The mathematical model of Schizosaccharomyces pombe : Batch and repeated batch simulations.

Mathematical models are playing an important part in the current developments in engineering, science and biotechnology. Within this field the most fashionable and representative organisms are the ones who are genetically and physiologically tractable. Since the fission yeast Schizosaccharomyces pombe plays a role model among them and its behaviour has medical, genetic and industrial links (related to cancer research, metabolic pathways and beer production), this makes it a particularly interesting organism for study. This dissertation presents the first physiological model ever developed for the yeast S. pombe. The model allows for the simulation and prediction of batch and repeated batch experiments which are an important engineering tool in terms of optimization of industrial processes improving yield in bioreactors by predicting precise values of harvest fraction (HF) and dilution cycle times (DCT). The model has been developed within the generic modelling framework of CelCyMUS (Cell Cycle Model University of Surrey). As part of the research being carried out CelCyMUS has been up-dated by introducing the new Fortran 95 features and utilities in order to exploit its powerful new features and to keep the generic model in pace with technological software advancements. The model is a one-dimensional age-based population balance for the fission yeast S. pombe. It includes the four typical phases (S, G2, M and G1) with the G2 phase divided into two phases (G2A, G2B) and two checkpoints that govern the movement of cells between G1 and S, and G2B and M phases. The transitions (movement of cells between phases) are determined by a probability function related to the consumption of glucose. The G2B-M transition is also dependent on cell size, but since individual growth of cells is related to the consumption of the carbon source (in this case glucose), cell size is dependent upon the amount of glucose consumed per cell. The model also includes a phase for cells facing starvation before going into a meiotic cycle, with some chance of coming back to the mitotic cycle, and a death phase that accounts for cells dying with no chance of recovering at all. Parameters in the S. pombe model have been gathered from experimental data in batch culture reported in literature. Data generated from this specific model have been compared with data from experiments (Fotuhi, 2002) in batch and repeated batch cultures of S. pombe following the behaviour of population balance, consumption of nutrients, and production of metabolites. The new code was tested by successftilly reproducing data from mm-321 hybridoma cell line, the first specific model of a cell line developed in CelCyMUS. As a new feature a model of mass transfer has been incorporated in the generic framework. This mass transfer module accounts for interactions of metabolites (oxygen and carbon dioxide) in the gas and liquid phase of bioreactors. The new S. pombe model was fitted to the experiments of Creanor (1992) on synchronised cultures where the consumption of oxygen was being measured. Such experiments identify two points (G2B and G1) where the rate of oxygen uptake increased in the cycle, doubling the consumption at the end of every cycle. With the model fitted to experimental results in synchronised cultures of S. pombe the model was then used to simulate desynchronised cultures. S. pombe was successfully tested when reproducing experimental data generated by Fotuhi (2002) in S.pombe for batch and repeated batch bioreactors. The S. pombe model was able to simulate cell number, oxygen and glucose consumption. Carbon dioxide and ATP production were predicted by the model however there was no experimental data to compare with. Now that the S. pombe model has been tested against experimental data it will be applied in a model-based observer strategy for the online control of bioreactors

Novel bioinformatics approach for encoding and interrogating the progression and modulation of the mammalian cell cycle

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell growth and proliferation in tissues - while abnormal control and modulation of the cell cycle are characteristic of cancer cells, particularly in response to therapy. A central theme in cancer biology is to resolve and understand the origin and nature of innate and induced heterogeneity at the cell population level. Cellular heterogeneity - comprising structural, temporal and functional dimensions - is a confounding factor in the analysis of cell population dynamics and has implications at physiological, pathological and therapeutic levels. There is an exceptional advancement in the applications of imaging and cell tracking technologies dedicated to the area of cytometric research, that demand an integrated bioinformatics environment for high-content data extraction and interrogation. Image-derived cell-based analyses, where time is the quality parameter also demand unique solutions with the aim of enabling image encoding of spatiotemporal cellular events within complex cell populations. The perspective for this thesis is the complex yet poorly understood nature of cancer and the opportunities offered by rapidly evolving cytometric technologies. The research addresses the intellectual aspects of a bioinformatics framework for cellular informatics that encompass integrated data encoding, archiving, mining and analysis tools and methods capable of producing in silico cellular fingerprints for the responses of cell populations to perturbing influences. The overall goal is to understand the effects of anti-cancer drugs in complex and potentially heterogeneous neoplastic cellular systems by providing hypothesis testing opportunities. Cell lineage maps encoded from timelapse microscopy image sequences sit at the core of the proposed bioinformatics infrastructure developed in the current work. Through a number of data mining, analysis and visualisation tools the interactions and relationships within and between lineages have provided dynamic patterns for the modulation of the cell cycle in disease and under stress. The lineage data, accessible through databases implemented during the current study, has provided a rich repository for pharmacodynamic (PD) modelling and validation and has thus laid the foundation for fabricating a comprehensive knowledge base for linking both cellular and molecular behaviour patterns. These provide the foundation for meeting the aspirations of systems biology and drug discovery.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Novel bioinformatics approach for encoding and interrogating the progression and modulation of the mammalian cell cycle

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell growth and proliferation in tissues - while abnormal control and modulation of the cell cycle are characteristic of cancer cells, particularly in response to therapy. A central theme in cancer biology is to resolve and understand the origin and nature of innate and induced heterogeneity at the cell population level. Cellular heterogeneity - comprising structural, temporal and functional dimensions - is a confounding factor in the analysis of cell population dynamics and has implications at physiological, pathological and therapeutic levels. There is an exceptional advancement in the applications of imaging and cell tracking technologies dedicated to the area of cytometric research, that demand an integrated bioinformatics environment for high-content data extraction and interrogation. Image-derived cell-based analyses, where time is the quality parameter also demand unique solutions with the aim of enabling image encoding of spatiotemporal cellular events within complex cell populations. The perspective for this thesis is the complex yet poorly understood nature of cancer and the opportunities offered by rapidly evolving cytometric technologies. The research addresses the intellectual aspects of a bioinformatics framework for cellular informatics that encompass integrated data encoding, archiving, mining and analysis tools and methods capable of producing in silico cellular fingerprints for the responses of cell populations to perturbing influences. The overall goal is to understand the effects of anti-cancer drugs in complex and potentially heterogeneous neoplastic cellular systems by providing hypothesis testing opportunities. Cell lineage maps encoded from timelapse microscopy image sequences sit at the core of the proposed bioinformatics infrastructure developed in the current work. Through a number of data mining, analysis and visualisation tools the interactions and relationships within and between lineages have provided dynamic patterns for the modulation of the cell cycle in disease and under stress. The lineage data, accessible through databases implemented during the current study, has provided a rich repository for pharmacodynamic (PD) modelling and validation and has thus laid the foundation for fabricating a comprehensive knowledge base for linking both cellular and molecular behaviour patterns. These provide the foundation for meeting the aspirations of systems biology and drug discovery

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

A cumulative index to the 1977 issues of a continuing bibliography on aerospace medicine and biology

This publication is a cumulative index to the abstracts contained in the Supplements 164 through 175 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes three indexes-- subject, personal author, and corporate source

Biocompatible tumour implant systems: towards an integrated biophotonic system

There is a need to perform comprehensive cell biology studies transferable across culture platforms using innovative cellular models. The higher purpose is to bridge the gap between in vitro cell culture and in vivo models. In this thesis a significant advance is presented in the embedding of an innovative optical biophotonic capability for the dynamic interrogation and single cell tracking of human osteosarcoma cells encapsulated in the hollow fiber (HF) platform. Two approaches have been implemented: quantum dot (QD) nanoparticles providing proliferative and cell cycle readouts and an in-fiber light illumination providing global features of particle and cell density. An in vitro HF encapsulation model was developed and characterised against standard two-dimensional tissue culture (TC) using the human osteosarcoma U-2 OS cell line expressing a cell cycle fluorescent reporter (cyclin Bl-GFP). Analysis of the packing and orientation of cells in the HF revealed that they grow like an anchorage dependent adherent layer. Overall cells in the fiber displayed a slower cell cycle traverse and a differential sensitivity to clinically relevant doses of the anticancer mitosis-inhibiting agent Taxol compared to cells under normal TC conditions. Comprehensive gene profiling, with bioinformatics and ontology network analysis, showed that the HF cells presented high steroid related but low differentiation gene expression. Specific biomarkers were indentified, and it is suggested that the HF model displays features that are closer to an in vivo tumour. A flow cytometry cell-tracking approach using QD labelling was validated and applied to the HF model for the first time. This represents an "embedded" biophotonic system where the QD sensors are integrated directly into the seeded cell population and then redistributed through the daughter cells, thus reflecting patterns of lineage expansion. This provides sub-population parameterized information on cell-cell heterogeneity and cell division. A biophotonic HF prototype comprising the integration of direct coupled-light excitation in the HF was conceived, this revealed the potential and limitations to detect die presence of cells inside the HF lumen by analysing light attenuation changes. Finally a "systems cytometry" acquisition concept has been proposed, comprising the use of embedded engineered nanoparticles as single cell "nano-memory" biophotonic intracellular probes

2008 IMSAloquium, Student Investigation Showcase

Marking its twentieth year, IMSA’s Student Inquiry and Research Program (SIR) is a powerful expression of the Academy’s mission, “to ignite and nurture creative ethical minds that advance the human condition.”https://digitalcommons.imsa.edu/archives_sir/1000/thumbnail.jp