Cellular automata: Simulating alpine tundra vegetation dynamics in response to global warming

This study attempts to model alpine tundra vegetation dynamics in a tundra region in the Qinghai Province of China in response to global warming. We used Raster-based cellular automata and a Geographic Information System to study the spatial and temporal vegetation dynamics. The cellular automata model is implemented with IDRISI's Multi-Criteria Evaluation functionality to simulate the spatial patterns of vegetation change assuming certain scenarios of global mean temperature increase over time. The Vegetation Dynamic Simulation Model calculates a probability surface for each vegetation type, and then combines all vegetation types into a composite map, determined by the maximum likelihood that each vegetation type should distribute to each raster unit. With scenarios of global temperature increase of I to 3 degrees C, the vegetation types such as Dry Kobresia Meadow and Dry Potentilla Shrub that are adapted to warm and dry conditions tend to become more dominant in the study area.

UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

Computational fluid dynamics for improved bioreactor design and 3D culture

The complex relationship between the hydrodynamic environment and surrounding tissues directly impacts on the design and production of clinically useful grafts and implants. Tissue engineers have generally seen bioreactors as 'black boxes' within which tissue engineering constructs (TECs) are cultured. It is accepted that a more detailed description of fluid mechanics and nutrient transport within process equipment can be achieved by using computational fluid dynamics (CFD) technology. This review discusses applications of CFD for tissue engineering-related bioreactors -- fluid flow processes have direct implications on cellular responses such as attachment, migration and proliferation. We conclude that CFD should be seen as an invaluable tool for analyzing and visualizing the impact of fluidic forces and stresses on cells and TECs.

Analyzing real-time video microscopy : the dynamics and geometry of vesicles and tubules in endocytosis

With the advent of live cell imaging microscopy, new types of mathematical analyses and measurements are possible. Many of the real-time movies of cellular processes are visually very compelling, but elementary analysis of changes over time of quantities such as surface area and volume often show that there is more to the data than meets the eye. This unit outlines a geometric modeling methodology and applies it to tubulation of vesicles during endocytosis. Using these principles, it has been possible to build better qualitative and quantitative understandings of the systems observed, as well as to make predictions about quantities such as ligand or solute concentration, vesicle pH, and membrane trafficked. The purpose is to outline a methodology for analyzing real-time movies that has led to a greater appreciation of the changes that are occurring during the time frame of the real-time video microscopy and how additional quantitative measurements allow for further hypotheses to be generated and tested.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Cellular settings mediating Src Substrate switching between Focal Adhesion Kinase Tyrosine 861 and CUB-domain-containing protein 1 (CDCP1) Tyrosine 734

Reciprocal interactions between Src family kinases (SFKs) and focal adhesion kinase (FAK) are critical during changes in cell attachment. Recently it has been recognized that another SFK substrate, CUB-domain-containing protein 1 (CDCP1), is differentially phosphorylated during these events. However, the molecular processes underlying SFK-mediated phosphorylation of CDCP1 are poorly understood. Here we identify a novel mechanism in which FAK tyrosine 861 and CDCP1-Tyr-734 compete as SFK substrates and demonstrate cellular settings in which SFKs switch between these sites. Our results show that stable CDCP1 expression induces robust SFK-mediated phosphorylation of CDCP1-Tyr-734 with concomitant loss of p-FAK-Tyr-861 in adherent HeLa cells. SFK substrate switching in these cells is dependent on the level of expression of CDCP1 and is also dependent on CDCP1-Tyr-734 but is independent of CDCP1-Tyr-743 and -Tyr-762. In HeLa CDCP1 cells, engagement of SFKs with CDCP1 is accompanied by an increase in phosphorylation of Src-Tyr-416 and a change in cell morphology to a fibroblastic appearance dependent on CDCP1-Tyr-734. SFK switching between FAK-Tyr-861 and CDCP1-Tyr-734 also occurs during changes in adhesion of colorectal cancer cell lines endogenously expressing these two proteins. Consistently, increased p-FAK-Tyr-861 levels and a more epithelial morphology are seen in colon cancer SW480 cells silenced for CDCP1. Unlike protein kinase Cδ, FAK does not appear to form a trimeric complex with Src and CDCP1. These data demonstrate novel aspects of the dynamics of SFK-mediated cell signaling that may be relevant during cancer progression.

Expression of PTRF in PC3 cells modulates cholesterol dynamics and the actin cytoskeleton impacting secretion pathways

Expression of caveolin-1 is up-regulated in prostate cancer metastasis and is associated with aggressive recurrence of the disease. Intriguingly, caveolin-1 is also secreted from prostate cancer cell lines and has been identified in secreted prostasomes. Caveolin-1 is the major structural component of the plasma membrane invaginations called caveolae. Co-expression of the coat protein Polymerase I and transcript release factor (PTRF) is required for caveolae formation. We recently found that expression of caveolin-1 in the aggressive prostate cancer cell line PC-3 is not accompanied by PTRF, leading to noncaveolar caveolin-1 lipid rafts. Moreover, ectopic expression of PTRF in PC-3 cells sequesters caveolin-1 into caveolae. Here we quantitatively analyzed the effect of PTRF expression on the PC-3 proteome using stable isotope labeling by amino acids in culture and subcellular proteomics. We show that PTRF reduced the secretion of a subset of proteins including secreted proteases, cytokines, and growth regulatory proteins, partly via a reduction in prostasome secretion. To determine the cellular mechanism accounting for the observed reduction in secreted proteins we analyzed total membrane and the detergent-resistant membrane fractions. Our data show that PTRF expression selectively impaired the recruitment of actin cytoskeletal proteins to the detergent-resistant membrane, which correlated with altered cholesterol distribution in PC-3 cells expressing PTRF. Consistent with this, modulating cellular cholesterol altered the actin cytoskeleton and protein secretion in PC-3 cells. Intriguingly, several proteins that function in ER to Golgi trafficking were reduced by PTRF expression. Taken together, these results suggest that the noncaveolar caveolin-1 found in prostate cancer cells generates a lipid raft microenvironment that accentuates secretion pathways, possibly at the step of ER sorting/exit. Importantly, these effects could be modulated by PTRF expression.

Stroma regulates increased epithelial lateral cell adhesion in 3D culture : a role for actin/cadherin dynamics

BACKGROUND: Cell shape and tissue architecture are controlled by changes to junctional proteins and the cytoskeleton. How tissues control the dynamics of adhesion and cytoskeletal tension is unclear. We have studied epithelial tissue architecture using 3D culture models and found that adult primary prostate epithelial cells grow into hollow acinus-like spheroids. Importantly, when co-cultured with stroma the epithelia show increased lateral cell adhesions. To investigate this mechanism further we aimed to: identify a cell line model to allow repeatable and robust experiments; determine whether or not epithelial adhesion molecules were affected by stromal culture; and determine which stromal signalling molecules may influence cell adhesion in 3D epithelial cell cultures. METHODOLOGY/PRINCIPAL FINDINGS: The prostate cell line, BPH-1, showed increased lateral cell adhesion in response to stroma, when grown as 3D spheroids. Electron microscopy showed that 9.4% of lateral membranes were within 20 nm of each other and that this increased to 54% in the presence of stroma, after 7 days in culture. Stromal signalling did not influence E-cadherin or desmosome RNA or protein expression, but increased E-cadherin/actin co-localisation on the basolateral membranes, and decreased paracellular permeability. Microarray analysis identified several growth factors and pathways that were differentially expressed in stroma in response to 3D epithelial culture. The upregulated growth factors TGFβ2, CXCL12 and FGF10 were selected for further analysis because of previous associations with morphology. Small molecule inhibition of TGFβ2 signalling but not of CXCL12 and FGF10 signalling led to a decrease in actin and E-cadherin co-localisation and increased paracellular permeability. CONCLUSIONS/SIGNIFICANCE: In 3D culture models, paracrine stromal signals increase epithelial cell adhesion via adhesion/cytoskeleton interactions and TGFβ2-dependent mechanisms may play a key role. These findings indicate a role for stroma in maintaining adult epithelial tissue morphology and integrity.

Intrinsics and dynamics of fat grafts : an in vitro study

Background Despite a revived interest in fat grafting procedures, clinicians still fail to demonstrate clearly the in vivo behavior of fat grafts as a dynamic tissue substitute. However, the basic principles in cellular biology teach us that cells can survive and develop, provided that a structural matrix exists that directs their behavior. The purpose of this in vitro study was to analyze that behavior of crude fat grafts, cultured on a three-dimensional laminin-rich matrix. Methods Nonprocessed, human fat biopsy specimens (approximately 1 mm) were inoculated on Matrigel-coated wells to which culture medium was added. The control group consisted of fat biopsy specimens embedded in medium alone. The cellular proliferation pattern was followed over 6 weeks. Additional cultures of primary generated cellular spheroids were performed and eventually subjected to adipogenic differentiation media. Results A progressive outgrowth of fibroblast-like cells from the core fat biopsy specimen was observed in both groups. Within the Matrigel group, an interconnecting three-dimensional network of spindle-shaped cells was established. This new cell colony reproduced spheroids that functioned again as solitary sources of cellular proliferation. Addition of differentiation media resulted in lipid droplet deposition in the majority of generated cells, indicating the initial steps of adipogenic differentiation. Conclusions The authors noticed that crude, nonprocessed fat biopsy specimens do have considerable potential for future tissue engineering-based applications, provided that the basic principles of developmental, cellular biology are respected. Spontaneous in vitro expansion of the stromal cells present in fat grafts within autologous and injectable matrices could create "off-the-shelf" therapies for reconstructive procedures.

On the effect of topology on cellular automata rule spaces

This thesis presents an empirical study of the effects of topology on cellular automata rule spaces. The classical definition of a cellular automaton is restricted to that of a regular lattice, often with periodic boundary conditions. This definition is extended to allow for arbitrary topologies. The dynamics of cellular automata within the triangular tessellation were analysed when transformed to 2-manifolds of topological genus 0, genus 1 and genus 2. Cellular automata dynamics were analysed from a statistical mechanics perspective. The sample sizes required to obtain accurate entropy calculations were determined by an entropy error analysis which observed the error in the computed entropy against increasing sample sizes. Each cellular automata rule space was sampled repeatedly and the selected cellular automata were simulated over many thousands of trials for each topology. This resulted in an entropy distribution for each rule space. The computed entropy distributions are indicative of the cellular automata dynamical class distribution. Through the comparison of these dynamical class distributions using the E-statistic, it was identified that such topological changes cause these distributions to alter. This is a significant result which implies that both global structure and local dynamics play a important role in defining long term behaviour of cellular automata.

A review of pedestrian group dynamics and methodologies in modelling pedestrian group behaviours

Observations conducted by researchers revealed that the group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour. However, most research currently undertaken by various researchers failed to consider the group dynamics when developing pedestrian flow models. This paper presented a critical review of pedestrian models that incorporates group behaviour. Models reviewed in this paper are mainly created by microscopic modelling approaches such as social force, cellular automata, and agent-based method. The purpose of this literature review is to improve the understanding of group dynamics among pedestrians and highlight the need for considering group dynamics when developing pedestrian simulation models.