frontiers of multidisciplinary research: mathematics
TRANSCRIPT
Frontiers of Multidisciplinary Research: Mathematics, Engineering and Biology21 – 24 September 2010 | reed Hall, UniverSity of exeter
Front cover image reproduced with the kind permission of Eckhard Völcker.
Sponsors
Welcome...to this unique meeting that aims to facilitate interaction and scientific exchange among mathematicians, physicists, engineers and biologists. The Frontiers workshop aspires to be unique for several reasons.
Firstly, it aims to achieve an unprecedented scientific breadth whilst maintaining focus and coherence. This is not an easy goal but we aim to realise it by selecting a large number of invited speakers who are leading scientists in their specific fields, yet maintain a broad view of science in other fields and the connections of their work to those fields.
Secondly, Frontiers aims to place “interaction” first and foremost. To achieve this we tried hard to provide a relaxed scientific platform with ample free time for both free and structured discussions. Half of the attendees will present a talk and posters will be at the centre stage of the meeting, being on display in the break out area at all times. The full daily program is set in an idyllic mansion in the campus of University of Exeter, with planned after-lunch walks that will allow for stretching the legs and mind.
Finally, Frontiers aims to motivate and attract young talent to interdisciplinary research. Some of the invited and most of the selected speakers are early-career academics, and we believe that they are indeed at the frontiers of interdisciplinary research. To foster such research in the coming generation of academics we will record all presentations (subject to speaker consent) and make them publicly available on the internet for the use of the wider scientific community.
Meetings are about people; no organisational aspiration can be met in a meeting without the right mix of participants. This fact is particularly true for Frontiers given its scientific breadth. We believe that with you, we have indeed gathered the right mix of people.
We look forward to seeing you at the next Frontiers meeting.
Orkun S Soyer, on behalf of the organising committee September 2010, University of Exeter
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FRONTIERS
Organisers and Sponsors
In addition to our official sponsors, we would like to thank all academic members of University of Exeter systems biology managerial board, Professor Nick Talbot and Professor David Butler. We would also like to acknowledge the support of the Research & Knowledge Transfer Office, and especially thank Maggie Smith, Andrew Richards and Pete Hodges.
Company of Biologists
Steven PorterUniversity of Exeter
Orkun S SoyerUniversity of Exeter
Ruth BakerUniversity of Oxford
Nicolas BuchlerDuke University
Özgür AkmanUniversity of Exeter
EPSRCEngineering and Physical Sciences Research Council
SIGNETThe Cell Signaling
Network
Microsoft Research
University of Exeter
Science StrategySystems
Biology Theme
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Programme* Indicates Invited Speaker
Tuesday, 21 September
12.00 -13.50 Registration
13.50 -14.00 Conference Open and Welcome
Professor Nick Talbot, Deputy Vice Chancellor for Research & Knowledge Transfer, University of Exeter
14.00 - 14.30 Pristionchus Pacificus – A Nematode Model for Integrative Studies in Evolution and Ecology, *Ralf Sommer, Max Planck Institute for Developmental Biology, Germany
14.35 - 15.05 Parasite Driven Redundancy in Signaling Networks, Orkun Soyer, University of Exeter
15.10 - 15.40 Complex Light Response in a One-Loop Model of the Ostreococcus Tauri Circadian Clock, Carl Troein, University of Edinburgh
15.40 - 16.10 Coffee Break
16.10 - 16.40 The Origins of Evolutionary Innovations, *Andreas Wagner, University of Zurich
16.45 - 17.15 Two-Domain DNA Strand Displacement, *Luca Cardelli, Microsoft Research
17.15 – 18.30 Posters session and free discussion with drinks reception
Wednesday, 22 September
09.00 - 09.30 Modelling and Analysis Tools for Biochemical Networks, Antonis Papachristodoulou, Oxford University
09.35 - 10.05 Phenotypes in the Design Space of Biochemical Systems, *Michael Savageau, UC Davis
10.05 - 10.35 Coffee Break
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10.35 - 11.05 Multidimensional Optimality of Microbial Metabolism, *Uwe Sauer, ETH Zurich
11.10 - 11.30 Uncovering the Design Principles of Polyamine Regulation in Yeast: An Integrated Modelling and Experimental Study, Svetlana Amirova, University of Exeter
11.35 - 12.05 A Synthetic Biology Approach to Recombinase Mediated Metabolic Pathway Engineering, *Susan Rosser, University of Glasgow
12.10 - 12.30 Surpassing Evolution: Using Synthetic Biology to Rewire and Repurpose Biological Systems, Travis Bayer, Imperial College London
12.30 - 13.30 Lunch
13.30 - 14.00 After lunch walk
14.00 - 14.30 Stochastic and Controlled Accumulation of Dynein at Microtubule Ends Prevents Endosomes from Falling off the Track, Gero Steinberg, University of Exeter
14.35 - 15.05 A Dynamic Spindle-Like Apparatus that Segregates Low Copy Number Plasmids, *Martin Howard, John Innes Centre
15.05 - 15.35 Coffee Break
15.35 - 16.05 Combinatorial Stress Responses in Yeast, Ken Haynes, University of Exeter.
16.10 - 16.40 The Gene Circuits of Plant Clocks, and the Infrastructure for Systems Biology, *Andrew Millar, University of Edinburgh
16.40 - 17.30 Discussion 1: Modelling in Light of Current Experimental Developments
17.30 - 18.30 Posters session and free discussion with drinks reception
Thursday, 23 September
09.00 - 09.30 The Propagation of Perturbations in Rewired Gene Networks, *Mark Isalan, Centre for Genomic Regulation (CRG), Barcelona, Spain
09.35 - 10.05 Cell-to-Cell Variability in the E. Coli TorS/TorR Signaling System, *Mark Goulian, University of Pennsylvania
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10.05 - 10.35 Coffee Break
10.35 - 11.05 Measuring Dynamics, Noise and Heterogeneity in Genes and Networks, *David Rand, University of Warwick
11.10 - 11.30 An Integrated Framework for Inference, Identifiability, Sensitivity and Robustess in Stochastic Models of Biochemical Reactions, Michal Komorowski, Imperial College London
11.35 - 12.05 Looking After the Neighbourhood: Noise Abatement and Genome Evolution, *Laurence Hurst, University of Bath
12.10 - 12.30 Transcriptional Noise Reduction and the Evolution of Negative Auto-Regulation, Max Reuter, University College London
12.30 - 13.30 Lunch
13.30 - 14.00 After lunch walk
14.00 - 14.30 Ultrasensitivity and Cellular Decision-Making, *Peter Swain, University of Edinburgh
14.35 - 15.05 Decision Making in Bacterial Chemotaxis, *Judy Armitage, Oxford University
15.05 - 15.35 Cream Tea Break
15.35 - 15.55 Robust Signal Processing in Living Cells, Ralf Steuer, Berlin
16.00 - 16.20 Chaste: A Computational Framework for Multiscale Modelling in Systems Biology, Alexander Fletcher, Oxford University
16.25 - 16.55 ... The Rest are Details: Model Selection in Systems and Evolutionary Biology, *Michael Stumpf, Imperial College London
16.55 - 17.30 Discussion 2: Experiment in Light of Current Theoretical Developments
17.30 Day session closes
17.30 Bar opens – Reed Hall.
19.00 Pre-dinner drinks – Reed Hall
19.30 Gala Dinner – Woodbridge Suite, Reed Hall
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Friday, 24 September
09.00 - 09.30 The Statistical Physics of Decision-Making in Insect Colonies, Patrick Hogan, University of Bristol
09.35 - 10.05 Social Evolution in Microbes, *Kevin Foster, Harvard University
10.05 - 10.35 Coffee Break
10.35 - 11.05 Horizontal Gene Transfer of the Secretome Drives the Evolution of Bacterial Cooperation and Virulence, Sam Brown, Oxford University
11.10 - 11.30 Microbial Evolution in Theory and Practice, Ivana Gudelj, Imperial College London
11.35 - 12.05 The Evolutionary Systems Biology of HIV-1 Drug Resistance, *Sebastian Bonhoeffer, ETH Zurich.
12.15 - 13.00 Final thanks and conference closes
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Posters1. Evolutionary Signatures of Mutagenic Processes
Associated with Transcription, Peter Arndt, Max Planck Institute for Molecular Genetics, Germany Co-authors: Paz Polak, Max Planck Institute for Molecular Genetics, Germany
2. Inferring Partially-Known Scale-Free Interaction Networks from Noisy Data, Carlo Cosentino, University of Catanzaro, Italy Co-authors: Francesco Montefusco ¹ ², Jongrae Kim ³, Declan G. Bates ², Francesco Amato ¹ (¹ University of Catanzaro, Italy; ² University of Exeter, UK; ³ University of Glasgow, UK)
3. A Kinetic Model for Predicting MHC Class I Presentation of Competing Peptides, Neil Dalchau, Microsoft Research, UK Co-authors: Andrew Phillips ¹, Leonard D Goldstein ², Mark Howarth ³, Luca Cardelli ¹, Tim Elliott 4, Joern M Werner 4 (¹ Microsoft Research, UK; ² University of Cambridge, UK; ³ University of Oxford, UK; 4 University of Southampton, UK)
4. Modelling the Oxidative Stress Response of Bacillus Subtilis Using Time Resolved Transcriptomics, Emma Denham, University Medical Center Groningen and University of Groningen, Netherlands
Co-authors: Leslie Aichaoui ¹, Lieke van Gijtenbeek ², Maria de Vries ², Vincent Fromion ¹, Jan Maarten van Dijl ², (¹ Laboratoire de Genetique Microbienne, France; ² University Medical Center Groningen and University of Groningen, Netherlands)
5. Inferring Hidden Transcription Factor Profiles by Thermodynamic Modelling of GFP-derived Promoter Activities, Luca Gerosa, ETH Zurich Co-authors: Bart Haverkorn van Rijsewijk ¹, Karl Kochanowski ¹, Matthias Heinemann ¹, Uwe Sauer ¹, (¹ Institute of Molecular Systems Biology, ETH Zurich, Switzerland)
6. The Mathematical Structures of the Brain. Category Theory in Neural Science, Manuel G Bedia, University of Zaragoza, Spain Co-authors: Ricardo Sanz ¹, Jaime Gonzalez-Ramirez ¹ (¹ Technical University Madrid, Spain)
7. On Types and Roles of Interdisciplinarity within Systems Biology Research, Karen Kastenhofer, Austrian Academy of Sciences
8. Evolution of an Environmental Response Network, Chris Knight, University of Manchester
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Co-authors: Heather Robinson ¹, Bharat Rash ¹ (¹ University of Manchester)
9. Identification of a Metabolic Flux Sensor in Escherichia Coli, Karl Kochanowski, Institute of Molecular Systems Biology, ETH Zurich, Switzerland Co-authors: Benjamin Volkmer ¹, Luca Gerosa ¹, Bart Haverkorn van Rijsewijk ¹, Matthias Heinemann ¹, (¹ Institute of Molecular Systems Biology, ETH Zurich, Switzerland)
10. Stochasticity in Protein Levels Drives Colinearity of Gene Order and Enzymatic Steps in Metabolic Operons of Escherichia Coli, Karoly Kovacs, Biological Research Center, Institute of Biochemistry, Szeged, Hungary Co-authors: Laurence D. Hurst ¹, Balazs Papp ², (¹ University of Bath, Department of Biology and Biochemistry, UK; ² Biological Research Center, Institute of Biochemistry, Szeged, Hungary)
11. Functional Trade-Offs in Allosteric Sensing, Bruno Martins, Centre for Systems Biology at Edinburgh, The University of Edinburgh Co-authors: Peter S. Swain, Centre for Systems Biology at Edinburgh, The University of Edinburgh
12. Philosophical Issues of Multidisciplinarity: Integration and Translation in Systems and Synthetic Biology, Maureen O’Malley, University of Exeter
13. New Generation Methods of Spiral-Pairs and 3D Patterns, Avinoam Rabinovitch, Ben-Gurion University, Israel Co-authors: Y. Bitona ¹, D. Braunsteinc ¹, M. Friedmand ¹, I. Aviram ¹, (¹ Ben-Gurion University, Israel)
14. Modelling DivIVA-Mediated Branching in
Streptomyces, David Richards, John Innes Centre Co-authors: Martin Howard, John Innes Centre
15. Collective Behaviour of Free-Swimming Rhodobacter Sphaeroides, Gabriel Rosser, University of Oxford, Centre for Mathematical Biology Co-authors: C. Yates ¹, T.M. Wood ¹, D. Wilkinson ¹, P.K. Maini ¹, M.C. Leake¹, (¹ University of Oxford, Centre for Mathematical Biology)
16. Modelling a Community Effect in Animal Development: A Mechanism for the Coordinated Differentiation of a Cell Population, Yasushi Saka, University of Aberdeen Co-authors: Cedric Lhoussaine ¹, Celine Kuttler ¹, (¹ IRI-CNRS, Universite de Lille, France)
17. Generic Dynamic Modelling of Metabolic Systems, Jean-Marc Schwartz, University of Manchester
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Co-authors: Delali Adiamah, University of Manchester
18. The Feasibility of Lab Evolution of Subtle Genomic Traits, Yishay Shoval, Weizmann Institute of Science, Israel Co-authors: Yitzhak Pilpel, Weizmann Institute of Science, Israel
19. Data integration for Systems Biology, Andrea Splendiani, Rothamsted Research
20. Real-Time Control of Gene Expression, Jannis Uhlendorf, INRIA Paris – Rocquencourt, France Co-authors: Pascal Hersen ¹, Samuel Bottani ¹, Gregory Batt ², (¹ University Paris Diderot, France; ² INRIA Paris – Rocquencourt, France)
21. Queueing Induced by Bidirectional Motor Motion Near the End of a Microtubule, Congpin Lin, University of Exeter, UK Co-authors: Peter Ashwin ¹, Gero Steinberg ¹, (¹ University of Exeter, UK)
22. Preventing Ventilator Associated Lung Injury using Systems Engineering, Anup Das, University of Exeter, UK
Co-authors: Z. Gao ¹, P.P. Menon ³, J.G. Hardman ², D.G. Bates ³ (¹ University of Liverpool, UK; ² University of Nottingham, UK; ³ University of Exeter, UK)
23. Response Dynamics and Evolution in Signalling Networks Regulating Bacterial Chemotaxis, Munia Amin, University of Exeter Co-authors: Orkun S. Soyer ¹, Steven L. Porter ¹, (¹ University of Exeter, UK)
24. Systems Biology of the Burkholderia glycome: A Search for the Best Targets, Nicholas Harmer, University of Exeter, UK
25. Genomics of Emerging Crop Pathogens, David Studholme, University of Exeter, UK
26. Digital Clocks: Boolean Modelling of Circadian Networks, Ozgur Akman, University of Exeter, UK Co-authors: Steven Watterson ¹, Andrew Parton ¹, Nigel Binns ¹, (¹ University of Edinburgh, UK)
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Talk AbstractsRalf Sommer Max Planck Institute for Developmental Biology, Germany
Pristionchus Pacificus – A Nematode Model for Integrative Studies in Evolution and Ecology
Pristionchus pacificus has been established as a model system in evolutionary biology with genetic, genomic and transgenic tools. Detailed investigations of vulva formation and other developmental processes reveal that developmental mechanisms differ strongly between C. elegans and P. pacificus. While evo-devo can provide fundamental insight into morphological evolution, the limitations of its gene-centered and development-centered view necessitate the synthesis of evo-devo with other areas of evolutionary biology. Synthesis with “population genetics” can reveal how phenotypic evolution is initiated at the micro-evolutionary level and synthesis with “evolutionary ecology” can add an ecological perspective to these evolutionary processes.
The well-defined association of P. pacificus with scarab beetles, the apparent plasticity of this beetle association, and the ability of this widespread species to thrive in a variety of geographic ranges and ecological conditions, make P. pacificus an ideal model organism for the fusion of evo-devo, population genetics and evolutionary ecology. We have started to analyze the ecological interactions of P. pacificus in the beetle ecosystem and more than 400 strains of P. pacificus have been isolated from around the world. Here I report from our most recent work focusing on the evolution
of novel morphological structures. P. pacificus forms a mouth dimorphism that allows predatory behaviour on fungi and other nematodes. We begun studying how a novel predatory behaviour is integrated into an existing nervous system and have started the reconstruction of the P. pacificus nervous system from more than 3,500 thin sections of the nematode head. Our studies reveal tremendous differences in the connectivity of cells and at other systems-levels, rather than simple changes in the number of cells.
Orkun Soyer University of Exeter Co-authors: Murray Grant, University of Exeter
Parasite Driven Redundancy in Signaling Networks
The antagonistic interaction between a parasite and its host result in an ever-running arms race. While it is well-established that such an arms race extends to the molecular level, we still lack a system level understanding of host-pathogen interaction at the level of signaling and metabolic networks. Our recent work using toy models suggest that parasite interference with host networks can lead to high level of redundancy based robustness in the latter. Motivated by that work, we performed a system level analysis of molecular interactions between Arabidopsis thaliana and one of its pathogens Pseudomonas syringae.
In particular, Arabidopsis thaliana uses jasmonic acid (JA) as a central signaling hormone to respond to a variety of environmental clues including pathogen attack. This central role of JA is exploited
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by P. syringae, which uses a JA mimic, coronatine, to interfere with host signaling and gene regulation. We analyse the response dynamics in the JA signaling networks and find that it can exhbit bistability. The observed high number of duplicates in this network has direct effect on this dynamics, changing threhsold levels of the bistable switch. Combined with experimental work, this analysis allows us to better understand the underpinning dynamics between host and parasite and its evolution.
Carl Troein University of Edinburgh, UK Co-authors: Laura Dixon ¹, Gerben van Ooijen ¹, Florence Corellou ², Francois-Yves Bouget ², (¹ University of Edinburgh, UK; ² Université Pierre et Marie Curie – Paris 6, France)
Complex Light Response in a One-Loop Model of the Ostreococcus Tauri Circadian Clock
The biochemical oscillations of the circadian clock helps living cells cope with the daily rhythms in the environment. In plants and other eukaryotes the clock depends on transcriptional feedback between genes. In Arabidopsis, the major clock components are genes with many homologues, which together form an interconnected and highly on-trivial system, making accurate modelling a great challenge. In contrast, the tiny alga Ostreococcus tauri has a highly reduced genome with few clock genes, but its clock can nonetheless entrain to light/dark cycles in both long and short day conditions.
We have modelled the Ostreococcus clock as a single negative feedback loop between the two clock genes TOC1 and CCA1. Our model
reproduces experimental data from luciferase assays, not only under periodic light/dark cycles and in constant light but also transiently across changes in the light conditions. This is unexpected from a model with such a simple structure. The clock shows a complex phase response and avoids locking its oscillations to dusk or dawn, something which in the Arabidopsis model required multiple feedback loops. To explain how the clock may achieve this feat, we have systematically altered the model to reveal the effects of the individual light inputs. We find that the existence of several light inputs grants the system a degree of flexibility and autonomy from the input signal usually assumed to require a greater number of clock components.
Andreas Wagner University of Zurich, Switzerland
The Origins of Evolutionary Innovations
Life can be viewed as a four billion year long history of innovations. These range from dramatic macroscopic innovations like the evolution of wings or eyes, to a myriad of molecular changes that form the basis of macroscopic innovations. We know many examples of innovations – qualitatively new phenotypes that can provide a critical advantage in the right environment – but have no systematic understanding of the principles that allow organisms to innovate. Most phenotypic innovations result from changes in three classes of systems: metabolic networks, regulatory circuits, and protein or RNA molecules. I will discuss evidence that these classes of systems share two important features that are essential for their ability to innovate.
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Luca Cardelli Microsoft Research, UK
Two-Domain DNA Strand Displacement
We investigate the computing power of a restricted class of DNA strand displacement ‘gates’ that are structurally very simple: they are made of double strands with nicks (interruptions) in the top strand. The single strands (‘signals’) they interact with are also structurally simple: they consist of two-domain strands with one toehold domain and one recognition domain. We study the implementation of fork and join signal processing gates based on these structures, and we show that these systems are amenable to formalisation and to mechanical verification.
Antonis Papachristodoulou University of Oxford Co-authors: James Anderson ¹, Yo-Cheng Chang¹, Edward Hancock ¹, (¹ University of Oxford)
Modelling and Analysis Tools for Biochemical Networks
Mathematical modelling is key for understanding the properties of dynamic processes inside cells, which usually consist of complicated networks of interacting genes/proteins. However, modelling and analysing such pathways presents a number of mathematical and computational challenges, as the models considered are usually complicated nonlinear differential equations with several time-scales and unknown parameters.
Here we discuss a number of issues related to the analysis of biological networks and specifically address scalability issues. We focus on new mathematical and algorithmic tools to understand the properties of complex signaling pathways. We
consider nonlinear differential equation models for these systems, for which the vector fields are polynomial or rational (as typically results from Mass Action or Michaelis-Menten kinetics). A method for algorithmically constructing certain functions that can verify, e.g., robust stability is then presented. The techniques presented are rooted in robust control and dynamical systems theory, but use recent developments in the theory of positive polynomials and Sum of Squares. Computation is done using convex optimisation algorithms (specifically Semidefinite Programming). Such an approach is desirable as the algorithms have an associated worst case polynomial time complexity. Even with this complexity bound, most realistic models that one would like to analyse are not guaranteed to result in tractable optimisation problems. Towards this end we are developing complementary analysis tools based on dynamic model decomposition and reduction that enable the algorithms presented to scale to a much larger class of system models. Our results are illustrated on the EGF-MAPK and Wnt signaling pathways.
Michael Savageau University of California, Davis, USA
Phenotypes in the Design Space of Biochemical Systems
Although characterisation of the genotype has undergone revolutionary advances as a result of the successful genome projects, the chasm between our understanding of a fully characterised gene sequence and the phenotypic repertoire of the organism is as broad and deep as it was in the pre-genomic era. There are fundamental unsolved problems in relating genotype to
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phenotype. We address one of these using the concept of a ‘system design space’. The important task of elucidating design principles of biological systems is facilitated by enumeration of regions within the system’s design space that correspond to qualitatively-distinct phenotypes that can be identified and counted, their relative fitness analysed and compared, and their tolerance to change measured. First, I will review design spaces that have proved useful in revealing design principles for elementary gene circuits. Second, I will present a generic construction of the system design space. This approach is grounded in the power-law equations that characterise traditional chemical kinetics and, by recasting, the rational functions that characterise biochemical kinetics. In steady state, the analysis of these equations can be reduced to that of linear algebraic equations. Third, these methods will be illustrated with an application to a well-studied gene circuit.
Uwe Sauer ETH Zurich, Switzerland Co-authors: Robert Schutz ¹, Matthias Heinemann¹, Nicola Zamboni ¹, (¹ Institute of Molecular Systems Biology, ETH Zurich, Switzerland)
Multidimensional Optimality of Microbial Metabolism
Great strides have been made in our ability to monitor metabolic responses, in particular in microorganisms, (1) but understanding and prediction of biological function and activity from such data has remained challenging (2) for in metabolism, biological function is represented by the enzyme-catalysed fluxes through the network.
Key questions are how a particular distribution of fluxes is manifested in an organism under a given
condition – and why this particular one. On the basis of large sets of experimentally determined in vivo flux data, we discuss here whether there are generally valid principles that describe the distribution of flux under different conditions and how such metabolic networks respond to perturbations? Using the computational framework of flux balance analysis, we test two fundamentally different families of hypotheses: are cells optimised during evolution towards one or more objectives (3) or are their responses optimised towards minimal adjustments?
References:
1. Zamboni N and Sauer U. Curr. Opin. Microbiol. 12: 553 (2009)
2. Heinemann M and Sauer U. Curr. Opin. Microbiol. 13: 337 (2010)
3. R Schütz, L Küpfer and U Sauer. Mol. Sys. Biol. 3:119 (2007)
Svetlana Amirova University of Exeter Co-authors: Claudia Rato da Silva ¹, Heather Wallace ¹, Ian Stansfield ¹, Declan G. Bates ², (¹ School of Medical Sciences, University of Aberdeen, UK; ² University of Exeter, UK)
Uncovering the Design Principles of Polyamine Regulation in Yeast: An Integrated Modelling and Experimental Study
A new complete predictive model of the polyamine metabolism in the yeast Saccharomyces cerevisiae is developed using a Systems Biology approach incorporating enzyme kinetics, statistical analysis, control engineering and experimental molecular biology of translation. The polyamine molecules putrescine, spermidine and spermine are involved in a number of important cellular
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processes such as transcriptional silencing, translation, protection from reactive oxygen species, coenzyme A synthesis and components of polyamine pathway are potential targets for cancer therapeutics. Unregulated polyamine synthesis can trigger uncontrolled cell proliferation. Conversely, polyamine depletion can cause apoptosis, and during development, defects leading to mental retardation in humans.
Our approach uncovers the multiple feedback control mechanisms in the polyamine metabolic pathway; also it provides a source of robustness and its associated dynamical properties. The main focus is highly conserved negative feedback loop regulating level of enzyme Spe1, the enzyme catalysing the first step in the polyamine biosynthesis pathway by the protein Antizyme synthesised by a polyamine-dependent translational frameshifting mechanism. Possible applications are in pharmacology; toxicology; preclinical drug development for cancer; and neurodegenerative disorders: anti-cancer drug DFMO, Snyder-Robinson Syndrome.
Susan Rosser University of Glasgow, UK
A Synthetic Biology Approach to Recombinase Mediated Metabolic Pathway Engineering
A grand challenge in synthetic biology is the need for technologies that enable the construction of novel and complex functions in biological systems. When these functions involve the expression, coordination and optimisation of multiple genes, building the genetic circuits becomes increasingly difficult. Assembling multigenic functions e.g. a metabolic pathway in an organism by an iterative approach is laborious, difficult and expensive. Such challenges have posed major hurdles to efforts
to engineer metabolism in microbes and plants. The goal of this project is to develop a synthetic system that harnesses the power of multiple recombination mechanisms to enable synthetic biologists to generate, diversify, and refine complex multigenic functions. We aim to develop a novel technology platform that harnesses the power of recombinases for continuous directed evolution of complex multigenic functions resulting an assembly of functionally coordinated genes theoretically facilitating the rapid evolution of new phenotypes.
Travis Bayer Imperial College London, UK
Surpassing Evolution: Using Synthetic Biology to Rewire and Repurpose Biological Systems
Cellular metabolism is controlled by complex networks of interacting regulators. Perturbing these networks can have profound effects on the fitness of organisms. This highlights an important challenge in biology: investigating how the network architectures we observe in Nature evolved in response to selective pressure; what that pressure might have been; or whether the architecture is a result of non-adaptive forces.
A complimentary issue is how we can rationally design metabolic and regulatory architecture to engineer biological systems for useful purposes. Synthetic biologists aim to construct artificial genetic systems to understand Nature and for a variety of biotechnological applications.
In this talk, I will highlight how engineering gene expression and metabolism allows us to test evolutionary hypotheses and potentially investigate the evolutionary paths not taken by extant organisms. I will also present several examples where biological systems have been repurposed as biological technologies.
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Gero Steinberg University of Exeter Co-authors: Martin Schuster ¹, CongPing Lin ², Peter Ashwin ², Nicholas J. Severs ³, Gero Steinberg ¹, (¹ School of Biosciences, University of Exeter, UK; ² Mathematics Research Institute, University of Exeter, UK; ³ Imperial College London, National Heart and Lung Institute, London, UK)
Stochastic and Controlled Accumulation of Dynein at Microtubule Ends Prevents Endosomes from Falling Off the Track
Cellular organisation and survival depends on active transport of organelles, vesicles or protein complexes along the filaments of the cytoskeleton. Long-distance transport involves microtubules and the opposing motor proteins kinesin and dynein. Numerous theoretical studies have described the behaviour of these motors. However, the motility parameters used were usually derived from in vitro studies, and might therefore not reflect the situation in the living cell. Here, we have visualised motors and their cargo in a fungal model system.
By combining a quantitative analysis of their motility with mathematical modelling we attempted to describe the behaviour of dynein and early endosomes at the end of microtubules. We found that a combination of stochastic accumulation and controlled retention of dynein at microtubule ends increases the probability of endosomes to get loaded onto dynein for retrograde transport. This mechanism prevents the organelles from falling of the microtubule.
Martin Howard John Innes Centre, UK
A Dynamic Spindle-Like Apparatus that Segregates Low Copy Number Plasmids
Low copy number plasmids require active partitioning (par) loci to ensure stable transmission at cell division. In the presence of the partition complex (ParB bound to parC), ParA of plasmid pB171 forms dynamic cytoskeletal-like structures that dynamically relocate over the nucleoid. Simultaneously, par distributes plasmids regularly over the nucleoid by an unknown mechanism. Here, we dissect this system using a combination of fluorescent imaging and mathematical modeling. Our experiments indicate that ParA forms dynamic filamentous structures that move plasmids by a pulling mechanism in a spindle like fashion. We propose a model for the Par dynamics where the plasmids themselves are responsible for ParA filament disassembly. The model makes firm predictions that we have validated experimentally, and reveals how dynamic ParA filaments can segregate plasmids into separate cell-halves before cell division.
K. Gerdes, M. Howard and F. Szardenings: Pushing and Pulling in Prokaryotic DNA Segregation. Cell 141 927-942 (2010)/S. Ringgaard, J. van Zon, M. Howard and K. Gerdes: Movement and Equipositioning of Plasmids by ParA Filament Disassembly. Proc. Natl. Acad. Sci 106 19369-19374 (2009)
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Ken Haynes University of Exeter, UK
Combinatorial Stress Responses in Yeast
In the mid 1990’s only a small number of genes had been sequenced cloned and inactivated in the fungal pathogen Candida glabrata. A decade and a half later, the genome has been sequenced, annotated, arrays designed and delivered to the community and a co-ordinated effort to construct a knock-out library and ORFeome is underway in our laboratory, and others around the world. Additionally the relatively close phylogentic relationship between C. glabrata and Saccharomyces cerevisiae has seen interest in this organism grow with respect to the evolution of protein-protein interactions. Preliminary attempts to utilise these resources to analyse the response of C. glabrata to hyperosmotic and oxidative stress, individually and in combination, will be presented.
We have performed time-series expression analysis, characterised phenotypic responses, protein-protein interactions and shown that the signaling pathway sensing osmostress differs in both structure and response to that seen in S. cerevisiae and Candida albicans. We have also shown that combinatorial stress elicits a response that is different to that seen in response to individual stresses. Data comparing responses in the three species will be presented.
In addition to the analyses that the modelling participants of CRISP are conducting we have attempted to analyse these data using tools that are easily accessible to biologists. We believe that these tools are inadequate to gain full value from the data. They do not allow us to gain a holistic view of our system, and we strongly believe that tools that allow biologists to integrate these varied
experimental approaches will add significant value to data interpretation. Finally, this presentation will hopefully show that non-model organisms are tractable at a systems level, and that you don’t have to be a super group to embark on such a voyage.
Collaboration between Imperial College London (Barahona, Gudelj, Haynes, Stark, Stumpf) and the University of Aberdeen (Brown, Coghill, Gow, Grebogi, Moura, Romano, Thiel). In addition I would like to thank all the SABR staff, and others (Lauren Ames, Emily Cook, Hsueh-lui Ho, Maxime Huvet, Piers Ingram, Mette Jacobsen, Despoina Kaloriti, Megan Lenardon, Andy McDonagh, Susanna Nilsson, Wei Pang, Melanie Puttnam, Elahe Radmaneshfar, Dan Silk, Anna Tillman, Tom Thorne and Tao You) who have contributed to this work. It is funded by the BBSRC SABR grant Combinatorial Responses in Stress Pathways (CRISP).
Andrew Millar University of Edinburgh, UK Co-authors: A. Pokhilko ¹, S. Hodge ¹, K. Knox¹, S. Gilmore ² ³, R. Adams ², A. Yamaguchi ², N. Hanlon ², N. Tsorman ², C. Tindal ¹, R. Muetzelfeldt 3 4, H. Ougham 5, (¹ School of Biological Sciences, University of Edinburgh; ² Centre for Systems Biology at Edinburgh; ³ School of Informatics, University of Edinburgh; 4 Simulistics Ltd., Loanhead; 5 IBERS, University of Wales-Aberystwyth)
The Gene Circuits of Plant Clocks and the Infrastructure for Systems Biology
The Centre for Systems Biology at Edinburgh (www.csbe.ed.ac.uk) develops infrastructure for Systems Biology research projects. CSBE’s core
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biological projects include RNA metabolism in yeast; the interferon and ErbB pathways in human cells; and our work on plant circadian rhythms.
The ubiquitous “biological clock” creates 24-hour rhythms that control much of plant physiology, from photoperiodism (Salazar et al. Cell 2009) to carbon metabolism. Our mathematical models of the clock mechanism have contributed to the practical design of experiments; to the identification of clock components by molecular genetics (Locke et al. Mol Syst Biol. 2005 and 2006); and to our understanding of broad principles of biological regulation (Rand et al. Interface 2004, Akman et al. Mol Syst. Biol. 2008). Why, for example, do the clock mechanisms in all organisms comprise many interlocked feedback loops (cf. work by Carl Troein and Ozgur Akman)? Why, in Arabidopsis, is one family of clock proteins represented by five members, which are expressed successively, in a wave from dawn to dusk? A new clock model separates the members to reveal the functional implications (Pokhilko et al., Mol. Syst. Biol. 2010, in press).
The Systems Biology Software Infrastructure, SBSI (www.sbsi.ed.ac.uk, led by Steven Gilmore) is a set of open-source, modular software that aims to streamline the process of modelling such dynamic biological systems. Global parameter searching on HPC platforms is a particular emphasis: SBSI is now available as a service on HECToR. A new entry-point has recently been provided to lower the technical barriers to this complex area: model optimisation via SBSI has recently been provided as a plugin to CellDesigner, the most popular graphical modelling application.
SBSI is now linked to the PlaSMo repository of private and public XML models (www.plasmo.ed.ac.uk), and a database of experimental results
is in development. From a broader perspective, Systems Biology offers a fresh opportunity to connect understanding of intracellular pathways to the performance of plant populations. The difficulties and the promise are worth considering, at a time when understanding of plant biology in the field is urgently required to respond to global challenges.
Mark Isalan Centre for Genomic Regulation (CRG), Barcelona, Spain
The Propagation of Perturbations in Rewired Gene Networks
There are many gene knock-out and overexpression studies available in different organisms, but no one previously explored the effect of systematically adding new links to biological networks. We therefore constructed 598 different combinations of promoters and transcription- or sigma-factor coding regions, in Escherichia coli, and added these shuffled combinations over the genetic background of the wild type. In a way, this is reminiscent of the gene duplication and regulatory drift processes thought to shape gene networks during evolution. The study showed that most perturbations were active and yet tolerated by the bacteria. We have recently extended our initial analysis by selecting 57 shuffled network constructs for microarray transcriptome analysis. The resulting view allows us to explore the extent to which rewiring perturbations propagate across the network.
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Mark Goulian University of Pennsylvania, USA
Cell-to-Cell Variability in the E. Coli TorS/TorR Signaling System
In the absence of oxygen, E. coli can respire by using trimethylamine-N-oxide (TMAO) as an electron acceptor. Expression of the torCAD operon, which encodes the proteins required for TMAO reduction, is controlled by the TorS/TorR two-component signaling system. TMAO signaling occurs through TorS, a hybrid sensor kinase that phosphorylates the response regulator TorR.
In an effort to explore the properties of hybrid kinases, we have been studying the behavior of this system in single cells using fluorescent transcriptional reporters. Surprisingly, transcription of the torC promoter shows considerable cell-to-cell variability, even when the system is maximally induced with TMAO. I will describe our efforts to understand this behaviour and present evidence that the variability arises from a step upstream of TorR in the phosphorelay.
David Rand University of Warwick, UK
Measuring Dynamics, Noise and Heterogeneity in Genes and Networks
I will describe some new mathematical approaches to measure dynamics and noise and probe system design principles both in individual genes and in signalling and regulatory networks. These approaches and techniques allow one to probe fundamental aspects such as uncertainty and heterogeneity in single cell behaviour, the variation between cells and the interaction between them that have not been investigated with conventional methods. To illustrate these ideas I will consider
a range of systems including circadian clocks, the NF-B signalling system, and prolactin transcription.
Michal Komorowski Imperial College London, UK Co-authors: Barbel Finkenstadt ¹, David Rand ¹, Michael Stumpf ², (¹ University of Warwick; ² Imperial College London, UK)
An Integrated Framework for Inference, Identifiability, Sensitivity and Robustness in Stochastic Models of Biochemical Reactions
The aim of the presentation is to introduce a novel, integrated theoretical framework for the analysis of stochastic biochemical kinetics models. Our framework includes efficient methods for statistical parameter estimation from experimental data, as well as tools to study parameter identifiability, sensitivity and robustness. Our methods provide novel conclusions about functionality and statistical properties of stochastic systems. We introduce a general model of chemical reactions described by the Chemical Master Equation that we approximate using the linear noise approximation. This allows us to write explicit expressions for the likelihood of experimental data, which lead to an efficient inference algorithm and a quick method for calculation of the Fisher Information Matrices.
We present a number of experimental and theoretical examples that show how our techniques can be used to extract information from the noise structure inherent to experimental data. Examples include a model of gene expression, Bayesian hierarchical model for estimation of degradation and transcription rates and a study of the p53 system. Novel insights into the causes and effects of stochasticity in biochemical systems are obtained by the analysis of the Fisher Information
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Matrices. Our methodology is the first which, not only allows parameter estimation, but can also be used to study sensitivity and to guide the design of experiments probing stochastic systems without the need for extensive Monte Carlo simulations.
Laurence Hurst University of Bath, UK Co-authors: Balazs Papp ¹, Karoly Kovacs ¹, Martin J. Lercher ², Guang-Zhong Wang ², (¹ Biological Research Center of the Hungarian Academy of Sciences, Hungary; ² Bioinformatics Group, Heinrich-Heine-University Düsseldorf, Germany)
Looking After the Neighbourhood: Noise Abatement and Genome Evolution
Noise in gene expression is both inevitable and likely to be selectively important, especially for more dose sensitive genes, such as those that result in inviability when knocked out (essential genes). Here I ask whether gene order and architecture adapt to modify expression in noise. In particular I show how bidirectional promoter architecture in yeast and co-linearity of metabolic genes in bacterial operons both appear to be noise abatement mechanisms. The noise model uniquely explains why co-linearity is strongest for lowly expressed operons. The bidirectional model correctly predicts the dearth of sub-telomeric bipromoter genes, the enrichment of essential genes associated with bidirectional promoters and explains why genes associated with cryptic unstable transcripts tend both to be essential and to have low noise levels.
Max Reuter University College London, UK Co-authors: Alexander Stewart, University College London, UK
Transcriptional Noise Reduction and the Evolution of Negative Auto-Regulation
Gene transcription is a key step in linking genotype to phenotype. Gaining insight into how gene regulation functions and evolves is therefore crucial to our understanding of the genotype-phenotype map. Transcriptional noise poses a significant challenge to maintaining optimal gene expression and therefore an organism’s phenotype. Transcriptional noise arises due to both fluctuations in the environment and the stochastic nature of transcription itself. Minimising the effect of noise on gene expression is an important function of gene regulatory networks and selection on the capacity to buffer noise is thought to be an important driver of regulatory evolution. Negative auto-regulation has been proposed as a powerful mechanism for noise-reduction. Stochastic models of gene expression have shown that negative auto-regulation reduces both the response time and noise in gene expression. Empirical support for the role of auto-regulation, however, is contradictory. In prokaryotes, the motif is very prevalent and about half of the transcription factors in Escherichia coli negatively auto-regulate. In eukaryotes, the situation is very different, for example only about ten percent of Saccharomyces cerevisiae transcription factors show this type of regulation.
We propose that the difference in prevalence of auto-regulation between pro- and eukaryotes is due to adverse effects of diploidy on the evolution of this type of regulation. We construct a model of negative auto-regulation in diploids and investigate the effects of mutations that alter the strength of negatively auto-regulating binding sites. We show
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that when selection acts to reduce noise in gene expression, an increase in the strength of negatively auto-regulating binding sites is frequently subject to under-dominance. This results in a barrier to the de novo evolution of negatively auto-regulating binding sites and explains the relative scarcity of negative auto-regulation in yeast.
Peter Swain University of Edinburgh, UK
ltrasensitivity and Cellular Decision-Making
Many genetic and signalling networks respond ultrasensitively as the concentration of the input to the network increases. We argue that ultrasensitive responses could occur because cells must infer changes in the state of the extracellular environment only from intracellular changes or from local changes at the membrane. Using synthetic biology to study gene expression in bacteria and systems biology to study signal transduction in yeast, we show that ultrasensitive responses are convergent in that they can be generated by very different biochemistry: through cooperative interactions between transcription factors or through a competition between a kinase and a phosphatase for multiple phosphorylation sites on a protein scaffold. We show that both responses may be understood as inference in a two-state environment.
Judy Armitage Oxford University, UK
Decision Making in Bacterial Chemotaxis
The majority of swimming bacterial species have more than one chemotactic pathway regulating the switching behaviour of the rotary flagellar
motor. How do bacteria balance the signals from apparently homologous pathways to bias the swimming towards an optimum environment for growth? All chemosensory pathways include receptors that regulate the activity of a histidine protein kinase (CheA), and this in turn regulates, through phosphorylation, the activity of a motor binding protein (CheY)-controlling switching- and an adaptation enzyme (CheB) resetting the signalling state of the receptors. Rhodobacter sphaeroides expressed two chemosensory pathways under normal laboratory conditions, and both are essential for a chemosensory response. The components of one pathway localise with transmembrane chemoreceptors in large quaternary complexes close to the cell poles, while the components of the second pathway localise with soluble chemoreceptors in a large complex close to the middle of a newly divided cell. A localisation system related to the ParA system of plasmid organisation ensures each daughter cell has a cytoplasmic cluster on division, emphasising the importance of each cell having both pathways.
While each cluster has all the components of a chemosensory pathway, they are unique homologues in each location and even when overexpressed the components never localise to the alternative site. Swapping the targeting domains of the CheAs in each pathway causes the kinases to localise to the “wrong” cluster. Biochemical studies have identified patterns of phosphotransfer from the CheAs to the CheYs and CheBs. Even when a kinase able to phosphotransfer to all CheYs and CheBs is localised to both cluster, it is unable to support chemotaxis, showing the need for specific localisation. Using a wide range of experimental data, from structural, through biochemical to in vivo localisation mathematical models have been developed to attempt to understand possible signalling pathways that
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could result in chemosensory responses in R.sphaeroides. These will be discussed, along with possible mechanisms for the controlled localisation of the cytoplasmic chemosensory proteins.
Ralf Steuer Humboldt University zu Berlin, Germany Co-authors: V. Sourjik ¹, M. Kollmann ², (¹ Universität Heidelberg, Germany; ² Humboldt University zu Berlin, Germany)
Robust Signal Processing in Living Cells
Cellular signaling has to operate reliably under conditions of uncertainty and in the face of constant perturbations. In this respect, a particular challenge for living cells is the necessity to keep the concentrations of certain active signaling molecules within narrowly defined ranges, despite a multitude of detrimental influences. Here, we present a novel formalism that pinpoints the necessary architecture for perfect concentration robustness in living cells. We show, supported by conclusive experimental evidence, that any signaling network can be constructed such that a set of possibly detrimental fluctuations has no effect on the active concentrations of signaling compounds, hence the function, of the network. Our mathematical framework accounts for diverse manifestations of cellular robustness and enables the predictive design of perfectly robust synthetic network topologies.
Alexander Fletcher Oxford University, UK Co-authors: J.M. Osborne ¹, D.J. Gavaghan ¹, P.K. Maini ¹, (¹ University of Oxford, UK)
Chaste: A Computational Framework for Multi-scale Modelling in Systems Biology
Problems in biology are intrinsically multi-scale, with processes occurring on many disparate spatial and temporal scales. We present a multi-scale framework for computational modelling of biological systems. Utilising the natural structural unit of the cell, the framework consists of three main scales: the tissue level (macro-scale); the cell level (meso-scale); and the sub-cellular level (micro-scale). Cells are modelled as discrete interacting entities using either an off-lattice tessellation, or a vertex-based model. The behaviour at the tissue level is represented by field equations for nutrient or messenger diffusion, with cells functioning as sinks and sources. The sub-cellular level concerns numerous metabolic processes and models interaction networks and signalling pathways by ordinary differential equations or rule-based models. The modular approach of the framework enables much more complicated sub-cellular behaviour to be considered. Interactions may occur between all spatial scales. The multi-scale framework is implemented in an open source software library called Chaste (http://web.comlab.ox.ac.uk/chaste), which is written in object-oriented C++ and developed using an agile approach. All software is tested, robust, reliable and extensible. We introduce Chaste and discuss both its functionality and development.
Michael Stumpf Imperial College London, UK
...The Rest are Details: Model Selection in Systems and Evolutionary Biology
For the vast majority of biological processes and systems we still lack suitable mechanistic models. Inference or reverse engineering of such models, however, remains a statistical challenge. In a
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recent landmark paper Sydney Brenner used the notorious difficulty of this so-called inverse problem to question the rationale underlying much of modern systems biology. In this talk I will argue that this is perhaps a slightly pessimistic assessment. In particular I will show that if we are ready, not to worry too much about details of such models – such as the precise values of rate constants – we can indeed learn a lot about biological systems from suitable high-throughput data.
I will discuss this in the context of analyses into the dynamics and evolutionary history of signal transduction and protein interaction networks, respectively. In particular so-called approximate Bayesian computation (ABC) techniques emerge as powerful tools for robust inference in systems, synthetic, population and evolutionary biology, and their power and flexibility will be illustrated. ABC (as well as other Bayesian approaches) allows us to identify the structure and dynamics of biological systems; more generally, and quite unlike conventional e.g. optimisation approaches, the Bayesian formalism gives much more detailed insights into what can be inferred about biological systems from data.
Patrick Hogan University of Bristol, UK Co-authors: Thomas Schlegel ¹, Nigel R. Franks ¹, James A. R. Marshall ², (¹ School of Biological Sciences, University of Bristol; ² Department of Computer Science, University of Bristol)
The Statistical Physics of Decision-Making in Insect Colonies
The stochastic methods of statistical physics provide tools to derive the emergent macroscopic
behaviour of a complex system from a microscopic description. We apply these techniques to analyse collective-decision making in social insect colonies, allowing us to derive the colony-level behaviour from an individual-level model. This contrasts with the traditional approach where a differential equation model, with or without arbitrary noise terms, is assumed.
Social insect colonies vary in size from on the order 100 to 10,000,000 individuals, and such a statistical physics approach allows us explicitly to derive equations for both the average behaviour and the noise in the system, across this entire scale. We develop such a framework by building upon an existing stochastic model of opinion formation to model the decision-making processes in emigrating ant colonies. This new model is both driven by and evaluated against results from experiments with the rock ant Temnothorax albipennis. We begin with a microscopic master equation description of relevant individual-level interactions in the colony. For biologically realistic colony sizes, we derive equations describing the emergent macroscopic behaviour of the whole colony, including the important stochastic fluctuations about this average via a Fokker-Planck equation. This allows us to elucidate rigorously the role played by the individual-level phenomena of direct switching in the colony-level decision-making process, which optimality theory has predicted to be of crucial importance, and which we compare with our experimental results. This illustrates the power of stochastic methods in statistical physics for understanding social insect colonies as complex systems.
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Kevin Foster Harvard University, USA
Social Evolution in Microbes
“If it could be proved that any part of the structure of any one species had been formed for the exclusive good of another species, it would annihilate my theory, for such could not have been produced through natural selection” Darwin (1859). Since Darwin, evolutionary biologists have been troubled by cooperative behaviour. Why do organisms frequently evolve social behaviours that promote others at an apparent cost to their own reproduction? For example, honeybee workers labour their whole life without reproducing; birds make alarm calls; and humans often help one another.
This fundamental question has received considerable attention over the last 50 years with the development of the field of sociobiology. This has resulted in a solid base of theory, centered on principles like inclusive-fitness, and a myriad of empirical tests. It is now widely accepted that cooperative behaviours evolve because they directly help the actor alongside any recipients, or they help individuals who share more alleles with the actor than predicted by chance (genetic relatedness), or both.
One major group that remains relatively unexplored, however, is the microbes, whose full spectrum of sociality only recently came to light. We ask how social environment and relatedness affects microbial behaviour in a number of model systems, including biofilm-forming bacteria and budding yeast. We find that microbes are extremely sensitive to social context – both in real time and over evolutionary time – and use them to better understand the genetic and genomics of social traits; a pursuit that is difficult in the more classical model organisms for social behaviour.
Sam Brown Oxford University, UK
Horizontal Gene Transfer of the Secretome Drives the Evolution of Bacterial Cooperation and Virulence
Microbes engage in a remarkable array of cooperative behaviours, secreting shared proteins that are essential for foraging, shelter, microbial warfare and virulence. These proteins are costly, rendering populations of cooperators vulnerable to exploitation by non-producing cheaters arising by gene loss or migration. In such conditions, how can cooperation persist? Our model predicts that differential gene mobility drives intragenomic variation in investment in cooperative traits. More mobile loci generate stronger among-individual genetic correlations at these loci (higher relatedness) and thereby allow the maintenance of more cooperative traits via kin selection.
By analyzing 21 Escherichia genomes, we confirm that genes coding for secreted proteins (the secretome) are very frequently lost and gained and are associated with mobile elements. We show that homologs of the secretome are overrepresented among human gut metagenomics samples, consistent with increased relatedness at secretome loci across multiple species. The biosynthetic cost of secreted proteins is shown to be under intense selective pressure, even more than for highly expressed proteins, consistent with a cost of cooperation driving social dilemmas.
Finally, we demonstrate that mobile elements are in conflict with their chromosomal hosts over the chimeric ensemble’s social strategy, with mobile elements enforcing cooperation on their otherwise selfish hosts via the cotransfer of secretome genes with ‘mafia strategy’ addictive systems. We conclude that horizontal transfer promoted by agents such as plasmids, phages or integrons
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shapes population genetic structure and drives microbial cooperation and virulence.
Ivana Gudelj Imperial College London
Microbial Evolution in Theory and Practice
Microbes are ubiquitous in nature and occupy virtually every environmental niche on earth. Contributing to this evolutionary success are diverse metabolic strategies as well as the ability to adapt to changing environments. Experimental evolution has provided an ideal setting for studying microbial diversification in action: experiments are conducted in controlled environments using culturable strains that are easily manipulated due to their known genetic structure. However, this simplified approach to evolution poses the following questions: how do we know whether a given experimental outcome is particular to the laboratory system? What can we learn from laboratory experiments about microorganisms in the wild?
In this talk I argue that through the use of systems mathematical models we can begin to bridge the gap between laboratory and nature. I will present a series of mathematical models of microbial evolution reflecting different types of selection pressures that microbes repeatedly encounter in nature: 1) Evolution of cooperative metabolic strategies and 2) Evolution of resistance to pathogens. I will demonstrate that such models can make good quantitative predictions of a given laboratory experimental setup and discuss which of these predictions can be generalised to other microbial systems.
Sebastian Bonhoeffer ETH Zurich, Switzerland
The Evolutionary Systems Biology of HIV-1 Drug Resistance
The development of a quantitative understanding of HIV-1 drug resistance represents a formidable challenge given the large number of available drugs and drug resistance mutations. We employ ridge regression based models to estimate main fitness effects and epistatic interactions of 1,857 mutations in HIV-1 protease and reverse transcriptase, using a data set of 60,000 virus samples assayed for in vitro replicative capacity in the absence of drugs as well as the presence of 15 individual drugs. The model predicts an average of 45.4% of the variance in replicative capacity across the 16 different environments and substantially outperform models based on main effects only. The model thus represents a realistic approximation of the fitness landscape underlying HIV-1 protease and reverse transcriptase.
We use our model of the HIV fitness landscape to determine generic properties that have long remained elusive in the absence of realistic fitness landscapes. We find that the fitness landscape is characterised by the presence of a large number of local optima and large neutral networks. Moreover, sequences that differ only by few mutations initially can evolve to optima that differ greatly both genetically and phenotypically. Thus our explorations of the HIV fitness landscape support the view that fitness landscapes are highly complex and that evolutionary trajectories depend sensitively on the initial conditions.
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ParticipantsÖzgür Akman University of Exeter, UK [email protected]
Munia Amin University of Exeter, UK [email protected]
Svetlana Amirova University of Exeter, UK [email protected]
Judy Armitage University of Oxford, UK [email protected]
Peter Arndt Max Planck Institute for Molecular Genetics, Germany [email protected]
Peter Ashwin University of Exeter, UK [email protected]
Ruth Baker University of Oxford, UK [email protected]
Ruth Bastow University of Warwick, UK [email protected]
Declan Bates University of Exeter, UK [email protected]
Travis Bayer Imperial College London, UK [email protected]
Manuel G. Bedia University of Zaragoza, Spain [email protected]
Sebastian Bonhoeffer ETH Zurich, Switzerland [email protected]
Sam Brown University of Oxford, UK [email protected]
Nicolas Buchler Duke University, USA [email protected]
Luca Cardelli Microsoft Research, UK [email protected]
Riccardo Cipelli University of Exeter, UK [email protected]
Carlo Cosentino University of Catanzaro, Italy [email protected]
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Neil Dalchau Microsoft Research, UK [email protected]
Anup Das University of Exeter, UK [email protected]
Emma Denham University Medical Center Groningen, Netherlands [email protected]
Alexander Fletcher University of Oxford, UK [email protected]
Kevin Foster Harvard University, USA [email protected]
Luca Gerosa ETH Zurich, Switzerland [email protected]
Murray Grant University of Exeter, UK [email protected]
Mark Goulian University of Pennsylvania, USA [email protected]
Ivana Gudelj Imperial College London, UK [email protected]
Nicholas Harmer University of Exeter, UK [email protected]
Ken Haynes University of Exeter, UK [email protected]
Patrick Hogan University of Bristol, UK [email protected]
Martin Howard John Innes Centre, UK [email protected]
Laurence Hurst University of Bath [email protected]
Mark Isalan Centre for Genomic Regulation (CRG), Barcelona, Spain [email protected]
Siddharth Jayaraman University of Exeter, UK [email protected]
Karen Kastenhofer Austrian Academy of Sciences, Austria [email protected]
Chris Knight University of Manchester [email protected]
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Karl Kochanowski ETH Zurich, Switzerland [email protected]
Michal Komorowski Imperial College London [email protected]
Karoly Kovacs Institute of Biochemistry, Hungary [email protected]
Congpin Lin University of Exeter, UK [email protected]
Bruno Martins University of Edinburgh, UK [email protected]
Colin Miles BBSRC, UK [email protected]
Andrew Millar University of Edinburgh [email protected]
Claudia Mueller University of Exeter [email protected]
Maureen O’Malley University of Exeter, UK [email protected]
Antonis Papachristodoulou University of Oxford, UK [email protected]
Steve Porter University of Exeter, UK [email protected]
Avinoam Rabinovitch Ben-Gurion University, Israel [email protected]
David Rand University of Warwick, UK [email protected]
Max Reuter University College London, UK [email protected]
David Richards John Innes Centre, UK [email protected]
Gabriel Rosser University of Oxford, UK [email protected]
Susan Rosser University of Glasgow, UK [email protected]
Yasushi Saka University of Aberdeen, UK [email protected]
Uwe Sauer ETH Zurich, Switzerland [email protected]
Michael Savageu University of California, USA [email protected]
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Jean-Marc Schwartz University of Manchester, UK [email protected]
Yishay Shoval Weizmann Institute of Science, Israel [email protected]
Nick Smirnoff University of Exeter, UK [email protected]
Ralf Sommer Max Planck Institute for Developmental Biology, Germany [email protected]
Orkun Soyer University of Exeter, UK [email protected]
Andrea Splendiani Rothamsted Research, UK [email protected]
Gero Steinberg University of Exeter, UK [email protected]
Ralf Steuer Humboldt-Universitaet zu Berlin, Germany [email protected]
David Studholme University of Exeter, UK [email protected]
Michael Stumpf Imperial College London, UK [email protected]
Peter Swain University of Edinburgh [email protected]
Nick Talbot University of Exeter [email protected]
Stuart Townley University of Exeter, UK [email protected]
Carl Troein University of Edinburgh, UK [email protected]
Jannis Uhlendorf INRIA Paris – Rocquencourt, France [email protected]
Andreas Wagner University of Zurich, Switzerland [email protected]
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