Biology subjects

Stelling, J.

Publications and source records attributed to Stelling, J..

23 records · Page 2Linked to original sources

bioRxiv2019

Efficient Manipulation and Generation of Kirchhoff Polynomials for the Analysis of Non-equilibrium Biochemical Reaction Networks

Kirchhoff polynomials are central for deriving symbolic steady-state expressions of models whose dynamics are governed by linear diffusion on graphs. In biology, such models have been unified under a common linear framework subsuming studies across areas such as enzyme kinetics, G-protein coupled receptors, ion channels, and gene regulation. Due to "history dependence" away from thermodynamic equilibrium these models suffer from a (super) exponential growth in the size of their symbolic steady-state expressions and respectively Kirchhoff polynomials. This algebraic explosion has limited applications of the linear framework. However, recent results on the graph-based prime factorisation of Kirchhoff polynomials may help subdue the combinatorial complexity. By prime decomposing the graphs contained in an expression of Kirchhoff polynomials and identifying the graphs giving rise to equal polynomials, we formulate a coarse-grained variant of the expression suitable for symbolic simplification. We devise criteria to efficiently test the equality of Kirchhoff polynomials and propose two heuristic algorithms to explicitly generate individual Kirchhoff polynomials in a compressed form; they are inspired by algebraic simplifications but operate on the corresponding graphs. We illustrate the practicality of the developed theory and algorithms for a diverse set of graphs of different sizes and for non-equilibrium gene regulation analyses.

bioinformatics↗

bioRxiv2019

See and Sequence: Integrating Whole-Genome Sequencing Within the National Antimicrobial Resistance Surveillance Program in the Philippines

Drug-resistant bacterial infections constitute a growing threat to public health globally 1. National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor the emergence and spread of resistance and are central to the dissemination of these data to AMR stakeholders 2. Whole-genome sequencing (WGS) can support these efforts by pinpointing resistance mechanisms and uncovering transmission patterns 3, 4. However, genomic surveillance is rare in low- and middle-income countries (LMICs), which are predicted to be the most affected by AMR 5. We implemented WGS within the established Antimicrobial Resistance Surveillance Program (ARSP) of the Philippines via ongoing technology transfer, capacity building in and binational collaboration. In parallel, we conducted an initial large-scale retrospective sequencing survey to characterize bacterial populations and dissect resistance phenotypes of key bug-drug combinations, which is the focus of this article. Starting in 2010, the ARSP phenotypic data indicated increasing carbapenem resistance rates for Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae and Escherichia coli. We first identified that this coincided with a marked expansion of specific resistance phenotypes. By then linking the resistance phenotypes to genomic data, we revealed the diversity of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying this expansion. We discovered a previously unreported plasmid-driven hospital outbreak of carbapenem-resistant K. pneumoniae, uncovered the interplay of carbapenem resistance genes and plasmids in the geographic circulation of epidemic K. pneumoniae ST147, and found that carbapenem-resistant E. coli ST410 consisted of diverse lineages of global circulation that carried both international and local plasmids, resulting in a combination of carbapenemase genes variants previously unreported for this organism. Thus, the WGS data provided an enhanced understanding of the interplay between strains, genes and vehicles driving the dissemination of carbapenem resistance in the Philippines. In addition, our retrospective survey served both as the genetic background to contextualize local prospective surveillance, and as a comprehensive dataset for training in bioinformatics and genomic epidemiology. Continued prospective sequencing, capacity building and collaboration will strengthen genomic surveillance of AMR in the Philippines and the translation of genomic data into public-health action. We generated a blueprint for the integration of WGS and genomic epidemiology into an established national system of laboratory-based surveillance of AMR through international collaboration that can be adapted and utilized within other locations to tackle the global challenge of AMR.

genomics↗

bioRxiv2019

Model-based integration of genomics and metabolomics reveals SNP functionality in Mycobacterium tuberculosis

Human tuberculosis is caused by members of the Mycobacterium tuberculosis complex (MTBC) and presents variable disease outcomes. The variation has primarily been attributed to host and environmental factors, but recent evidence indicates an additional role of genetic diversity among MTBC clinical strains. Here, we used metabolomics to unravel the potential role of genetic variations in conferring strain-specific adaptive capacity and vulnerability. To systematically identify functionality of single nucleotide polymorphisms (SNPs), we developed a constraint-based approach that integrates metabolomic and genomic data. Model-based predictions were systematically tested against independent metabolome data; they correctly classified SNP effects in pyruvate kinase and suggested a genetic basis for strain-specific sensitivity to the antibiotic para-aminosalicylic acid. Our method is broadly applicable to mutations in enzyme-encoding genes across microbial life, opening new possibilities for identifying strain-specific metabolic vulnerabilities that could lead to more selective treatment strategies.

systems biology↗

bioRxiv2018

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

systems biology↗

bioRxiv2017

Predicting Ligand-Dependent Tumors From Multi-Dimensional Signaling Features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using a novel approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.\n\nSummaryPrediction of ligand-induced growth of cancer cell lines, which correlates with ligand-blocking antibody efficacy, could be significantly improved by learning from features of a mechanistic signaling model, and was applied to reveal a correlation between growth factor expression and predicted response in patient samples.

systems biology↗