NOXclass: prediction of protein-protein interaction types

Background  

Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available.     

The response of VEGF-stimulated endothelial cells to angiostatic molecules is substrate-dependent

Background  

The microenvironment surrounding cells can exert multiple effects on their biological responses. In particular the extracellular matrix surrounding cells can profoundly influence their behavior. It has been shown that the extracellular matrix composition in tumors is vastly different than that found in normal tissue with increased amounts of certain matrices such as collagen I. It has been previously demonstrated that VEGF stimulation of endothelial cells growing on type I collagen results in the induction of bcl-2 expression and enhanced endothelial cell survival. We sought to investigate whether this increased endothelial cell survival resulted in the failure of angiostatic molecules to inhibit angiogenesis.     

Development of a one-step embryonic stem cell-based assay for the screening of sprouting angiogenesis

Background  

Angiogenesis assays are important tools for the identification of regulatory molecules and the potential development of therapeutic strategies to modulate neovascularization. Although numerous in vitro angiogenesis models have been developed in the past, they exhibit limitations since they do not recapitulate the entire angiogenic process or correspond to multi-step procedures that are not easy to use. Convenient, reliable, easily quantifiable and physiologically relevant assays are still needed for pharmacological screenings of angiogenesis.     

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery

Background  

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.     

Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

Background  

Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models.     

Context dependent substitution biases vary within the human genome

Background  

Models of sequence evolution typically assume that different nucleotide positions evolve independently. This assumption is widely appreciated to be an over-simplification. The best known violations involve biases due to adjacent nucleotides. There have also been suggestions that biases exist at larger scales, however this possibility has not been systematically explored.     

A model invalidation-based approach for elucidating biological signalling pathways,applied to the chemotaxis pathway in R. sphaeroides

Background  

Developing methods for understanding the connectivity of signalling pathways is a major challenge in biological research. For this purpose, mathematical models are routinely developed based on experimental observations, which also allow the prediction of the system behaviour under different experimental conditions. Often, however, the same experimental data can be represented by several competing network models.     

Modelling the correlation between the activities of adjacent genes in drosophila

Background  

Correlation between the expression levels of genes which are located close to each other on the genome has been found in various organisms, including yeast, drosophila and humans. Since such a correlation could be explained by several biochemical, evolutionary, genetic and technological factors, there is a need for statistical models that correspond to specific biological models for the correlation structure.     

An approach to multiscale modelling with graph grammars

Background and Aims

Functional–structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs.

Methods

A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL.

Key Results

Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas.

Conclusions

The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.     

Ontology-based instance data validation for high-quality curated biological pathways

Background

Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

Results

We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

Conclusions

A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

     

Zebrafish RNase T2 genes and the evolution of secretory ribonucleases in animals

Background  

Members of the Ribonuclease (RNase) T2 family are common models for enzymological studies, and their evolution has been well characterized in plants. This family of acidic RNases is widespread, with members in almost all organisms including plants, animals, fungi, bacteria and even some viruses. While several biological functions have been proposed for these enzymes in plants, their role in animals is unknown. Interestingly, in vertebrates most of the biological roles of plant RNase T2 proteins are carried out by members of a different family, RNase A. Still, RNase T2 proteins are conserved in these animals     

Exploration of biological network centralities with CentiBiN

Background  

The elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability.     

Thalidomide attenuates nitric oxide mediated angiogenesis by blocking migration of endothelial cells

Background  

Thalidomide is an immunomodulatory agent, which arrests angiogenesis. The mechanism of anti-angiogenic activity of thalidomide is not fully understood. As nitric oxide is involved in angiogenesis, we speculate a cross-talk between thalidomide and nitric oxide signaling pathway to define angiogenesis. The aim of present study is to understand the mechanistic aspects of thalidomide-mediated attenuation of angiogenesis induced by nitric oxide at the cellular level.     

Discriminating between rival biochemical network models: three approaches to optimal experiment design

Background  

The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures.     

Assessing and selecting gene expression signals based upon the quality of the measured dynamics

Background  

One of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited number of replicates, such that one can have confidence that the results of the analysis are capturing important salient dynamic features.     

Odefy - From discrete to continuous models

Background  

Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable.