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.     

Value creation dynamics in a project alliance

Project alliance requires all parties to work together in good faith, share project risks, and make unanimous decisions for the betterment of the project. A key feature of successful implementation of a project alliance is a focus on value creation and value for money. This paper proposes a qualitative system dynamics model to specify and explain dynamics of value creation processes in the context of project alliance. By synthesizing the existing literature and reports on project alliancing, this paper identifies four processes that have a strong influence on the value created in the project alliance context: work progression, rework, redesign and innovation, and rescheduling. In addition, we show how these value creation processes are interrelated and evolve over time. The effectiveness of these processes is influenced by the capability and motivation of the project alliance partners to discover works that do not fully utilize the available resources, and make quick decisions to capture these benefits.     

Visualization of a pharmaceutical unit operation: wet granulation

Recent developments in the field of process engineering and manufacturing sciences enable a new level of process understanding. However, extracting this understanding from increasing amounts of information is challenging. The aim of this study was to create a process vector from a model process describing all relevant information and, by that means, create a tool for combining and visualizing this information. Physical (impeller torque and temperature) and chemical (near-infrared spectroscopy) information from a small-scale high-shear granulation was used in the process vector. The vectors created were visualized by two different methods: principal component analysis (PCA) and the self-organizing map (SOM). None of the individual measurement techniques were able to describe the state of the process alone, although they provided important information about the process. By combining the data and visualizing it, an overview could be achieved. The SOM approach had two advantages over the PCA: it presented the results in terms of the original variables and enabled the analysis of nonlinear responses. However, both visualization methods could be used to describe the progress of the process and to increase the level of process understanding.     

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.     

The psychophysiology of James Bond: Phasic emotional responses to violent video game events.

The authors examined emotional valence- and arousal-related phasic psychophysiological responses to different violent events in the first-person shooter video game "James Bond 007: NightFire" among 36 young adults. Event-related changes in zygomaticus major, corrugator supercilii, and orbicularis oculi electromyographic (EMG) activity and skin conductance level (SCL) were recorded, and the participants rated their emotions and the trait psychoticism based on the Psychoticism dimension of the Eysenck Personality Questionnaire--Revised, Short Form. Wounding and killing the opponent elicited an increase in SCL and a decrease in zygomatic and orbicularis oculi EMG activity. The decrease in zygomatic and orbicularis oculi activity was less pronounced among high Psychoticism scorers compared with low Psychoticism scorers. The wounding and death of the player's own character (James Bond) elicited an increase in SCL and zygomatic and orbicularis oculi EMG activity and a decrease in corrugator activity. Instead of joy resulting from victory and success, wounding and killing the opponent may elicit high-arousal negative affect (anxiety), with high Psychoticism scorers experiencing less anxiety than low Psychoticism scorers. Although counterintuitive, the wounding and death of the player's own character may increase some aspect of positive emotion. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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.     

Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)

Journal of Mathematical Imaging and Vision - In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in...     

Detection of Sea Surface Temperature (SST) Using Infrared Band Data of Advanced Very High Resolution Radiometer (AVHRR) in the Gulf of Finland

This paper presents the detection of sea surface temperature (SST) in the Gulf of Finland using infrared band data of Advanced Very High Resolution Radiometer (AVHRR). AVHRR imagery is evaluated as a main data source for monitoring SST as a measure of upwelling's dynamic. Sea surface effects (SSE), however, cause a temperature difference between the sea surface skin and water below the surface. Therefore, SSE is taken into account as one of the major error factors in the SST esimation. Further studies will be investigated using both AVHRR and MODIS in the future.     

Depressive symptoms and the metabolic syndrome in childhood and adulthood: A prospective cohort study.

Objective: To examine the reciprocal associations between depressive symptoms and clinical definitions of the metabolic syndrome in childhood and adulthood. Design: Population-based prospective cohort study of 921 participants (538 women and 383 men) in Finland. The components of the metabolic syndrome were measured in childhood (mean age 12 years) and again in adulthood (mean age 33 years). A revised version of the Beck Depression Inventory was used to assess depressive symptoms at the mean ages of 24 and 33. Main Outcome Measures: Metabolic syndrome defined by the National Cholesterol Education Program Adult Treatment Panel III (NCEP), the European Group for the Study of Insulin Resistance, and the International Diabetes Federation criteria. Results: In women, depressive symptoms were associated with increased risk of the metabolic syndrome in adulthood (odds ratio for NCEP metabolic syndrome per 1 SD increase in depressive symptoms 1.40, 95% confidence interval 1.05-1.85). The metabolic syndrome in childhood, in turn, predicted higher levels of depressive symptoms in adulthood (p = .03). In men, no associations were found between depressive symptoms and the clinical definitions of the metabolic syndrome. Conclusion: The process linking depressive symptoms with the metabolic syndrome may go into both directions and may begin early in life. (PsycINFO Database Record (c) 2010 APA, all rights reserved)