DOCSLIB.ORG

Quantitative Cell Biology with the Virtual Cellq

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Quantitative Cell Biology with the Virtual Cellq 570 Review TRENDS in Cell Biology Vol.13 No.11 November 2003 Quantitative cell biology with the Virtual Cellq Boris M. Slepchenko1, James C. Schaff1, Ian Macara2 and Leslie M. Loew1 1Center For Biomedical Imaging Technology and National Resource for Cell Analysis and Modeling, Department of Physiology, University of Connecticut Health Center, Farmington, CT 06030, USA 2Center for Cell Signaling and Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA Cell biological processes are controlled by an interact- techniques such as patch-clamp single-channel recording. ing set of biochemical and electrophysiological events Once such data are acquired, a crucial and obvious that are distributed within complex cellular structures. challenge is to determine how these, often disparate and Computational models, comprising quantitative data complex, details can explain the cellular process under on the interacting molecular participants in these events, investigation. The ideal way to meet this challenge is to provide a means for applying the scientific method to integrate and organize the data into a predictive model. these complex systems. The Virtual Cell is a compu- Indeed, the value of computational approaches in cell tational environment designed for cell biologists, to biology has been increasingly recognized as demonstrated facilitate the construction of models and the generation by several recent reviews [4–6] and a book [37]. However, of predictive simulations from them. This review sum- for cell biologists, formulating a mathematical model marizes how a Virtual Cell model is assembled and based on quantitative data and solving its equations to describes the physical principles underlying the calcu- generate simulations can present significant technical lations that are performed. Applications to problems in challenges. Overcoming this barrier, in order to complete nucleocytoplasmic transport and intracellular calcium the cycle of hypothesis–prediction–experiment, is required dynamics will illustrate the power of this paradigm for to understand the biophysical mechanisms underlying the elucidating cell biology. function of a cellular subsystem. To meet these needs for quantitative cell biology, we have developed a software- Fundamental cell biological processes, such as growth, modeling environment called the Virtual Cell. [7–9]. signaling, differentiation and death, are extraordinarily The Virtual Cell provides a formal framework for complex, depending upon an enormous number of different modeling biochemical, electrophysiological, and transport molecules and molecular interactions. High-throughput phenomena while considering the subcellular localization methods for analysis of DNA and RNA (genomics) and of the molecules that take part in them. This localization protein (proteomics) are now providing an overwhelming can take the form of a three-dimensional (3D) arbitrarily amount of qualitative data, cataloguing the molecules shaped cell, where the molecular species might be hetero- involved in these complex pathways. The field of ‘systems geneously distributed. The geometry of the cell, including biology’ [1–3] uses computational analyses to sift through the locations and shapes of subcellular organelles, can be the large volume of data, to identify unique sets of mol- imported directly from microscope images. Such a model ecules involved in particular cellular responses, and to explicitly considers the diffusion of the molecules within develop statistical relationships between molecules that the geometry, as well as their reactions. Simulations from suggest causal interactions. Such largely qualitative such a ‘spatial’ model require the solution of partial dif- data and analyses can offer significant insights into the ferential equations. Alternatively, the localization can be operation of large, complex biochemical systems, such as more crudely represented by considering the average those involved in metabolic and signaling networks. concentrations of molecules within well-mixed compart- To develop a full understanding of the mechanisms ments that correspond to the cellular and subcellular underlying a cell biological event, however, the quantitat- structures. This corresponds to the assumption that dif- ive physical–chemical details must be elucidated. As a fusion is fast compared with the timescales of all the first prerequisite, this requires measurements of the biochemical transformations, so that any diffusible mol- concentrations, locations, biochemical reaction rates and ecule will be uniformly distributed as soon as it is produced membrane transport kinetics for the involved molecules. and no matter how convoluted the geometry of its com- Such data can come from traditional biochemical methods, partment; such a nonspatial model will result in ordinary but are also available through direct in vivo measurements differential equations that can be numerically solved much on live cells, using modern microscope imaging with more efficiently. Thus, these two classes of models result in fluorescent probes and indicators and electrophysiological very different mathematical forms, although the original quantitative mechanistic hypothesis could be the same. q Supplementary data associated with this article can be found at doi: 10.1016/j.tcb. 2003.09.002 Simulation results on the dynamic distributions of the Corresponding author: Leslie M. Loew ([email protected]). molecules controlling a cellular function are displayed http://ticb.trends.com 0962-8924/$ - see front matter q 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.tcb.2003.09.002 Review TRENDS in Cell Biology Vol.13 No.11 November 2003 571 format also cannot capture the quantitative relationships. Box 1. Access to the Virtual Cell and its database Model features are, therefore, most effectively expressed The Virtual Cell software is maintained at a central server within the in terms of diagrams linked to mathematical expressions. National Resource for Cell Analysis and Modeling (NRCAM) at the Figure 1a shows diagrams taken directly from the Virtual University of Connecticut Health Center, and is available to users Cell user interface for the compartmental distribution of worldwide through JAVA technology on a web browser. This web- the molecules, transport processes in the ER membrane, based client-server architecture has several advantages: it obviates and the assignment of compartments to the spatial geo- the task of software distribution and installation on heterogeneous hardware platforms – the Virtual Cell runs equally well on Windows, metry of a neuroblastoma cell. Furthermore, generating Linux and Macintosh machines; enhancements and bug fixes are quantitative predictions from a model requires solving available to the entire user community as soon as they are deployed; mathematical equations (i.e. simulations). Computational the computationally intensive calculations, which might be unac- tools such as the Virtual Cell facilitate both the process of ceptably slow on the computer of a user, are optimized for a model construction and the generation of simulations. dedicated cluster of fast computers at NRCAM; and, most impor- tantly, a central database of user models, maintained at NRCAM, facilitates the online sharing of model components, collaboration, Building a model in the Virtual Cell and interactive publication of models. Building a biological model in the Virtual Cell is an iterative process that should start with a simple model that is incrementally validated against simulations and experimental observations. In our calcium dynamics illu- within the software environment and can also be exported stration, for example, an initial assumption was that the as arrays of numerical values, sets of images or digital Ins(1,4,5)P3 receptor was distributed uniformly through- movies. Box 1 provides details of the software and how to out the cell. The simulations produced under this assump- access it through the Internet. tion gave calcium amplitudes that were much too high in the neurite and too low in the soma compared with Quantitative cell biology the experimental calcium dynamics. This prompted us The paradigm that we term quantitative cell biology is to investigate the Ins(1,4,5)P3 receptor distribution by described in Figure 1, where a study of calcium dynamics immunofluorescence, which revealed that the calcium in a neuronal cell [10,11] is used as a concrete illustration. density in the soma was twice as high as that in the In this study, the neuromodulator bradykinin applied to neurite; the simulation resulting from this new elabora- the cells produced a calcium wave that starts in the neurite tion of the model provided an excellent match to the and spreads to the soma and growth cones. The calcium experimental calcium wave. Furthermore, a model (or wave was monitored using digital microscope imaging of a hypothesis) should lead to predictions that can be further fluorescent calcium indicator. The hypothesis was that tested with new experiments. For example, our model interaction of bradykinin with its receptor on the plasma predicted that if bradykinin were applied to different membrane induced the production of inositol (1,4,5)- regions of the cell, rather than globally, application to the trisphosphate (Ins(1,4,5)P ) that diffused to its receptor proximal neurite would be sufficient to produce a calcium 3 wave; but activation of bradykinin receptors in only the on the endoplasmic reticulum (ER), leading to calcium growth cone
Load more

Recommended publications
  • Rule-Based Modeling of Biochemical Systems with Bionetgen
    Rule-Based Modeling of Biochemical Systems with BioNetGen James R. Faeder1, Michael L. Blinov2, and William S. Hlavacek3 Once a model specification is processed by BNG2, Short Abstract — Rule-based modeling involves the models can be exported in one of a variety of formats, representation of molecules as structured objects and including MATLAB M-files and Systems Biology Markup molecular interactions as rules for transforming the attributes language (SBML) (3). BNG2 can also generate a network of of these objects. The approach allows systematic incorporation species and reactions for simulation using either an ODE of site-specific details about protein-protein interactions into a solver or Gillespie’s stochastic simulation algorithm (4). The model for the dynamics of a signal-transduction system, as well as other applications. The consequences of protein-protein dashed line in Fig. 1 indicates that the network generation interactions are difficult to specify and track with a and stochastic simulation engines are coupled so that for conventional modeling approach because of the large number large reaction networks species and reactions can be of protein phosphoforms and protein complexes that these generated on-the-fly as needed (5, 6). Recently, particle- interactions potentially generate. Here, we focus on how a rule- based simulation methods (7, 8) have been developed that based model is specified in the BioNetGen language (BNGL) avoid the computational costs associated with explicit and how a model specification is analyzed using the BioNetGen generation of the reachable network of species and software tool. We also discuss new developments in rule-based interactions, which can become a major bottleneck for the modeling that should enable the construction and analysis of simulation of even moderate-sized rule-based models (9).
    [Show full text]
  • Modeling Without Borders: Creating and Annotating Vcell Models Using the Web
    Modeling without Borders: Creating and Annotating VCell Models Using the Web Michael L. Blinov, Oliver Ruebenacker, James C. Schaff, and Ion I. Moraru Center for Cell Analysis and Modeling University of Connecticut Health Center Farmington, CT 06030, USA {blinov,oruebenacker,schaff,moraru}@exchange.uchc.edu http:vcell.org/sybil Abstract. Biological research is becoming increasingly complex and data-rich, with multiple public databases providing a variety of resources: hundreds of thousands of substances and interactions, hundreds of ready to use models, controlled terms for locations and reaction types, links to reference materials (data and/or publications), etc. Mathematical mod- eling can be used to integrate this complex data and create quantita- tive, testable predictions based on the current state of knowledge of a biological process. Data retrieval, visualization, flexible querying, and model annotation for future reuse, are some of the important require- ments for modeling-based research in the modern age. Here we describe an approach that we implement within the popular Virtual Cell (VCell) modeling and simulation framework in order to help connect the mod- eling community with the web of machine-processable systems biology knowledge. A new software application, called SyBiL (Systems Biology Linker), has been designed and developed for simultaneous querying of multiple systems biology knowledge bases and data sources, such as web repositories, databases, and user files, and converting the extracted and refined data into model elements. Integration of SyBiL as a component of VCell makes these capabilities easily available to a wide modeling community. Keywords: Biological databases, mathematical modeling, VCell, data conversion. 1 Introduction Mathematical modeling is increasingly necessarry to investigate the function of molecular pathways and networks, and a growing number of resources that fa- cilitate computational approaches are becoming available.
    [Show full text]
  • Vcell Quick Start Guide
    Virtual Cell Quick Start Guide 1. Pre-requisites: a. Java (version 1.4.2 or later; 1.5.x recommended) b. An Internet connection (broadband, no firewall recommended). 2. Go to http://www.vcell.org/ a. If you are a new user, first register and create a user name and password. b. If you are registered, navigate to the “VCell” page. 3. Click on one of the 2 links to run VCell as an application or as an applet: a. The software will download and start automatically (it can take a couple of minutes the first time or whenever VCell was updated since your last login) – unless you have pop-up blockers enabled and you are trying to run it as an applet, in which case you must click on the new link “Run the Virtual Cell” after logging in. • Tip: Most operating systems and browsers will work as long as you are running Java 1.4 or later, and there are no real hardware requirements. However, check the “Technical requirements” link for recommended configurations. b. VCell as an application will us Java webstart and will give the option to install a local shortcut. • Tip: Whenever you launch VCell from the local shortcut, if you are connected to the Internet, it will automatically check for and download any updated version. • Tip: You can run the local VCell application without an Internet connection and without logging in to the model database; in this case, however, you will only be able to save your work by using File Æ Export, and you will not be able to run simulations and view results until logging in.
    [Show full text]
  • Biodynamo: a New Platform for Large-Scale Biological Simulation
    Lukas Johannes Breitwieser, BSc BioDynaMo: A New Platform for Large-Scale Biological Simulation MASTER’S THESIS to achieve the university degree of Diplom-Ingenieur Master’s degree programme Software Engineering and Management submitted to Graz University of Technology Supervisor Ass.Prof. Dipl.-Ing. Dr.techn. Christian Steger Institute for Technical Informatics Advisor Ass.Prof. Dipl.-Ing. Dr.techn. Christian Steger Dr. Fons Rademakers (CERN) November 23, 2016 AFFIDAVIT I declare that I have authored this thesis independently, that I have not used other than the declared sources/resources, and that I have explicitly indicated all ma- terial which has been quoted either literally or by content from the sources used. The text document uploaded to TUGRAZonline is identical to the present master‘s thesis. Date Signature This thesis is dedicated to my parents Monika and Fritz Breitwieser Without their endless love, support and encouragement I would not be where I am today. iii Acknowledgment This thesis would not have been possible without the support of a number of people. I want to thank my supervisor Christian Steger for his guidance, support, and input throughout the creation of this document. Furthermore, I would like to express my deep gratitude towards the entire CERN openlab team, Alberto Di Meglio, Marco Manca and Fons Rademakers for giving me the opportunity to work on this intriguing project. Furthermore, I would like to thank them for vivid discussions, their valuable feedback, scientific freedom and possibility to grow. My gratitude is extended to Roman Bauer from Newcastle University (UK) for his helpful insights about computational biology detailed explanations and valuable feedback.
    [Show full text]
  • Emerging Whole-Cell Modeling Principles and Methods
    Available online at www.sciencedirect.com ScienceDirect Emerging whole-cell modeling principles and methods 1,2,3 1,2,3 1,2,3 Arthur P Goldberg , Bala´ zs Szigeti , Yin Hoon Chew , 1,2,3 1,2 1,2 John AP Sekar , Yosef D Roth and Jonathan R Karr Whole-cell computational models aim to predict cellular models should predict, or how to achieve WC models. phenotypes from genotype by representing the entire genome, Nevertheless, we and others are beginning to leverage the structure and concentration of each molecular species, advances in measurement technology, bioinformatics, each molecular interaction, and the extracellular environment. rule-based modeling, and multi-algorithmic simulation Whole-cell models have great potential to transform to develop WC models [4 ,5 ,6,7 ,8 ,9 ]. However, sub- bioscience, bioengineering, and medicine. However, numerous stantial work remains to achieve WC models [10 ,11 ]. challenges remain to achieve whole-cell models. Nevertheless, researchers are beginning to leverage recent progress in To build consensus on WC modeling, we propose a set of measurement technology, bioinformatics, data sharing, rule- key physical and chemical mechanisms that WC models based modeling, and multi-algorithmic simulation to build the should aim to represent, and a set of key phenotypes that first whole-cell models. We anticipate that ongoing efforts to WC models should aim to predict. We also summarize develop scalable whole-cell modeling tools will enable the experimental and computational progress that is dramatically more comprehensive and more accurate models, making WC modeling feasible, and outline several tech- including models of human cells. nological advances that would help accelerate WC modeling.
    [Show full text]
  • 1 a Simple and Efficient Pipeline for Construction, Merging, Expansion
    bioRxiv preprint doi: https://doi.org/10.1101/2020.11.09.373407; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models Cemal Erdem1,*, Ethan M. Bensman2, Arnab Mutsuddy1, Michael M. Saint-Antoine3, Mehdi Bouhaddou4, Robert C. Blake5, Will Dodd1, Sean M. Gross6, Laura M. Heiser6, F. Alex Feltus7,8,9, and Marc R. Birtwistle1,10,* 1 Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC 2 Computer Science, School of Computing, Clemson University, Clemson, SC 3 Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 4 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 5 Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 6 Department of Biomedical Engineering, Oregon Health & Sciences University, Portland, OR 7 Department of Genetics and Biochemistry, Clemson University, Clemson, SC 8 Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC 9 Center for Human Genetics, Clemson University, Clemson, SC 10 Lead Contact * Correspondence: [email protected] (C.E.) and [email protected] (M.R.B.) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.09.373407; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ABSTRACT The current era of big biomedical data accumulation and availability brings data integration opportunities for leveraging its totality to make new discoveries and/or clinically predictive models.
    [Show full text]
  • Real-Time Computer Simulations of Excitable Media: JAVA As a Scientific Language and As a Wrapper for C and FORTRAN Programs
    BioSystems 64 (2002) 73–96 www.elsevier.com/locate/biosystems Real-time computer simulations of excitable media: JAVA as a scientific language and as a wrapper for C and FORTRAN programs Flavio H. Fenton a,b,*, Elizabeth M. Cherry a, Harold M. Hastings a, Steven J. Evans b a Department of Physics, Hofstra Uni6ersity, Hempstead, NY 11549, USA b The Heart Institute, Beth Israel Medical Center, New York, NY 10003, USA Received 4 August 2001; accepted 1 September 2001 Abstract We describe a useful setting for interactive, real-time study of mathematical models of cardiac electrical activity, using implicit and explicit integration schemes implemented in JAVA. These programs are intended as a teaching aid for the study and understanding of general excitable media. Particularly for cardiac cell models and the ionic currents underlying their basic electrical dynamics. Within the programs, excitable media properties such as thresholds and refractoriness and their dependence on parameter values can be analyzed. In addition, the cardiac model applets allow the study of reentrant tachyarrhythmias using premature stimuli and conduction blocks to induce or to terminate reentrant waves of electrical activation in one and two dimensions. The role of some physiological parameters in the transition from tachycardia to fibrillation also can be analyzed by varying the maximum conductances of ion channels associated with a given model in real time during the simulations. These applets are available for download at http://arrhythmia.hofstra.edu or its mirror site http://stardec.ascc.neu.edu/fenton. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Computer simulations; JAVA; Programs 1.
    [Show full text]
  • In Silico Systems Analysis of Biopathways
    In Silico Systems Analysis of Biopathways Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universität Bielefeld. Vorgelegt von Ming Chen Bielefeld, im März 2004 MSc. Ming Chen AG Bioinformatik und Medizinische Informatik Technische Fakultät Universität Bielefeld Email: [email protected] Genehmigte Dissertation zur Erlangung des akademischen Grades Doktor der Naturwissenschaften (Dr.rer.nat.) Von Ming Chen am 18 März 2004 Der Technischen Fakultät an der Universität Bielefeld vorgelegt Am 10 August 2004 verteidigt und genehmiget Prüfungsausschuß: Prof. Dr. Ralf Hofestädt, Universität Bielefeld Prof. Dr. Thomas Dandekar, Universität Würzburg Prof. Dr. Robert Giegerich, Universität Bielefeld PD Dr. Klaus Prank, Universität Bielefeld Dr. Dieter Lorenz / Dr. Dirk Evers, Universität Bielefeld ii Abstract In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved: 1) Modeling and simulation of biological processes; 2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and 3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways.
    [Show full text]
  • Implementing Virtual Environments for Education and Research at NDSU
    Implementing Virtual Environments for Education and Research at NDSU Aaron Bergstrom, John Bauer, Brad Vender, Jeffrey Clark, Paul Juell, Phil McClean, Bernhardt Saini-Eidukat, Donald P. Schwert, Brian M. Slator, Alan R. White, Francis Larson, Otto Borchert, Robert Cosmano, Richard Frovarp, Guy Hokanson, Christina Johnson, James Landrum, Mei Li, John Opgrande, Rebecca Potter, Lai Ong Teo, Anurag Tokhi, Shannon Tomac, Joy Turnbull, Qiang Xiao, Melissa Zuroff World Wide Web Instructional Committee North Dakota State University Contact: [email protected] Abstract The World Wide Web Instructional Committee (WWWIC) at North Dakota State University (NDSU) develops and distributes immersive educational and research oriented virtual environments that are accessible through the world-wide-web and implemented by means of “Interactive 3D” user interfaces. This type of delivery of 3D content is known as Web3D. WWWIC's major Web3D projects include the Virtual Cell for cell biology education, the Geology Explorer for geology education, and the Digital Archive Network for Anthropology intended for use in archaeology and anthropology research. This paper will highlight the development history for each of these projects, and the development of WWWIC’s next generation interactive Java3D user interfaces. Introduction Over the past several years, the World Wide Web Instructional Committee (WWWIC) at North Dakota State University (NDSU) has built upon its previous successful developments in the area of web-based, interactive, immersive environments for education and research. Though past text- based computer technologies were found to be effective, WWWIC felt that further compelling visual enhancements could supplement project goals. Today, technologies such as real-time, desktop graphics accelerators and broadband technologies such as Internet2, DSL, and cable modems are available that allow these compelling enhancements to be delivered dynamically over the Internet (Borchert et al., 2001; Walsh and Bourges-Sévenier, 2001:7-13).
    [Show full text]
  • (SBML) Level 1: Structures and Facilities for Basic Model Definitions
    Systems Biology Markup Language (SBML) Level 1: Structures and Facilities for Basic Model Definitions Michael Hucka, Andrew Finney, Herbert Sauro, Hamid Bolouri mhucka,afinney,hsauro,hbolouri @cds.caltech.edu f Systems Biology Workbench Developmentg Group ERATO Kitano Systems Biology Project Control and Dynamical Systems, MC 107-81 California Institute of Technology, Pasadena, CA 91125, USA http://www.cds.caltech.edu/erato Principal Investigators: John Doyle and Hiroaki Kitano SBML Level 1, Version 1 (Final), 2 March 2001 Contents 1 Introduction 2 1.1 Scope and Limitations .............................................. 2 1.2 Notational Conventions ............................................. 2 2 Overview of SBML 3 3 Preliminary Definitions 4 3.1 Type SBase ................................................... 4 3.2 Guidelines for the Use of the annotations Field in SBase .......................... 5 3.3 Type SName ................................................... 6 3.4 Component Names and Namespaces in SBML ................................. 6 3.5 Formulas ..................................................... 7 4 SBML Components 8 4.1 Models ...................................................... 8 4.2 Unit Definitions ................................................. 9 4.3 Compartments .................................................. 11 4.4 Species ...................................................... 12 4.5 Parameters .................................................... 12 4.6 Rules ......................................................
    [Show full text]
  • An Introduction to Whole-Cell Modeling Release 0.0.1
    An introduction to whole-cell modeling Release 0.0.1 Jonathan Karr Arthur Goldberg Jun 26, 2020 Contents 1 Introduction 3 1.1 Motivation for WC modeling......................................4 1.1.1 Biological science: understand how genotype influences phenotype.............4 1.1.2 Medicine: personalize medicine for individual genomes....................5 1.1.3 Synthetic biology: rationally design microbial genomes....................5 1.2 The biology that WC models should aim to represent and predict...................5 1.2.1 Phenotypes that WC models should aim to predict.......................5 1.2.2 Physics and chemistry that WC models should aim to represent................6 1.3 Fundamental challenges to WC modeling................................7 1.3.1 Integrating molecular behavior to the cell level over several spatiotemporal scales......7 1.3.2 Assembling a unified molecular understanding of cells from imperfect data.........8 1.3.3 Selecting, calibrating and validating high-dimensional models................ 10 1.4 Feasibility of WC models........................................ 10 1.4.1 Experimental methods, data, and repositories......................... 10 1.4.2 Modeling and simulation tools................................. 18 1.4.3 Models of individual pathways and model repositories.................... 23 1.4.4 Models of multiple pathways.................................. 26 1.5 Emerging principles and methods for WC modeling.......................... 28 1.5.1 Principles of WC modeling................................... 29 1.5.2 Methods for WC modeling................................... 30 1.6 Latest WC models and their limitations................................. 34 1.6.1 Coarse-grained models..................................... 34 1.6.2 Genomically-centric bottom-up fine-grained models...................... 34 1.6.3 Physiologically-centric top-down fine-grained models..................... 35 1.6.4 Spatially-centric bottom-up fine-grained models.......................
    [Show full text]
  • Virtual Reality Training for Micro-Robotic Cell Injection
    Virtual Reality Training for Micro-robotic Cell Injection by Syafizwan Nizam Mohd Faroque BSc (Hons), MEd Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Deakin University August, 2016 Abstract Micro-robotic cell injection is a procedure requiring a high degree of precision to manoeuvre a motorised micropipette and deposit a small quantity of material at a particular location inside a cell. Performing the procedure requires three-dimensional movement of the micro-robot with individual or simultaneous control of the three normal Cartesian axes. Currently the micro-robotic cell injection procedure is performed manually by an expert bio-operator where lengthy training and a great deal of experience are required to become proficient. In order to reduce the training cost, duration and ethical issues surrounding the use of real cells for training, this thesis introduces two virtual reality micro-robotic cell injection training platforms. The first is a desktop-sized haptically-enabled virtual reality training system which supports portability and flexibility and employs common personal computer or laptop peripherals. A reconfigurable multipurpose haptic interface was also developed as part of the platform in order to provide convenience in setting up and mobility. The second platform provides a large-scale virtual reality platform for micro-robotic cell injection training. It utilises three large screens which are arranged in different display configurations to provide immersive virtual environment. The three display configurations project a large virtual replication of a micro-robotic cell injection setup in two-dimensional, three-dimensional and three-dimensional i immersive displays respectively. The large display area of the two- dimensional large-scale display can provide a very detailed representation of the environment.
    [Show full text]