Modeling Tuberculosis in Lung and Central Nervous System

Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis (Mtb). Most cases of TB are pulmonary, i.e. the main infection site is in the lung. In this work, we consider pulmonary TB as well as tuberculous meningitis (TBM). The latter is caused by infection of the meninges in the central nervous system (CNS) with Mtb. TBM is the most severe extra-pulmonary manifestation of TB; when left untreated it results in death of the patient. Even if the patient is treated, severe sequelae may result such as spasticity, other handicaps and serious mental problems. We start this thesis by presenting the relevant biological background information, after which we proceed to describe and extend an existing agent-based model of the immune response in pulmonary TB. In addition, we introduce a new agent-based model describing the immune response to Mtb in the CNS. We have implemented the models by developing novel software that employs various visualization techniques. After subjecting both models to an experimental evaluation, we conclude by discussing possible future work

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples

The copy-number tree mixture deconvolution problem and applications to multi-sample bulk sequencing tumor data

Cancer is an evolutionary process driven by somatic mutation. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the mutational complexity of cancer and the fact that nearly all cancer sequencing is of bulk tissue, measuring a super-position of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy number data from multiple samples of a tumor. CNTMD generalizes two approaches that have been researched intensively in recent years: deconvolution/factorization algorithms that aim to infer the number and proportions of clones in a mixed tumor sample; and phylogenetic models of copy number evolution that model the dependencies between copy number events that affect the same genomic loci. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that perform either deconvolution or phylogenetic tree construction under the assumption of a single tumor clone per sample. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher-resolution view of copy number evolution of this cancer than published analyses

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

13-Series resolvins mediate the leukocyte-platelet actions of atorvastatin and pravastatin in inflammatory arthritis

This work was supported by funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (Grant 677542), a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant 107613/Z/15/Z), and the Barts Charity (Grant MGU0343). This work was also funded, in part, by Medical Research Council Advance Course Masters (Grant MR/J015741/1). The authors declare no conflicts of interest

Formyl Peptide Receptor as a Novel Therapeutic Target for Anxiety-Related Disorders

Formyl peptide receptors (FPR) belong to a family of sensors of the immune system that detect microbe-associated molecules and inform various cellular and sensorial mechanisms to the presence of pathogens in the host. Here we demonstrate that Fpr2/3-deficient mice show a distinct profile of behaviour characterised by reduced anxiety in the marble burying and light-dark box paradigms, increased exploratory behaviour in an open-field, together with superior performance on a novel object recognition test. Pharmacological blockade with a formyl peptide receptor antagonist, Boc2, in wild type mice reproduced most of the behavioural changes observed in the Fpr2/3(-/-) mice, including a significant improvement in novel object discrimination and reduced anxiety in a light/dark shuttle test. These effects were associated with reduced FPR signalling in the gut as shown by the significant reduction in the levels of p-p38. Collectively, these findings suggest that homeostatic FPR signalling exerts a modulatory effect on anxiety-like behaviours. These findings thus suggest that therapies targeting FPRs may be a novel approach to ameliorate behavioural abnormalities present in neuropsychiatric disorders at the cognitive-emotional interface