EQML- An Evolutionary Qualitative Model Learning Framework

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QML-Morven : A Novel Framework for Learning Qualitative Models

Publisher PD

An immune network approach to learning qualitative models of biological pathways

ACKNOWLEDGMENT GMC is supported by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative. WP and GMC are also supported by the partnership fund from dot.rural, RCUK Digital Economy research.Postprin

Fuzzy qualitative simulation with multivariate constraints

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Stochastic modelling of eukaryotic cell cycle

Stochastic models are developed to capture the inherent stochasticity of the biochemical networks associated to many biological processes. The objective of the present thesis is to present a detailed picture of stochastic approach for the mathematical modeling of eukaryotic cell cycle, to demonstrate an important application of such model in chemotherapy and to present a methodology for selecting the model parameters. The stochastic cell cycle model, developed using stochastic chemical kinetics approach, leads to the formation of an infinite dimensional differential equation in probabilities of system being in a specific state. Using Monte Carlo simulations of this model, dynamics of populations of eukaryotic cells such as yeasts or mammalian cells are obtained. Simulations are stochastic in nature and therefore exhibit variability among cells that is similar to the variability observed in natural populations. The model’s capability to predict heterogeneities in cell populations is used as a basis to implement it in a chemotherapic modeling framework to demonstrate how the model can be used to assist in the drug development stage by investigating drug administration strategies that can have different killing effect on cancer cells and healthy cells. Finally, basic cell cycle model is refined in a systematic way to make it more suitable for describing the population characteristics of budding yeast. Selection of model parameters using an evolutionary optimization strategy referred to as insilico evolution is described. The benefits of this approach lie in the fact that it generates an initial guess of reasonable set of parameters which in turn can be used in the least squares fitting of model to the steady state distributions obtained from flow cytometry measurements. The Insilco evolution algorithm serves as a tool for sensitivity analysis of the model parameters and leads to a synergistic approach of model and experiments guiding each other. To conclude, the stochastic model based on single cell kinetics will be useful for predicting the population distribution on whole organism level. Such models find applications in wide areas of biological and biomedical applications. Evolutionary optimization strategies can be used in parameter estimation methods based on steady state distributions

Methods in Computational Biology

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measuremen

Tracking and analysis of movement at different scales: from endosomes to humans

Movement is apparent across all spatio-temporal scales in biology and can have a significant effect on the survival of the individual. For this reason, it has been the object of study in a wide range of research fields, i.e. in molecular biology, pharmaceutics, medical research but also in behavioural biology and ecology. The aim of the thesis was to provide methodologies and insight on the movement patterns seen at different spatio-temporal scales in biology; the intra-cellular, the cellular and the organism level. At the intra-cellular level, current thesis studied the compartmental inheritance in Human Osteosarcoma (U2-OS) cells. The inheritance pattern of the endosomal quantum dot fluorescence across two consecutive generations was for first time empirically revealed. In addition, a in silico model was developed to predict the inheritance across multiple generations. At the cellular level, a semi-automated routine was developed that can realize long-term nuclei tracking in U2-OS cell populations labeled with a cell cycle marker in their cytoplasm. A method to extract cell cycle information without the need to explicitly segment the cells was proposed. The movement behaviour of the cellular population and their possible inter-individual differences was also studied. Lastly, at the organism level, the focus of the thesis was to study the emergence of coordination in unfamiliar free-swimming stickleback fish shoals. It was demonstrated that there exist two different phases, the uncoordinated and the coordinated. In addition, the significance of uncoordinated phase to the establishment of the group’s social network was for first time evinced. The adaptation of the stickleback collectives was also studied over time, i.e. the effect of group’s repeated interactions on the emergence of coordination. Findings at the intra-cellular and cellular level can have significant implications on medical and pharmaceutical research. Findings at the organism level can also contribute to the understanding of how social interactions are formed and maintained in animal collectives

Quantifying the Generation of T Cell Immunity using a Systems Biology Approach.

The immune system is our defense against pathogens. Quantitatively predicting its response to foreign stimulation is key toward developing tools to interfere with or prevent infection (e.g. vaccines and immunotherapies). I use a systems biology approach and develop computational models describing dynamics occurring within lymph nodes, sites where activated immune cells are generated. These effector cells circulate out into blood and to sites of infection participating in immunity. I both quantitatively and qualitatively study dynamics of immune cells during a generalized infection as well as during infection with Mycobacterium tuberculosis (Mtb). The models predict that their 3-dimensional configuration enables the lymph nodes to support rare antigen-specific T cells to efficiently search for antigen-bearing dendritic cells, and this efficiency is not reduced when the precursor frequency increases in a wide range. The models also predict strategies to manipulate the differentiation of immune cells to maximize specific subtypes of T cells populations, depending on different immunomodulation goals. When coupled with Mtb infection models, our models are able to assist vaccine design by finding correlations between immune cell subsets and protection against Mtb, and also help identify mechanisms controlling different disease outcomes at host level.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113563/1/changgon_1.pd

Computational Imaging Biomarkers For Precision Medicine: Characterizing Heterogeneity In Breast Cancer

In the United States, 1 in 8 women are diagnosed with breast cancer. Breast tumor heterogeneity is well-established, with intratumor heterogeneity manifesting spatially and temporally. Increased heterogeneity is associated with adverse clinical outcomes. Current critical disease treatment decisions are made on the basis of biomarkers acquired from tissue samples, largely under sampling the heterogeneous disease burden. In order to drive precision medicine treatment strategies for cancer, personalized biomarkers are needed to truly characterize intratumor heterogeneity. Medical imaging can provide anon-invasive, whole tumor sampling of disease burden at the time of diagnosis and allows for longitudinal monitoring of disease progression. The studies outlined in this thesis introduce analytical tools developed through computer vision, bioinformatics, and machine learning and use diagnostic and longitudinal clinical images of breast cancer to develop computational imaging biomarkers characterizing intratumor heterogeneity. Intrinsic imaging phenotypes of spatial heterogeneity, identified in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images at the time of diagnosis, were identified and validated, demonstrating improved prognostic value over conventional histopathologic biomarkers when predicting 10-year recurrence free survival. Intrinsic phenotypes of longitudinal change in spatial heterogeneity in response to neoadjuvant treatment, identified in DCE-MRI were identified and leveraged as prognostic and predictive biomarkers, demonstrating augmented prognostic value when added to conventional histopathologic and personalized molecular biomarkers. To better characterize 4-D spatial and temporal heterogeneity, illuminated through dynamic positron emission tomography imaging, a novel 4-D segmentation algorithm was developed to identify spatially constrained, functionally discrete intratumor sub-regions. Quantifying the identified sub-regions through a novel imaging signature demonstrated the prognostic value of characterizing intratumor heterogeneity when predicting recurrence free survival, demonstrating prognostic improvement over established histopathologic biomarkers and conventional kinetic model derived parameters. Collectively, the studies in this thesis demonstrate the value of leveraging computational imaging biomarkers to characterize intratumor heterogeneity. Such biomarkers have the potential to be utilized towards precision medicine for cancer care