phage–bacteria infection networks: from nestedness to modularity and back again

170 Pages·2014·3.34 MB·English

PHAGE–BACTERIA INFECTION NETWORKS: FROM
NESTEDNESS TO MODULARITY AND BACK AGAIN
A Thesis
Presented to
The Academic Faculty
by
C´esar Omar Flores Garc´ıa
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy in the
School of Physics
Georgia Institute of Technology
December 2014
Copyright c 2014 by C´esar Omar Flores Garc´ıa
(cid:13)
PHAGE–BACTERIA INFECTION NETWORKS: FROM
NESTEDNESS TO MODULARITY AND BACK AGAIN
Approved by:
Professor Joshua S. Weitz, Advisor Professor James C. Gumbart
School of Physics School of Physics
Georgia Institute of Technology Georgia Institute of Technology
Doctor Sergi Valverde Professor Flavio H. Fenton
Complex Systems Lab and Institute of School of Physics
Evolutionary Biology Georgia Institute of Technology
Universitat Pompeu Fabra
Professor Kurt Wiesenfeld Date Approved: 15 August 2014
School of Physics
Georgia Institute of Technology
To all my family, who have always supported me,
and the memory of my father.
iii
ACKNOWLEDGEMENTS
I would like to thank my advisor, Professor Joshua S. Weitz, for his patience and sup-
port. The stimulating atmosphere at his lab made the ideas possible that eventually
culminated in this thesis. Many thanks to the current and former group members
who gave me feedback and comments during all my research: Dr. Richard I. Joh, Dr.
Gabriel J. Mitchell, Luis F. Jover, Bradford P. Taylor, Abhiram Das, and Charles
H. Wigington. I would like to thank Lauren Farr, who did an splendid job in col-
lecting and digitizing the data in which this work is based. My thanks also goes to
Dr. Michael H. Cortez, Dr. Lauren M. Childs, and Dr. Olga Symonova for offering
me guidance at certain stages of my PhD studies. Very special thanks goes to Dr.
Alexander Bucksh, who I considered my second PhD mentor.
I am greatly indebted with Dr. Sergi Valverde who was before a collaborator,
a mentor who share his very broad knowledge of complex networks to me. I am
also thankful to Dr. Justin R. Meyer, who besides being a collaborator, give me the
opportunity of learning some of the basis in Experimental Microbiology when he was
a student of Richard Lenski, who I am also thankful for the opportunity.
My thanks also goes to Dr. Drew Purves and all the people of the Computational
Ecology and Environmental Science at Microsoft Research Cambridge for giving me
the big opportunity of doing an internship there. I had an unique experience of
interactingwithsomeofthesmartestscientistsinmyfield. MyinternshipatMicrosoft
helped me to open and formulate new questions besides my main PhD research topic.
Finally, I would like to thank my family and friends for their encouragement and
support. Special thanks goes to my mother and brothers that have been always there,
when I need them the most.
iv
My research was supported by CONACyT fellowship for graduate studies and
grants from the Burroughs Wellcome Fund and the National Science Foundation
Division of Biological Oceanography awarded to Joshua S. Weitz.
v
TABLE OF CONTENTS
DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . iv
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
I INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 A little bit of history in complex networks . . . . . . . . . . . 1
1.1.2 Complex networks in ecology . . . . . . . . . . . . . . . . . . 2
1.2 Phage–bacteria interactions: Who-kills-whom? . . . . . . . . . . . . 3
1.2.1 Possible scenarios . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 And then, what are the phage–bacteria network features? . . . . . . 4
1.3.1 From Nestedness ... . . . . . . . . . . . . . . . . . . . . . . 6
1.3.2 ...To Modularity ... . . . . . . . . . . . . . . . . . . . . . . 6
1.3.3 ...And back again . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 BiMAT: a software for performing bipartite network analysis . . . . . 7
II STATISTICALSTRUCTUREOFHOST–PHAGEINTERACTIONS 8
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Compiling a Large–Scale Host–Phage Interaction Dataset . . 11
2.2.2 Host–PhageInfectionStatisticsDoNotVarywithStudyType
or Show Significant Cross-Correlations . . . . . . . . . . . . . 14
2.2.3 Host–Phage Infection Assays Are Typically Nested and Not
Modular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.4 Previously Overlooked Nested Patterns Uncovered . . . . . . 21
2.2.5 Addressing Sample Composition Biases as Potential Drivers
of Network Structure . . . . . . . . . . . . . . . . . . . . . . 22
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2.2.6 Possible Scale Dependence of Host–Phage Interactions: From
Nestedness to Modularity? . . . . . . . . . . . . . . . . . . . 24
2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.1 Summary of Major Results . . . . . . . . . . . . . . . . . . . 25
2.3.2 Mechanisms Responsible for Nestedness: Biophysical, Ecolog-
ical, and Evolutionary . . . . . . . . . . . . . . . . . . . . . . 25
2.3.3 Dispelling and Recognizing Potential Biases . . . . . . . . . . 28
2.3.4 Prospective View . . . . . . . . . . . . . . . . . . . . . . . . 29
2.4 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4.1 Network Statistics . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4.2 Host–Phage Infection Assay . . . . . . . . . . . . . . . . . . 32
III MULTI-SCALE STRUCTURE AND GEOGRAPHIC DRIVERS
OF CROSS-INFECTION WITHIN MARINE BACTERIA AND
PHAGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.1 Data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.2 Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.3 Multi-scale Analysis . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.4 Geographical Analysis . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Characteristics of a large-scale phage-bacteria infection network 45
3.3.2 Evaluating modularity at the whole-network scale . . . . . . 46
3.3.3 Evaluating nestedness at the whole-network scale . . . . . . . 47
3.3.4 Network analysis at the intra-module scale . . . . . . . . . . 50
3.3.5 Geographical diversity of interactions . . . . . . . . . . . . . 52
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
IV BIMAT : A MATLAB(cid:13)R PACKAGE TO FACILITATE THE ANALYSIS
AND VISUALIZATION OF BIPARTITE NETWORKS . . . . . 59
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
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4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.1 Bipartite ecological network . . . . . . . . . . . . . . . . . . 61
4.2.2 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.3 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3 The BiMAT package . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.3.1 Usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3.2 Comparison with other software . . . . . . . . . . . . . . . . 72
4.3.3 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3.4 License and bug tracking . . . . . . . . . . . . . . . . . . . . 73
4.3.5 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3.6 Objected-Oriented Programming Scheme . . . . . . . . . . . 74
4.3.7 Input/Output . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.8 Functional alternative . . . . . . . . . . . . . . . . . . . . . . 77
4.3.9 Plotting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.3.10 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4.1 Example I: Meta-analysis . . . . . . . . . . . . . . . . . . . . 79
4.4.2 Example II: Multi-scale analysis . . . . . . . . . . . . . . . . 82
4.5 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.6 Citation of methods implemented in BiMAT . . . . . . . . . . . . . 87
V CONCLUSIONS AND FUTURE DIRECTIONS . . . . . . . . . . 88
5.1 Summary of major contributions . . . . . . . . . . . . . . . . . . . . 88
5.1.1 Phage–Bacteria cross infection data collection . . . . . . . . . 88
5.1.2 Phage–Bacteria networks are nested . . . . . . . . . . . . . . 89
5.1.3 Phage–Bacterianetworksaremodularasthestudyscaleincrease 89
5.1.4 Phage–bacteria network structure changes with size . . . . . 90
5.1.5 Release of BiMAT . . . . . . . . . . . . . . . . . . . . . . . . 90
5.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
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5.2.1 Network Structure: Individual vs Species Density . . . . . . 91
5.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
APPENDIX A — SUPPLEMENTARY MATERIALS FOR CHAP-
TER 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
APPENDIX B — SUPPLEMENTARY MATERIALS FOR CHAP-
TER 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
VITA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
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LIST OF TABLES
1 General properties of a large-scale phage–bacteria infection network . 46
2 Significance of the nestedness of the MN matrix using alternative al-
gorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3 Network properties of the largest 15 modules identified using the mod-
ularity analysis (see Table 1 for definitions of all quantities) . . . . . . 50
4 Bipartite Ecological libraries . . . . . . . . . . . . . . . . . . . . . . . 73
5 Some useful calls using the OOP approach . . . . . . . . . . . . . . . 75
6 Useful calls in the functional approach . . . . . . . . . . . . . . . . . 77
7 Characteristicsofcompletehost-phagenetworksincludedinthepresent
study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
8 Characteristicsofcompletehost-phagenetworksincludedinthepresent
study, including additional information on biological context of each
study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
9 Global properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
10 PCA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
11 Correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
12 Isolation bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
13 Geographical data of microbial stations . . . . . . . . . . . . . . . . . 124
14 Global properties of the extracted modules . . . . . . . . . . . . . . . 125
15 Geographical biodiversity indexes . . . . . . . . . . . . . . . . . . . . 126
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