Getting started in biological pathway construction and analysis

Omics and systems view of innate immune pathways

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.

Medial Sclerosis-Epidemiology and Clinical Significance

BACKGROUND

Medial sclerosis (MeS) is a chronic systemic vascular disease that mainly affects the arteries of the lower limb. Its prevalence in the general population is approximately 2.5% (range: 1.6% to 10.0%). It is more common in men than in women.

METHODS

This review is based on pertinent publications retrieved by a selective search in PubMed.

RESULTS

MeS is the final common pathway of a wide variety of diseases; its pathogenesis is not fully understood. It often remains clinically silent for decades and is usually diagnosed as an incidental finding or in a late stage. MeS with or without atherosclerosis is the most common histologic finding after limb amputation. MeS of the below-the-knee arteries is a major risk factor for chronic critical leg ischemia (OR:13.25, 95% confidence interval: [1.69; 104.16]) and amputation (RR 2.27, [1.89; 2.74]). Patients with peripheral arterial occlusive disease and marked calcification have a much higher risk of amputation (OR 2.88, [1.18; 12.72]) and a higher mortality (OR 5.16, [1.13; 21.61]). MeS is a risk factor for the failure of endovascular treatment of the pedal arteries (OR 4.0, [1.1; 16.6]). The more marked the calcification, the higher the risk of major amputation (HR 10.6 [1.4; 80.7] to HR 15.5 [2.0; 119]). Patients with vascular calcifications have been found to have lower patency rates and higher treatment failure rates two years after open surgical revascularization of the below-the-knee arteries. No pharmacotherapy for MeS is available to date.

CONCLUSION

MeS is an important risk factor for chronic critical lower limb ischemia, amputation, morbidity, and complications, particularly after endovascular and surgical procedures.

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

BACKGROUND

There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings.

METHODS

This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods.

RESULTS

We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models.

CONCLUSIONS

The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.

An automatic integrative method for learning interpretable communities of biological pathways

Although knowledge of biological pathways is essential for interpreting results from computational biology studies, the growing number of pathway databases complicates efforts to efficiently perform pathway analysis due to high redundancies among pathways from different databases, and inconsistencies in how pathways are created and named. We introduce the PAthway Communities (PAC) framework, which reconciles pathways from different databases and reduces pathway redundancy by revealing informative groups with distinct biological functions. Uniquely applying the Louvain community detection algorithm to a network of 4847 pathways from KEGG, REACTOME and Gene Ontology databases, we identify 35 distinct and automatically annotated communities of pathways and show that they are consistent with expert-curated pathway categories. Further, we demonstrate that our pathway community network can be queried with new gene sets to provide biological context in terms of related pathways and communities. Our approach, combined with an interpretable web tool we provide, will help computational biologists more efficiently contextualize and interpret their biological findings.

Biomimetic Scaffolds Modulate the Posttraumatic Inflammatory Response in Articular Cartilage Contributing to Enhanced Neoformation of Cartilaginous Tissue In Vivo

Focal chondral lesions of the knee are the most frequent type of trauma in younger patients and are associated with a high risk of developing early posttraumatic osteoarthritis. The only current clinical solutions include microfracture, osteochondral grafting, and autologous chondrocyte implantation. Cartilage tissue engineering based on biomimetic scaffolds has become an appealing strategy to repair cartilage defects. Here, a chondrogenic collagen-chondroitin sulfate scaffold is tested in an orthotopic Lapine in vivo model to understand the beneficial effects of the immunomodulatory biomaterial on the full chondral defect. Using a combination of noninvasive imaging techniques, histological and whole transcriptome analysis, the scaffolds are shown to enhance the formation of cartilaginous tissue and suppression of host cartilage degeneration, while also supporting tissue integration and increased tissue regeneration over a 12 weeks recovery period. The results presented suggest that biomimetic materials could be a clinical solution for cartilage tissue repair, due to their ability to modulate the immune environment in favor of regenerative processes and suppression of cartilage degeneration.

Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.

[Applications of artificial intelligence to new drug development]

Artificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning. The convergence of technological advances in those fields allows to integrate massive data and build probabilistic models of a problem. The latter can be continuously updated by incorporating new data to inform decision-making and predict the future. In support of drug discovery and development, AI allows to generate disease models using data obtained following extensive molecular profiling of patients. Given its superior computational power, AI can integrate those big multimodal data to generate models allowing: (i) to represent patient heterogeneity; and (ii) identify therapeutic targets with inferences of causality in the pathophysiology. Additional computational analyses can help identifying and optimizing drugs interacting with these targets, or even repurposing existing molecules for a new indication. AI-based modeling further supports the identification of biomarkers of efficacy, the selection of appropriate combination therapies and the design of innovative clinical studies with virtual placebo groups. The convergence of biotechnologies, drug sciences and AI is fostering the emergence of a computational precision medicine predicted to yield therapies or preventive measures precisely tailored to patient characteristics in terms of their physiology, disease features and environmental risk exposure.

Cancer subtype classification and modeling by pathway attention and propagation

MOTIVATION

Biological pathway is an important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only one-third of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification.

RESULTS

We present an explainable deep-learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. Then, a multi-attention-based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer datasets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions.

AVAILABILITY AND IMPLEMENTATION

The source code is available at http://biohealth.snu.ac.kr/software/GCN_MAE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems

Since its inception, the main goal of the lipidomics field has been to characterize lipid species and their respective biological roles. However, difficulties in both full speciation and biological interpretation have rendered these objectives extremely challenging and as a result, limited our understanding of lipid mechanisms and dysregulation. While mass spectrometry-based advancements have significantly increased the ability to identify lipid species, less progress has been made surrounding biological interpretations. We have therefore developed a Structural-based Connectivity and Omic Phenotype Evaluations (SCOPE) cheminformatics toolbox to aid in these evaluations. SCOPE enables the assessment and visualization of two main lipidomic associations: structure/biological connections and metadata linkages either separately or in tandem. To assess structure and biological relationships, SCOPE utilizes key lipid structural moieties such as head group and fatty acyl composition and links them to their respective biological relationships through hierarchical clustering and grouped heatmaps. Metadata arising from phenotypic and environmental factors such as age and diet is then correlated with the lipid structures and/or biological relationships, utilizing Toxicological Prioritization Index (ToxPi) software. Here, SCOPE is demonstrated for various applications from environmental studies to clinical assessments to showcase new biological connections not previously observed with other techniques.

Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making it accessible, or biocuration, is an important aspect of biomedical research and discovery. However, because maintaining sophisticated biocuration is highly resource dependent, it continues to lag behind the continually being generated biomedical data. Another critical aspect of cancer research, pathway analysis, has proven to be an efficient method for gaining insight into the underlying biology associated with cancer. We propose a deep-learning-based model, Stacked Denoising Autoencoder Multi-Label Learning (SdaMLL), for facilitating gene multi-function discovery and pathway completion. SdaMLL can capture intermediate representations robust to partial corruption of the input pattern and generate low-dimensional codes superior to conditional dimension reduction tools. Experimental results indicate that SdaMLL outperforms existing classical multi-label algorithms. Moreover, we found some gene functions, such as Fused in Sarcoma (FUS, which may be part of transcriptional misregulation in cancer) and p27 (which we expect will become a member viral carcinogenesis), that can be used to complete the related pathways. We provide a visual tool (https://www.keaml.cn/gpvisual) to view the new gene functions in cancer pathways.

The Cytotoxicity of the Ajoene Analogue BisPMB in WHCO1 Oesophageal Cancer Cells Is Mediated by CHOP/GADD153

Garlic is a food and medicinal plant that has been used in folk medicine since ancient times for its beneficial health effects, which include protection against cancer. Crushed garlic cloves contain an array of small sulfur-rich compounds such as ajoene. Ajoene is able to interfere with biological processes and is cytotoxic to cancer cells in the low micromolar range. BisPMB is a synthetic ajoene analogue that has been shown in our laboratory to have superior cytotoxicity to ajoene. In the current study we have performed a DNA microarray analysis of bisPMB-treated WHCO1 oesophageal cancer cells to identify pathways and processes that are affected by bisPMB. The most significantly enriched biological pathways as assessed by gene ontology, KEGG and ingenuity pathway analysis were those involving protein processing in the endoplasmic reticulum (ER) and the unfolded protein response. In support of these pathways, bisPMB was found to inhibit global protein synthesis and lead to increased levels of ubiquitinated proteins. BisPMB also induced alternate splicing of the transcription factor XBP-1; increased the expression of the ER stress sensor GRP78 and induced expression of the ER stress marker CHOP/GADD153. CHOP expression was found to be central to the cytotoxicity of bisPMB as its silencing with siRNA rendered the cells resistant to bisPMB. The MAPK proteins, JNK and ERK1/2 were activated following bisPMB treatment. However JNK activation was not critical in the cytotoxicity of bisPMB, and ERK1/2 activation was found to play a pro-survival role. Overall the ajoene analogue bisPMB appears to induce cytotoxicity in WHCO1 cells by activating the unfolded protein response through CHOP/GADD153.

Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information

Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author's webpage.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Systems toxicology identifies mechanistic impacts of 2-amino-4,6-dinitrotoluene (2A-DNT) exposure in Northern Bobwhite

Background

A systems toxicology investigation comparing and integrating transcriptomic and proteomic results was conducted to develop holistic effects characterizations for the wildlife bird model, Northern bobwhite (Colinus virginianus) dosed with the explosives degradation product 2-amino-4,6-dinitrotoluene (2A-DNT). A subchronic 60d toxicology bioassay was leveraged where both sexes were dosed via daily gavage with 0, 3, 14, or 30 mg/kg-d 2A-DNT. Effects on global transcript expression were investigated in liver and kidney tissue using custom microarrays for C. virginianus in both sexes at all doses, while effects on proteome expression were investigated in liver for both sexes and kidney in males, at 30 mg/kg-d.

Results

As expected, transcript expression was not directly indicative of protein expression in response to 2A-DNT. However, a high degree of correspondence was observed among gene and protein expression when investigating higher-order functional responses including statistically enriched gene networks and canonical pathways, especially when connected to toxicological outcomes of 2A-DNT exposure. Analysis of networks statistically enriched for both transcripts and proteins demonstrated common responses including inhibition of programmed cell death and arrest of cell cycle in liver tissues at 2A-DNT doses that caused liver necrosis and death in females. Additionally, both transcript and protein expression in liver tissue was indicative of induced phase I and II xenobiotic metabolism potentially as a mechanism to detoxify and excrete 2A-DNT. Nuclear signaling assays, transcript expression and protein expression each implicated peroxisome proliferator-activated receptor (PPAR) nuclear signaling as a primary molecular target in the 2A-DNT exposure with significant downstream enrichment of PPAR-regulated pathways including lipid metabolic pathways and gluconeogenesis suggesting impaired bioenergetic potential.

Conclusion

Although the differential expression of transcripts and proteins was largely unique, the consensus of functional pathways and gene networks enriched among transcriptomic and proteomic datasets provided the identification of many critical metabolic functions underlying 2A-DNT toxicity as well as impaired PPAR signaling, a key molecular initiating event known to be affected in di- and trinitrotoluene exposures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1798-4) contains supplementary material, which is available to authorized users.

Transcriptome alterations of mitochondrial and coagulation function in schizophrenia by cortical sequencing analysis

Background

Transcriptome sequencing of brain samples provides detailed enrichment analysis of differential expression and genetic interactions for evaluation of mitochondrial and coagulation function of schizophrenia. It is implicated that schizophrenia genetic and protein interactions may give rise to biological dysfunction of energy metabolism and hemostasis. These findings may explain the biological mechanisms responsible for negative and withdraw symptoms of schizophrenia and antipsychotic-induced venous thromboembolism.

We conducted a comparison of schizophrenic candidate genes from literature reviews and constructed the schizophrenia-mediator network (SCZMN) which consists of schizophrenic candidate genes and associated mediator genes by applying differential expression analysis to BA22 RNA-Seq brain data. The network was searched against pathway databases such as PID, Reactome, HumanCyc, and Cell-Map. The candidate complexes were identified by MCL clustering using CORUM for potential pathogenesis of schizophrenia.

Results

Published BA22 RNA-Seq brain data of 9 schizophrenic patients and 9 controls samples were analyzed. The differentially expressed genes in the BA22 brain samples of schizophrenia are proposed as schizophrenia candidate marker genes (SCZCGs). The genetic interactions between mitochondrial genes and many under-expressed SCZCGs indicate the genetic predisposition of mitochondria dysfunction in schizophrenia. The biological functions of SCZCGs, as listed in the Pathway Interaction Database (PID), indicate that these genes have roles in DNA binding transcription factor, signal and cancer-related pathways, coagulation and cell cycle regulation and differentiation pathways.

In the query-query protein-protein interaction (QQPPI) network of SCZCGs, TP53, PRKACA, STAT3 and SP1 were identified as the central "hub" genes. Mitochondrial function was modulated by dopamine inhibition of respiratory complex I activity. The genetic interaction between mitochondria function and schizophrenia may be revealed by DRD2 linked to NDUFS7 through protein-protein interactions of FLNA and ARRB2.

The biological mechanism of signaling pathway of coagulation cascade was illustrated by the PPI network of the SCZCGs and the coagulation-associated genes. The relationship between antipsychotic target genes (DRD2/3 and HTR2A) and coagulation factor genes (F3, F7 and F10) appeared to cascade the following hemostatic process implicating the bottleneck of coagulation genetic network by the bridging of actin-binding protein (FLNA).

Conclusions

It is implicated that the energy metabolism and hemostatic process have important roles in the pathogenesis for schizophrenia. The cross-talk of genetic interaction by these co-expressed genes and reached candidate genes may address the key network in disease pathology. The accuracy of candidate genes evaluated from different quantification tools could be improved by crosstalk analysis of overlapping genes in genetic networks.

MiRNome and transcriptome aided pathway analysis in human regulatory T cells

Owing to their manifold immune regulatory functions, regulatory T cells (Treg) have received tremendous interest as targets for therapeutic intervention of diverse immunological pathologies or cancer. Directed manipulation of Treg will only be achievable with extensive knowledge about the intrinsic programs that define their regulatory function. We simultaneously analyzed miR and mRNA transcript levels in resting and activated human Treg cells in comparison with non-regulatory conventional T cells (Tcon). Based on experimentally validated miR-target information, both transcript levels were integrated into a comprehensive pathway analysis. This strategy revealed characteristic signal transduction pathways involved in Treg biology such as T-cell receptor-, Toll-like receptor-, transforming growth factor-β-, JAK/STAT (Janus kinase/signal transducers and activators of transcription)- and mammalian target of rapamycin signaling, and allowed for the prediction of specific pathway activities on the basis of miR and mRNA transcript levels in a probabilistic manner. These data encourage new concepts for targeted control of Treg cell effector functions.

Signalling pathway database usability: lessons learned

BACKGROUND

issues and limitations related to accessibility, understandability and ease of use of signalling pathway databases may hamper or divert research workflow, leading, in the worst case, to the generation of confusing reference frameworks and misinterpretation of experimental results. In an attempt to retrieve signalling pathway data related to a specific set of test genes, we queried and analysed the results from six of the major curated signalling pathway databases: Reactome, PathwayCommons, KEGG, InnateDB, PID, and Wikipathways.

FINDINGS

although we expected differences - often a desirable feature for the integration of each individual query, we observed variations of exceptional magnitude, with disproportionate quality and quantity of the results. Some of the more remarkable differences can be explained by the diverse conceptual designs and purposes of the databases, the types of data stored and the structure of the query, as well as by missing or erroneous descriptions of the search procedure. To go beyond the mere enumeration of these problems, we identified a number of operational features, in particular inner and cross coherence, which, once quantified, offer objective criteria to choose the best source of information.

CONCLUSIONS

in silico biology heavily relies on the information stored in databases. To ensure that computational biology mirrors biological reality and offers focused hypotheses to be experimentally validated, coherence of data codification is crucial and yet highly underestimated. We make practical recommendations for the end-user to cope with the current state of the databases as well as for the maintainers of those databases to contribute to the goal of the full enactment of the open data paradigm.

A network-based kernel machine test for the identification of risk pathways in genome-wide association studies

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

A primer for disease gene prioritization using next-generation sequencing data

High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. The challenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that are different from the reference genome, and to prioritize/rank the variants for the question of interest. The recent development of many computational algorithms and programs has vastly improved the ability to translate sequence data into valuable information for disease gene identification. However, the NGS data analysis is complex and could be overwhelming for researchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the most commonly used principles and tools for analyzing NGS data for disease gene identification.

SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria

Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.

Modeling of the hypothalamic-pituitary-adrenal axis-mediated interaction between the serotonin regulation pathway and the stress response using a Boolean approximation: a novel study of depression

Major depressive disorder (MDD) is a multifactorial disorder known to be influenced by both genetic and environmental factors. MDD presents a heritability of 37%, and a genetic contribution has also been observed in studies of family members of individuals with MDD that imply that the probability of suffering the disorder is approximately three times higher if a first-degree family member is affected. Childhood maltreatment and stressful life events (SLEs) have been established as critical environmental factors that profoundly influence the onset of MDD. The serotonin pathway has been a strong candidate for genetic studies, but it only explains a small proportion of the heritability of the disorder, which implies the involvement of other pathways. The serotonin (5-HT) pathway interacts with the stress response pathway in a manner mediated by the hypothalamic-pituitary-adrenal (HPA) axis. To analyze the interaction between the pathways, we propose the use of a synchronous Boolean network (SBN) approximation. The principal aim of this work was to model the interaction between these pathways, taking into consideration the presence of selective serotonin reuptake inhibitors (SSRIs), in order to observe how the pathways interact and to examine if the system is stable. Additionally, we wanted to study which genes or metabolites have the greatest impact on model stability when knocked out in silico. We observed that the biological model generated predicts steady states (attractors) for each of the different runs performed, thereby proving that the system is stable. These attractors changed in shape, especially when anti-depressive drugs were also included in the simulation. This work also predicted that the genes with the greatest impact on model stability were those involved in the neurotrophin pathway, such as CREB, BDNF (which has been associated with major depressive disorder in a variety of studies) and TRkB, followed by genes and metabolites related to 5-HT synthesis.

A nutrigenomic framework to identify time-resolving responses of hepatic genes in diet-induced obese mice

Obesity and its related complications have emerged as global health problems; however, the pathophysiological mechanism of obesity is still not fully understood. In this study, C57BL/6J mice were fed a normal (ND) or high-fat diet (HFD) for 0, 2, 4, 6, 8, 12, 20, and 24 weeks and the time course was systemically analyzed specifically for the hepatic transcriptome profile. Genes that were differentially expressed in the HFD-fed mice were clustered into 49 clusters and further classified into 8 different expression patterns: long-term up-regulated (pattern 1), long-term downregulated (pattern 2), early up-regulated (pattern 3), early down-regulated (pattern 4), late up-regulated (pattern 5), late down-regulated (pattern 6), early up-regulated and late down-regulated (pattern 7), and early down-regulated and late up-regulated (pattern 8) HFD-responsive genes. Within each pattern, genes related with inflammation, insulin resistance, and lipid metabolism were extracted, and then, a protein-protein interaction network was generated. The pattern specific sub-network was as follows: pattern 1, cellular assembly and organization, and immunological disease, pattern 2, lipid metabolism, pattern 3, gene expression and inflammatory response, pattern 4, cell signaling, pattern 5, lipid metabolism, molecular transport, and small molecule biochemistry, pattern 6, protein synthesis and cell-to cell signaling and interaction and pattern 7, cell-to cell signaling, cellular growth and proliferation, and cell death. For pattern 8, no significant sub-networks were identified. Taken together, this suggests that genes involved in regulating gene expression and inflammatory response are up-regulated whereas genes involved in lipid metabolism and protein synthesis are down-regulated during diet-induced obesity development.

Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes

Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation. This article provides an overview of the methods currently available and reviews the advantages and challenges of this approach. In detail, it highlights the particular issues of de novo pathway discovery based on genome-wide association studies data, for which multiple different strategies have been proposed.

Knowledge-Driven, Data-Assisted Integrative Pathway Analytics

Target and biomarker selection in drug discovery relies extensively on the use of various genomics platforms. These technologies generate large amounts of data that can be used to gain novel insights in biology. There is a strong need to mine these information-rich datasets in an effective and efficient manner. Pathway and network based approaches have become an increasingly important methodology to mine bioinformatics datasets derived from ‘omics’ technologies. These approaches also find use in exploring the unknown biology of a disease or functional process. This chapter provides an overview of pathway databases and network tools, network architecture, text mining and existing methods used in knowledge-driven data analysis. It shows examples of how these databases and tools can be used integratively to apply existing knowledge and network-based approach in data analytics.

siRNA Genome Screening Approaches to Therapeutic Drug Repositioning

Bridging high-throughput screening (HTS) with RNA interference (RNAi) has allowed for rapid discovery of the molecular basis of many diseases, and identification of potential pathways for developing safe and effective treatments. These features have identified new host gene targets for existing drugs paving the pathway for therapeutic drug repositioning. Using RNAi to discover and help validate new drug targets has also provided a means to filter and prioritize promising therapeutics. This review summarizes these approaches across a spectrum of methods and targets in the host response to pathogens. Particular attention is given to the utility of drug repurposing utilizing the promiscuous nature of some drugs that affect multiple molecules or pathways, and how these biological pathways can be targeted to regulate disease outcome.

HAT: a novel statistical approach to discover functional regions in the genome

Tiling arrays are useful for exploring local functions of regions of the genome in an unbiased fashion. The exact determination of those genomic regions based on tiling-array data, e.g., generated by means of hybridization with immunopreciptated DNA-fragments to the arrays is a challenge. Many different statistical methodologies have been developed to find biological relevant regions-of-interest (ROI) by using the quantitative signal intensity of each probe. We previously developed a method called Hypergeometric Analysis of Tiling arrays (HAT) for the analysis of tiling-array data, but it is developed such that it can also be used to study data derived by genome-wide deep sequencing approaches. Here we applied HAT to analyze two publicly available tiling-array data sets. After the detection of statistically significant ROI, these are often used in additional analysis for hypothesis testing. We therefore discuss, by using the results of the tiling-array experiment, pathway and motif analyses.

Atypical activation of the unfolded protein response in cystic fibrosis airway cells contributes to p38 MAPK-mediated innate immune responses

Inflammatory lung disease is the major cause of morbidity and mortality in cystic fibrosis (CF); understanding what produces dysregulated innate immune responses in CF cells will be pivotal in guiding the development of novel anti-inflammatory therapies. To elucidate the molecular mechanisms that mediate exaggerated inflammation in CF following TLR signaling, we profiled global gene expression in immortalized human CF and non-CF airway cells at baseline and after microbial stimulation. Using complementary analysis methods, we observed a signature of increased stress levels in CF cells, specifically characterized by endoplasmic reticulum (ER) stress, the unfolded protein response (UPR), and MAPK signaling. Analysis of ER stress responses revealed an atypical induction of the UPR, characterized by the lack of induction of the PERK-eIF2α pathway in three complementary model systems: immortalized CF airway cells, fresh CF blood cells, and CF lung tissue. This atypical pattern of UPR activation was associated with the hyperinflammatory phenotype in CF cells, as deliberate induction of the PERK-eIF2α pathway with salubrinal attenuated the inflammatory response to both flagellin and Pseudomonas aeruginosa. IL-6 production triggered by ER stress and microbial stimulation were both dependent on p38 MAPK activity, suggesting a molecular link between both signaling events. These data indicate that atypical UPR activation fails to resolve the ER stress in CF and sensitizes the innate immune system to respond more vigorously to microbial challenge. Strategies to restore ER homeostasis and normalize the UPR activation profile may represent a novel therapeutic approach to minimize lung-damaging inflammation in CF.

Boolean modeling and fault diagnosis in oxidative stress response

BACKGROUND

Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean network model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean network model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer.

RESULTS

In this paper, we have developed a Boolean network model for the oxidative stress response. This model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI3k/Akt pathway.

CONCLUSIONS

It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological network with feedback will need to be further simplified before it can be even considered for practical biological validation.

A quick guide to biomolecular network studies: construction, analysis, applications, and resources

Over the past decade, a rapid increase in network data including signaling, transcription regulation, metabolic reaction, protein-protein interaction and genetic interaction has been observed. Many biology issues have been investigated by analyzing these diverse networks, providing new insights into biology. Networks also play an important role in disease studies including disease gene screening and clinical diagnosis. Large amounts of databases and software have been developed to facilitate the storage, exchange, integration, and analysis of network data and network analysis is becoming a routine procedure for biologists to infer biological information. In this review, several main aspects of network studies are discussed, including network construction, analysis, application, and resources.

Analysis of bacterial biofilms using NMR-based metabolomics

Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this 'social structure' of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.

Cell cycle gene networks are associated with melanoma prognosis

Background

Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways.

Methodology/Principal Findings

Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro.

Conclusions/Significance

This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.

Novel anti-cancer compounds for developing combinatorial therapies to target anoikis-resistant tumors

Anoikis, a cell death pathway induced by loss of normal cell-matrix attachment or upon adhesion to a non-native matrix, ensures the balance between proliferative potential of normal cells and maintenance of tissue integrity. Thereby, anoikis serves as a potential molecular barrier against oncogenic transformation of normal cells. Cancer cells acquire anoikis resistance for survival and distant metastatic progression. During the acquisition of anoikis resistance, tumors modulate multiple cell signaling parameters through changes in the expression of up-stream receptors and by dynamically calibrating the dependency on down-stream signaling cascades. Many compounds that target the tumor-acquired switches in integrins, tumor antigens, growth factors, metabolic pathways, oxidative and osmotic-stress signaling are in various phases of pre-clinical and clinical development. Combinatorial approaches maximize the therapeutic efficacy and minimize the activation of alternate signaling pathways, which will otherwise contribute to drug resistance. In this regard, an integrated analysis of the mechanisms of action of potential drugs and lead compounds that can target significant nodes of anoikis signaling networks will provide a rational frame-work for further development and clinical use of respective agents, by formulating more effective combinatorial therapies, in patients with distinct drug-sensitivity profiles.

Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset

BACKGROUND

Though rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence.

FINDINGS

Two algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest.

CONCLUSIONS

Raw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease.

Molecular network of microRNA targets in Alzheimer's disease brains

MicroRNAs (miRNAs) are a group of small noncoding RNAs that regulate translational repression of target mRNAs. The vast majority of presently identified miRNAs are expressed in the brain where they fine-tune the expression of a wide range of target molecules essential for neuronal and glial development, differentiation, proliferation, apoptosis and metabolism. Aberrant expression and dysfunction of brain-enriched miRNAs induce development of neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD). Because a single miRNA concurrently downregulates hundreds of target mRNAs, the set of miRNA target genes coregulated by an individual miRNA generally constitutes the biologically integrated network of functionally associated molecules. Recent advances in systems biology enable us to characterize the global molecular network of experimentally validated targets for individual miRNAs by using pathway analysis tools of bioinformatics endowed with comprehensive knowledgebase. This review is conducted to summarize accumulating studies focused on aberrant miRNA expression in AD brains, and to propose the systems biological view that abnormal regulation of cell cycle progression as a result of deregulation of miRNA target networks plays a central role in the pathogenesis of AD.

An integrated mathematical model of thrombin-, histamine-and VEGF-mediated signalling in endothelial permeability

BACKGROUND

Endothelial permeability is involved in injury, inflammation, diabetes and cancer. It is partly regulated by the thrombin-, histamine-, and VEGF-mediated myosin-light-chain (MLC) activation pathways. While these pathways have been investigated, questions such as temporal effects and the dynamics of multi-mediator regulation remain to be fully studied. Mathematical modeling of these pathways facilitates such studies. Based on the published ordinary differential equation models of the pathway components, we developed an integrated model of thrombin-, histamine-, and VEGF-mediated MLC activation pathways.

RESULTS

Our model was validated against experimental data for calcium release and thrombin-, histamine-, and VEGF-mediated MLC activation. The simulated effects of PAR-1, Rho GTPase, ROCK, VEGF and VEGFR2 over-expression on MLC activation, and the collective modulation by thrombin and histamine are consistent with experimental findings. Our model was used to predict enhanced MLC activation by CPI-17 over-expression and by synergistic action of thrombin and VEGF at low mediator levels. These may have impact in endothelial permeability and metastasis in cancer patients with blood coagulation.

CONCLUSION

Our model was validated against a number of experimental findings and the observed synergistic effects of low concentrations of thrombin and histamine in mediating the activation of MLC. It can be used to predict the effects of altered pathway components, collective actions of multiple mediators and the potential impact to various diseases. Similar to the published models of other pathways, our model can potentially be used to identify important disease genes through sensitivity analysis of signalling components.

Comprehensive analysis of human microRNA target networks

Background

MicroRNAs (miRNAs) mediate posttranscriptional regulation of protein-coding genes by binding to the 3' untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation, depending on the degree of sequence complementarity. In general, a single miRNA concurrently downregulates hundreds of target mRNAs. Thus, miRNAs play a key role in fine-tuning of diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. However, it remains to be fully elucidated whether a set of miRNA target genes regulated by an individual miRNA in the whole human microRNAome generally constitute the biological network of functionally-associated molecules or simply reflect a random set of functionally-independent genes.

Methods

The complete set of human miRNAs was downloaded from miRBase Release 16. We explored target genes of individual miRNA by using the Diana-microT 3.0 target prediction program, and selected the genes with the miTG score ≧ 20 as the set of highly reliable targets. Then, Entrez Gene IDs of miRNA target genes were uploaded onto KeyMolnet, a tool for analyzing molecular interactions on the comprehensive knowledgebase by the neighboring network-search algorithm. The generated network, compared side by side with human canonical networks of the KeyMolnet library, composed of 430 pathways, 885 diseases, and 208 pathological events, enabled us to identify the canonical network with the most significant relevance to the extracted network.

Results

Among 1,223 human miRNAs examined, Diana-microT 3.0 predicted reliable targets from 273 miRNAs. Among them, KeyMolnet successfully extracted molecular networks from 232 miRNAs. The most relevant pathway is transcriptional regulation by transcription factors RB/E2F, the disease is adult T cell lymphoma/leukemia, and the pathological event is cancer.

Conclusion

The predicted targets derived from approximately 20% of all human miRNAs constructed biologically meaningful molecular networks, supporting the view that a set of miRNA targets regulated by a single miRNA generally constitute the biological network of functionally-associated molecules in human cells.

Integration of tight junctions and claudins with the barrier functions of the retinal pigment epithelium

The retinal pigment epithelium (RPE) forms the outer blood-retinal barrier by regulating the movement of solutes between the fenestrated capillaries of the choroid and the photoreceptor layer of the retina. Blood-tissue barriers use various mechanisms to accomplish their tasks including membrane pumps, transporters, and channels, transcytosis, metabolic alteration of solutes in transit, and passive but selective diffusion. The last category includes tight junctions, which regulate transepithelial diffusion through the spaces between neighboring cells of the monolayer. Tight junctions are extraordinarily complex structures that are dynamically regulated. Claudins are a family of tight junctional proteins that lend tissue specificity and selectivity to tight junctions. This review discusses how the claudins and tight junctions of the RPE differ from other epithelia and how its functions are modulated by the neural retina. Studies of RPE-retinal interactions during development lend insight into this modulation. Notably, the characteristics of RPE junctions, such as claudin composition, vary among species, which suggests the physiology of the outer retina may also vary. Comparative studies of barrier functions among species should deepen our understanding of how homeostasis is maintained in the outer retina. Stem cells provide a way to extend these studies of RPE-retinal interactions to human RPE.

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.

Encoding the states of interacting proteins to facilitate biological pathways reconstruction

Background

In a systems biology perspective, protein-protein interactions (PPI) are encoded in machine-readable formats to avoid issues encountered in their retrieval for the reconstruction of comprehensive interaction maps and biological pathways. However, the information stored in electronic formats currently used doesn't allow a valid automatic reconstruction of biological pathways.

Results

We propose a logical model of PPI that takes into account the "state" of proteins before and after the interaction. This information is necessary for proper reconstruction of the pathway.

Conclusions

The adoption of the proposed model, which can be easily integrated into existing machine-readable formats used to store the PPI data, would facilitate the automatic or semi-automated reconstruction of biological pathways.

Reviewers

This article was reviewed by Dr. Wen-Yu Chung (nominated by Kateryna Makova), Dr. Carl Herrmann (nominated by Dr. Purificación López-García) and Dr. Arcady Mushegian.

PINT: Pathways INtegration Tool

New pathway databases generally display pathways by retrieving information from a database dynamically. Some of them even provide their pathways in SBML or other exchangeable formats. Integrating these models is a challenging work, because these models were not built in the same way. Pathways integration Tool (PINT) may integrate the standard SBML files. Since these files may be obtained from different sources, any inconsistency in component names can be revised by using an annotation editor upon uploading a pathway model. This integration function greatly simplifies the building of a complex model from small models. To get new users started, about 190 curated public models of human pathways were collected by PINT. Relevant models can be selected and sent to the workbench by using a user-friendly query interface, which also accepts a gene list derived from high-throughput experiments. The models on the workbench, from either a public or a private source, can be integrated and painted. The painting function is useful for highlighting important genes or even their expression level on a merged pathway diagram, so that the biological significance can be revealed. This tool is freely available at http://csb2.ym.edu.tw/pint/.

Coregulation mapping based on individual phenotypic variation in response to virus infection

Background

Gene coregulation across a population is an important aspect of the considerable variability of the human immune response to virus infection. Methodology to investigate it must rely on a number of ingredients ranging from gene clustering to transcription factor enrichment analysis.

Results

We have developed a methodology to investigate the gene to gene correlations for the expression of 34 genes linked to the immune response of Newcastle Disease Virus (NDV) infected conventional dendritic cells (DCs) from 145 human donors. The levels of gene expression showed a large variation across individuals. We generated a map of gene co-expression using pairwise correlation and multidimensional scaling (MDS). The analysis of these data showed that among the 13 genes left after filtering for statistically significant variations, two clusters are formed. We investigated to what extent the observed correlation patterns can be explained by the sharing of transcription factors (TFs) controlling these genes. Our analysis showed that there was a significant positive correlation between MDS distances and TF sharing across all pairs of genes. We applied enrichment analysis to the TFs having binding sites in the promoter regions of those genes. This analysis, after Gene Ontology filtering, indicated the existence of two clusters of genes (CCL5, IFNA1, IFNA2, IFNB1) and (IKBKE, IL6, IRF7, MX1) that were transcriptionally co-regulated. In order to facilitate the use of our methodology by other researchers, we have also developed an interactive coregulation explorer web-based tool called CorEx. It permits the study of MDS and hierarchical clustering of data combined with TF enrichment analysis. We also offer web services that provide programmatic access to MDS, hierarchical clustering and TF enrichment analysis.

Conclusions

MDS mapping based on correlation in conjunction with TF enrichment analysis represents a useful computational method to generate predictions underlying gene coregulation across a population.

Industrial systems biology

The chemical industry is currently undergoing a dramatic change driven by demand for developing more sustainable processes for the production of fuels, chemicals, and materials. In biotechnological processes different microorganisms can be exploited, and the large diversity of metabolic reactions represents a rich repository for the design of chemical conversion processes that lead to efficient production of desirable products. However, often microorganisms that produce a desirable product, either naturally or because they have been engineered through insertion of heterologous pathways, have low yields and productivities, and in order to establish an economically viable process it is necessary to improve the performance of the microorganism. Here metabolic engineering is the enabling technology. Through metabolic engineering the metabolic landscape of the microorganism is engineered such that there is an efficient conversion of the raw material, typically glucose, to the product of interest. This process may involve both insertion of new enzymes activities, deletion of existing enzyme activities, but often also deregulation of existing regulatory structures operating in the cell. In order to rapidly identify the optimal metabolic engineering strategy the industry is to an increasing extent looking into the use of tools from systems biology. This involves both x-ome technologies such as transcriptome, proteome, metabolome, and fluxome analysis, and advanced mathematical modeling tools such as genome-scale metabolic modeling. Here we look into the history of these different techniques and review how they find application in industrial biotechnology, which will lead to what we here define as industrial systems biology.

Pandora, a pathway and network discovery approach based on common biological evidence

Motivation: Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information.

Results: Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein–protein interaction, genetic interaction, domain–domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view.

Availability: The method has been implemented in Perl and is available for downloading from http://www.oicr.on.ca/research/ouellette/pandora. It is distributed under the terms of GPL (http://opensource.org/licenses/gpl-2.0.php)

Contact:francis@oicr.on.ca

Supplementary information:Supplementary data are available at Bioinformatics online.