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Mathematical Modeling in Microbial Ecology PDF

284 Pages·1998·5.74 MB·English
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Chapman Ii Hall licrobiololY Series Physiology/Ecology/Molecular Biology/Biotechnology SERIES EDITORS C.A Reddy, Editor-in-Chief Department of Microbiology Michigan State University East Lansing, MI 48824-1101 AM. Chakrabarty Department of Microbiology and Immunology University of Illinois Medical Center 835 S. Wolcott Avenue Chicago, IL 60612 Arnold L. Demain Department of Biology, Rm 68-223 Massachusetts Institute of Technology Cambridge, MA 02139 James M. Tiedje Center for Microbial Ecology Department of Crop and Soil Sciences Michigan State University East Lansing, MI 48824 Olher Publicalions in Ihe Chapman Ii Hall licrabiololJ Series Methanogenesis; James G. Ferry, ed. Acetogenesis; Harold L. Drake, ed. Gastrointestinal Microbiology, Volume 1; Roderick I. Mackie and Bryan A White, eds. Gastrointestinal Microbiology, Volume 2; Roderick I. Mackie, Bryan A White, and Richard E. Isaacson, eds. Bacteria in Oligotrophic Environments; Richard Y. Morita forlhcomiRI Tilles in Ihe Chapman Ii HIli li~robiololY Series Oxygen Regulation of Gene Regulation in Bacteria; Rob Gunsalus, ed. Metal Ions in Gene Regulation; Simon Silver and William Walden, eds. Chapman &H ali Mlcpoblology Seples Arthur L. Koch Incltana Universlty Department of alology Bloomington, IN .J'oseph A. Robill1son UPJohn laboratofle5 Blostatlstlc and Envlronmental Research Kalamazoo, MI George A. M;ll;ken Kansas State: Unrverslty Depanment of StatlStlCS Manhattan, KS mS PRINGER-$CIENCE+BU$INESS MEDIA, a.v. Cover design: Curtis Tow Graphics Copyright © 1998 by Springer Science+Business Media Dordrecht Originally published by Chapman & Hali in 1998 Softcover reprint of the hardcover I st edition 1998 AII rights reserved. No part of this book covered by the copyright hereon may be reproduced or used in any form or by any means-graphic, electronic, or mechanical, including photocopying, recording, taping, or information storage and retrieval systems-without the written permission of the publisher. 1 2 3 4 5 6 7 8 9 10 XJCX 01 00 99 98 Library of Congress Cataloging-in-Publication Data Mathematical modeling in microbial ecology ! edited by Arthur L. Koch, Joseph A. Robinson, George A. Milliken. p. cm. Includes bibliographical references and index. TSBN 978-1-4613-6826-7 TSBN 978-1-4615-4078-6 (eBook) DOI 10.1007/978-1-4615-4078-6 1. Microbial ecology--Mathematical models. 1. Koch, Arthur L. II. Robinson, Joseph Arlen. III. Milliken, George A. QR100.M38 1997 579'.IT0l5118--DC21 97-10463 CIP British Library Cataloguing in Publication Data available Contents Preface IX Contributors xi 1. What is Happening to Microbial Ecology? 1 Arthur L. Koch 1. Introduction 1 2. Analytical Methods 2 3. Kinetic Aspects 6 4. Principles of Kinetic Modeling 9 5. Progress in Statistical Methods 10 6. Conclusions 12 2. Modeling Microbial Processes: An Overview of Statistical Considerations 14 Joseph A. Robinson 1. Introduction 14 2. Model Identification versus Discrimination 15 3. The Least-Squares Criterion 17 4. Model Identification 18 5. Model Discrimination 23 6. Optimal Experiments for Parameter Estimation 28 7. Concluding Remarks 29 References 30 3. Analysis of Repeated Measures Data Using Nonlinear Models 32 George A. Milliken and April J. Milliken-MacKinnon 1. Introduction 32 2. The Model 34 3. Parameter Estimation 35 vi Contents 4. Comparing the Treatments 36 5. Constructing Confidence Bands for the Models 38 6. Example 1: Growing Cookies 41 7. Example 2: Cumulative Radioactive CO2 Production 48 8. Summary 61 References 61 4. The Monod Model and Its Alternatives 62 Arthur L. Koch 1. Jacques Monod: His Life and Work 62 2. The Monod Model and Its Derivations 69 3. Limitation of the Hyperbolic Model 72 4. The Blackman (1905) Model and the Best (1955) Model 73 5. Still More Complication: The Phosphotransferase System 79 6. Still More Complications: The Kinetic Contribution of Porins and Passage through the Outer Membrane 82 7. The Experimental Measurement of Glucose Consumption 82 8. Selection of a Mutant Growing More Avidly at Low Glucose Concentrations 83 9. The Data Fitting: The Role of Models 83 10. The Statistical Fitting 85 11. Diffusion Limitation and Effect of Multiple Layers 87 12. The Effect of the Variation of the Surface Area to Volume during the Cell Cycle 88 13. Grave Omissions 90 14. Conclusions 90 References 91 5. Using Transport Model Interpretations of Tracer Tests to Study Microbial Processes in Groundwater 94 Richard L. Smith and Stephen P. Garabedian 1. Introduction 94 2. The Groundwater Environment 95 3. Measuring Microbial Processes in an Aquifer 101 4. Tracer-Test Technology 102 5. Transport-Process Models 105 6. Assessing Methane Oxidation 108 7. Assessing Denitrification 112 8. Future Applications and Limitations 120 References 121 Contents vii 6. Modeling of Pesticide Biodegradation in Soil 124 Daniel R. Shelton, Michael A. Doherty, Timothy B. Parkin, and Joseph A. Robinson 1. Introduction 124 2. Modeling 125 3. More Elaborate Models 127 4. Effect of Microbial Numbers 130 5. Role of Sorption 131 6. Summary 137 References 138 7. Modeling Nitrogen Transformation in Soil 142 David D. Myrold 1. Introduction 142 2. Using Models to Calculate Data 142 3. Using Models to Understand N Cycle Transformations and Their Regulation 147 4. Using Models to Make Predictions about N Cycling 154 5. Summary 157 References 157 8. Construction and Analysis of Static, Structured Models of Nitrogen Cycling in Coastal Ecosystems 162 Robert R. Christian, Mariachiara Naldi, and Pierluigi Viaroli 1. Introduction 162 2. Methods 165 3. Model Development 170 4. Analysis Results and Interpretation 177 5. Conclusions and Subsequent Directions 189 References 192 9. A Modeling Approach to Elucidating the Distribution and Rates of Microbially Catalyzed Redox Reactions in Anoxic Groundwater 196 Derek R. Lovley and Francis H. Chapelle 1. Introduction 196 2. Use of H2 Concentrations to Predict Terminal Electron-Accepting Processes in Anoxic Groundwater 197 3. Estimating Rates of Microbial Processes with Geochemical Modeling 201 4. Conclusions 206 References 207 viii Contents 10. From the Ground Up: The Development and Demonstrated Utility of the Ruminal Ecosystem Model 210 R. L. Baldwin and K. C. Donovan 1. Introduction 210 2. Balance Models of Rumen Digestion 211 3. Dynamic Models of Ruminant Digestion 214 4. Early Dynamic Models 214 5. Current Dynamic Models 219 References 224 11. Mathematical Models of Bacterial Chemotaxis 228 Roseanne M. Ford and Peter T. Cummings 1. Introduction 228 2. Population Balance Models 235 3. Cellular Dynamics Simulation 245 4. Comparison of Modeling Approaches 248 5. Application to Multiple Stimuli 256 6. Concluding Remarks 264 References 267 Index 271 Preface Mathematical modeling has become a staple in the inferential tool kit used by microbial ecologists to discern patterns in data. The wide availability of powerful microcomputers and statistical software have contributed to this development. Microbial ecologists can now easily explore the opportunities offered by mathe- matical modeling such as using models as (1) heuristic devices, (2) means of generating hypotheses about biological systems, and (3) aids in making decisions (i.e., the testing of hypotheses statistically). The chapters in this text provide examples of each of these different, but complementary aims of mathematical modeling. For microbial ecologists, the range of systems being subjected to modeling analyses runs the gamut from subcellar systems to ecosystems. Throughout, the objective is to express the system of interest in mathematical terms and to test whether the model provides an adequate representation of the system, depending on the researcher's objectives. The path between this objective and the develop- ment of a parsimonious mathematical model winds through various techniques, including parameter estimation, sensitivity analysis, model discrimination, and experimental design. It is hoped that the readers of this text will be served well by the information contained herein such that the biology remains the focus, when that focus is appropriate, and is not lost among the mathematical and statistical tools. The editors wish to thank Mr. Greg Payne and Mr. Henry Flesh of Chapman and Hall for their exceptional patience during the long gestation period required for this text. Contributors R. L. Baldwin, Department of Michael A. Doherty, USDA-ARS- Animal Science NRI-ECL University of California-Davis Building 001 Davis, CA 95616 BARC-West 10300 Baltimore Avenue Francis H. Chapelle, Water Beltsville, MD 20705 Resources Division K. C. Donovan, Department of U.S. Geological Survey Animal Science Stephenson Center, Suite 129 University of California-Davis Gracern Road Davis, CA 95616 Columbia, SC 29210 Roseanne M. Ford, Department of Robert R. Christian, Biology Chemical Engineering Department University of Virginia East Carolina University Charlottesville, VA 22903 Greenville, NC 27834 Stephen P. Garabedian, U.S. Geological Survey Peter T. Cummings, Department of 28 Lord Road, Suite 208 Chemical Engineering Marlborough, MA 01752 University of Tennessee Knoxville, TN 37996 Arthur L. Koch, Department of and Biology Chemical Technology Division Indiana University Oak Ridge National Laboratory Jordan Hall Oak Ridge, TN 37831 Bloomington, IN 47405

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