Modelling biological processes using workflow and Petri Net models

MOTIVATION: Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. RESULTS: We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model. AVAILABILITY: The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.     

Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer

MOTIVATION: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed. RESULTS: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others. AVAILABILITY: the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm     

A systems‐based approach to investigate dose‐ and time‐dependent methylmercury‐induced gene expression response in C57BL/6 mouse embryos undergoing neurulation

BACKGROUND: Aberrations during neurulation due to genetic and/or environmental factors underlie a variety of adverse developmental outcomes, including neural tube defects (NTDs). Methylmercury (MeHg) is a developmental neurotoxicant and teratogen that perturbs a wide range of biological processes/pathways in animal models, including those involved in early gestation (e.g., cell cycle, cell differentiation). Yet, the relationship between these MeHg‐linked effects and changes in gestational development remains unresolved. Specifically, current information lacks mechanistic comparisons across dose or time for MeHg exposure during neurulation. These detailed investigations are crucial for identifying sensitive indicators of toxicity and for risk assessment applications. METHODS: Using a systems‐based toxicogenomic approach, we examined dose‐ and time‐dependent effects of MeHg on gene expression in C57BL/6 mouse embryos during cranial neural tube closure, assessing for significantly altered genes and associated Gene Ontology (GO) biological processes. Using the GO‐based application GO‐Quant, we quantitatively assessed dose‐ and time‐dependent effects on gene expression within enriched GO biological processes impacted by MeHg. RESULTS: We observed MeHg to significantly alter expression of 883 genes, including several genes (e.g., Vangl2, Celsr1, Ptk7, Twist, Tcf7) previously characterized to be crucial for neural tube development. Significantly altered genes were associated with development cell adhesion, cell cycle, and cell differentiation–related GO biological processes. CONCLUSIONS: Our results suggest that MeHg‐induced impacts within these biological processes during gestational development may underlie MeHg‐induced teratogenic and neurodevelopmental toxicity outcomes. Birth Defects Res (Part B) 89:188–200, 2010. © 2010 Wiley‐Liss, Inc.     

BaSAR-A tool in R for frequency detection

Many biological processes are periodic, for example cell cycle expression, circadian rhythms and calcium oscillations. However, measured time series from these processes are commonly short and noisy, and finding frequencies in such data can be challenging. Here we present BaSAR, Bayesian Spectrum Analysis in R, a package for extracting frequency information from time series data. The software uses advanced techniques of Bayesian inference that are well suited for handling typical biological data. The core functions are designed for detecting a single key frequency, without the need for data pre-processing such as detrending. The package is freely available at CRAN - The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/BaSAR.     

Continuous hidden process model for time series expression experiments

MOTIVATION: When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up- or down-regulated under the experimental condition studied. Current approaches, including clustering expression profiles and averaging the expression profiles of genes known to participate in specific processes, fail to provide an accurate estimate of the activity levels of many biological processes. RESULTS: We introduce a probabilistic continuous hidden process Model (CHPM) for time series expression data. CHPM can simultaneously determine the most probable assignment of genes to processes and the level of activation of these processes over time. To estimate model parameters, CHPM uses multiple time series datasets and incorporates prior biological knowledge. Applying CHPM to yeast expression data, we show that our algorithm produces more accurate functional assignments for genes compared to other expression analysis methods. The inferred process activity levels can be used to study the relationships between biological processes. We also report new biological experiments confirming some of the process activity levels predicted by CHPM. AVAILABILITY: A Java implementation is available at http:\\www.cs.cmu.edu\~yanxins\chpm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Searching the literature for proteins facilitates the identification of biological processes,if advanced methods of analysis are linked: a case study on microgravity-caused changes in cells

Background: More than one hundred reports were published about the characterization of cells from malignant and healthy tissues, as well as of endothelial cells and stem cells exposed to microgravity conditions.

Methods: We retrieved publications about microgravity related studies on each type of cells, extracted the proteins mentioned therein and analyzed them aiming to identify biological processes affected by microgravity culture conditions.

Results: The analysis revealed 66 different biological processes, 19 of them were always detected when papers about the four types of cells were analyzed.

Conclusion: Since a response to the removal of gravity is common to the different cell types, some of the 19 biological processes could play a role in cellular adaption to microgravity. Applying computer programs, to extract and analyze proteins and genes mentioned in publications becomes essential for scientists interested to get an overview of the rapidly growing fields of gravitational biology and space medicine.     

Applications of integrative OMICs approaches to gene regulation studies

Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms. Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.     

VIS-O-BAC: exploratory visualization of functional genome studies from bacteria

SUMMARY: The visualization-aided exploration of complex datasets will allow the research community to formulate novel functional hypotheses leading to a better understanding of biological processes at all levels. Therefore, we have developed a web resource termed VIS-O-BAC designed for the functional investigation of expression data for model systems, such as bacterial pathogens based on a graphical display. Genome-scale datasets derived from typical 'omic' approaches can directly be explored with respect to three biologically relevant aspects, the genome structure (operon organization), the organization of genes in pathways (KEGG) and the gene function with Gene Ontology (GO) terms. The integrated viewers can be used in parallel and combine expression data and functional annotations from different external data repositories. The graphical visualizations evidently accelerate both the validation of regulatory information and the detection of affected biological processes. AVAILABILITY: http://leger2.gbf.de/cgi-bin/vis-o-bac.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Timing‐Mechanismen in der Natur: Biologische Uhren

Increasingly timing mechanisms are detected on all levels of organisation which control the temporal order and coordination of biological processes. The respective mechanisms are designated as „biological clocks”︁. They are based on two principles: oscillations and unidirectional processes (hour‐glass). Oscillating biological clocks such as circadian,clunar or annual clocks coordinate biological events with respect to certain time points (phases)of external daily, lunar or annual hanges in the environment, while hourglass mechanisms mainly determine the duration of steps in development or aging.Complex biological timing mechanisms may comprise endogenous clocks and hourglass processes as well as external signals. Timing of biological events is often coupled with reaching defined thresholds within the underlying clock mechanism.     

Create and assess protein networks through molecular characteristics of individual proteins

MOTIVATION: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. RESULTS: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. AVAILABILITY: http://www.rostlab.org/services/pinat.     

Sediment-associated contaminants — an overview of scientific bases for developing remedial options

The review article covers three major aspects of scientific research on sediment-associated contaminants during the last 20 years: (i) identification and monitoring of sources and distribution (sampling; sample preparation; analyses, mainly of non-residual fractions; estimation of pollution potential); (ii) study of processes and mechanisms of pollutant transfer (interactions between dissolved and particulate element species; particle environments; transport and diagenesis: colloids; surface microlayers; particle related processes; bioturbation; dredging operations; remobilization of toxic elements; bioaccumulation of organic chemicals: solid/dissolved distribution of contaminants); (iii) assessment of the environmental impact of particle-bound pollutations (chemical extraction sequence; sediment bioassay; combined chemical/biological test procedures). Enstead, empirical tests developed from multi-disciplinary research on biological, chemical and physical factors are applied for assessing the reactivity, mobility and bioavailability of sediment-bound pollutations and for estimating the validity of remedial measures.     

Das Gesetz der absoluten Geschwindigkeiten biologischer Prozesse

The dependency of the velocity of biological processes from the temperature is described by the “Law of absolute velocity of biological processes”, which has only the individual parameters energy of activation ΔE, and the universal constant C. The law holds for all biological processes and is expressed by the equation: where C is: .     

Gene expression patterns define pathways correlated with loss of differentiation in lung adenocarcinomas

An analysis of microarray data from 86 lung adenocarcinomas reveals hundreds of genes significantly correlated with tumor cell differentiation. A bioinformatics approach of linking these genes to public information from the Locuslink and KEGG databases yields evidence for a loss of tumor cell differentiation being associated with biological processes of cell division, protein degradation, pyrimidine and purine metabolism, oxidative phosphorylation, glyoxylate and dicarboxylate metabolism, folate biosynthesis, and glutamate metabolism. The increased expression of genes involved in these processes is consistent with increased proliferation and metabolism characteristics of poorly differentiated tumors. The complete results of this analysis are available at http://dot.ped.med.umich.edu:2000/pub/diff/index.htm.     

Bayesian modelling of shared gene function

MOTIVATION: Biological assays are often carried out on tissues that contain many cell lineages and active pathways. Microarray data produced using such material therefore reflect superimpositions of biological processes. Analysing such data for shared gene function by means of well-matched assays may help to provide a better focus on specific cell types and processes. The identification of genes that behave similarly in different biological systems also has the potential to reveal new insights into preserved biological mechanisms. RESULTS: In this article, we propose a hierarchical Bayesian model allowing integrated analysis of several microarray data sets for shared gene function. Each gene is associated with an indicator variable that selects whether binary class labels are predicted from expression values or by a classifier which is common to all genes. Each indicator selects the component models for all involved data sets simultaneously. A quantitative measure of shared gene function is obtained by inferring a probability measure over these indicators. Through experiments on synthetic data, we illustrate potential advantages of this Bayesian approach over a standard method. A shared analysis of matched microarray experiments covering (a) a cycle of mouse mammary gland development and (b) the process of in vitro endothelial cell apoptosis is proposed as a biological gold standard. Several useful sanity checks are introduced during data analysis, and we confirm the prior biological belief that shared apoptosis events occur in both systems. We conclude that a Bayesian analysis for shared gene function has the potential to reveal new biological insights, unobtainable by other means. AVAILABILITY: An online supplement and MatLab code are available at http://www.sykacek.net/research.html#mcabf     

Representing and analysing molecular and cellular function using the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model.     

ADGO: analysis of differentially expressed gene sets using composite GO annotation

MOTIVATION: Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets. RESULTS: Here we propose a method for discovering composite biological themes from expression data. We intersected two annotated gene sets from different categories of Gene Ontology (GO). We then scored the expression changes of all the single and intersected sets. In this way, we were able to uncover, for example, a gene set with the molecular function F and the cellular component C that showed significant expression change, while the changes in individual gene sets were not significant. We provided an exemplary analysis for HIV-1 immune response. In addition, we tested the method on 20 public datasets where we found many 'filtered' composite terms the number of which reached approximately 34% (a strong criterion, 5% significance) of the number of significant unary terms on average. By using composite annotation, we can derive new and improved information about disease and biological processes from expression data. AVAILABILITY: We provide a web application (ADGO: http://array.kobic.re.kr/ADGO) for the analysis of differentially expressed gene sets with composite GO annotations. The user can analyze Affymetrix and dual channel array (spotted cDNA and spotted oligo microarray) data for four species: human, mouse, rat and yeast. CONTACT: chu@kribb.re.kr SUPPLEMENTARY INFORMATION: http://array.kobic.re.kr/ADGO.     

Change Is Good: Variations in Common Biological Mechanisms in the Epsilonproteobacterial Genera Campylobacter and Helicobacter

Summary: Microbial evolution and subsequent species diversification enable bacterial organisms to perform common biological processes by a variety of means. The epsilonproteobacteria are a diverse class of prokaryotes that thrive in diverse habitats. Many of these environmental niches are labeled as extreme, whereas other niches include various sites within human, animal, and insect hosts. Some epsilonproteobacteria, such as Campylobacter jejuni and Helicobacter pylori, are common pathogens of humans that inhabit specific regions of the gastrointestinal tract. As such, the biological processes of pathogenic Campylobacter and Helicobacter spp. are often modeled after those of common enteric pathogens such as Salmonella spp. and Escherichia coli. While many exquisite biological mechanisms involving biochemical processes, genetic regulatory pathways, and pathogenesis of disease have been elucidated from studies of Salmonella spp. and E. coli, these paradigms often do not apply to the same processes in the epsilonproteobacteria. Instead, these bacteria often display extensive variation in common biological mechanisms relative to those of other prototypical bacteria. In this review, five biological processes of commonly studied model bacterial species are compared to those of the epsilonproteobacteria C. jejuni and H. pylori. Distinct differences in the processes of flagellar biosynthesis, DNA uptake and recombination, iron homeostasis, interaction with epithelial cells, and protein glycosylation are highlighted. Collectively, these studies support a broader view of the vast repertoire of biological mechanisms employed by bacteria and suggest that future studies of the epsilonproteobacteria will continue to provide novel and interesting information regarding prokaryotic cellular biology.