Search for dissertations about: "mathematical modelling with bayesian"

Showing result 1 - 5 of 19 swedish dissertations containing the words mathematical modelling with bayesian.

  1. 1. Development of mathematical modelling for the glycosylation of IgG in CHO cell cultures

    Author : Liang Zhang; Véronique Chotteau; Michael J. Betenbaugh; KTH; []
    Keywords : ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Chinese hamster ovary cells; glycosylation; IgG; mathematical modelling; experimental design; perfusion; carbon sources; GReBA; Bayesian network; probabilistic graphic model; Bioteknologi; Biotechnology;

    Abstract : Chinese hamster ovary (CHO) cells are the most popular expression system for the production of biopharmaceuticals. More than 80% of the approved monoclonal antibodies (mAbs) or immunoglobulin G (IgG) are produced with these cells. Glycosylation is a usual post- translational modification important for therapeutic mAbs. READ MORE

  2. 2. Modelling of drug-effect on time-varying biomarkers

    Author : Felix Held; Chalmers University of Technology; Göteborgs universitet; Gothenburg University; []
    Keywords : NATURVETENSKAP; MEDICIN OCH HÄLSOVETENSKAP; NATURAL SCIENCES; MEDICAL AND HEALTH SCIENCES; cortisol; TNFα; hierarchical modelling; turnover model; pharmacokinetic pharmacodynamic modelling; cortisol TNFα hierarchical modelling turnover model pharmacokinetic pharmacodynamic modelling;

    Abstract : Model-based quantification of drug effect is an efficient tool during pre-clinical and clinical phases of drug trials. Mathematical modelling can lead to improved understanding of the underlying biological mechanisms, help in finding shortcomings of experimental design and suggest improvements, or be an effective tool in simulation-based analyses. READ MORE

  3. 3. Enhanced block sparse signal recovery and bayesian hierarchical models with applications

    Author : Jianfeng Wang; Jun Yu; Anders Garpebring; Jia Li; Umeå universitet; []
    Keywords : NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Magnetic resonance imaging; Bayesian hierarchical models; Weather Research and Forecasting; Compressive sensing; Block sparsity; Multivariate isotropic symmetric a-stable distribution; q-ratio block constrained minimal singular value;

    Abstract : This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). READ MORE

  4. 4. Statistical Inference on Interacting Particle Systems

    Author : Gustav Lindwall; Chalmers University of Technology; []
    Keywords : NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; NATURAL SCIENCES; stochastic process; mathematical biology; agent based modelling; glioblastoma; Bayesian inference;

    Abstract : Interacting particle systems, and more specifically stochastic dynamical systems, is a mathematical framework which allows for condensed and elegant modelling of complex phenomena undergoing both deterministic and random dynamics. This thesis is concerned with the topic of statistical inference on large systems of interacting particles, with the specific application of in vitro migration of cancer cells. READ MORE

  5. 5. Accelerating Monte Carlo methods for Bayesian inference in dynamical models

    Author : Johan Dahlin; Thomas B. Schön; Fredrik Lindsten; Richard Everitt; Linköpings universitet; []
    Keywords : NATURAL SCIENCES; NATURVETENSKAP; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; TEKNIK OCH TEKNOLOGIER; NATURAL SCIENCES; ENGINEERING AND TECHNOLOGY; Computational statistics; Monte Carlo; Markov chains; Particle filters; Machine learning; Bayesian optimisation; Approximate Bayesian Computations; Gaussian processes; Particle Metropolis-Hastings; Approximate inference; Pseudo-marginal methods;

    Abstract : Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. READ MORE