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Submit a ProposalThe Mathematical Biology of Diatoms
 | Edited by Janice L. Pappas Series: Diatoms: Biology and Applications Copyright: 2023 | Status: Published ISBN: 9781119749851 | Hardcover | 470 pages | 140 illustrations Price: $245 USD  |
One Line Description
This book contains unique, advanced applications using mathematics, algorithmic
techniques, geometric analysis, and other computational methods in diatom research.
Audience
This book is intended for use by those in the biological sciences, theoretical biologists, applied mathematics, computational sciences, informatics, computer vision sciences, algorithm sciences, pattern recognition sciences, nanoscience, and applied engineering.
This book is intended for use by those in the biological sciences, theoretical biologists, applied mathematics, computational sciences, informatics, computer vision sciences, algorithm sciences, pattern recognition sciences, nanoscience, and applied engineering.
Description
Historically, diatom research has centered on taxonomy and systematics. While these topics are of the utmost importance, other aspects of this important group of unicells have been increasingly explored in the biological sciences. While mathematical applications are still rare, they are starting take hold and provide an extensive avenue of new diatom research, including applications in multidisciplinary fields.
The work contained in this volume is an eclectic mix of analytical studies on diatoms. Mathematical treatment of the various biological disciplines covered in this book range from implicit, but succinct studies to more elaborate detailed computational studies. Topics include growth models, nanostructure, nanoengineering, cell growth, araphid diatoms, valve ontogeny, diatom metabolism, diatom motility, synchronization, diatom kinematics, photonics, biogenic sensors, photochemistry, diatom light response, colony growth, siliceous unicells, algal kinetics, diatom structure, diatom imaging, functional morphology, geometric structure, biomineralization, high-resolution imaging, non-destructive imaging, and 3D structure. This wide-ranging volume provides an introductory as
well as an advanced treatment of recent interests in diatom research. The mathematical research in this volume may be applicable to studies of other unicells, biomechanics, biological processes, physio-chemical analyses, or nanoscience.
Back to Top
Historically, diatom research has centered on taxonomy and systematics. While these topics are of the utmost importance, other aspects of this important group of unicells have been increasingly explored in the biological sciences. While mathematical applications are still rare, they are starting take hold and provide an extensive avenue of new diatom research, including applications in multidisciplinary fields.
The work contained in this volume is an eclectic mix of analytical studies on diatoms. Mathematical treatment of the various biological disciplines covered in this book range from implicit, but succinct studies to more elaborate detailed computational studies. Topics include growth models, nanostructure, nanoengineering, cell growth, araphid diatoms, valve ontogeny, diatom metabolism, diatom motility, synchronization, diatom kinematics, photonics, biogenic sensors, photochemistry, diatom light response, colony growth, siliceous unicells, algal kinetics, diatom structure, diatom imaging, functional morphology, geometric structure, biomineralization, high-resolution imaging, non-destructive imaging, and 3D structure. This wide-ranging volume provides an introductory as
well as an advanced treatment of recent interests in diatom research. The mathematical research in this volume may be applicable to studies of other unicells, biomechanics, biological processes, physio-chemical analyses, or nanoscience.
Back to Top
Author / Editor Details
Janice L. Pappas has BA, BS and PhD degrees from the University of Michigan and an MA degree from Drake University. She is a mathematical biologist researching diatoms and invertebrates. She is a Great Lakes aquatic ecologist with studies on-board research vessels and in the lab, resulting in computational analyses of fish distributions in coastal wetlands and ecological informatics analysis of phytoplankton seasonal succession. Other studies include applications to diatom studies using Morse theory and morphospace dynamics, fuzzy measures in systematics, vector spaces in ecological analysis, information theory and Hamiltonian mechanics in morphogenesis, optimization, group and probability theory in macroevolutionary processes, and applied computer vision techniques in diatom imaging studies.
Janice L. Pappas has BA, BS and PhD degrees from the University of Michigan and an MA degree from Drake University. She is a mathematical biologist researching diatoms and invertebrates. She is a Great Lakes aquatic ecologist with studies on-board research vessels and in the lab, resulting in computational analyses of fish distributions in coastal wetlands and ecological informatics analysis of phytoplankton seasonal succession. Other studies include applications to diatom studies using Morse theory and morphospace dynamics, fuzzy measures in systematics, vector spaces in ecological analysis, information theory and Hamiltonian mechanics in morphogenesis, optimization, group and probability theory in macroevolutionary processes, and applied computer vision techniques in diatom imaging studies.
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Table of Contents
List of Figures
List of Tables
Preface
Part I: Diatom Form and Size Dynamics
1. Modeling the Stiffness of Diploneis Species Based on Geometry of the Frustule Cut with Focused Ion Beam Technology
Andrzej Witkowski, Romuald Dobosz, Tomasz Płociński, Przemysław Dąbek, Izabela Zgłobicka, Horst Lange-Bertalot, Thomas G. Bornman, Renata Dobrucka, Michał Gloc and Krzysztof J. Kurzydłowski
1.1 Introduction
1.2 Material and Methods
1.2.1 Focused Ion Beam (FIB) Milling
1.2.2 Modeling
1.3 Results
1.3.1 FIB Processing
1.3.2 Modeling
1.4 Discussion
1.4.1 Practical Meaning of the Frustule Geometric Characters
1.4.2 Documenting the Mechanical Strength of the Diatom Frustule
Acknowledgments
References
2. Size-Resolved Modeling of Diatom Populations: Old Findings and New Insights
Jonas Ziebarth, Werner M. Seiler and Thomas Fuhrmann-Lieker
2.1 Introduction
2.2 The MacDonald–Pfitzer Rule and the Need for Matrix Descriptions
2.3 Cardinal Points and Cycle Lengths
2.3.1 Considered Cardinal Parameters
2.3.2 Factors Determining Cardinal Points
2.3.3 Experimental Data
2.4 Asymmetry, Delay and Fibonacci Growth
2.4.1 The Müller Model
2.4.2 The Laney Model
2.5 Continuous vs. Discrete Modeling
2.5.1 Discrete Dynamical Systems
2.5.2 The Perron-Frobenius Theorem
2.5.3 Continuous Dynamical Systems
2.5.4 Extensions and Generalizations
2.5.5 Individual-Based Models
2.6 Simulation Models
2.6.1 The Schwarz et al. Model
2.6.2 The D’Alelio et al. Model
2.6.3 The Hense–Beckmann Model
2.6.4 The Terzieva–Terziev Model
2.6.5 The Fuhrmann-Lieker et al. Model
2.7 Oscillatory Behavior
2.7.1 Reproduction of Experimental Data
2.7.2 Coupling to External Rhythms
2.8 Conclusion
Acknowledgment
References
3. On the Mathematical Description of Diatom Algae: From Siliceous Exoskeleton Structure and Properties to Colony Growth Kinetics, and Prospective Nanoengineering Applications
Alexey I. Salimon, Julijana Cvjetinovic, Yuliya Kan, Eugene S. Statnik, Patrick Aggrey, Pavel A. Somov, Igor A. Salimon, Joris Everaerts, Yekaterina D. Bedoshvili, Dmitry A. Gorin, Yelena V. Likhoshway, Philipp V. Sapozhnikov, Nikolai A. Davidovich, Olga Y. Kalinina, Kalin Dragnevski and Alexander M. Korsunsky
3.1 Introduction
3.2 Hierarchical Structuring of Matter: Diatom Algae and the Bio-Assisted Nanostructured Additive Manufacturing Paradigm
3.3 Structural Design of Diatom Frustules
3.4 Mechanical Performance of Diatom Frustules – Experimental Characterization
3.4.1 Nanoindentation Testing of Diatom Frustules
3.4.2 AFM Studies of Diatom Frustules
3.5 Engineering Applications of Diatomaceous Earth
3.6 NEMS/MEMS Perspective
3.7 On the Mathematical Description of Self-Organized Diatom Frustule Growth
3.8 On the Kinetics of Diatom Colony Growth
3.9 Advanced Pattern Analysis of the Hierarchical Structure of Diatom Frustules
3.10 Concluding Remarks
Acknowledgement
References
Part II: Diatom Development, Growth and Metabolism
4. Ring to the Linear: Valve Ontogeny Indicates Two Potential Evolutionary Pathways of Core Araphid Diatoms
Shigeki Mayama and Momoko Kushida
4.1 Introduction
4.2 Material and Methods
4.2.1 Fragilaria mesolepta
4.2.2 Staurosira binodis
4.2.3 Induction of Synchronous Division
4.2.4 Electron Microscopy
4.3 Results
4.3.1 Fragilaria mesolepta
4.3.2 Staurosira binodis
4.4 Discussion
4.5 Conclusion
References
5. Mathematical Basis for Diatom Growth Modeling
Dariush Sardari
5.1 Introduction
5.2 General Physiology of Diatoms
5.3 Mathematical View of Diatom Growth
5.4 Physical Basis for Diatom Modeling
5.4.1 Diatom Dimensions
5.4.2 Ambient Temperature
5.4.3 Light Intensity and Duration
5.5 Review of Existing Mathematical Models
5.5.1 Gompertz Model
5.5.2 Monod Model
5.5.3 Michaelis-Menten Model
5.5.4 Droop Model
5.5.5 Aquaphy Model
5.5.6 Mechanistic Model
5.6 Results
5.7 Conclusion
5.8 Prospects
References
6. Diatom Growth: How to Improve the Analysis of Noisy Data
Olga Kourtchenko, Kai T. Lohbeck, Björn Andersson and Tuomas Rajala
6.1 Introduction
6.1.1 What is a Growth Curve?
6.1.2 Why Measure Growth?
6.1.3 Diatoms and Their Growth
6.1.4 Growth Data Analysis and Growth Parameter Estimation
6.2 Simulation Trials
6.2.1 Methodology for the Simulation Trials
6.2.2 Candidate Methods for Estimating the Specific Growth Rate
6.2.3 Simulation Trials Results
6.2.3.1 Results with Only the Noise Challenge
6.2.3.2 Results when Crashing Occurs
6.2.3.3 Results when Censoring Occurs
6.2.3.4 Overall Results and Ranking of the Methods
6.3 Empirical Example
6.4 Conclusions and Recommendations
References
7. Integrating Metabolic Modeling and High-Throughput Data to Characterize Diatoms Metabolism
Juan D. Tibocha-Bonilla, Manish Kumar, Karsten Zengler and Cristal Zuniga
7.1 Introduction
7.2 Characterization of Diatom Genomes
7.2.1 Available Genomics Data
7.2.2 Computational Tools to Allocate Gene Functions by Subcellular Localization
7.3 Metabolic Modeling of Diatoms: Data and Outcomes
7.3.1 Using Genomic Information to Build Genome-Scale Metabolic Models
7.3.2 Comprehensive Diatom Omic Datasets Are Useful to Constrain Metabolic Models
7.3.3 Unraveling New Knowledge About Central Carbon Metabolism of Diatoms
7.3.4 Light-Driven Metabolism that Enables Acclimation to High Light Intensities
7.4 Modeling Applications to Study Bioproduction and Genome Changes in Diatoms
7.4.1 Predicting Diatom-Heterotroph Interactions and Horizontal Gene Transfer Using Community Metabolic Models
7.4.2 Optimization and Scale-Up of the Production of Valuable Metabolites
7.4.3 Potential for the Study of Proteome Allocation in Diatoms
7.5 Conclusions
References
Part III: Diatom Motility
8. Modeling the Synchronization of the Movement of Bacillaria paxillifer by a Kuramoto Model with Time Delay
Thomas Harbich
8.1 Introduction
8.2 Materials and Methods
8.3 Time Dependence of the Relative Motion of Adjacent Diatoms
8.4 Modeling Interacting Oscillators of a Bacillaria Colony
8.4.1 Observation of the Movement Activity at Uncovered Rhaphes
8.4.2 Interaction of Neighboring Diatoms
8.4.3 Coupled Oscillators
8.5 Kuramoto Model
8.5.1 Adaptation of the Kuramoto Model for a Bacillaria Colony
8.5.2 Analyses and Approximations
8.5.3 Critical Coupling
8.5.3.1 Uncoupled Oscillators
8.5.3.2 Two Oscillators
8.5.3.3 Chains without Retardation
8.5.3.4 Identical Oscillator Frequencies and Sufficiently Small Delay
8.5.3.5 Remarks on the General Case
8.5.4 Statistical Considerations and Monte Carlo Simulations
8.5.4.1 Expected Value and Standard Deviation
8.5.4.2 Distribution of Critical Coupling
8.5.5 Simulation of Non-Synchronous States
8.5.5.1 Numerical Integration
8.5.5.2 Visualization of the Transient
8.5.5.3 Discrete Fourier Transform
8.5.6 Coupling to a Periodic Light Source
8.6 Discussion
Acknowledgment
References
9. The Psychophysical World of the Motile Diatom Bacillaria paradoxa
Bradly Alicea, Richard Gordon and Jesse Parent
Abbreviations
9.1 Introduction
9.1.1 Aneural Architecture of Bacillaria
9.1.2 Aneural Cognition in a Broader Context
9.1.3 Psychophysics as Diatom Information Processing
9.1.4 Information Processing and Aneural Cognition
9.1.5 Hebbian Intelligence and Predictive Processing
9.2 Measurement Techniques
9.2.1 Weber-Fechner Law
9.2.2 Connectionist Network
9.2.3 Algorithmic Information
9.2.4 Collective Pattern Generator
9.2.5 Dynamical States of the CoPG
9.3 CPGs vs. CoPGs
9.3.1 Potential of Predictive Processing
9.3.2 Phase Transitions in Bacillaria Movement
9.4 Aneural Regulation
9.5 Broader Picture of Intelligence and Emergence
9.5.1 Pseudo-Intelligence
9.6 Discussion
Acknowledgments
References
10. Pattern Formation in Diatoma vulgaris Colonies: Observations and Description by a Lindenmayer-System
Thomas Harbich
10.1 Introduction
10.2 Materials and Methods
10.2.1 Cultivation and Recording of Images
10.2.2 Formal Notation of Types of Concatenation and Splitting Processes
10.2.3 Methods to Observe the Processes
10.2.3.1 Basic Options
10.2.3.2 Long-Term Observations
10.2.3.3 Analysis of Single Images
10.3 Results
10.3.1 Observation of Elementary Splitting Processes
10.3.2 Observation of Synchronism
10.3.3 Observation of the Processes and Appearance of Colonies
10.3.3.1 Splitting of Elements of Types c and d
10.3.3.2 Splitting of Elements of Types a and b – Dynamic Analysis
10.3.3.3 Separation of Elements of Types a and b – Static Analysis
10.3.3.4 Dependence on Environmental Parameters
10.3.4 Theory Formation
10.3.4.1 Description of the Asymmetry
10.3.4.2 Lindenmayer System
10.3.5 Outer Shape of the Colonies
10.4 Discussion
Acknowledgment
Appendix 10A: Calculation Scheme
Appendix 10B: Accordance with the D0L-System
References
11 RAPHE: Simulation of the Dynamics of Diatom Motility at the Molecular Level – The Domino Effect Hydration Model with Concerted Diffusion
Shruti Raj Vansh Singh, Krishna Katyal and Richard Gordon
11.1 Introduction
11.2 Parameters
11.3 Ising Lattice Modeling
11.4 Allowing Bias
11.5 Computer Representation
11.6 The Roles of the Cell Membrane, Canal Raphes, and the Diatotepum
11.7 Raphan and the Raphe
11.8 The Jerky Motion of Diatoms
11.9 Diffusion and Concerted Diffusion of Raphan
11.10 Shear and Janus-Faced Causation: Motility and Raphan Tilting
11.11 The Domino Effect Causes Size Independence of Diatom Speed
11.12 Quantitating the Swelling of Raphan in the Diatom Trail
11.13 A Schematic of Raphan Discharge
11.14 Transitions of Raphan
11.15 The Roles of the Diatom Trail
11.16 Outline of the Simulation
11.17 Results
11.18 Discussion
11.19 Conclusion
Dedication
Appendix 11.1
Appendix 11.2
References
Part IV: Diatom Ecological and Environmental Analysis
12. Following the Photons Route: Mathematical Models Describing the Interaction of Diatoms with Light
Edoardo De Tommasi, Alessandra Rogato, Diego Caratelli, Luciano Mescia and Johan Gielis
12.1 Introduction
12.2 The Underwater Light Field
12.2.1 The Travel of Light from the Sun into Water Bodies
12.2.2 Numerical Computation of the Underwater Optical Field
12.3 Novel Geometrical Models for Diatoms
12.3.1 Gielis Transformations
12.3.2 Laplace and Fourier Revisited
12.4 Going Through the Wall: Simulating Light Propagation in the Frustule
12.4.1 Plane Wave Expansion (PWE) Method
12.4.2 Finite Difference Time Domain (FDTD) Method
12.4.3 Wide-Angle Beam Propagation Method (WA-BPM)
12.4.4 Fast Fourier Transform (FFT) Approach
12.5 Fractional Calculus for Diatoms
12.5.1 Fractional Calculus Based Dielectric Dispersion Model
12.5.2 Basic Time–Marching Scheme
12.5.3 Uniaxial Perfectly Matched Layer Boundary Conditions
12.6 Beyond the Glass Cage: The Fate of Light Inside the Cell
12.6.1 The Diatom Chloroplast and its Evolution
12.6.2 The Photosynthetic and Electron Transport Chain
12.6.3 The Photoprotection Mechanism
12.6.4 The Diatom Photoreceptors
12.6.5 Chlorophyll Optical Signals for Satellite Population Monitoring
12.7 Conclusions
References
13. A Generalized Model for the Light Response of the Nonphotochemical
Quenching of Chlorophyll Fluorescence of Diatoms
João Serôdio and Johann Lavaud
13.1 Introduction
13.2 Model Formulation
13.2.1 Nonphotochemical Quenching Indices NPQ and Y(NPQ)
13.2.2 Standard Model for NPQ LCs
13.2.3 Generalized Model for NPQ LCs
13.2.4 Model Fitting and Parameter Estimation
13.3 Results
13.4 Discussion
13.4.1 Model Assumptions
13.4.2 Fitting to Experimental Data
13.4.3 Application
Acknowledgments
References
14. Coscinodiscus wailesii as Biogenic Charge-Based Sensors for Heavy Metal Ion Contamination Detection
Rajeshwari Taruvai Kalyana Kumar, Diem-Thuy Le, Antra Ganguly and Shalini Prasad
14.1 Introduction
14.2 Materials and Methods
14.2.1 Chemicals and Reagents
14.2.2 Cell Culture
14.2.3 Heavy Metal Doping and Characterization
14.2.4 Electrophoretic Measurements
14.3 Results and Discussion
14.3.1 Effect of Heavy Metal Doping on Cell Culture
14.3.2 Effect of Heavy Metal Doping on Zeta Potential
14.3.3 Dependency of pH on Surface Charge Potential
14.3.4 FTIR Characterization
14.4 Conclusion
Acknowledgments
References
Index
Back to Top
List of Figures
List of Tables
Preface
Part I: Diatom Form and Size Dynamics
1. Modeling the Stiffness of Diploneis Species Based on Geometry of the Frustule Cut with Focused Ion Beam Technology
Andrzej Witkowski, Romuald Dobosz, Tomasz Płociński, Przemysław Dąbek, Izabela Zgłobicka, Horst Lange-Bertalot, Thomas G. Bornman, Renata Dobrucka, Michał Gloc and Krzysztof J. Kurzydłowski
1.1 Introduction
1.2 Material and Methods
1.2.1 Focused Ion Beam (FIB) Milling
1.2.2 Modeling
1.3 Results
1.3.1 FIB Processing
1.3.2 Modeling
1.4 Discussion
1.4.1 Practical Meaning of the Frustule Geometric Characters
1.4.2 Documenting the Mechanical Strength of the Diatom Frustule
Acknowledgments
References
2. Size-Resolved Modeling of Diatom Populations: Old Findings and New Insights
Jonas Ziebarth, Werner M. Seiler and Thomas Fuhrmann-Lieker
2.1 Introduction
2.2 The MacDonald–Pfitzer Rule and the Need for Matrix Descriptions
2.3 Cardinal Points and Cycle Lengths
2.3.1 Considered Cardinal Parameters
2.3.2 Factors Determining Cardinal Points
2.3.3 Experimental Data
2.4 Asymmetry, Delay and Fibonacci Growth
2.4.1 The Müller Model
2.4.2 The Laney Model
2.5 Continuous vs. Discrete Modeling
2.5.1 Discrete Dynamical Systems
2.5.2 The Perron-Frobenius Theorem
2.5.3 Continuous Dynamical Systems
2.5.4 Extensions and Generalizations
2.5.5 Individual-Based Models
2.6 Simulation Models
2.6.1 The Schwarz et al. Model
2.6.2 The D’Alelio et al. Model
2.6.3 The Hense–Beckmann Model
2.6.4 The Terzieva–Terziev Model
2.6.5 The Fuhrmann-Lieker et al. Model
2.7 Oscillatory Behavior
2.7.1 Reproduction of Experimental Data
2.7.2 Coupling to External Rhythms
2.8 Conclusion
Acknowledgment
References
3. On the Mathematical Description of Diatom Algae: From Siliceous Exoskeleton Structure and Properties to Colony Growth Kinetics, and Prospective Nanoengineering Applications
Alexey I. Salimon, Julijana Cvjetinovic, Yuliya Kan, Eugene S. Statnik, Patrick Aggrey, Pavel A. Somov, Igor A. Salimon, Joris Everaerts, Yekaterina D. Bedoshvili, Dmitry A. Gorin, Yelena V. Likhoshway, Philipp V. Sapozhnikov, Nikolai A. Davidovich, Olga Y. Kalinina, Kalin Dragnevski and Alexander M. Korsunsky
3.1 Introduction
3.2 Hierarchical Structuring of Matter: Diatom Algae and the Bio-Assisted Nanostructured Additive Manufacturing Paradigm
3.3 Structural Design of Diatom Frustules
3.4 Mechanical Performance of Diatom Frustules – Experimental Characterization
3.4.1 Nanoindentation Testing of Diatom Frustules
3.4.2 AFM Studies of Diatom Frustules
3.5 Engineering Applications of Diatomaceous Earth
3.6 NEMS/MEMS Perspective
3.7 On the Mathematical Description of Self-Organized Diatom Frustule Growth
3.8 On the Kinetics of Diatom Colony Growth
3.9 Advanced Pattern Analysis of the Hierarchical Structure of Diatom Frustules
3.10 Concluding Remarks
Acknowledgement
References
Part II: Diatom Development, Growth and Metabolism
4. Ring to the Linear: Valve Ontogeny Indicates Two Potential Evolutionary Pathways of Core Araphid Diatoms
Shigeki Mayama and Momoko Kushida
4.1 Introduction
4.2 Material and Methods
4.2.1 Fragilaria mesolepta
4.2.2 Staurosira binodis
4.2.3 Induction of Synchronous Division
4.2.4 Electron Microscopy
4.3 Results
4.3.1 Fragilaria mesolepta
4.3.2 Staurosira binodis
4.4 Discussion
4.5 Conclusion
References
5. Mathematical Basis for Diatom Growth Modeling
Dariush Sardari
5.1 Introduction
5.2 General Physiology of Diatoms
5.3 Mathematical View of Diatom Growth
5.4 Physical Basis for Diatom Modeling
5.4.1 Diatom Dimensions
5.4.2 Ambient Temperature
5.4.3 Light Intensity and Duration
5.5 Review of Existing Mathematical Models
5.5.1 Gompertz Model
5.5.2 Monod Model
5.5.3 Michaelis-Menten Model
5.5.4 Droop Model
5.5.5 Aquaphy Model
5.5.6 Mechanistic Model
5.6 Results
5.7 Conclusion
5.8 Prospects
References
6. Diatom Growth: How to Improve the Analysis of Noisy Data
Olga Kourtchenko, Kai T. Lohbeck, Björn Andersson and Tuomas Rajala
6.1 Introduction
6.1.1 What is a Growth Curve?
6.1.2 Why Measure Growth?
6.1.3 Diatoms and Their Growth
6.1.4 Growth Data Analysis and Growth Parameter Estimation
6.2 Simulation Trials
6.2.1 Methodology for the Simulation Trials
6.2.2 Candidate Methods for Estimating the Specific Growth Rate
6.2.3 Simulation Trials Results
6.2.3.1 Results with Only the Noise Challenge
6.2.3.2 Results when Crashing Occurs
6.2.3.3 Results when Censoring Occurs
6.2.3.4 Overall Results and Ranking of the Methods
6.3 Empirical Example
6.4 Conclusions and Recommendations
References
7. Integrating Metabolic Modeling and High-Throughput Data to Characterize Diatoms Metabolism
Juan D. Tibocha-Bonilla, Manish Kumar, Karsten Zengler and Cristal Zuniga
7.1 Introduction
7.2 Characterization of Diatom Genomes
7.2.1 Available Genomics Data
7.2.2 Computational Tools to Allocate Gene Functions by Subcellular Localization
7.3 Metabolic Modeling of Diatoms: Data and Outcomes
7.3.1 Using Genomic Information to Build Genome-Scale Metabolic Models
7.3.2 Comprehensive Diatom Omic Datasets Are Useful to Constrain Metabolic Models
7.3.3 Unraveling New Knowledge About Central Carbon Metabolism of Diatoms
7.3.4 Light-Driven Metabolism that Enables Acclimation to High Light Intensities
7.4 Modeling Applications to Study Bioproduction and Genome Changes in Diatoms
7.4.1 Predicting Diatom-Heterotroph Interactions and Horizontal Gene Transfer Using Community Metabolic Models
7.4.2 Optimization and Scale-Up of the Production of Valuable Metabolites
7.4.3 Potential for the Study of Proteome Allocation in Diatoms
7.5 Conclusions
References
Part III: Diatom Motility
8. Modeling the Synchronization of the Movement of Bacillaria paxillifer by a Kuramoto Model with Time Delay
Thomas Harbich
8.1 Introduction
8.2 Materials and Methods
8.3 Time Dependence of the Relative Motion of Adjacent Diatoms
8.4 Modeling Interacting Oscillators of a Bacillaria Colony
8.4.1 Observation of the Movement Activity at Uncovered Rhaphes
8.4.2 Interaction of Neighboring Diatoms
8.4.3 Coupled Oscillators
8.5 Kuramoto Model
8.5.1 Adaptation of the Kuramoto Model for a Bacillaria Colony
8.5.2 Analyses and Approximations
8.5.3 Critical Coupling
8.5.3.1 Uncoupled Oscillators
8.5.3.2 Two Oscillators
8.5.3.3 Chains without Retardation
8.5.3.4 Identical Oscillator Frequencies and Sufficiently Small Delay
8.5.3.5 Remarks on the General Case
8.5.4 Statistical Considerations and Monte Carlo Simulations
8.5.4.1 Expected Value and Standard Deviation
8.5.4.2 Distribution of Critical Coupling
8.5.5 Simulation of Non-Synchronous States
8.5.5.1 Numerical Integration
8.5.5.2 Visualization of the Transient
8.5.5.3 Discrete Fourier Transform
8.5.6 Coupling to a Periodic Light Source
8.6 Discussion
Acknowledgment
References
9. The Psychophysical World of the Motile Diatom Bacillaria paradoxa
Bradly Alicea, Richard Gordon and Jesse Parent
Abbreviations
9.1 Introduction
9.1.1 Aneural Architecture of Bacillaria
9.1.2 Aneural Cognition in a Broader Context
9.1.3 Psychophysics as Diatom Information Processing
9.1.4 Information Processing and Aneural Cognition
9.1.5 Hebbian Intelligence and Predictive Processing
9.2 Measurement Techniques
9.2.1 Weber-Fechner Law
9.2.2 Connectionist Network
9.2.3 Algorithmic Information
9.2.4 Collective Pattern Generator
9.2.5 Dynamical States of the CoPG
9.3 CPGs vs. CoPGs
9.3.1 Potential of Predictive Processing
9.3.2 Phase Transitions in Bacillaria Movement
9.4 Aneural Regulation
9.5 Broader Picture of Intelligence and Emergence
9.5.1 Pseudo-Intelligence
9.6 Discussion
Acknowledgments
References
10. Pattern Formation in Diatoma vulgaris Colonies: Observations and Description by a Lindenmayer-System
Thomas Harbich
10.1 Introduction
10.2 Materials and Methods
10.2.1 Cultivation and Recording of Images
10.2.2 Formal Notation of Types of Concatenation and Splitting Processes
10.2.3 Methods to Observe the Processes
10.2.3.1 Basic Options
10.2.3.2 Long-Term Observations
10.2.3.3 Analysis of Single Images
10.3 Results
10.3.1 Observation of Elementary Splitting Processes
10.3.2 Observation of Synchronism
10.3.3 Observation of the Processes and Appearance of Colonies
10.3.3.1 Splitting of Elements of Types c and d
10.3.3.2 Splitting of Elements of Types a and b – Dynamic Analysis
10.3.3.3 Separation of Elements of Types a and b – Static Analysis
10.3.3.4 Dependence on Environmental Parameters
10.3.4 Theory Formation
10.3.4.1 Description of the Asymmetry
10.3.4.2 Lindenmayer System
10.3.5 Outer Shape of the Colonies
10.4 Discussion
Acknowledgment
Appendix 10A: Calculation Scheme
Appendix 10B: Accordance with the D0L-System
References
11 RAPHE: Simulation of the Dynamics of Diatom Motility at the Molecular Level – The Domino Effect Hydration Model with Concerted Diffusion
Shruti Raj Vansh Singh, Krishna Katyal and Richard Gordon
11.1 Introduction
11.2 Parameters
11.3 Ising Lattice Modeling
11.4 Allowing Bias
11.5 Computer Representation
11.6 The Roles of the Cell Membrane, Canal Raphes, and the Diatotepum
11.7 Raphan and the Raphe
11.8 The Jerky Motion of Diatoms
11.9 Diffusion and Concerted Diffusion of Raphan
11.10 Shear and Janus-Faced Causation: Motility and Raphan Tilting
11.11 The Domino Effect Causes Size Independence of Diatom Speed
11.12 Quantitating the Swelling of Raphan in the Diatom Trail
11.13 A Schematic of Raphan Discharge
11.14 Transitions of Raphan
11.15 The Roles of the Diatom Trail
11.16 Outline of the Simulation
11.17 Results
11.18 Discussion
11.19 Conclusion
Dedication
Appendix 11.1
Appendix 11.2
References
Part IV: Diatom Ecological and Environmental Analysis
12. Following the Photons Route: Mathematical Models Describing the Interaction of Diatoms with Light
Edoardo De Tommasi, Alessandra Rogato, Diego Caratelli, Luciano Mescia and Johan Gielis
12.1 Introduction
12.2 The Underwater Light Field
12.2.1 The Travel of Light from the Sun into Water Bodies
12.2.2 Numerical Computation of the Underwater Optical Field
12.3 Novel Geometrical Models for Diatoms
12.3.1 Gielis Transformations
12.3.2 Laplace and Fourier Revisited
12.4 Going Through the Wall: Simulating Light Propagation in the Frustule
12.4.1 Plane Wave Expansion (PWE) Method
12.4.2 Finite Difference Time Domain (FDTD) Method
12.4.3 Wide-Angle Beam Propagation Method (WA-BPM)
12.4.4 Fast Fourier Transform (FFT) Approach
12.5 Fractional Calculus for Diatoms
12.5.1 Fractional Calculus Based Dielectric Dispersion Model
12.5.2 Basic Time–Marching Scheme
12.5.3 Uniaxial Perfectly Matched Layer Boundary Conditions
12.6 Beyond the Glass Cage: The Fate of Light Inside the Cell
12.6.1 The Diatom Chloroplast and its Evolution
12.6.2 The Photosynthetic and Electron Transport Chain
12.6.3 The Photoprotection Mechanism
12.6.4 The Diatom Photoreceptors
12.6.5 Chlorophyll Optical Signals for Satellite Population Monitoring
12.7 Conclusions
References
13. A Generalized Model for the Light Response of the Nonphotochemical
Quenching of Chlorophyll Fluorescence of Diatoms
João Serôdio and Johann Lavaud
13.1 Introduction
13.2 Model Formulation
13.2.1 Nonphotochemical Quenching Indices NPQ and Y(NPQ)
13.2.2 Standard Model for NPQ LCs
13.2.3 Generalized Model for NPQ LCs
13.2.4 Model Fitting and Parameter Estimation
13.3 Results
13.4 Discussion
13.4.1 Model Assumptions
13.4.2 Fitting to Experimental Data
13.4.3 Application
Acknowledgments
References
14. Coscinodiscus wailesii as Biogenic Charge-Based Sensors for Heavy Metal Ion Contamination Detection
Rajeshwari Taruvai Kalyana Kumar, Diem-Thuy Le, Antra Ganguly and Shalini Prasad
14.1 Introduction
14.2 Materials and Methods
14.2.1 Chemicals and Reagents
14.2.2 Cell Culture
14.2.3 Heavy Metal Doping and Characterization
14.2.4 Electrophoretic Measurements
14.3 Results and Discussion
14.3.1 Effect of Heavy Metal Doping on Cell Culture
14.3.2 Effect of Heavy Metal Doping on Zeta Potential
14.3.3 Dependency of pH on Surface Charge Potential
14.3.4 FTIR Characterization
14.4 Conclusion
Acknowledgments
References
Index
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