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

Mathematical Modeling Describing the Effect of Fishing and Dispersion on Hermaphrodite Population Dynamics

International audienceIn order to study the impact of fishing on a grouper population, we propose in this paper to model the dynamics of a grouper population in a fishing territory by using structured models. For that purpose, we have integrated the natural population growth, the fishing, the competition for shelter and the dispersion. The dispersion was considered as a consequence of the competition. First we prove, that the grouper stocks may be less sensitive to the removal of large male individuals if female population are totally protected. Second, we show that fishing does not disturb the demographic structure of the population. Finally, we prove that female selective fisheries have the potential of drastically reduce reproductive rates. We also prove that male fishing decreases competition and then increases the total population number

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

Optimal vaccination strategies for a heterogenous population using multiple objectives: The case of L1 and L2-formulations

The choice of the objective functional in optimization problems coming from biomedical and epidemiological applications plays a key role in optimal control outcomes. In this study, we investigate the role of the objective functional on the structure of the optimal control solution for an epidemic model for sexually transmitted infections that includes a core group with higher sexual activity levels than the rest of the population. An optimal control problem is formulated to find a targeted vaccination program able to control the spread of the infection with minimum vaccine deployment. Both $L_{1}-$ and $L_{2}-$objectives are considered as an attempt to explore the trade-offs between control dynamics and the functional form characterizing optimality. The results show that the optimal vaccination policies for both the $L_{1}-$ and the $L_{2}-$formulation share one important qualitative property, that is, immunization of the core group should be prioritized by policymakers to achieve a fast reduction of the epidemic. However, quantitative aspects of this result can be significantly affected depending on the choice of the control weights between formulations. Overall, the results suggest that with appropriate weight constants, the optimal control outcomes are reasonably robust with respect to the $L_{1}-$ or $L_{2}-$formulation. This is particularly true when the monetary cost of the control policy is substantially lower than the cost associated with the disease burden. Under these conditions, even if the $L_{1}-$formulation is more realistic from a modeling perspective, the $L_{2}-$formulation can be used as an approximation and yield qualitatively comparable outcomes

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

Investigating the Efficacy of Oily Water Treatment at a De-Oiling Facility in the Northern Industrial Center of CINA - Hassi Messaoud: A Statistical Physics Assessment

Environmental protection is becoming a primary objective when choosing processes andtechnologies for treating oily waters resulting from hydrocarbon production. The focus of treatingoily waters is to eliminate contaminants from effluents before discharging them into the receivingenvironment while adhering to discharge standards. Our work aims to analyze the treatment processfor oily waters in the API unit of the North Industrial Center (CINA), which involves a physical–chemical treatment using activated silica and Kurifix. The study aims to improve the treatmentprocess used for oily water treatment to ensure water conservation for reuse. To enhance the CINAHMDoily water treatment process, we created coagulants with different doses (C1–C6) using sodiumsilicate, activated silicate (sodium silicate + sulfuric acid), distilled water, Kurifix, and an oxygenscavenger while measuring pH, HC (hydrocarbon), and SS (suspended solids) levels. Coagulationand flocculation processes were used to remove suspended solids and hydrocarbons from the water.The results show that the coagulant used reduces suspended matter and hydrocarbon content andincreases pH. The best treatment is achieved with a coagulant prepared with distilled water, 5%sodium silicate, and 2% sulfuric acid. The optimal dose of coagulant is 16 ppm. Automatic dosingprovides better efficiency than manual dosing. The study recommends continuous verification ofchemical preparation and injection, periodic cleaning of settling tanks, and monitoring of oily waterentering the station