de novo modeling of neurodevelopmental and neurodegenerative diseases. Translational applications include deciphering brain development, function, pathologies, traditional medications, and drug discovery for novel pharmacotherapeutics. For the latter, human stem cell-based assays represent a physiologically relevant and high-throughput means to assess toxicity and other undesirable effects early in the drug development pipeline, avoiding late-stage attrition whilst expediting proof-of-concept of genuine drug candidates. Here we consider the potential of human embryonic, adult, and induced pluripotent stem cells for studying neurological disorders and preclinical drug development.     

Models for contact-mediated pattern formation: cells that form parallel arrays

Kinetic continuum models are derived for cells that crawl over a 2D substrate, undergo random reorientation, and turn in response to contact with a neighbor. The integro-partial differential equations account for changes in the distribution of orientations in the population. It is found that behavior depends on parameters such as total mass, random motility, adherence, and sloughing rates, as well as on broad aspects of the contact response. Linear stability analysis, and numerical, and cellular automata simulations reveal that as parameters are varied, a bifurcation leads to loss of stability of a uniform (isotropic) steady state, in favor of an (anisotropic) patterned state in which cells are aligned in parallel arrays.     

Terrestrial biosphere model performance for inter‐annual variability of land‐atmosphere CO2 exchange

Interannual variability in biosphere‐atmosphere exchange of CO₂ is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter‐annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long‐term measurements of biosphere‐atmosphere CO₂ exchange made with eddy‐covariance flux towers at 11 forested sites in North America. Instead of focusing on model‐data agreement we take a systematic, variability‐oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.     

中国仓鼠卵巢工程细胞无血清流加培养关键工艺参数的考察

主要考察流加培养基中不同营养成分、流加起始时间及初始接种密度对11G-S细胞无血清流加培养的影响。在研究中以悬浮适应的表达尿激酶原 (Pro-urokinase,Pro-UK) CHO工程细胞系11G-S为研究对象,在100 mL的摇瓶中无血清悬浮流加培养11G-S细胞,同时以活细胞密度、细胞活力及Pro-UK活性为评价依据。结果表明在培养基中氨基酸、无血清添加成分及无机盐对促进细胞生长、细胞活力维持及蛋白表达起着较为重要的作用;且流加起始时间为72 h及初始接种密度为3×105~4×105 cells/     

Fluorescent proteins in animal cells for process development: optimization of sodium butyrate treatment as an example.

Fluorescent proteins expressed in mammalian cells can be quantified quickly and noninvasively with a standard fluorescence plate reader. We have previously exploited this quality in cell growth assessment (Hunt et al., 1999b). In this work, different CHO cell lines constitutively expressing fluorescent proteins were evaluated as model systems for process development and optimization. Our results demonstrate that the fluorescence of these cell lines quickly reveals conditions that might improve the overall productivity. Sodium butyrate, a well-known yet unpredictable enhancer of production, was chosen for this study. Due to the competing effects of sodium butyrate ("butyrate") on expression and cell number, the maximal overall productivity represents a compromise between enhancement of production and toxicity. Based on fluorescence only, it is possible to separate effects on cell number and specific production by combining microplate fluorescence measurements with data obtained by flow cytometry. This allows for rapid screening of different clones without counting cells or quantifying the recombinant protein, a highly attractive feature if the expression of green fluorescent protein (GFP) was correlated to that of a protein of interest. For all clones tested, negative effects of butyrate on proliferation were similar, while net enhancement of expression was characteristic for each clone. Therefore, it is necessary to optimize treatment for each individual clone. This work demonstrates that, based on the fluorescence of GFP-expresssing cell lines, it is possible to examine noninvasively three critical, generic parameters of butyrate treatment: butyrate concentration, exposure time, and culture phase at the time of addition.