Mathematical Modeling in Microbial Ecology

284 Pages·1998·5.74 MB·English

by  Arthur L. Koch (auth.), Arthur L. Koch, Joseph A. Robinson, George A. Milliken (eds.)

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
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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
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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