Modelling the Structure and Dynamics of Biological Pathways

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

SPIDE: A single cell potency inference method based on the local cell-specific network entropy

For a given single cell RNA-seq data, it is critical to pinpoint key cellular stages and quantify cells' differentiation potency along a differentiation pathway in a time course manner. Currently, several methods based on the entropy of gene functions or PPI network have been proposed to solve the problem. Nevertheless, these methods still suffer from the inaccurate interactions and noises originating from scRNA-seq profile. In this study, we proposed a cell potency inference method based on cell-specific network entropy, called SPIDE. SPIDE introduces the local weighted cell-specific network for each cell to maintain cell heterogeneity and calculates the entropy by incorporating gene expression with network structure. In this study, we compared three cell entropy estimation models on eight scRNA-Seq datasets. The results show that SPIDE obtains consistent conclusions with real cell differentiation potency on most datasets. Moreover, SPIDE accurately recovers the continuous changes of potency during cell differentiation and significantly correlates with the stemness of tumor cells in Colorectal cancer. To conclude, our study provides a universal and accurate framework for cell entropy estimation, which deepens our understanding of cell differentiation, the development of diseases and other related biological research.

Graphia: A platform for the graph-based visualisation and analysis of high dimensional data

Graphia is an open-source platform created for the graph-based analysis of the huge amounts of quantitative and qualitative data currently being generated from the study of genomes, genes, proteins metabolites and cells. Core to Graphia’s functionality is support for the calculation of correlation matrices from any tabular matrix of continuous or discrete values, whereupon the software is designed to rapidly visualise the often very large graphs that result in 2D or 3D space. Following graph construction, an extensive range of measurement algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are available, for graph exploration and analysis. Combined, these provide a powerful solution for the interpretation of high-dimensional data from many sources, or data already in the form of a network or equivalent adjacency matrix. Several use cases of Graphia are described, to showcase its wide range of applications in the analysis biological data. Graphia runs on all major desktop operating systems, is extensible through the deployment of plugins and is freely available to download from https://graphia.app/.

Graphia is a new visual analytics platform specifically created for the network-based analysis of large and complex data, such as that generated in huge amounts by modern biological analyses. It works in a data agnostic, hypothesis-free manner to generate correlation networks from any table of numerical or discrete values, thereafter providing a means to rapidly visualise the often very large networks that result, in either 2D or 3D space. Following network construction, the tool offers an extensive range of analysis algorithms, routines for network transformation, and options for the visualisation of metadata. This provides a powerful analysis solution for the exploration and interpretation of high-dimensional data from any source, as well as any data already defined as a network. Several use cases of Graphia are described to showcase its wide range of applications in the analysis biological data. Graphia is open source and free to all.

Expression Profile of Genes Related to the Th17 Pathway in Macrophages Infected by Leishmania major and Leishmania amazonensis: The Use of Gene Regulatory Networks in Modeling This Pathway

Leishmania amazonensis and Leishmania major are the causative agents of cutaneous and mucocutaneous diseases. The infections‘ outcome depends on host–parasite interactions and Th1/Th2 response, and in cutaneous form, regulation of Th17 cytokines has been reported to maintain inflammation in lesions. Despite that, the Th17 regulatory scenario remains unclear. With the aim to gain a better understanding of the transcription factors (TFs) and genes involved in Th17 induction, in this study, the role of inducing factors of the Th17 pathway in Leishmania–macrophage infection was addressed through computational modeling of gene regulatory networks (GRNs). The Th17 GRN modeling integrated experimentally validated data available in the literature and gene expression data from a time-series RNA-seq experiment (4, 24, 48, and 72 h post-infection). The generated model comprises a total of 10 TFs, 22 coding genes, and 16 cytokines related to the Th17 immune modulation. Addressing the Th17 induction in infected and uninfected macrophages, an increase of 2- to 3-fold in 4–24 h was observed in the former. However, there was a decrease in basal levels at 48–72 h for both groups. In order to evaluate the possible outcomes triggered by GRN component modulation in the Th17 pathway. The generated GRN models promoted an integrative and dynamic view of Leishmania–macrophage interaction over time that extends beyond the analysis of single-gene expression.

INDRA-IPM: interactive pathway modeling using natural language with automated assembly

SUMMARY

INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats.

AVAILABILITY AND IMPLEMENTATION

INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Visualization and analysis of RNA-Seq assembly graphs

RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

A genomic analysis and transcriptomic atlas of gene expression in Psoroptes ovis reveals feeding- and stage-specific patterns of allergen expression

Background

Psoroptic mange, caused by infestation with the ectoparasitic mite, Psoroptes ovis, is highly contagious, resulting in intense pruritus and represents a major welfare and economic concern for the livestock industry Worldwide. Control relies on injectable endectocides and organophosphate dips, but concerns over residues, environmental contamination, and the development of resistance threaten the sustainability of this approach, highlighting interest in alternative control methods. However, development of vaccines and identification of chemotherapeutic targets is hampered by the lack of P. ovis transcriptomic and genomic resources.

Results

Building on the recent publication of the P. ovis draft genome, here we present a genomic analysis and transcriptomic atlas of gene expression in P. ovis revealing feeding- and stage-specific patterns of gene expression, including novel multigene families and allergens. Network-based clustering revealed 14 gene clusters demonstrating either single- or multi-stage specific gene expression patterns, with 3075 female-specific, 890 male-specific and 112, 217 and 526 transcripts showing larval, protonymph and tritonymph specific-expression, respectively. Detailed analysis of P. ovis allergens revealed stage-specific patterns of allergen gene expression, many of which were also enriched in “fed” mites and tritonymphs, highlighting an important feeding-related allergenicity in this developmental stage. Pair-wise analysis of differential expression between life-cycle stages identified patterns of sex-biased gene expression and also identified novel P. ovis multigene families including known allergens and novel genes with high levels of stage-specific expression.

Conclusions

The genomic and transcriptomic atlas described here represents a unique resource for the acarid-research community, whilst the OrcAE platform makes this freely available, facilitating further community-led curation of the draft P. ovis genome.

A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures

The lack of integrated resources depicting the complexity of the innate immune response in cancer represents a bottleneck for high-throughput data interpretation. To address this challenge, we perform a systematic manual literature mining of molecular mechanisms governing the innate immune response in cancer and represent it as a signalling network map. The cell-type specific signalling maps of macrophages, dendritic cells, myeloid-derived suppressor cells and natural killers are constructed and integrated into a comprehensive meta map of the innate immune response in cancer. The meta-map contains 1466 chemical species as nodes connected by 1084 biochemical reactions, and it is supported by information from 820 articles. The resource helps to interpret single cell RNA-Seq data from macrophages and natural killer cells in metastatic melanoma that reveal different anti- or pro-tumor sub-populations within each cell type. Here, we report a new open source analytic platform that supports data visualisation and interpretation of tumour microenvironment activity in cancer.

The complexity of the innate immune response to cancer makes interpretation of large data sets challenging. Here, the authors provide an integrated multi-scale map of signalling networks representing the different immune cells and their interactions and show its utility for data interpretation.

Reconstruction of endosomal organization and function by a combination of ODE and agent-based modeling strategies

BACKGROUND

Reproducing cell processes using an in silico system is an essential tool for understanding the underlying mechanisms and emergent properties of this extraordinary complex biological machine. However, computational models are seldom applied in the field of intracellular trafficking. In a cell, numerous molecular interactions occur on the surface or in the interior of membrane-bound compartments that continually change position and undergo dynamic processes of fusion and fission. At present, the available simulation tools are not suitable to develop models that incorporate the dynamic evolution of the cell organelles.

RESULTS

We developed a modeling platform combining Repast (Agent-Based Modeling, ABM) and COPASI (Differential Equations, ODE) that can be used to reproduce complex networks of molecular interactions. These interactions occur in dynamic cell organelles that change position and composition over the course of time. These two modeling strategies are fundamentally different and comprise of complementary capabilities. The ODEs can easily model the networks of molecular interactions, signaling cascades, and complex metabolic reactions. On the other hand, ABM software is especially suited to simulate the movement, interaction, fusion, and fission of dynamic organelles. We used the combined ABM-ODE platform to simulate the transport of soluble and membrane-associated cargoes that move along an endocytic route composed of early, sorting, recycling and late endosomes. We showed that complex processes that strongly depend on transport can be modeled. As an example, the hydrolysis of a GM2-like glycolipid was programmed by adding a trans-Golgi network compartment, lysosomal enzyme trafficking, endosomal acidification, and cholesterol processing to the simulation model.

CONCLUSIONS

The model captures the highly dynamic nature of cell compartments that fuse and divide, creating different conditions for each organelle. We expect that this modeling strategy will be useful to understand the logic underlying the organization and function of the endomembrane system.

REVIEWERS

This article was reviewed by Drs. Rafael Fernández-Chacón, James Faeder, and Thomas Simmen.

Use of Graph Theory to Characterize Human and Arthropod Vector Cell Protein Response to Infection With Anaplasma phagocytophilum

One of the major challenges in modern biology is the use of large omics datasets for the characterization of complex processes such as cell response to infection. These challenges are even bigger when analyses need to be performed for comparison of different species including model and non-model organisms. To address these challenges, the graph theory was applied to characterize the tick vector and human cell protein response to infection with Anaplasma phagocytophilum, the causative agent of human granulocytic anaplasmosis. A network of interacting proteins and cell processes clustered in biological pathways, and ranked with indexes representing the topology of the proteome was prepared. The results demonstrated that networks of functionally interacting proteins represented in both infected and uninfected cells can describe the complete set of host cell processes and metabolic pathways, providing a deeper view of the comparative host cell response to pathogen infection. The results demonstrated that changes in the tick proteome were driven by modifications in protein representation in response to A. phagocytophilum infection. Pathogen infection had a higher impact on tick than human proteome. Since most proteins were linked to several cell processes, the changes in protein representation affected simultaneously different biological pathways. The method allowed discerning cell processes that were affected by pathogen infection from those that remained unaffected. The results supported that human neutrophils but not tick cells limit pathogen infection through differential representation of ras-related proteins. This methodological approach could be applied to other host-pathogen models to identify host derived key proteins in response to infection that may be used to develop novel control strategies for arthropod-borne pathogens.

Modelling steroidogenesis: a framework model to support hypothesis generation and testing across endocrine studies

Objective

Steroid hormones are responsible for the control of a wide range of physiological processes such as development, growth, reproduction, metabolism, and aging. Because of the variety of enzymes, substrates and products that take part in steroidogenesis and the compartmentalisation of its constituent reactions, it is a complex process to visualise and document. One of the goals of systems biology is to quantitatively describe the behaviour of complex biological systems that involve the interaction of many components. This can be done by representing these interactions visually in a pathway model and then optionally constructing a mathematical model of the interactions.

Results

We have used the modified Edinburgh Pathway Notation to construct a framework diagram describing human steroidogenic pathways, which will be of use to endocrinologists. To demonstrate further utility, we show how such models can be parameterised with empirical data within the software Graphia Professional, to recapitulate specific examples of steroid hormone production, and also to mimic gene knockout. These framework models support in silico hypothesis generation and testing with utility across endocrine endpoints, with significant potential to reduce costs, time and animal numbers, whilst informing the design of planned studies.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3365-y) contains supplementary material, which is available to authorized users.

A graphical and computational modeling platform for biological pathways

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net-based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.

From word models to executable models of signaling networks using automated assembly

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF‐V600E‐mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

An Interactive Macrophage Signal Transduction Map Facilitates Comparative Analyses of High-Throughput Data

Macrophages (Mϕs) are key players in the coordination of the lifesaving or detrimental immune response against infections. The mechanistic understanding of the functional modulation of Mϕs by pathogens and pharmaceutical interventions at the signal transduction level is still far from complete. The complexity of pathways and their cross-talk benefits from holistic computational approaches. In the present study, we reconstructed a comprehensive, validated, and annotated map of signal transduction pathways in inflammatory Mϕs based on the current literature. In a second step, we selectively expanded this curated map with database knowledge. We provide both versions to the scientific community via a Web platform that is designed to facilitate exploration and analysis of high-throughput data. The platform comes preloaded with logarithmic fold changes from 44 data sets on Mϕ stimulation. We exploited three of these data sets-human primary Mϕs infected with the common lung pathogens Streptococcus pneumoniae, Legionella pneumophila, or Mycobacterium tuberculosis-in a case study to show how our map can be customized with expression data to pinpoint regulated subnetworks and druggable molecules. From the three infection scenarios, we extracted a regulatory core of 41 factors, including TNF, CCL5, CXCL10, IL-18, and IL-12 p40, and identified 140 drugs targeting 16 of them. Our approach promotes a comprehensive systems biology strategy for the exploitation of high-throughput data in the context of Mϕ signal transduction. In conclusion, we provide a set of tools to help scientists unravel details of Mϕ signaling. The interactive version of our Mϕ signal transduction map is accessible online at https://vcells.net/macrophage.