Parsimonious Migration History Problem: Complexity and Algorithms

In many evolutionary processes we observe extant taxa in different geographical or anatomical locations. To reconstruct the migration history from a given phylogenetic tree T, one can model locations using an additional character and apply parsimony criteria to assign a location to each internal vertex of T. The migration criterion assumes that migrations are independent events. This assumption does not hold for evolutionary processes where distinct taxa from different lineages comigrate from one location to another in a single event, as is the case in metastasis and in certain infectious diseases. To account for such cases, the comigration criterion was recently introduced, and used as an optimization criterion in the Parsimonious Migration History (PMH) problem. In this work, we show that PMH is NP-hard. In addition, we show that a variant of PMH is fixed parameter tractable (FPT) in the number of locations. On simulated instances of practical size, we demonstrate that our FPT algorithm outperforms a previous integer linear program in terms of running time

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 \nthe patient is treated, severe sequelae may result such as spasticity, other handicaps and serious mental problems. \n \nWe 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

Phyolin: Identifying a Linear Perfect Phylogeny in Single-Cell DNA Sequencing Data of Tumors

Cancer arises from an evolutionary process where somatic mutations occur and eventually give rise to clonal expansions. Modeling this evolutionary process as a phylogeny is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. However, cancer phylogeny inference from single-cell DNA sequencing data of tumors is challenging due to limitations with sequencing technology and the complexity of the resulting problem. Therefore, as a first step some value might be obtained from correctly classifying the evolutionary process as either linear or branched. The biological implications of these two high-level patterns are different and understanding what cancer types and which patients have each of these trajectories could provide useful insight for both clinicians and researchers. Here, we introduce the Linear Perfect Phylogeny Flipping Problem as a means of testing a null model that the tree topology is linear and show that it is NP-hard. We develop Phyolin and, through both in silico experiments and real data application, show that it is an accurate, easy to use and a reasonably fast method for classifying an evolutionary trajectory as linear or branched

Parsimonious Clone Tree Integration in cancer

BACKGROUND: Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. RESULTS: To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. CONCLUSION: PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods

Parsimonious Clone Tree Reconciliation in Cancer

Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a reconciliation problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce a mixed integer linear programming formulation to solve it exactly. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our reconciliation approach provides a higher resolution view of tumor evolution than previous studies

eXamine: a Cytoscape app for exploring annotated modules in networks

Background. Biological networks have growing importance for the interpretation of high-throughput "omics" data. Statistical and combinatorial methods allow to obtain mechanistic insights through the extraction of smaller subnetwork modules. Further enrichment analyses provide set-based annotations of these modules. Results. We present eXamine, a set-oriented visual analysis approach for annotated modules that displays set membership as contours on top of a node-link layout. Our approach extends upon Self Organizing Maps to simultaneously lay out nodes, links, and set contours. Conclusions. We implemented eXamine as a freely available Cytoscape app. Using eXamine we study a module that is activated by the virally-encoded G-protein coupled receptor US28 and formulate a novel hypothesis about its functioning