Dissertations / Theses on the topic 'Biological Mathematics'
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1
Magi, Ross. "Dynamic behavior of biological membranes." Thesis, The University of Utah, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680576.
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Biological membranes are important structural units in the cell. Composed of a lipid bilayer with embedded proteins, most exploration of membranes has focused on the proteins. While proteins play a vital role in membrane function, the lipids themselves can behave in dynamic ways which affect membrane structure and function. Furthermore, the dynamic behavior of the lipids can affect and be affected by membrane geometry. A novel fluid membrane model is developed in which two different types of lipids flow in a deforming membrane, modelled as a two-dimensional Riemannian manifold that resists bending. The two lipids behave like viscous Newtonian fluids whose motion is determined by realistic physical forces. By examining the stability of various shapes, it is shown that instability may result if the two lipids forming the membrane possess biophysical qualities, which cause them to respond differently to membrane curvature. By means of numerical simulation of a simplified model, it is shown that this instability results in curvature induced phase separation. Applying the simplified model to the Golgi apparatus, it is hypothesized that curvature induced phase separation may occur in a Golgi cisterna, aiding in the process of protein sorting.
In addition to flowing tangentially in the membrane, lipids also flip back and forth between the two leaflets in the bilayer. While traditionally assumed to occur very slowly, recent experiments have indicated that lipid flip-flop may occur rapidly. Two models are developed that explore the effect of rapid flip-flop on membrane geometry and the effect of a pH gradient on the distribution of charged lipids in the leaflets of the bilayer. By means of a stochastic model, it is shown that even the rapid flip-flop rates observed are unlikely to be significant inducers of membrane curvature. By means of a nonlinear Poisson- Boltzmann model, it is shown that pH gradients are unlikely to be significant inducers of bilayer asymmetry under physiological conditions.
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Chindelevitch, Leonid Alexandrovich. "Extracting information from biological networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/64607.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 175-194).
Systems biology, the study of biological systems in a holistic manner, has been catalyzed by a dramatic improvement in experimental techniques, coupled with a constantly increasing availability of biological data. The representation and analysis of this heterogeneous data is facilitated by the powerful abstraction of biological networks. This thesis examines several types of these networks and looks in detail at the kind of information their analysis can yield. The first part discusses protein interaction networks. We introduce a new algorithm for the pairwise alignment of these networks. We show that these alignments can provide important clues to the function of proteins as well as insights into the evolutionary history of the species under examination. The second part discusses regulatory networks. We present an approach for validating putative drug targets based on the information contained in these networks. We show how this approach can also be used to discover drug targets. The third part discusses metabolic networks. We provide new insights into the structure of constraint-based models of cell metabolism and describe a methodology for performing a complete analysis of a metabolic network. We also present an implementation of this methodology and discuss its application to a variety of problems related to the metabolism of bacteria. The final part describes an application of our methodology to Mycobacterium tuberculosis, the pathogen responsible for almost 2 million deaths around the world every year. We introduce a method for reconciling metabolic network reconstructions and apply it to merge the two published networks for tuberculosis. We analyze the merged network and show how it can be refined based on available experimental data to improve its predictive power. We conclude with a list of potential drug targets.
by Leonid Alexandrovich Chindelevitch.
Ph.D.
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Altschul, Stephen Frank. "Aspects of biological sequence comparison." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/102708.
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Thesis (Ph. D)--Massachusetts Institute of Technology, Dept. of Mathematics, 1987.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Bibliography: leaves 165-168.
by Stephen Frank Altschul.
Ph.D
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Orme, Belinda Abigail Amanda. "Biological mixing and chaos." Thesis, University of Birmingham, 2002. http://etheses.bham.ac.uk//id/eprint/7637/.
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We consider a problem from the field of biological fluid mechanics which considers the flow associated with the motion of a flagellum on a sessile micro-organism. Motivation is taken from the movement of fluid around a species of choanoflagellate, \(Salpingoeca\) \(Amphoridium\). Choanoflagellates are a class of organism in the phylum Protozoa. Because the length scales and velocities are very low, the flow is one dominated by viscous forces and the environment is characterised by a low Reynolds number. The flow caused by the flagellum is initially modelled via a point force. These microorganisms operate in more than one location and the motion they create is modelled in a qualitative sense by using two stokeslets (appropriate to Stokes' flow) whose orientation and position is varied with time. The sessile micro-organism resides above a boundary which is modelled, most generally, as an interface between two fluids possessing different properties. Efficiency of feeding currents generated by the flagellum motion is studied. The resulting dynamics are investigated using chaotic measures, which examine the stretching and consequent mixing of elements within the fluid. Different point force locations lead to various eddy structures such that their superposition results in chaotic advection. The model is developed to examine the flow of particles around a three-dimensional realisation of a micro-organism which involves a flagellum and a cell body attached to a substrate. Green's functions are used to satisfy a number of boundary conditions simultaneously. Particle paths of a tracer introduced into the fully three-dimensional model are investigated. Comparisons with experimental data illustrate good agreement between theoretical and experimental results. Further extensions to the model are suggested.
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Tucker, George Jay. "Statistical methods to infer biological interactions." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89874.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
169
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 153-170).
Biological systems are extremely complex, and our ability to experimentally measure interactions in these systems is limited by inherent noise. Technological advances have allowed us to collect unprecedented amounts of raw data, increasing the need for computational methods to disentangle true interactions from noise. In this thesis, we focus on statistical methods to infer two classes of important biological interactions: protein-protein interactions and the link between genotypes and phenotypes. In the first part of the thesis, we introduce methods to infer protein-protein interactions from affinity purification mass spectrometry (AP-MS) and from luminescence-based mammalian interactome mapping (LUMIER). Our work reveals novel context dependent interactions in the MAPK signaling pathway and insights into the protein homeostasis machinery. In the second part, we focus on methods to understand the link between genotypes and phenotypes. First, we characterize the effects of related individuals on standard association statistics for genome-wide association studies (GWAS) and introduce a new statistic that corrects for relatedness. Then, we introduce a statistically powerful association testing framework that corrects for confounding from population structure in large scale GWAS. Lastly, we investigate regularized regression for phenotype prediction from genetic data.
by George Jay Tucker.
Ph. D.
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Breitsch, Nathan W. "Techniques for the Study of Biological Coupled Oscillator Systems." Ohio University Honors Tutorial College / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1399892563.
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Montenegro-Johnson, Thomas D. "Microscopic swimming in biological fluids." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4220/.
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Fluid interactions are ubiquitous in the natural world; all organisms must find strategies to generate, utilise or resist flow in order to be successful. A process fundamental to all life on earth is reproduction, which in many cases entails the swimming of sperm cells. Cell swimming arises from coupled interactions between physical and biological processes. We will focus on the effects of changing fluid rheology on microscopic swimmers, with a particular application to the study of internal mammalian fertilisation. To reach the egg, mammalian sperm must navigate the convoluted geometry of the female reproductive tract, actively bending their flagella in order to propel themselves through cervical mucus: a suspension of polymer chains that twist, tangle and align with flow, giving it complex properties. Whilst recent work has examined the effects of fluid viscoelasticity on sperm-like swimmers, relatively less attention has been given to the shear-thinning property. We develop a new finite element technique to simulate free swimmers with prescribed beat kinematics in shear-thinning fluids with nonlinear governing equations. This technique is then applied to three qualitatively different viscous swimmers in order to examine the different phenomena that arise from swimmer interactions with of shear-thinning fluid.
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Seier, Edith, and Karl H. Joplin. "Introduction to STATISTICS in a Biological Context." Digital Commons @ East Tennessee State University, 2011. http://amzn.com/1463613377.
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Caberlin, Martin D. "Stiff ordinary and delay differential equations in biological systems." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29416.
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The Santillan-Mackey model of the tryptophan operon was developed to characterize the anthranilate synthase activity in cultures of Escherichia coli. Similarly, the GABA reaction scheme was formulated to characterize the response of the GABAA receptor at a synapse, and the Hodgkin-Huxley model was developed to characterize the action potential of a squid giant axon. While the Hodgkin-Huxley model has been studied in great detail from a mathematical vantage, much less is known about the preceding two models in this regard. This work examines the stiffness of all three models; a novel perspective for both the Santillan-Mackey model and the GABA reaction. The characterization of the stiffness in these problems gives theoretical biologists insight into the dynamics of the reactions. It also enables them to select more computationally efficient methods for numerical simulations. The discovery of invariant manifolds in the Santillan-Mackey model and the GABA reaction in this work present experimentalists with concrete assays, against which the models can be tested.
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Yu, Yun William. "Compressive algorithms for search and storage in biological data." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112879.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 187-197).
Disparate biological datasets often exhibit similar well-defined structure; efficient algorithms can be designed to exploit this structure. In this doctoral thesis, we present a framework for similarity search based on entropy and fractal dimension; here, we prove that a clustered search algorithm scales in time with metric entropy number of covering hyperspheres-if the fractal dimension is low. Using these ideas, entropy-scaling versions of standard bioinformatics search tools can be designed, including for small-molecule, metagenomics, and protein structure search. This 'compressive acceleration' approach taking advantage of redundancy and sparsity in biological data can be leveraged also for next-generation sequencing (NGS) read mapping. By pairing together a clustered grouping over similar reads and a homology table for similarities in the human genome, our CORA framework can accelerate all-mapping by several orders of magnitude. Additionally, we also present work on filtering empirical base-calling quality scores from Next Generation Sequencing data. By using the sparsity of k-mers of sufficient length in the human genome and imposing a human prior through the use of frequent k-mers in a large corpus of human DNA reads, we are able to quickly discard over 90% of the information found in those quality scores while retaining or even improving downstream variant-calling accuracy. This filtering step allows for fast lossy compression of quality scores.
by Yun William Yu.
Ph. D.
11
Armond, Jonathan William. "Forces in a biological context." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/4480/.
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Theoretical modelling of the microtubule-Dam1-ring force generation mechanism and the pulling of tubes from surface-supported lipid bilayers are presented and discussed. Atomic force microscopy (AFM) force data of tube pulling experiments is analysed and compared with theoretical predictions. Featurescommonto recent computational models are simplified and examined independently where possible. In particular, the steric confinement of the Dam1 ring on a microtubule (MT) by protofilaments (PFs), the powerstroke produced by curling PFs, the depolymerisation of the MT, and the binding attraction between Dam1 and the MT are modelled. Model parameters are fitted to data. Functional force generation is equally demonstrated when attachment is maintained by steric confinement alone (protofilament model) or by a binding attraction alone (binding model). Moreover, parameters amenable to experimental modification are shown to induce differences between the protofilament model and the binding model. Changing the depolymerisation rate of MTs, the diffusion coefficient of the Dam1 ring, or applying an oscillating load force will allow discrimination of these two different mechanisms of force generation and kinetochore attachment. A previously described theoretical model of pulling lipid bilayer tubes from vesicles is modified for the case of pulling tubes from surface-supported lipid bilayers. A shape equation for axisymmetric membranes is derived variationally and solved numerically for zero pressure. Free energy profiles and force curves are calculated for various AFM probe sizes and compared to experimental data where a ground flat AFM probe is used to pull tubes from surface-supported lipid bilayers. The predicted force curves partially fit the experimental data, although not at short distances, and estimates of the bilayer surface tension are given. Pressure and volume profiles are calculated for the extension of the model to the nonzero pressure case.
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Sinfield, James Lister. "Synchronization and causality in biological networks." Thesis, University of Warwick, 2009. http://wrap.warwick.ac.uk/3789/.
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Rackauckas, Christopher Vincent. "Simulation and Control of Biological Stochasticity." Thesis, University of California, Irvine, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10827971.
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Stochastic models of biochemical interactions elucidate essential properties of the network which are not accessible to deterministic modeling. In this thesis it is described how a network motif, the proportional-reversibility interaction with active intermediate states, gives rise to the ability for the variance of biochemical signals to be controlled without changing the mean, a property designated as mean-independent noise control (MINC). This noise control is demonstrated to be essential for macro-scale biological processes via spatial models of the zebrafish hindbrain boundary sharpening. Additionally, the ability to deduce noise origin from the aggregate noise properties is shown.
However, these large-scale stochastic models of developmental processes required significant advances in the methodology and tooling for solving stochastic differential equations. Two improvements to stochastic integration methods, an efficient method for time stepping adaptivity on high order stochastic Runge-Kutta methods termed Rejection Sampling with Memory (RSwM) and optimal-stability stochastic Runge-Kutta methods, are combined to give over 1000 times speedups on biological models over previously used methodologies. In addition, a new software for solving differential equations in the Julia programming language is detailed. Its unique features for handling complex biological models, along with its high performance (routinely benchmarking as faster than classic C++ and Fortran integrators of similar implementations) and new methods, give rise to an accessible tool for simulation of large-scale stochastic biological models.
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Mao, Dong. "Biological time series classification via reproducing kernels and sample entropy." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2008. http://wwwlib.umi.com/cr/syr/main.
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Wittenberg, Ralf W. "Models of self-organization in biological development." Master's thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/17405.
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Bibliography: p. 297-320.In this thesis we thus wish to consider the concept of self-organization as an overall paradigm within which various theoretical approaches to the study of development may be described and evaluated. In the process, an attempt is made to give a fair and reasonably comprehensive overview of leading modelling approaches in developmental biology, with particular reference to self-organization. The work proceeds from a physical or mathematical perspective, but not unduly so - the major mathematical derivations and results are relegated to appendices - and attempts to fill a perceived gap in the extant review literature, in its breadth and attempted impartiality of scope. A characteristic of the present account is its markedly interdisciplinary approach: it seeks to place self-organization models that have been proposed for biological pattern formation and morphogenesis both within the necessary experimentally-derived biological framework, and in the wider physical context of self-organization and the mathematical techniques that may be employed in its study. Hence the thesis begins with appropriate introductory chapters to provide the necessary background, before proceeding to a discussion of the models themselves. It should be noted that the work is structured so as to be read sequentially, from beginning to end; and that the chapters in the main text were designed to be understood essentially independently of the appendices, although frequent references to the latter are given. In view of the vastness of the available information and literature on developmental biology, a working knowledge of embryological principles must be assumed. Consequently, rather than attempting a comprehensive introduction to experimental embryology, chapter 2 presents just a few biological preliminaries, to 'set the scene', outlining some of the major issues that we are dealing with, and sketching an indication of the current status of knowledge and research on development. The chapter is aimed at furnishing the necessary biological, experimental background, in the light of which the rest of the thesis should be read, and which should indeed underpin and motivate any theoretical discussions. We encounter the different hierarchical levels of description in this chapter, as well as some of the model systems whose experimental study has proved most fruitful, some of the concepts of experimental embryology, and a brief reference to some questions that will not be addressed in this work. With chapter 3, we temporarily move away from developmental biology, and consider the wider physical and mathematical concepts related to the study of self-organization. Here we encounter physical and chemical examples of spontaneous structure formation, thermodynamic considerations, and different approaches to the description of complexity. Mathematical approaches to the dynamical study of self-organization are also introduced, with specific reference to reaction-diffusion equations, and we consider some possible chemical and biochemical realizations of self-organizing kinetics. The chapter may be read in conjunction with appendix A, which gives a somewhat more in-depth study of reaction-diffusion equations, their analysis and properties, as an example of the approach to the analysis of self-organizing dynamical systems and mathematically-formulated models. Appendix B contains a more detailed discussion of the Belousov-Zhabotinskii reaction, which provides a vivid chemical paradigm for the concepts of symmetry-breaking and self-organization. Chapter 3 concludes with a brief discussion of a model biological system, the cellular slime mould, which displays rudimentary development and has thus proved amenable to detailed study and modelling. The following two chapters form the core of the thesis, as they contain discussions of the detailed application of theoretical concepts and models, largely based on self-organization, to various developmental situations. We encounter a diversity of models which has arisen largely in the last quarter century, each of which attempts to account for some aspect of biological pattern formation and morphogenesis; an aim of the discussion is to assess the extent of the underlying unity of these models in terms of the self-organization paradigm. In chapter 4 chemical pre-patterns and positional information are considered, without the overt involvement of cells in the patterning. In chapter 5, on the other hand, cellular interactions and activities are explicitly taken into account; this chapter should be read together with appendix C, which contains a brief introduction to the mathematical formulation and analysis of some of the models discussed. The penultimate chapter, 6, considers two other approaches to the study of development; one of these has faded away, while the other is still apparently in the ascendant. The assumptions underlying catastrophe theory, the value of its applications to developmental biology and the reasons for its decline in popularity, are considered. Lastly, discrete approaches, including the recently fashionable cellular automata, are dealt with, and the possible roles of rule-based interactions, such as of the so-called L-systems, and of fractals and chaos are evaluated. Chapter 7 then concludes the thesis with a brief assessment of the value of the self-organization concept to the study of biological development.
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O'Keeffe, Stephen George. "The mechanics of growth and residual stress in biological cylinders." Thesis, University of Oxford, 2015. http://ora.ox.ac.uk/objects/uuid:493473f6-b952-4ce3-a2e5-1a79e97afb7f.
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Biological tissue differs from other materials in many ways. Perhaps the most crucial difference is its ability to grow. Growth processes may give rise to stresses that exist in a body in the absence of applied loads and these are known as residual stresses. Residual stress is present in many biological systems and can have important consequences on the mechanical response of a body. Mathematical models of biological structures must therefore be able to capture accurately the effects of differential growth and residual stress, since greater understanding of the roles of these phenomena may have applications in many fields. In addition to residual stresses, biological structures often have a complex morphology. The theory of 3-D elasticity is analytically tractable in modelling mechanical properties in simple geometries such as a cylinder. On the other hand, rod theory is well-suited for geometrically-complex deformations, but is unable to account for residual stress. In this thesis, we aim to develop a map between the two frameworks. Firstly, we use 3-D elasticity to determine effective mechanical properties of a growing cylinder and map them into an effective rod. Secondly, we consider a growing filament embedded in an elastic foundation. Here, we estimate the degree of transverse reinforcement the foundation confers on the filament in terms of its material properties. Finally, to gain a greater understanding of the role of residual stress in biological structures, we consider a case study: the chameleon's tongue. In particular we consider the role of residual stress and anisotropy in aiding the rapid projection of the tongue during prey capture. We construct a mechanical model of the tongue and use it to investigate a proposed mechanism of projection by means of an energy balance argument.
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Njagarah, Hatson John Boscoh. "Modelling water-borne infections : the impact of hygiene, metapopulation movements and the biological control of cholera." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95972.
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Thesis (PhD)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Water-borne infections have been a menace in many countries around the globe, claiming millions of lives. Cholera in particular has spread to all continents and now on its seventh epidemic. Although control measures have been continually developed through sanitation, vaccination and rehydration, the infection still devastates populations whenever there is an outbreak. In this research work, mathematical models for cholera transmission dynamics with focus on the impact of sanitation and hygiene, metapopulation spread, optimal control and biological control using a bacteriophage specific for pathogenic Vibrio cholerae are constructed and analysed. Vital analyses for the models are precisely given as well as numerical results depicting long term behaviour and the evolution of populations over time. The results of our analysis indicate that; improved sanitation and hand-hygiene are vital in reducing cholera infections; the spread of disease across metapopulations characterised by exchange of individuals and no cross community infection is associated with synchronous fluctuation of populations in both adjacent communities; during control of cholera, the control measures/efforts ought to be optimal especially at the beginning of the epidemic where the outbreak is often explosive in nature; and biological control if well implemented would avert many potential infections by lowering the concentration of pathogenic vibrios in the aquatic environment to values lower than the infectious dose.
AFRIKAANSE OPSOMMING: Water-infeksies is ’n bedreiging in baie lande regoor die wêreld en eis miljoene lewens. Cholera in die besonder, het op sy sewende epidemie na alle kontinente versprei. Hoewel beheermaatreëls voortdurend ontwikkel word deur middel van higiëne, inentings en rehidrasie, vernietig die infeksie steeds bevolkings wanneer daar ’n uitbraak voorkom. In hierdie navorsingswerk, word wiskundige modelle vir cholera-oordrag dinamika met die fokus op die impak van higiëne, metabevolking verspreiding, optimale beheer en biologiese beheer met behulp van ’n bakteriofaag spesifiek vir patogene Vibrio cholerae gebou en ontleed. Noodsaaklike ontledings vir die modelle is gegee sowel as numeriese resultate wat die langtermyn gedrag uitbeeld en die ontwikkeling van die bevolking oor tyd. Die resultate van ons ontleding dui daarop dat; verbeterde higiëne is noodsaaklik in die vermindering van cholera infeksies; die verspreiding van die siekte oor metapopulaties gekenmerk deur die uitruil van individue en geen kruis gemeenskap infeksie wat verband houmet sinchrone skommeling van bevolkings in beide aangrensende gemeenskappe; tydens die beheer van cholera,behoort die beheermaatreëls/pogings optimaal te wees veral aan die begin van die epidemie waar die uitbreking dikwels plofbaar in die natuur is; en biologiese beheer, indien dit goed geïmplementeer word, kan baie potensiële infeksies voorkom deur ’n vermindering in die konsentrasie van patogene vibrio in die water tot waardes laer as die aansteeklike dosis.
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Gill, Mandeep Singh. "Application of software engineering methodologies to the development of mathematical biological models." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:35178f3a-7951-4f1c-aeab-390cdd622b05.
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Mathematical models have been used to capture the behaviour of biological systems, from low-level biochemical reactions to multi-scale whole-organ models. Models are typically based on experimentally-derived data, attempting to reproduce the observed behaviour through mathematical constructs, e.g. using Ordinary Differential Equations (ODEs) for spatially-homogeneous systems. These models are developed and published as mathematical equations, yet are of such complexity that they necessitate computational simulation. This computational model development is often performed in an ad hoc fashion by modellers who lack extensive software engineering experience, resulting in brittle, inefficient model code that is hard to extend and reuse. Several Domain Specific Languages (DSLs) exist to aid capturing such biological models, including CellML and SBML; however these DSLs are designed to facilitate model curation rather than simplify model development. We present research into the application of techniques from software engineering to this domain; starting with the design, development and implementation of a DSL, termed Ode, to aid the creation of ODE-based biological models. This introduces features beneficial to model development, such as model verification and reproducible results. We compare and contrast model development to large-scale software development, focussing on extensibility and reuse. This work results in a module system that enables the independent construction and combination of model components. We further investigate the use of software engineering processes and patterns to develop complex modular cardiac models. Model simulation is increasingly computationally demanding, thus models are often created in complex low-level languages such as C/C++. We introduce a highly-efficient, optimising native-code compiler for Ode that generates custom, model-specific simulation code and allows use of our structured modelling features without degrading performance. Finally, in certain contexts the stochastic nature of biological systems becomes relevant. We introduce stochastic constructs to the Ode DSL that enable models to use Stochastic Differential Equations (SDEs), the Stochastic Simulation Algorithm (SSA), and hybrid methods. These use our native-code implementation and demonstrate highly-efficient stochastic simulation, beneficial as stochastic simulation is highly computationally intensive. We introduce a further DSL to model ion channels declaratively, demonstrating the benefits of DSLs in the biological domain. This thesis demonstrates the application of software engineering methodologies, and in particular DSLs, to facilitate the development of both deterministic and stochastic biological models. We demonstrate their benefits with several features that enable the construction of large-scale, reusable and extensible models. This is accomplished whilst providing efficient simulation, creating new opportunities for biological model development, investigation and experimentation.
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Degasperi, Andrea. "Multi-scale modelling of biological systems in process algebra." Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2946/.
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There is a growing interest in combining different levels of detail of biological phenomena into unique multi-scale models that represent both biochemical details and higher order structures such as cells, tissues or organs. The state of the art of multi-scale models presents a variety of approaches often tailored around specific problems and composed of a combination of mathematical techniques. As a result, these models are difficult to build, compose, compare and analyse. In this thesis we identify process algebra as an ideal formalism to multi-scale modelling of biological systems. Building on an investigation of existing process algebras, we define process algebra with hooks (PAH), designed to be a middle-out approach to multi-scale modelling. The distinctive features of PAH are: the presence of two synchronisation operators, distinguishing interactions within and between scales, and composed actions, representing events that occur at multiple scales. A stochastic semantics is provided, based on functional rates derived from kinetic laws. A parametric version of the algebra ensures that a model description is compact. This new formalism allows for: unambiguous definition of scales as processes and interactions within and between scales as actions, compositionality between scales using a novel vertical cooperation operator and compositionality within scales using a traditional cooperation operator, and relating models and their behaviour using equivalence relations that can focus on specified scales. Finally, we apply PAH to define, compose and relate models of pattern formation and tissue growth, highlighting the benefits of the approach.
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Price, Candice Renee. "A biological application for the oriented skein relation." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3369.
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The traditional skein relation for the Alexander polynomial involves an oriented knot, K+, with a distinguished positive crossing; a knot K−, obtained by changing the distinguished positive crossing of K+ to a negative crossing; and a link K0, the orientation preserving resolution of the distinguished crossing. We refer to (K+,K−,K0) as the oriented skein triple.
A tangle is defined as a pair (B, t) of a 3-dimensional ball B and a collection of disjoint, simple, properly embedded arcs, denoted t. DeWitt Sumners and Claus Ernst developed the tangle model which uses the mathematics of tangles to model DNA-protein binding. The protein is seen as the 3-ball and the DNA bound by the protein as properly embedded curves in the 3-ball. Topoisomerases are proteins that break one segment of DNA allowing a DNA segment to pass through before resealing the break. Effectively, the action of these proteins can be modeled as K− ↔ K+. Recombinases are proteins that cut two segments of DNA and recombine them in some manner. While recombinase local action varies, most are mathematically equivalent to a resolution, i.e. K± ↔ K0. The oriented triple is now viewed as K− = circular DNA substrate, K+ = product of topoisomerase action, K0 = product of recombinase action.
The theorem stated in this dissertation gives a relationship between two 2-bridge knots, K+ and K−, that differ by a crossing change and a link, K0 created from the oriented resolution of that crossing. We apply this theorem to difference topology experiments using topoisomerase proteins to study SMC proteins.
In recent years, link homology theories have become a popular invariant to develop and study. One such invariant knot Floer homology, was constructed by Peter Ozsváth, Zoltán Szabó, and independently Jacob Rasmussen, denoted by HFK. It is also a refinement of a classical invariant, the Alexander polynomial.
The study of DNA knots and links are of great interest to molecular biologists as they are present in many cellular process. The variety of experimentally observed DNA knots and links makes separating and categorizing these molecules a critical issue. Thus, knowing the knot Floer homology will provide restrictions on knotted and linked products of protein action.
We give a summary of the combinatorial version of knot Floer homology from known work, providing a worked out example. The thesis ends with reviewing knot Floer homology properties of three particular sub-families of biologically relevant links known as (2, p)- torus links, clasp knots and 3-strand pretzel links.
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Wang, Shu. "Information Theoretic Analysis of A Biological Signal Transduction System." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1526393370364409.
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Moses, Gregory J. "Dynamical Systems In Biological Modeling: Clustering In the Cell Division Cycle of Yeast." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1438170442.
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Harrison, Nigel. "Modelling chemotactic motion of cells in biological tissue with applications to embryogenesis." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/10093/.
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Perhaps one of the most amazing events that occurs in nature, is in the emergence and growth of biological life. Emergence speaks of the well-coined phrase Primordial ooze from which the chemical building blocks of life first gave rise to the complicated molecular structure of Deoxyribonucleic acid (DNA), that has the mind boggling task of encoding every chemical and physical attribute and trait of the organism for which it is encoded. This incredible feat of nature is only equalled by the ability of single fertilized cell (zygote) to undergo a seemingly magical transformation through enlargement, growth and change to give rise to a fully formed animal (or plant). The study and body of knowledge of this latter process is called Developmental Biology, and it seeks to define and explain all of the intricate sub-stages and bio-chemical, molecular and physical processes along the time-line of this transformation, that is from fertilization to birth, hatching or germination and beyond. One might consider, and quite reasonably, that the variety of different processes leading to the development of a complete biological organism would be so vast as to render the problem untenable. Indeed the almost inconceivable amount of genetic information contained within the nucleus of the simplest of cells would seem to corroborate this assumption. However when one takes a more holistic view, we can see that the development of any complex biological organism can be reduced to a set of five distinct processes, all of which are orchestrated to define structures from a body of cells. Viewed in this light the generation of any complex multi-cellular organism, be it small or large, must involve: cell-division, differentiation, pattern formation, change in form and growth [1]. To mediate and orchestrate these different processes during the development of the embryo are a enumerable number of bio-chemicals that are produced within the cells that can diffuse into the surrounding environment, activating (and de-activating) inter/intra-cellular signalling pathways that trigger further productions and possibly one or more of the processes suggested above. One such case of this, and which is of particular interest in this thesis, is in the role of morphogens in the growth of vertebrate embryos, where it is known that interacting morphogen gradients can give rise to spatially stable concentrations [2] that are known to be involved in organ growth [3], primitive streak formation [4] and the extension and patterning of the primary body axis [5, 6, 7]. In this thesis we are considering one such problem involving these mechanisms/processes, during the primary body axis extension in the chick embryo. During this phase of development the early brain is beginning to form and the central nervous system (CNS) is beginning to extend unilaterally in a posterior direction defining the main anteroposterior (head to tail) body axis; in simple terms one may see this as the generation of the spinal cord and surrounding structures. Extension of this axis is known to be orchestrated by a small cellular structure located at the posterior-most tip of the extension, encompassing what is known as the primary organising centre in the chick embryo: Hensen’s node. This structure including the node is known to move independently/autonomously of the rest of the embryo and as it does so the cells in the region are growing and proliferating, and ultimately differentiating and leaving this region to literally fuel the axial extension. This broad description leads us to the heart of our thesis, and which will preoccupy the rest of this dissertation. We postulate that the motile behaviour of the group is as a result of biochemical gradients to which the group is attracted toward areas of highest concentration or towards areas of lowest concentration of some as yet unnamed morphogen. That is we assume that the group moves as a result of a chemotaxis. Furthermore, the growth and subsequent differentiation of cells exiting the group, contributing to the growth of the CNS, are also regulated by the same morphogen. Therefore we propose that a singular bio-chemical mechanism can account for the motile and growth behaviour observed during CNS extension.
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Keller, Peter, Sylvie Roelly, and Angelo Valleriani. "A quasi-random-walk to model a biological transport process." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6358/.
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Transport Molecules play a crucial role for cell viability. Amongst others, linear motors transport cargos along rope-like structures from one location of
the cell to another in a stochastic fashion. Thereby each step of the motor, either forwards or backwards, bridges a fixed distance. While moving along the rope the motor can also detach and is lost. We give here a mathematical formalization of such dynamics as a random process which is an extension of Random Walks, to which we add an absorbing state to model the detachment of the motor from the rope. We derive particular properties of such processes that have not been available before. Our results include description of the maximal distance reached from the starting point and the position from which detachment takes place. Finally, we apply our theoretical results to a concrete established model of the transport molecule Kinesin V.
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Kirkham, Sharon Kaye. "On the mathematical modelling of cerebral autoregulation." Thesis, University of Southampton, 2001. https://eprints.soton.ac.uk/50623/.
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Cerebral autoregulation is the process by which blood flow to the brain is maintained despite changes in arterial blood pressure. Experiments using transcranial Doppler ultrasonography allow rapid measurements of blood flow velocity in the middle cerebral artery. Measurements of this velocity and a subject's arterial blood pressure are used in the assessment of the dynamic cerebral autoregulatory response. Two mathematical models representing the dynamic cerebral autoregulation response as a feedback mechanism, dependent on pressure and flow respectively, are derived. For each model two parameters are introduced, a rate of restoration and a time delay. Solutions for both flow between fixed plates and flow in a rigid pipe are obtained using Laplace transform methods. In both cases solutions for the velocity are found for a general arterial blood pressure, allowing the model to be applied to any experiment that uses changes in arterial blood pressure to assess dynamic cerebral autoregulation. Velocity profiles are determined for the thigh cuff and vacuum box experiments, modelled as a step change and sinusoidal variation in pressure gradient in the middle cerebral artery respectively. The influence of the underlying heart and breathing cycles on measurements obtained from the vacuum box experiments is assessed, before results derived using the mathematical model with a flow dependent feedback mechanism are compared with data from the two experiments. The comparisons yield similar estimates for the rate of restoration and time delay suggesting that these parameters could be independent of the pressure change stimulus and depend only on the main features of the dynamic cerebral autoregulation process. The modelling also indicates that for imposed oscillatory variations in arterial blood pressure a small phase difference between the pressure and velocity waveforms does not necessarily imply impaired autoregulation. The ratio between the percentage variation in maximum velocity and pressure can be used, along with the phase difference, to indicate more accurately the nature of the autoregulatory response. Finally, the relationship between arterial blood pressure and pressure gradient in the middle cerebral artery is modelled using electrical analogue theory. The influence of this relationship on the autoregulation model for flow in a rigid pipe is investigated.
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Warren, Christopher. "Synthesis, Characterization, and Functionalization of Magnetic Iron Nanoparticles for Enhanced Biological Applications." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3283.
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The transition metal ferrites of composition MFe2O4 where M is Fe, Co, or Ni are well established materials for various biological applications due to their interesting magnetic properties. Their elemental and stochiometric composition can be easily manipulated which allows further tuning of their ferrimagnetic properties. By changing the identity of M and by changing the crystallite size of the ferrites, nanocrystals with diverse magnetic properties can be systematically produced. Furthermore, ferrites are more stable in diverse chemical environments, as compared to metallic nanoparticles, which make ferrites particularly useful for a broad range of biomedical applications, especially in the field of magnetic resonance imaging and cell labeling. In this work, spinel ferrites of composition CoFe2O4, NiFe2O4, and Ni.5Co.5 Fe2O4 were synthesized by a polyol method utilizing ethylene glycol as the solvent, reducing agent, and surfactant. The nanoparticles produced were surface coated with 3-aminopropyltriethoxy silane to increase solubility as well as to serve as an anchor for further conjugation with targeting substrates such as peptides and antibodies. The first part of this dissertation was focused on using the polyol method to produce nanoparticles of various metallic compositions. In each case, the polyol method provided an easy one-pot method to produce metallic as well as metal oxide nanocrystals. Utilizing the polyol method, ferrites of CoFe2O4, NiFe2O4, and Ni.5Co.5 Fe2O4 were produced with size ranges between 20 nm and 50 nm depending on the reaction time in the polyol. The second part of this dissertation was concerned with the functionalization of the nanoparticles to serve as an anchor for further conjugation with targeting substrates in the immunoaffinity separation of food borne pathogens. These nanoparticles were functionalized using an anti-E. coli O157:H7 antibody, mixed with a food matrix, and then subsequently removed from the food matrix by an external magnet in order to be analyzed by Matrix Assisted Laser Desorption Ionization/Time of Flight (MALDI/TOF) Mass Spectrometry as a rapid identification method of bacterial pathogens. Furthermore, magnetic resonance imaging (MRI) was carried out on the polyol produced ferrites in order to measure the transverse relaxation time (T2) of the nanoparticles in order to investigate the size dependence and crystallite composition of the particles ability to affect the transverse relaxivity rarte (r2). Further understanding of how ferrite composition and crystallite size affect their magnetic properties and resulting MRI contrast abilities will provide insight into the best materials for the next generation of contrast agents. Lastly, the ability of nanoparticles to serve as a stationary phase material for reversed phase ultrahigh pressure liquid chromatography will be discussed as a novel separation technique.
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Mangan, Niall Mari. "Organization and diffusion in biological and material fabrication problems." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11130.
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This thesis is composed of two problems. The first is a systems level analysis of the carbon concentrating mechanism in cyanobacteria. The second presents a theoretical analysis of femtosecond laser melting for the purpose of hyperdoping silicon with sulfur. While these systems are very distant, they are both relevant to the development of alternative energy (production of biofuels and methods for fabricating photovoltaics respectively). Both problems are approached through analysis of the underlying diffusion equations.
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Huang, Tien Liang. "Design, synthesis, and biological evaluation of inhibitors for N⁸-acetyspermidine deacetglase and spermidine N⁸-acetytransferase." Scholarly Commons, 1989. https://scholarlycommons.pacific.edu/uop_etds/2179.
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Previous studies active-site-directed metal coordinating reagents and substrate analogues of (N8-AcSpd) have indicated that the catalytic mechanism of N8-AcSpd deacetylase involved a transition state metal. Several ω-amino substituted carboxylic acids were tested and results confirmed the probability of the deacetylase having a mechanism similar to the metalloproteases. Based on these results, several analogues of spermidine containing metal coordinating ligands were designed and synthesized. In addition, a potential transition state analogue of the nuclear enzyme, spermidine N8-acetyltransferase (N8-SAT) was also synthesized. All compounds were assayed in a 100,000g cytosol fraction form rat liver for their ability to inhibit the acetylation of radiolabeled substrate, [acetyl-3H]-N8-AcSpd. The apparent Ki (app Ki) values were determined from Dixon plots. The apparent Km of N8-AcSpd for the cytosolic deacetylase is 11.0 μM.
Based on the results of this investigation and results obtained earlier in this laboratory, hypothetical models for the binding interactions of substrates and inhibitors to the active sites of N8-AcSpd deacetylase and N8-SAT are drawn schematically. The mechanism of catalysis of N8-AcSpd deacetylase also is proposed.
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Gupta, Manish. "Complexity Reduction for Near Real-Time High Dimensional Filtering and Estimation Applied to Biological Signals." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493389.
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Real-time processing of physiological signals collected from wearable sensors that can be done with low computational power is a requirement for continuous health monitoring. Such processing involves identifying underlying physiological state x from a measured biomedical signal y, that are related stochastically: y = f(x; e) (here e is a random variable). Often the state space of x is large, and the dimensionality of y is low: if y has dimension N and S is the state space of x then |S| >> N, since the purpose is to infer a complex physiological state from minimal measurements. This makes real-time inference a challenging task. We present algorithms that address this problem by using lower dimensional approximations of the state. Our algorithms are based on two techniques often used for state dimensionality reduction: (a) decomposition where variables can be grouped into smaller sets, and (b) factorization where variables can be factored into smaller sets. The algorithms are computationally inexpensive, and permit online application. We demonstrate their use in dimensionality reduction by successfully solving two real complex problems in medicine and public safety.
Motivated originally by the problem of predicting cognitive fatigue state from EEG (Chapter 1), we developed the Correlated Sparse Signal Recovery (CSSR) algorithm and successfully applied it to the problem of elimination of blink artifacts in EEG from awake subjects (Chapter 2). Finding the decomposition x = x1+ x2 into a low dimensional representation of the artifact signal x1 is a non-trivial problem and currently there are no online real-time methods accurately solve the problem for small N (dimensionality of y). By using a skew-Gaussian dictionary and a novel method to represent group statistical structure, CSSR is able to identify and remove blink artifacts even from few (e.g. 4-6) channels of EEG recordings in near real-time. The method uses a Bayesian framework. It results in more effective decomposition, as measured by spectral and entropy properties of the decomposed signals, compared to some state-of-the-art artifact subtraction and structured sparse recovery methods. CSSR is novel in structured sparsity: unlike existing group sparse methods (such as block sparse recovery) it does not rely on the assumption of a common sparsity profile. It is also a novel EEG denoising method: unlike state-of-the art artifact removal technique such as independent components analysis, it does not require manual intervention, long recordings or high density (e.g. 32 or more channels) recordings. Potentially this method of denoising is of tremendous utility to the medical community since EEG artifact removal is usually done manually, which is a lengthy tedious process requiring trained technicians and often making entire epochs of data unuseable. Identification of the artifact in itself can be used to determine some physiological state relevant from the artifact properties (for example, blink duration and frequency can be used as a marker of fatigue). A potential application of CSSR is to determine if structurally decomposed cortical EEG (i.e. non-spectral ) representation can instead be used for fatigue prediction.
A new E-M based active learning algorithm for ensemble classification is presented in Chapter 3 and applied to the problem of detection of artifactual epochs based upon several criteria including the sparse features obtained from CSSR. The algorithm offers higher accuracy than existing ensemble methods for unsupervised learning such as similarity- and graph-based ensemble clustering, as well as higher accuracy and lower computational complexity than several active learning methods such as Query-by-Committee and Importance-Weighted Active Learning when tested on data comprising of noisy Gaussian mixtures. In one case we were to successfully identify artifacts with approximately 98% accuracy based upon 31-dimensional data from 700,000 epochs in a matter of seconds on a personal laptop using less than 10% active labels. This is to be compared to a maximum of 94% from other methods. As far as we know, the area of active learning for ensemble-based classification has not been previously applied to biomedical signal classification including artifact detection; it can also be applied to other medical areas, including classification of polysomnographic signals into sleep stages.
Algorithms based upon state-space factorization in the case where there is unidirectional dependence amongst the dynamics groups of variables ( the "Cascade Markov Model") are presented in Chapters 4. An algorithm for estimation of factored state where dynamics follow a Markov model from observations is developed using E-M (i.e. a version of Baum-Welch algorithm on factored state spaces) and applied to real-time human gait and fall detection. The application of factored HMMs to gait and fall detection is novel; falls in the elderly are a major safety issue. Results from the algorithm show higher fall detection accuracy (95%) than that achieved with PCA based estimation (70%). In this chapter, a new algorithm for optimal control on factored Markov decision processes is derived. The algorithm, in the form of decoupled matrix differential equations, both is (i) computationally efficient requiring solution of a one-point instead of two-point boundary value problem and (ii) obviates the "curse of dimensionality" inherent in HJB equations thereby facilitating real-time solution. The algorithm may have application to medicine, such as finding optimal schedules of light exposure for correction of circadian misalignment and optimal schedules for drug intervention in patients.
The thesis demonstrates development of new methods for complexity reduction in high dimensional systems and that their application solves some problems in medicine and public safety more efficiently than state-of-the-art methods.Engineering and Applied Sciences - Applied Math
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Yates, Phillip. "An Inferential Framework for Network Hypothesis Tests: With Applications to Biological Networks." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2200.
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The analysis of weighted co-expression gene sets is gaining momentum in systems biology. In addition to substantial research directed toward inferring co-expression networks on the basis of microarray/high-throughput sequencing data, inferential methods are being developed to compare gene networks across one or more phenotypes. Common gene set hypothesis testing procedures are mostly confined to comparing average gene/node transcription levels between one or more groups and make limited use of additional network features, e.g., edges induced by significant partial correlations. Ignoring the gene set architecture disregards relevant network topological comparisons and can result in familiar n
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Patke, Usha. "Inquiry-based laboratory investigations and student performance on standardized tests in biological science." ScholarWorks, 2011. https://scholarworks.waldenu.edu/dissertations/1089.
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Achievement data from the 3rd International Mathematics and Sciences Study and Program for International Student Assessment in science have indicated that Black students from economically disadvantaged families underachieve at alarming rates in comparison to White and economically advantaged peer groups. The study site was a predominately Black, urban school district experiencing underachievement. The purpose of this correlational study was to examine the relationship between students' use of inquiry-based laboratory investigations and their performance on the Biology End of Course Test, as well as to examine the relationship while partialling out the effects of student gender. Constructivist theory formed the theoretical foundation of the study. Students' perceived levels of experience with inquiry-based laboratory investigations were measured using the Laboratory Program Variable Inventory (LPVI) survey. LPVI scores of 256 students were correlated with test scores and were examined by student gender. The Pearson correlation coefficient revealed a small direct correlation between students' experience in inquiry-based laboratory investigation classes and standardized test scores on the Biology EOCT. A partial correlational analysis indicated that the correlation remained after controlling for gender. This study may prompt a change from teacher-centered to student-centered pedagogy at the local site in order to increase academic achievement for all students. The results of this study may also influence administrators and policy makers to initiate local, state, or nationwide curricular development. A change in curriculum may promote social change as students become more competent, and more able, to succeed in life beyond secondary school.
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Qin, Yu. "Computations and Algorithms in Physical and Biological Problems." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11478.
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This dissertation presents the applications of state-of-the-art computation techniques and data analysis algorithms in three physical and biological problems: assembling DNA pieces, optimizing self-assembly yield, and identifying correlations from large multivariate datasets. In the first topic, in-depth analysis of using Sequencing by Hybridization (SBH) to reconstruct target DNA sequences shows that a modified reconstruction algorithm can overcome the theoretical boundary without the need for different types of biochemical assays and is robust to error. In the second topic, consistent with theoretical predictions, simulations using Graphics Processing Unit (GPU) demonstrate how controlling the short-ranged interactions between particles and controlling the concentrations optimize the self-assembly yield of a desired structure, and nonequilibrium behavior when optimizing concentrations is also unveiled by leveraging the computation capacity of GPUs. In the last topic, a methodology to incorporate existing categorization information into the search process to efficiently reconstruct the optimal true correlation matrix for multivariate datasets is introduced. Simulations on both synthetic and real financial datasets show that the algorithm is able to detect signals below the Random Matrix Theory (RMT) threshold. These three problems are representatives of using massive computation techniques and data analysis algorithms to tackle optimization problems, and outperform theoretical boundary when incorporating prior information into the computation.Engineering and Applied Sciences
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Shimizu, Kristen N. M. "Water quality monitoring of biological contaminants -- rapid, on-site detection technologies." Scholarly Commons, 2012. https://scholarlycommons.pacific.edu/uop_etds/824.
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Access to a safe drinking water supply is a critical issue in a number of places across the globe. Conventional water quality monitoring technologies are slow, often taking at least two days to produce results. In recent years, there has been extensive research into emerging technologies that provide real-time results; however, there is no technology which detects all classes of biological contaminants. Biological contaminants are 5 particularly difficult to detect and quantify due to low concentrations typically present. It is not feasible to identify all biological contaminants present in a water supply; therefore, it is necessary to determine which are of highest concern based factors such as health significance, infectivity, and prevalence. The intent of this research is to evaluate microbiological detection methods and technologies based on their applicability for continuous real-time detection. This research will complement current research on biological water quality monitoring technologies for use in developing regions or in the event of an emergency and will provide suggestions for future research efforts.
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Gilbert, Mark. "Modelling species invasions in heterogeneous landscapes." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:944d15d3-257a-47e5-acb9-9bdfba26985b.
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Biological invasions are devastating ecosystems and economies world-wide, while many native species' survival depends on their ability to track climate change. Characterising the spread of biological populations is therefore of utmost importance, and can be studied with spatially explicit, discrete-time integro-difference equations (IDEs), which reflect numerous species' processes of demography and dispersal. While spatial variation has often been ignored when implementing IDE models, real landscapes are rarely spatially uniform and environmental variation is crucial in determining biological spread. To address this, we use novel methods to characterise population spread in heterogeneous landscapes. Asymptotic analysis is used for highly fragmented landscapes, where habitat patches are isolated and smaller than the dispersal scale, and in landscapes with low environmental variation, where the ecological parameters vary by no more than a small factor from their mean values. We find that the choice of dispersal kernel determines the effect of landscape structure on spreading speed, indicating that accurately fitting a kernel to data is important in accurately predicting speed. For the low-variation case, the spreading speeds in the heterogeneous and homogeneous landscapes differ by ϵ2, where ϵ governs the degree of variation, suggesting that in many cases, a simpler homogeneous model gives similar spread rates. For irregular landscapes, analytical methods become intractable and numerical simulation is needed to predict spread. Accurate simulation requires high spatial resolution, which, using existing techniques, requires prohibitive amounts of computational resources (RAM, CPU etc). We overcome this by developing and implementing a novel algorithm that uses adaptive mesh refinement. The approximations and simulation algorithm produce accurate results, with the adaptive algorithm providing large improvements in efficiency without significant losses of accuracy compared to non-adaptive simulations. Hence, the adaptive algorithm enables faster simulation at previously unfeasible scales and resolutions, permitting novel areas of scientific research in species spread modelling.
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Venkataraman, Chandrasekhar. "Reaction-diffusion systems on evolving domains with applications to the theory of biological pattern formation." Thesis, University of Sussex, 2011. http://sro.sussex.ac.uk/id/eprint/6908/.
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In this thesis we investigate a model for biological pattern formation during growth development. The pattern formation phenomenon is described by a reaction-diffusion system on a time-dependent domain. We prove the global existence of solutions to reaction-diffusion systems on time-dependent domains. We extend global existence results for a class of reaction-diffusion systems on fixed domains to the same systems posed on spatially linear isotropically evolving domains. We demonstrate that the analysis is applicable to many systems that commonly arise in the theory of pattern formation. Our results give a mathematical justification to the widespread use of computer simulations of reaction-diffusion systems on evolving domains. We propose a finite element method to approximate the solutions to reaction-diffusion systems on time-dependent domains. We prove optimal convergence rates for the error in the method and we derive a computable error estimator that provides an upper bound for the error in the semidiscrete (space) scheme. We have implemented the method in the C programming language and we verify our theoretical results with benchmark computations. The method is a robust tool for the study of biological pattern formation, as it is applicable to domains with irregular geometries and nonuniform evolution. This versatility is illustrated with extensive computer simulations of reaction-diffusion systems on evolving domains. We observe varied pattern transitions induced by domain evolution, such as stripe to spot transitions, spotsplitting, spot-merging and spot-annihilation. We also illustrate the striking effects of spatially nonuniform domain evolution on the position, orientation and symmetry of patterns generated by reaction-diffusion systems. To improve the efficiency of the method, we have implemented a space-time adaptive algorithm where spatial adaptivity is driven by an error estimator and temporal adaptivity is driven by an error indicator. We illustrate with numerical simulations the dramatic improvements in accuracy and efficiency that are achieved via adaptivity. To demonstrate the applicability and generality of our methodology, we examine the process of parr mark pattern formation during the early development of the Amago trout. By assuming the existence of chemical concentrations residing on the surface of the Amago fish which react and diffuse during surface evolution, we model the pattern formation process with reactiondiffusion systems posed on evolving surfaces. An important generalisation of our study is the experimentally driven modelling of the fish's developing body surface. Our results add weight to the feasibility of reaction-diffusion system models of fish skin patterning, by illustrating that a reaction-diffusion system posed on an evolving surface generates transient patterns consistent with those experimentally observed on the developing Amago trout. Furthermore, we conclude that the surface evolution profile, the surface geometry and the curvature are key factors which play a pivotal role in pattern formation via reaction-diffusion systems.
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Kabolizadeh, Peyman. "Mechanisms of Accumulation and Biological Consequences of Polynuclear Platinum Compounds." VCU Scholars Compass, 2007. http://hdl.handle.net/10156/1913.
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Bickham, Anna V. "Microfabricated Fluidic Devices for Biological Assays and Bioelectronics." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8470.
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Microfluidics miniaturizes many benchtop processes and provides advantages of low cost, reduced reagent usage, process integration, and faster analyses. Microfluidic devices have been fabricated from a wide variety of materials and methods for many applications. This dissertation describes four such examples, each employing different features and fabrication methods or materials in order to achieve their respective goals. In the first example of microfluidic applications in this dissertation, thermoplastics are hot embossed to form t-shaped channels for microchip electrophoresis. These devices are used to separate six preterm birth (PTB) biomarkers and establish a limit of detection for each. The next chapter describes 3D printed devices with reversed-phase monoliths for solid-phase extraction and on-chip fluorescent labeling of PTB biomarkers. I demonstrate the optimization of the monolith and selective retention of nine PTB biomarkers, the first microchip study to perform an analysis on this entire panel. The third project describes the iterative design and fabrication of glass/polydimethylsiloxane (PDMS) devices with gold and nickel electrodes for the self-assembly of DNA nanotubes for site-selective placement of nanowires. Simple flow channels and “patch electrode” devices were successfully used, and DNA seeding was achieved on gold electrodes. Finally, a 3D printed device for cancer drug screening was developed as a replacement for one previously fabricated in PDMS. Devices of increasing complexity were fabricated, and those tested found to give good control over fluid flow for multiple inlets and valves. Although the applications and methods of these projects are varied, the work in this dissertation demonstrates the potential of microfluidics in several fields, particularly for diagnostics, therapeutics, and nanoelectronics. Furthermore, it demonstrates the importance of applying appropriate tools to each problem to gain specific advantages. Each of the described devices has the potential for increased complexity and integration, which further emphasizes the advantages of miniaturized analyses and the potential for microfluidics for analytical testing in years to come.
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Smith, Martha Anne. "The organizational culture of the academic department: A case study of a Department of Biological Sciences." W&M ScholarWorks, 1992. https://scholarworks.wm.edu/etd/1539618811.
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The purpose of this study was to examine theories of organizational culture typically applied to the university level of organization and their applicability to the academic department. Chaffee and Tierney's (1988) theory of organizational culture, dimensions of culture, and leadership strategies became the basis for a qualitative case study of a Department of Biological Sciences in a metropolitan university.;Interviews of current faculty members, current and former deans, and other administrators were conducted. Observations were made of faculty meetings and retreats and of departmental governance committee meetings. Extensive review of documents and correspondence covering more that twenty years provided additional data.;Interview and observation transcripts and documents were analyzed in terms of Chaffee and Tierney's (1988) concepts of the structural, environmental, and values dimensions of the department. Linear, adaptive, and interpretive strategies of faculty members and the department chair were identified.;The department was found to have what Clark (1972) refers to as strong organizational saga, or a sense of unique accomplishment which serves to maintain and perpetuate the integrity of the culture. Central to the value system of the Department of Biological Sciences is the shared sense that the department is unique in the degree to which faculty members work together cooperatively for the good of the department. These strong values were rooted in an earlier era when the department was experiencing growth and development of its research programs under adverse circumstances.;The primary usefulness of the results of this study go far beyond the particular findings for this individual academic department. Most important is the demonstration of the value of using this method of organizational analysis to understand the role of culture in shaping and perpetuating the organization. Administrators, department chairs, and faculty members can enhance their understanding of the departmental organization by applying concepts of organizational culture.;Further study and analysis are needed to evaluate disciplinary and institutional similarities and differences in departmental culture and to expand the existing theory to accommodate the variety of academic departments in colleges and universities.
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Karmakar, Saurav. "Statistical Stability and Biological Validity of Clustering Algorithms for Analyzing Microarray Data." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/math_theses/3.
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Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.
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Benkirane, Soufiene. "Process algebra for epidemiology : evaluating and enhancing the ability of PEPA to describe biological systems." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/3603.
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Modelling is a powerful method for understanding complex systems, which works by simplifying them to their most essential components. The choice of the components is driven by the aspects studied. The tool chosen to perform this task will determine what can be modelled, the maximum number of components which can be represented, as well as the analyses which can be performed on the system. Performance Evaluation Process Algebra (PEPA) was initially developed to tackle computer systems issues. Nevertheless, it possesses some interesting properties which could be exploited for the study of epidemiological systems. PEPA's main advantage resides in its capacity to change scale: the assumptions and parameter values describe the behaviour of a single individual, while the resulting model provides information on the population behaviour. Additionally, stochasticity and continuous time have already proven to be useful features in epidemiology. While each of these features is already available in other tools, to find all three combined in a single tool is novel, and PEPA is proposed as a useful addition to the epidemiologist's toolbox. Moreover, an algorithm has been developed which allows converting a PEPA model into a system of Ordinary Differential Equations (ODEs). This provides access to countless additional software and theoretical analysis methods which enable the epidemiologist to gain further insight into the model. Finally, most existing tools require a deep understanding of the logic they are based on and the resulting model can be difficult to read and modify. PEPA's grammar, on the other hand, is easy to understand since it is based on few, yet powerful concepts. This makes it a very accessible formalism for any epidemiologist. The objective of this thesis is to determine precisely PEPA's ability to describe epidemiological systems, as well as extend the formalism when required. This involved modelling two systems: the bubonic plague in prairie dogs, and measles in England and Wales. These models were chosen as they exhibit a good range of typical features, allowing to thoroughly test PEPA. All features required in each of these models have been analysed in detail, and a solution has been provided for representing each of these features. While some of them could be expressed in a straightforward manner, PEPA did not provide the tools to express others. In those cases, we determined methods to approach the desired behaviour, and the limitations of said methods were carefully analysed. In the case of models with a structured population, PEPA was extended to simplify their expression and facilitate the writing process of the PEPA model. The work also required the development of an algorithm to derive ODEs adapted to the type of models encountered. Finally, the PEPAdum software was developed to assist the modeller in the generation and analysis of PEPA models, by simplifying the process of writing a PEPA model with compartments, performing the average of stochastic simulations and deriving and explicitly providing the ODEs using the Stirling Amendment.
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吳寶明 and Baoming Wu. "Image-based monitoring and wavelet multi-rhythm analysis of long-term locomotor activity." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31242467.
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Wu, Baoming. "Image-based monitoring and wavelet multi-rhythm analysis of long-term locomotor activity." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23273148.
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Hengenius, James B. "Quantitative modeling of spatiotemporal systems| Simulation of biological systems and analysis of error metric effects on model fitting." Thesis, Purdue University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3687049.
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Understanding the biophysical processes underlying biological and biotechnological processes is a prerequisite for therapeutic treatments and technological innovation. With the exponential growth of computational processing speed, experimental findings in these fields have been complemented by dynamic simulations of developmental signaling and genetic interactions. Models provide means to evaluate "emergent" properties of systems sometimes inaccessible by reductionist approaches, making them test beds for biological inference and technological refinement.
The complexity and interconnectedness of biological processes pose special challenges to modelers; biological models typically possess a large number of unknown parameters relative to their counterparts in other physical sciences. Estimating these parameter values requires iterative testing of parameter values to find values that produce low error between model and data. This is a task whose length grows exponentially with the number of unknown parameters. Many biological systems require spatial representation (i.e., they are not well-mixed systems and change over space and time). Adding spatial dimensions complicates parameter estimation by increasing computational time for each model evaluation. Defining error for model-data comparison is also complicated on spatial domains. Different metrics compare different features of data and simulation, and the desired features are dependent on the underlying research question.
This dissertation documents the modeling, parameter estimation, and simulation of two spatiotemporal modeling studies. Each study addresses an unanswered research question in the respective experimental system. The former is a 3D model of a nanoscale amperometric glucose biosensor; the model was used to optimize the sensor's design for improved sensitivity to glucose. The latter is a 3D model of the developmental gap gene system that helps establish the bodyplan of Drosophila melanogaster; I wished to determine if the embryo's geometry alone was capable of accounting for observed spatial distributions of gap gene products and to infer feasible genetic regulatory networks (GRNs) via parameter estimation of the GRN interaction terms. Simulation of the biosensor successfully predicted an optimal electrode density on the biosensor surface, allowing us to fabricate improved biosensors. Simulation of the gap gene system on 1D and 3D embryonic demonstrated that geometric effects were insufficient to produce observed distributions when simulated with previously reported GRNs. Noting the effects of the error definition on the outcome of parameter estimation, I conclude with a characterization of assorted error definitions (objective functions), describe data characteristics to which they are sensitive, and end with a suggested procedure for objective function selection. Choice of objective function is important in parameter estimation of spatiotemporal system models in varied biological and biotechnological disciplines.
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Riojas, Amanda G. "Application of the Correlation Consistent Composite Approach to Biological Systems and Noncovalent Interactions." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801886/.
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Advances in computing capabilities have facilitated the application of quantum mechanical methods to increasingly larger and more complex chemical systems, including weakly interacting and biologically relevant species. One such ab initio-based composite methodology, the correlation consistent composite approach (ccCA), has been shown to be reliable for the prediction of enthalpies of formation and reaction energies of main group species in the gas phase to within 1 kcal mol-1, on average, of well-established experiment, without dependence on experimental parameterization or empirical corrections. In this collection of work, ccCA has been utilized to determine the proton affinities of deoxyribonucleosides within an ONIOM framework (ONIOM-ccCA) and to predict accurate enthalpies of formation for organophosphorus compounds. Despite the complexity of these systems, ccCA is shown to result in enthalpies of formation to within ~2 kcal mol-1 of experiment and predict reliable reaction energies for systems with little to no experimental data. New applications for the ccCA method have also been introduced, expanding the utility of ccCA to solvated systems and complexes with significant noncovalent interactions. By incorporating the SMD solvation model into the ccCA formulation, the Solv-ccCA method is able to predict the pKa values of nitrogen systems to within 0.7 pKa unit (less than 1.0 kcal mol-1), overall. A hydrogen bonding constant has also been developed for use with weakly interacting dimers and small cluster compounds, resulting in ccCA interaction energies for water clusters and dimers of the S66 set to within 1.0 kcal mol-1 of well-established theoretical values.
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Benedetti, Brad. "Drug Design, Biological Activity, and Metabolic Consequences of Cytotoxic Platinum Compounds: Utilizing Fluorescent Tagging to Understand Drug Action and Metabolism." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/195.
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Platinum drugs are among the most commonly used chemotherapeutics for the treatment of testicular, head and neck, ovarian, small cell lung, and colorectal carcinomas. Although the current set of platinum chemotherapeutics has proven somewhat successful, the overall success of platinum based drugs is limited due to acquired drug resistance and a limited range of tumor types that are treatable with the current regime. The development of novel cytotoxic platinum based compounds, both trans- and polynuclear, provides for the promising treatment of clinical platinum drug resistant tumors. While the cytotoxic activity of platinum drugs provides for a hopeful outlook, the ultimate factors that affect the success of chemotherapeutics are the fine balance between cytotoxic activity and metabolic deactivation. In general, this work reports the drug design/drug action, and pharmacokinetic consequences of anticancer compounds aimed to fight mechanisms of cisplatin resistance. In the first project, we report the biological and biophysical studies aimed at understanding and improving upon the pharmacokinetic properties of chemotherapeutics; specifically, understanding their interactions with serum proteins. This work resulted in the discovery of using carboxylate ligands to modulate the reactivity of trans-platinum based compounds towards sulfur containing proteins with consequent effects on drug efficacy. In addition, we report an in depth look into the biological consequences of non-covalent platinum drug-protein interactions on drug efficacy, and introduce the use of novel Platinum-NBD fluorescent conjugates as probes for drug metabolism. In the second project we report the design, synthesis, and biological consequences of fluorescent drug derivatives based on the NBD fluorophore, for use in understanding drug action and drug metabolism. As a result of this fluorescent drug labeling, TriplatinNC, a non-covalent platinum based chemotherapeutic, was found to specifically target nucleolar DNA/RNA, due to its high charge, and inhibit ribosomal RNA production in cancer cells. The use of fluorescent derivatization also resulted in the development of a series of novel water-soluble trans-platinum complexes, with greater cytotoxicity than cisplatin. Therefore, these data resulted in the understanding of, and improvement upon the pharmacokinetic profile of platinum chemotherapeutics, as well as the development of novel fluorescent platinum conjugates with novel metabolic and cytotoxic profiles.
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Vera-Licona, Martha Paola. "Algorithms for modeling and simulation of biological systems; applications to gene regulatory networks." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28073.
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Systems biology is an emergent field focused on developing a system-level understanding of biological systems. In the last decade advances in genomics, transcriptomics and proteomics have gathered a remarkable amount data enabling the possibility of a system-level analysis to be grounded at a molecular level. The reverse-engineering of biochemical networks from experimental data has become a central focus in systems biology. A variety of methods have been proposed for the study and identification of the systemâ s structure and/or dynamics.
The objective of this dissertation is to introduce and propose solutions to some of the challenges inherent in reverse-engineering of biological systems.
First, previously developed reverse engineering algorithms are studied and compared using data from a simulated network. This study draws attention to the necessity for a uniform benchmark that enables an ob jective comparison and performance evaluation of reverse engineering methods.
Since several reverse-engineering algorithms require discrete data as input (e.g. dynamic Bayesian network methods, Boolean networks), discretization methods are being used for this purpose. Through a comparison of the performance of two network inference algorithms that use discrete data (from several different discretization methods) in this work, it has been shown that data discretization is an important step in applying network inference methods to experimental data.
Next, a reverse-engineering algorithm is proposed within the framework of polynomial dynamical systems over finite fields. This algorithm is built for the identification of the underlying network structure and dynamics; it uses as input gene expression data and, when available, a priori knowledge of the system. An evolutionary algorithm is used as the heuristic search method for an exploration of the solution space. Computational algebra tools delimit the search space, enabling also a description of model complexity. The performance and robustness of the algorithm are explored via an artificial network of the segment polarity genes in the D. melanogaster.
Once a mathematical model has been built, it can be used to run simulations of the biological system under study. Comparison of simulated dynamics with experimental measurements can help refine the model or provide insight into qualitative properties of the systems dynamical behavior. Within this work, we propose an efficient algorithm to describe the phase space, in particular to compute the number and length of all limit cycles of linear systems over a general finite field.
This research has been partially supported by NIH Grant Nr. RO1GM068947-01.Ph. D.
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Kirkegaard, Julius Bier. "Physical and stochastic aspects of microorganism behaviour." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277543.
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This thesis studies physical and stochastic aspects of microorganisms. From the point of view of $\textit{physics}$, the studies in this thesis are motivated by the goal of gaining biological insight using the machinery of physics and mathematics. From the point of view of $\textit{biology}$, the studies in this thesis focus primarily on choanoflagellates, eukaryotes that are the closest living unicellular relatives of animals. This choice of model organism was motivated by the important biological question of the origin of multicellularity. Why was it that single-celled organisms evolved to become multicellular? In particular, we study closely the species $\textit{Salpingoeca rosetta}$, which has the ability to form colonies that resemble true multicellular organisms. A large part of this thesis deals with the random walks of microorganisms. We study these active random walks both for single cells and those composed of individual organisms adhered together. The latter colonial random walkers are typified by choanoflagellates. We develop quantitative theories and use these to extract physical parameters. The increasing ocean oxygen levels in the Precambrian era are thought to be an important factor in the emergence of complex multicellular, animal life. As a first step, we address this situation by studying the response of $\textit{S. rosetta}$ to oxygen gradients. We find that $\textit{S. rosetta}$ displays positive aerotaxis. Analysis of the spatial population distributions provides evidence for logarithmic sensing of oxygen, which enhances sensing in low oxygen neighbourhoods. Analysis of search strategy models on the experimental colony trajectories finds that choanoflagellate aerotaxis is consistent with stochastic navigation, the statistics of which are captured using an effective continuous version of classical run-and-tumble chemotaxis. We compare this continuous run-to-tumble with the run-and-tumble seen in bacteria by formulating a general model for persistent run-and-tumble. We find that although an optimal persistence does exist for a given tumble frequency, in the full parameter space there is a continuum of optimal solutions. We develop this model further by introducing finite tumble times. Efficient uptake of prey and nutrients from the environment is an important component in the fitness of all microorganisms, and its dependence on size may reveal clues to the origins of evolutionary transitions to multicellularity. We examine these issues in depth for choanoflagellates, finding that in the absence of other requirements and in a homogeneously nutritious environment, the optimal strategy to maximise filter feeding is to swim fast which favours swimming unicells. In contrast, in large external flows, a sessile form becomes advantageous. Effects of prey diffusion are discussed and are also found to be advantageous for the swimming unicell. Finally, we consider the switching between synchronous and anti-synchronous beating of flagella in the green alga $\textit{Chlamydomonas}$, a phenomenon that results in run-and-tumble behaviour in eukaryotes. We develop a theoretical model to describe this beating and use it to argue that the synchrony itself is obtained intracellularly, whereas the flagella shapes are most likely strongly influenced by hydrodynamic interactions.
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Alexopoulos, Eftichia. "Crystallographic and modeling studies of intermolecular interactions of biological interest." Doctoral thesis, [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972659137.
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Weber, Nathanial. "Native and Community College Transfer Students in Biological Sciences at a Four-Year Institution: A Comparative Study." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3329.
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The purpose of this study was to investigate differences between native and community college transfer students and identify factors that predicted upper-level biology course grade-point average and final overall grade-point average at a four-year institution in biological sciences. The results of this study indicated four-year institution persistence was not significantly related to gender, high school grade-point average, or ACT composite score. Persistence was significantly related to transfer status; whether the student was a native or community college transfer student with native students persisting at a higher rate at the four-year institution than community college transfer students. Furthermore, ACT composite score, high school grade-point average, final overall grade-point average, and upper level biology course grade-point average were significantly related to transfer status. Multiple regression analyses indicated high school grade-point average and ACT composite score were significantly predictive of upper-level biology course grade-point average while high school grade-point average, ACT composite score, and Pell eligibility were significantly predictive of final overall grade-point average.
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Wolfrum, Bernhard. "Cavitation and shock wave effects on biological systems." Doctoral thesis, [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971895082.
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