Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways.

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models. We used an ordinary differential equation based model for the Fas apoptotic pathway as an example. The first step in our approach was to cluster simulation outputs generated from models with varied protein initial concentrations. Subsequently, decision tree analysis was applied, in which we used protein concentrations to predict the simulation outcomes. Our results suggest that no single subset of proteins can determine the pathway behaviour. Instead, different subsets of proteins with different concentrations ranges can be important. We also used the resulting decision tree to identify the minimal number of perturbations needed to change pathway behaviours. In conclusion, our framework provides a novel approach to understand the multivariate dependencies among molecules in complex networks, and can potentially be used to identify combinatorial targets for therapeutic interventions.     

On the bonding of porcelain on titanium

The interface structure and bond strength between Ti and porcelain were studied using various firing times and vacuum levels. During firing an interfacial oxide layer was formed between Ti and porcelain. Fracture occurred between this oxide layer and Ti. A correlation was observed between the thickness of the interfacial layer and the bond strength: the thicker the layer, the weaker was bonding. An improved vacuum was found to increase the bond strength. Oxygen was observed by ESCA to dissolve into Ti, causing brittleness in the uppermost Ti layer with prolonged firing time.     

Effect of Crystalline Leucite on Porcelain Bonding on Titanium

The effect of elimination of crystalline leucite on porcelain bonding to Ti was studied. The thermal expansion coefficient of low-fusing dental porcelain was decreased by eliminating the crystalline leucite phase. As a result, minimal thermal mismatch and better bonding between Ti and porcelain were obtained.     

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes.     

Modeling calcium dissolution from oil shale ash: Part 2.: Continuous washing of the ash layer

In the present work a possible approach to the utilization of oil shale ash containing free lime in precipitated calcium carbonate (PCC) production is elucidated. This paper investigates the Ca (calcium) dissolution process during continuous washing of pulverized firing (PF) and fluidized bed combustion (FBC) oil shale ash layers in a packed-bed leaching column. The main characteristics of the Ca dissolution process from ash are established. The effect of water flow rate is investigated by conducting leaching experiments of oil shale ashes formed in boilers operating with different combustion technologies. The values of the overall and liquid phase mass transfer coefficients are evaluated based on experiments using the developed ash layer washing model. The model is a set of partial differential equations that describe the changes in Ca content in the stagnant layer of ash and in the water flowing through the ash layer. An example in which the model is applied to environmental assessment and estimation of Ca leaching from industrial oil shale ash fields is provided.     

Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps

cDNA microarrays permit massively parallel gene expression analysis and have spawned a new paradigm in the study of molecular biology. One of the significant challenges in this genomic revolution is to develop sophisticated approaches to facilitate the visualization, analysis, and interpretation of the vast amounts of multi-dimensional gene expression data. We have applied self-organizing map (SOM) in order to meet these challenges. In essence, we utilize U-matrix and component planes in microarray data visualization and introduce general procedure for assessing significance for a cluster detected from U-matrix. Our case studies consist of two data sets. First, we have analyzed a data set containing 13,824 genes in 14 breast cancer cell lines. In the second case we show an example of the SOM in drug treatment of prostate cancer cells. Our results indicate that (1) SOM is capable of helping finding certain biologically meaningful clusters, (2) clustering algorithms could be used for finding a set of potential predictor genes for classification purposes, and (3) comparison and visualization of the effects of different drugs is straightforward with the SOM. In summary, the SOM provides an excellent format for visualization and analysis of gene microarray data, and is likely to facilitate extraction of biologically and medically useful information.     

Modeling calcium dissolution from oil shale ash: Part 1. Ca dissolution during ash washing in a batch reactor

Batch dissolution experiments were carried out to investigate Ca leachability from oil shale ashes formed in boilers operating with different combustion technologies. The main characteristics of Ca dissolution equilibrium and dynamics, including Ca internal mass transfer through effective diffusion coefficients inside the ash particle were evaluated. Based on the collected data, models allowing simulation of the Ca dissolution process from oil shale ashes during ash washing in a batch reactor were developed. The models are a set of differential equations that describe the changes in Ca content in the solid and liquid phase of the ash-water suspension.     

The effect of indium on porcelain bonding between porcelain and Au−Pd−In alloy

The effect of small In additions on oxide structure and porcelain adherence to Au–Pd alloys was studied. In was oxidized internally as In₂O₃. No uniform external oxide layer could be seen. Small In addition (1 at %) did not have any detectable effect on porcelain adherence (12 MPa), whereas higher In concentration (5 at %) caused significant increase in bond strength (26 MPa). This increase was probably a result of higher In₂O₃ concentration at the surface.