The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways

<p>Abstract</p> <p>Background</p> <p>There is general agreement amongst biologists about the need for good pathway diagrams and a need to formalize the way biological pathways are depicted. However, implementing and agreeing how best to do this is currently the subject of some debate.</p> <p>Results</p> <p>The modified Edinburgh Pathway Notation (mEPN) scheme is founded on a notation system originally devised a number of years ago and through use has now been refined extensively. This process has been primarily driven by the author's attempts to produce process diagrams for a diverse range of biological pathways, particularly with respect to immune signaling in mammals. Here we provide a specification of the mEPN notation, its symbols, rules for its use and a comparison to the proposed Systems Biology Graphical Notation (SBGN) scheme.</p> <p>Conclusions</p> <p>We hope this work will contribute to the on-going community effort to develop a standard for depicting pathways and will provide a coherent guide to those planning to construct pathway diagrams of their biological systems of interest.</p

Falsifiable Network Models. A Network-based Approach to Predict Treatment Efficacy in Ulcerative Colitis

This work is focused on understanding the treatment efficacy of patients with ulcerative colitis (UC) using a network-based approach. UC is one of two forms of inflammatory bowel disease (IBD) along with Crohn’s disease. UC is a debilitating condition characterized by chronic inflammation and ulceration of the colon and rectum. UC symptoms occur gradually rather than abruptly, and the degree of symptoms differs across UC patients. Only around 20% of all UC cases can be explained by known genetic variations, implying a more ambiguous aetiology that is yet not fully understood but is thought to involve a complex interplay between genetic and environmental factors. The available therapy for UC substantially reduces symptoms and achieves long-term remission. However, about one-third of UC patients fail to respond to anti-TNFα therapy and consequently develop long-term side effects due to medication. Non-response to existing antibody-based therapies in subgroups of UC patients is a major challenge and incurs a healthcare burden. Therefore, the disease markers for predicting therapy response to assist individualized therapy decisions are needed. To date, no quantitative computational framework is available to predict treatment response in UC. We developed a quantitative framework that uses gene expression data and existing biological background information on signalling pathways to quantify network connectivity from receptors to transcription factors (TF) that are involved in UC pathogenesis. Variations in network connectivity in UC patients can be used to identify responders and non-responders to anti-TNFα and anti-Integrin treatment. Our findings allow us to summarize the effect of small gene expression changes on the overall connectivity of a signalling network and estimate the effect this will have on the individual patients' responses. Estimating the network connectivity associated with varied drug responses may provide an understanding of individualized treatment outcomes. Our model could be used to generate testable hypotheses about how individual genes act together in networks to cause inflammation in UC as well as other immune-inflammatory diseases such as psoriasis, asthma, and rheumatoid arthritis

VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus–host interaction networks

Infectious diseases caused by viral agents kill millions of people every year. The improvement of prevention and treatment of viral infections and their associated diseases remains one of the main public health challenges. Towards this goal, deciphering virus–host molecular interactions opens new perspectives to understand the biology of infection and for the design of new antiviral strategies. Indeed, modelling of an infection network between viral and cellular proteins will provide a conceptual and analytic framework to efficiently formulate new biological hypothesis at the proteome scale and to rationalize drug discovery. Therefore, we present the first release of VirHostNet (Virus–Host Network), a public knowledge base specialized in the management and analysis of integrated virus–virus, virus–host and host–host interaction networks coupled to their functional annotations. VirHostNet integrates an extensive and original literature-curated dataset of virus–virus and virus–host interactions (2671 non-redundant interactions) representing more than 180 distinct viral species and one of the largest human interactome (10 672 proteins and 68 252 non-redundant interactions) reconstructed from publicly available data. The VirHostNet Web interface provides appropriate tools that allow efficient query and visualization of this infected cellular network. Public access to the VirHostNet knowledge-based system is available at http://pbildb1.univ-lyon1.fr/virhostnet

Bayesian Network Approaches for Refining and Expanding Cellular and Immunological Pathways.

This thesis focuses on computational analysis of cellular and immune pathways of living cells in response to molecular signals using Bayesian networks (BN). Although Bayesian networks have been applied to the reconstruction and expansion of gene regulatory and protein signaling pathways using existing biological data, the results generated from existing BN methods show high false positive and false negative rates. To resolve these issues, two major Bayesian network approaches were developed to allow refinement and expansion of known biological pathways to identify new interactions and molecular entities participating in the pathway. How to refine existing Bayesian networks to identify the best-supported interactions predicted using underlying biological data was explored initially. A posterior probability-based EdgeClipper refinement algorithm was developed to identify well-supported interaction hypotheses in distributions of saved BNs. EdgeClipper incorporates posterior weighting to prioritize and clip interactions. This approach identified many known interactions in synthetic and Escherichia coli reactive oxygen species (ROS) pathways as well as novel interactions and improved specificity with decreasing sensitivity. Second, an expansion approach called BN+1 was introduced to identify unknown though potentially novel pathway members which likely influence biological pathways. BN+1 was applied to the expansion of several synthetic, prokaryotic, and eukaryotic pathways. Major findings included the identification of genetic interactions between genes gadX and uspE and their direct regulation of biofilm activities in E.coli, which was verified experimentally. Finally, the expansion and refinement algorithms were combined to recover a known acid fitness island and new putative acid fitness regulators using E.coli ROS pathway members, and later applied towards understanding Jak/Stat pathway regulation during human progressive kidney disease in glomerular and tubule compartments. The Jak/Stat pathway showed relatively low overlap in supported interactions for the two compartments, though recovered BN+1 genes reflected relevant biological functions and stages of disease progression for the respective kidney compartments. Overall, the results demonstrate that it is possible to refine and expand protein-level signaling pathways using transcriptional microarray data and the introduced expansion and refinement algorithms. The methods are applicable to other biological and computational systems, and are available as publicly-accessible software tools.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89840/1/aphodges_1.pd

Protein dissection approach as a powerful tool to identify new potential drugs

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina. Fecha de lectura: 20-05-2020Esta tesis tiene embargado el acceso al texto completo hasta el 20-11-202

Quantitative Analysis of EGFR Phosphorylation and SH2 Domain Binding in vivo

The work presented in the following thesis dissertation examines the regulation of phosphotyrosine (pY) signaling, an essential cellular process that relies on the activity of three major protein classes: Tyrosine kinases (TKs) which induce pY signaling by phosphorylating tyrosine residues on substrate proteins, Protein tyrosine phosphatases (PTPs) which suppress pY signaling by removing phosphate moieties from tyrosine phosphorylated proteins and Src-Homology 2 (SH2) containing proteins which bind to tyrosine phosphorylated proteins and connect them to downstream signaling pathways. The effects of kinase localization, temporal changes in kinase activation, SH2 protein concentration, and negative feedback from downstream signaling pathways are all examined by the research presented here. This is accomplished by exploiting the Epidermal Growth Factor Receptor (EGFR), a clinically important transmembrane TK, and its SH2 protein mediated downstream pathways. Using EGFR signaling as a tool, this dissertation research attempts to define innate properties of pY signaling systems which are broadly applicable and advance our understanding of the field