Frontiers of Multidisciplinary Research: Mathematics, Engineering and 21 – 24 September 2010 | Reed Hall, University of Exeter Sponsors

Front cover image reproduced with the kind permission of Eckhard Völcker. Welcome... to this unique meeting that aims to facilitate Finally, Frontiers aims to motivate and attract young interaction and scientific exchange among talent to interdisciplinary research. Some of the mathematicians, physicists, engineers and invited and most of the selected speakers are early- biologists. The Frontiers workshop aspires career academics, and we believe that they are to be unique for several reasons. indeed at the frontiers of interdisciplinary research. To foster such research in the coming generation of Firstly, it aims to achieve an unprecedented academics we will record all presentations (subject scientific breadth whilst maintaining focus and to speaker consent) and make them publicly coherence. This is not an easy goal but we aim available on the internet for the use of the wider to realise it by selecting a large number of invited scientific community. speakers who are leading scientists in their specific fields, yet maintain a broad view of science in Meetings are about people; no organisational other fields and the connections of their work aspiration can be met in a meeting without the to those fields. right mix of participants. This fact is particularly true for Frontiers given its scientific breadth. We believe Secondly, Frontiers aims to place “interaction” first that with you, we have indeed gathered the right and foremost. To achieve this we tried hard to mix of people. provide a relaxed scientific platform with ample free time for both free and structured discussions. We look forward to seeing you at the next Half of the attendees will present a talk and posters Frontiers meeting. will be at the centre stage of the meeting, being on display in the break out area at all times. The Orkun S Soyer, full daily program is set in an idyllic mansion in the on behalf of the organising committee campus of University of Exeter, with planned after- September 2010, University of Exeter lunch walks that will allow for stretching the legs and mind. FRONTIERS

1 Organisers and Sponsors

Orkun S Soyer Ruth Baker University of Exeter

Steven Porter Nicolas Buchler Özgür Akman University of Exeter Duke University University of Exeter

EPSRC Engineering and Physical Sciences Research Council

University SIGNET of Exeter Microsoft Company of The Cell Signaling Science Strategy Research Biologists Network Systems Biology Theme

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.

2 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 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

3 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

4 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

5 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,

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 , *Sebastian Bonhoeffer, ETH Zurich.

12.15 - 13.00 Final thanks and conference closes

6

Posters

1. Evolutionary Signatures of Mutagenic Processes Co-authors: Leslie Aichaoui ¹, Lieke van Associated with , Peter Arndt, Gijtenbeek ², Maria de Vries ², Vincent Max Planck Institute for Molecular , Fromion ¹, Jan Maarten van Dijl ², Germany (¹ Laboratoire de Genetique Microbienne, France; ² University Medical Center Groningen Co-authors: Paz Polak, Max Planck Institute for and University of Groningen, Netherlands) Molecular Genetics, Germany

5. Inferring Hidden Transcription Factor Profiles 2. Inferring Partially-Known Scale-Free Interaction by Thermodynamic Modelling of GFP-derived Networks from Noisy Data, Carlo Cosentino, Promoter Activities, Luca Gerosa, ETH Zurich University of Catanzaro, Italy Co-authors: Bart Haverkorn van Rijsewijk ¹, Co-authors: Francesco Montefusco ¹ ², Karl Kochanowski ¹, Matthias Heinemann ¹, Jongrae Kim ³, Declan G. Bates ², Francesco Uwe Sauer ¹, (¹ Institute of Molecular Systems Amato ¹ (¹ University of Catanzaro, Italy; Biology, ETH Zurich, Switzerland) ² University of Exeter, UK; ³ University of Glasgow, UK) 6. The Mathematical Structures of the Brain. Category Theory in Neural Science, Manuel G 3. A Kinetic Model for Predicting MHC Class Bedia, University of Zaragoza, Spain I Presentation of Competing Peptides, Neil Dalchau, Microsoft Research, UK Co-authors: Ricardo Sanz ¹, Jaime Gonzalez- Ramirez ¹ (¹ Technical University Madrid, Co-authors: Andrew Phillips ¹, Leonard D Spain) Goldstein ², Mark Howarth ³, Luca Cardelli ¹, Tim Elliott 4, Joern M Werner 4 (¹ Microsoft Research, UK; ² , UK; 7. On Types and Roles of Interdisciplinarity within 4 ³ University of Oxford, UK; University of Systems Biology Research, Karen Kastenhofer, Southampton, UK) Austrian Academy of Sciences

4. Modelling the Oxidative Stress Response 8. Evolution of an Environmental Response of Bacillus Subtilis Using Time Resolved Network, Chris Knight, University of Transcriptomics, Emma Denham, University Manchester Medical Center Groningen and University of Groningen, Netherlands

7 Co-authors: Heather Robinson ¹, Bharat 13. New Generation Methods of Spiral-Pairs and 3D Rash ¹ (¹ University of Manchester) Patterns, Avinoam Rabinovitch, Ben-Gurion University, Israel

9. Identification of a Metabolic Flux Sensor in Co-authors: Y. Bitona ¹, D. Braunsteinc ¹, Escherichia Coli, Karl Kochanowski, Institute M. Friedmand ¹, I. Aviram ¹, (¹ Ben-Gurion of Molecular Systems Biology, ETH Zurich, University, Israel) Switzerland

Co-authors: Benjamin Volkmer ¹, Luca 14. Modelling DivIVA-Mediated Branching in Gerosa ¹, Bart Haverkorn van Rijsewijk ¹, Streptomyces, David Richards, John Innes Matthias Heinemann ¹, (¹ Institute of Molecular Centre Systems Biology, ETH Zurich, Switzerland) Co-authors: Martin Howard, John Innes Centre 10. Stochasticity in Protein Levels Drives Colinearity of Gene Order and Enzymatic Steps in Metabolic Operons of Escherichia Coli, Karoly 15. Collective Behaviour of Free-Swimming Kovacs, Biological Research Center, Institute Rhodobacter Sphaeroides, Gabriel Rosser, of , Szeged, Hungary University of Oxford, Centre for Mathematical Biology Co-authors: Laurence D. Hurst ¹, Balazs Papp ², (¹ University of Bath, Department of Co-authors: C. Yates ¹, T.M. Wood ¹, Biology and Biochemistry, UK; ² Biological D. Wilkinson ¹, P.K. Maini ¹, M.C. Leake¹, Research Center, Institute of Biochemistry, (¹ University of Oxford, Centre for Szeged, Hungary) Mathematical Biology)

11. Functional Trade-Offs in Allosteric Sensing, 16. Modelling a Community Effect in Animal Bruno Martins, Centre for Systems Biology Development: A Mechanism for the Coordinated at Edinburgh, The University of Edinburgh Differentiation of a Cell Population, Yasushi Saka, University of Aberdeen Co-authors: Peter S. Swain, Centre for Systems Biology at Edinburgh, The University Co-authors: Cedric Lhoussaine ¹, Celine of Edinburgh Kuttler ¹, (¹ IRI-CNRS, Universite de Lille, France)

12. Philosophical Issues of Multidisciplinarity: Integration and Translation in Systems and 17. Generic Dynamic Modelling of Metabolic Synthetic Biology, Maureen O’Malley, University Systems, Jean-Marc Schwartz, University of Exeter of Manchester

8 Co-authors: Delali Adiamah, University of Co-authors: Z. Gao ¹, P.P. Menon ³, J.G. Manchester Hardman ², D.G. Bates ³ (¹ University of Liverpool, UK; ² University of Nottingham, UK; ³ University of Exeter, UK) 18. The Feasibility of Lab Evolution of Subtle Genomic Traits, Yishay Shoval, Weizmann Institute of Science, Israel 23. Response Dynamics and Evolution in Signalling Networks Regulating Bacterial Chemotaxis, Co-authors: Yitzhak Pilpel, Weizmann Institute Munia Amin, University of Exeter of Science, Israel Co-authors: Orkun S. Soyer ¹, Steven L. Porter ¹, (¹ University of Exeter, UK) 19. Data integration for Systems Biology, Andrea Splendiani, Rothamsted Research 24. Systems Biology of the Burkholderia glycome: A Search for the Best Targets, Nicholas Harmer, University of Exeter, UK 20. Real-Time Control of Gene Expression, Jannis Uhlendorf, INRIA Paris – Rocquencourt, France 25. Genomics of Emerging Crop Pathogens, David Studholme, University of Exeter, UK Co-authors: Pascal Hersen ¹, Samuel Bottani ¹, Gregory Batt ², (¹ University Paris Diderot, France; ² INRIA Paris – Rocquencourt, France) 26. Digital Clocks: Boolean Modelling of Circadian Networks, Ozgur Akman, University of Exeter, UK 21. Queueing Induced by Bidirectional Motor Motion Co-authors: Steven Watterson ¹, Andrew Near the End of a Microtubule, Congpin Lin, Parton ¹, Nigel Binns ¹, (¹ University of University of Exeter, UK Edinburgh, 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

9 Talk Abstracts

Ralf Sommer of novel morphological structures. P. pacificus Max Planck Institute for Developmental Biology, forms a mouth dimorphism that allows predatory Germany behaviour on fungi and other nematodes. We Pristionchus Pacificus – A Nematode Model for begun studying how a novel predatory behaviour Integrative Studies in Evolution and Ecology is integrated into an existing nervous system and have started the reconstruction of the P. pacificus Pristionchus pacificus has been established nervous system from more than 3,500 thin as a model system in evolutionary biology sections of the nematode head. Our studies reveal with genetic, genomic and transgenic tools. tremendous differences in the connectivity of cells Detailed investigations of vulva formation and and at other systems-levels, rather than simple other developmental processes reveal that changes in the number of cells. developmental mechanisms differ strongly between C. elegans and P. pacificus. While evo-devo can provide fundamental insight into morphological evolution, the limitations of its Orkun Soyer gene-centered and development-centered view University of Exeter necessitate the synthesis of evo-devo with other Co-authors: Murray Grant, University of Exeter areas of evolutionary biology. Synthesis with Parasite Driven Redundancy in Signaling Networks “population genetics” can reveal how phenotypic The antagonistic interaction between a parasite evolution is initiated at the micro-evolutionary level and its host result in an ever-running arms race. and synthesis with “evolutionary ecology” can add While it is well-established that such an arms an ecological perspective to these evolutionary race extends to the molecular level, we still lack processes. a system level understanding of host-pathogen The well-defined association of P. pacificus with interaction at the level of signaling and metabolic scarab beetles, the apparent plasticity of this beetle networks. Our recent work using toy models association, and the ability of this widespread suggest that parasite interference with host species to thrive in a variety of geographic ranges networks can lead to high level of redundancy and ecological conditions, make P. pacificus an based robustness in the latter. Motivated by that ideal model organism for the fusion of evo-devo, work, we performed a system level analysis of population genetics and evolutionary ecology. We molecular interactions between Arabidopsis have started to analyze the ecological interactions thaliana and one of its pathogens Pseudomonas of P. pacificus in the beetle ecosystem and more syringae. than 400 strains of P. pacificus have been isolated In particular, Arabidopsis thaliana uses jasmonic from around the world. Here I report from our acid (JA) as a central signaling hormone to respond most recent work focusing on the evolution to a variety of environmental clues including pathogen attack. This central role of JA is exploited

10 by P. syringae, which uses a JA mimic, coronatine, reproduces experimental data from luciferase to interfere with host signaling and gene regulation. assays, not only under periodic light/dark cycles We analyse the response dynamics in the JA and in constant light but also transiently across signaling networks and find that it can exhbit changes in the light conditions. This is unexpected bistability. The observed high number of duplicates from a model with such a simple structure. The in this network has direct effect on this dynamics, clock shows a complex phase response and avoids changing threhsold levels of the bistable switch. locking its oscillations to dusk or dawn, something Combined with experimental work, this analysis which in the Arabidopsis model required multiple allows us to better understand the underpinning feedback loops. To explain how the clock may dynamics between host and parasite and its achieve this feat, we have systematically altered the evolution. 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 Carl Troein require a greater number of clock components. 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 Andreas Wagner et Marie Curie – Paris 6, France) University of Zurich, Switzerland Complex Light Response in a One-Loop Model of The Origins of Evolutionary Innovations the Ostreococcus Tauri Circadian Clock Life can be viewed as a four billion year long The biochemical oscillations of the circadian clock history of innovations. These range from dramatic helps living cells cope with the daily rhythms in macroscopic innovations like the evolution of the environment. In plants and other eukaryotes wings or eyes, to a myriad of molecular changes the clock depends on transcriptional feedback that form the basis of macroscopic innovations. between genes. In Arabidopsis, the major clock We know many examples of innovations – components are genes with many homologues, qualitatively new phenotypes that can provide a which together form an interconnected and highly critical advantage in the right environment – but on-trivial system, making accurate modelling have no systematic understanding of the principles a great challenge. In contrast, the tiny alga that allow organisms to innovate. Most phenotypic Ostreococcus tauri has a highly reduced genome innovations result from changes in three classes of with few clock genes, but its clock can nonetheless systems: metabolic networks, regulatory circuits, entrain to light/dark cycles in both long and short and protein or RNA molecules. I will discuss day conditions. evidence that these classes of systems share two We have modelled the Ostreococcus clock as important features that are essential for their ability a single negative feedback loop between the to innovate. two clock genes TOC1 and CCA1. Our model

11 Luca Cardelli consider nonlinear differential equation models Microsoft Research, UK for these systems, for which the vector fields are Two-Domain DNA Strand Displacement polynomial or rational (as typically results from Mass Action or Michaelis-Menten kinetics). A We investigate the computing power of a method for algorithmically constructing certain restricted class of DNA strand displacement ‘gates’ functions that can verify, e.g., robust stability is that are structurally very simple: they are made of then presented. The techniques presented are double strands with nicks (interruptions) in the top rooted in robust control and dynamical systems strand. The single strands (‘signals’) they interact theory, but use recent developments in the with are also structurally simple: they consist of theory of positive polynomials and Sum of two-domain strands with one toehold domain Squares. Computation is done using convex and one recognition domain. We study the optimisation algorithms (specifically Semidefinite implementation of fork and join signal processing Programming). Such an approach is desirable gates based on these structures, and we show that as the algorithms have an associated worst case these systems are amenable to formalisation and polynomial time complexity. Even with this to mechanical verification. 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 Antonis Papachristodoulou tools based on dynamic model decomposition and University of Oxford reduction that enable the algorithms presented to Co-authors: James Anderson ¹, Yo-Cheng Chang¹, scale to a much larger class of system models. Our Edward Hancock ¹, (¹ University of Oxford) results are illustrated on the EGF-MAPK and Wnt Modelling and Analysis Tools for Biochemical signaling pathways. Networks Mathematical modelling is key for understanding the properties of dynamic processes inside cells, Michael Savageau which usually consist of complicated networks of University of California, Davis, USA interacting genes/proteins. However, modelling and analysing such pathways presents a number Phenotypes in the Design Space of of mathematical and computational challenges, as Biochemical Systems the models considered are usually complicated Although characterisation of the genotype has nonlinear differential equations with several time- undergone revolutionary advances as a result scales and unknown parameters. of the successful genome projects, the chasm between our understanding of a fully characterised Here we discuss a number of issues related to gene sequence and the phenotypic repertoire the analysis of biological networks and specifically of the organism is as broad and deep as it was address scalability issues. We focus on new in the pre-genomic era. There are fundamental mathematical and algorithmic tools to understand unsolved problems in relating genotype to the properties of complex signaling pathways. We

12 phenotype. We address one of these using the condition – and why this particular one. On the concept of a ‘system design space’. The important basis of large sets of experimentally determined task of elucidating design principles of biological in vivo flux data, we discuss here whether there systems is facilitated by enumeration of regions are generally valid principles that describe the within the system’s design space that correspond distribution of flux under different conditions to qualitatively-distinct phenotypes that can be and how such metabolic networks respond to identified and counted, their relative fitness perturbations? Using the computational framework analysed and compared, and their tolerance of flux balance analysis, we test two fundamentally to change measured. First, I will review design different families of hypotheses: are cells optimised spaces that have proved useful in revealing design during evolution towards one or more objectives principles for elementary gene circuits. Second, I (3) or are their responses optimised towards will present a generic construction of the system minimal adjustments? design space. This approach is grounded in the power-law equations that characterise traditional References: chemical kinetics and, by recasting, the rational 1. Zamboni N and Sauer U. Curr. Opin. functions that characterise biochemical kinetics. Microbiol. 12: 553 (2009) In steady state, the analysis of these equations can be reduced to that of linear algebraic equations. 2. Heinemann M and Sauer U. Curr. Opin. Third, these methods will be illustrated with an Microbiol. 13: 337 (2010) application to a well-studied gene circuit. 3. R Schütz, L Küpfer and U Sauer. Mol. Sys. Biol. 3:119 (2007)

Uwe Sauer ETH Zurich, Switzerland Svetlana Amirova Co-authors: Robert Schutz ¹, Matthias University of Exeter Heinemann¹, Nicola Zamboni ¹, (¹ Institute Co-authors: Claudia Rato da Silva ¹, Heather of Molecular Systems Biology, ETH Zurich, Wallace ¹, Ian Stansfield ¹, Declan G. Bates ², Switzerland) (¹ School of Medical Sciences, University of Multidimensional Optimality of Microbial Aberdeen, UK; ² University of Exeter, UK) Metabolism Uncovering the Design Principles of Polyamine Great strides have been made in our ability Regulation in Yeast: An Integrated Modelling to monitor metabolic responses, in particular and Experimental Study in microorganisms, (1) but understanding and A new complete predictive model of the prediction of biological function and activity from polyamine metabolism in the yeast Saccharomyces such data has remained challenging (2) for in cerevisiae is developed using a Systems Biology metabolism, biological function is represented by approach incorporating enzyme kinetics, statistical the enzyme-catalysed fluxes through the network. analysis, control engineering and experimental molecular biology of translation. The polyamine Key questions are how a particular distribution of molecules putrescine, spermidine and spermine fluxes is manifested in an organism under a given are involved in a number of important cellular

13 processes such as transcriptional silencing, to engineer metabolism in microbes and plants. translation, protection from reactive oxygen The goal of this project is to develop a synthetic species, coenzyme A synthesis and components of system that harnesses the power of multiple polyamine pathway are potential targets for cancer recombination mechanisms to enable synthetic therapeutics. Unregulated polyamine synthesis can biologists to generate, diversify, and refine complex trigger uncontrolled cell proliferation. Conversely, multigenic functions. We aim to develop a novel polyamine depletion can cause apoptosis, and technology platform that harnesses the power of during development, defects leading to mental recombinases for continuous directed evolution of retardation in humans. complex multigenic functions resulting an assembly of functionally coordinated genes theoretically Our approach uncovers the multiple feedback facilitating the rapid evolution of new phenotypes. control mechanisms in the polyamine metabolic pathway; also it provides a source of robustness and its associated dynamical properties. The Travis Bayer main focus is highly conserved negative feedback Imperial College London, UK loop regulating level of enzyme Spe1, the enzyme catalysing the first step in the polyamine Surpassing Evolution: Using Synthetic Biology biosynthesis pathway by the protein Antizyme to Rewire and Repurpose Biological Systems synthesised by a polyamine-dependent Cellular metabolism is controlled by complex translational frameshifting mechanism. Possible networks of interacting regulators. Perturbing applications are in pharmacology; toxicology; these networks can have profound effects on the preclinical drug development for cancer; and fitness of organisms. This highlights an important neurodegenerative disorders: anti-cancer drug challenge in biology: investigating how the network DFMO, Snyder-Robinson Syndrome. architectures we observe in Nature evolved in response to selective pressure; what that pressure might have been; or whether the architecture is Susan Rosser a result of non-adaptive forces. University of Glasgow, UK A complimentary issue is how we can rationally A Synthetic Biology Approach to Recombinase design metabolic and regulatory architecture to Mediated Metabolic Pathway Engineering engineer biological systems for useful purposes. A grand challenge in synthetic biology is the need Synthetic biologists aim to construct artificial for technologies that enable the construction of genetic systems to understand Nature and for novel and complex functions in biological systems. a variety of biotechnological applications. When these functions involve the expression, In this talk, I will highlight how engineering gene coordination and optimisation of multiple genes, expression and metabolism allows us to test building the genetic circuits becomes increasingly evolutionary hypotheses and potentially investigate difficult. Assembling multigenic functions e.g. a the evolutionary paths not taken by extant metabolic pathway in an organism by an iterative organisms. I will also present several examples approach is laborious, difficult and expensive. Such where biological systems have been repurposed challenges have posed major hurdles to efforts as biological technologies.

14 Gero Steinberg Martin Howard University of Exeter John Innes Centre, UK Co-authors: Martin Schuster ¹, CongPing A Dynamic Spindle-Like Apparatus that Segregates Lin ², Peter Ashwin ², Nicholas J. Severs ³, Gero Low Copy Number Plasmids Steinberg ¹, (¹ School of Biosciences, University of Exeter, UK; ² Mathematics Research Institute, Low copy number plasmids require active University of Exeter, UK; ³ Imperial College partitioning (par) loci to ensure stable transmission London, National Heart and Lung Institute, at cell division. In the presence of the partition London, UK) complex (ParB bound to parC), ParA of plasmid Stochastic and Controlled Accumulation of pB171 forms dynamic cytoskeletal-like structures Dynein at Microtubule Ends Prevents Endosomes that dynamically relocate over the nucleoid. from Falling Off the Track Simultaneously, par distributes plasmids regularly over the nucleoid by an unknown mechanism. Cellular organisation and survival depends on Here, we dissect this system using a combination active transport of organelles, vesicles or protein of fluorescent imaging and mathematical modeling. complexes along the filaments of the cytoskeleton. Our experiments indicate that ParA forms dynamic Long-distance transport involves microtubules and filamentous structures that move plasmids by a the opposing motor proteins kinesin and dynein. pulling mechanism in a spindle like fashion. We Numerous theoretical studies have described the propose a model for the Par dynamics where behaviour of these motors. However, the motility the plasmids themselves are responsible for ParA parameters used were usually derived from in filament disassembly. The model makes firm vitro studies, and might therefore not reflect the predictions that we have validated experimentally, situation in the living cell. Here, we have visualised and reveals how dynamic ParA filaments can motors and their cargo in a fungal model system. segregate plasmids into separate cell-halves before By combining a quantitative analysis of their motility cell division. with mathematical modelling we attempted K. Gerdes, M. Howard and F. Szardenings: to describe the behaviour of dynein and early . endosomes at the end of microtubules. We found Pushing and Pulling in Prokaryotic DNA Segregation Cell 141 927-942 (2010)/S. Ringgaard, J. van that a combination of stochastic accumulation and Zon, M. Howard and K. Gerdes: controlled retention of dynein at microtubule ends Movement increases the probability of endosomes to get and Equipositioning of Plasmids by ParA Filament . Proc. Natl. Acad. Sci 106 19369- loaded onto dynein for retrograde transport. This Disassembly 19374 (2009) mechanism prevents the organelles from falling of the microtubule.

15 Ken Haynes experimental approaches will add significant value University of Exeter, UK to data interpretation. Finally, this presentation Combinatorial Stress Responses in Yeast will hopefully show that non-model organisms are tractable at a systems level, and that you In the mid 1990’s only a small number of genes don’t have to be a super group to embark on had been sequenced cloned and inactivated in such a voyage. the fungal pathogen Candida glabrata. A decade and a half later, the genome has been sequenced, Collaboration between Imperial College London annotated, arrays designed and delivered to (Barahona, Gudelj, Haynes, Stark, Stumpf) and the community and a co-ordinated effort to the University of Aberdeen (Brown, Coghill, construct a knock-out library and ORFeome is Gow, Grebogi, Moura, Romano, Thiel). In underway in our laboratory, and others around addition I would like to thank all the SABR staff, the world. Additionally the relatively close and others (Lauren Ames, Emily Cook, Hsueh- phylogentic relationship between C. glabrata and lui Ho, Maxime Huvet, Piers Ingram, Mette has seen interest in this Jacobsen, Despoina Kaloriti, Megan Lenardon, organism grow with respect to the evolution of Andy McDonagh, Susanna Nilsson, Wei Pang, protein-protein interactions. Preliminary attempts Melanie Puttnam, Elahe Radmaneshfar, Dan Silk, to utilise these resources to analyse the response Anna Tillman, Tom Thorne and Tao You) who of C. glabrata to hyperosmotic and oxidative have contributed to this work. It is funded by the stress, individually and in combination, will be BBSRC SABR grant Combinatorial Responses in presented. Stress Pathways (CRISP).

We have performed time-series expression analysis, characterised phenotypic responses, protein-protein interactions and shown that Andrew Millar the signaling pathway sensing osmostress differs University of Edinburgh, UK in both structure and response to that seen in Co-authors: A. Pokhilko ¹, S. Hodge ¹, K. Knox¹, S. cerevisiae and Candida albicans. We have also S. Gilmore ² ³, R. Adams ², A. Yamaguchi ², shown that combinatorial stress elicits a response N. Hanlon ², N. Tsorman ², C. Tindal ¹, R. that is different to that seen in response to Muetzelfeldt 3 4, H. Ougham 5, (¹ School of individual stresses. Data comparing responses Biological Sciences, University of Edinburgh; in the three species will be presented. ² Centre for Systems Biology at Edinburgh; ³ School of Informatics, University of Edinburgh; In addition to the analyses that the modelling 4 Simulistics Ltd., Loanhead; 5 IBERS, University of participants of CRISP are conducting we have Wales-Aberystwyth) attempted to analyse these data using tools that The Gene Circuits of Plant Clocks and the are easily accessible to biologists. We believe that Infrastructure for Systems Biology these tools are inadequate to gain full value from the data. They do not allow us to gain a holistic The Centre for Systems Biology at Edinburgh view of our system, and we strongly believe that (www.csbe.ed.ac.uk) develops infrastructure for tools that allow biologists to integrate these varied Systems Biology research projects. CSBE’s core

16 biological projects include RNA metabolism in is in development. From a broader perspective, yeast; the interferon and ErbB pathways in human Systems Biology offers a fresh opportunity to cells; and our work on plant circadian rhythms. connect understanding of intracellular pathways to the performance of plant populations. The The ubiquitous “biological clock” creates 24-hour difficulties and the promise are worth considering, rhythms that control much of plant physiology, at a time when understanding of plant biology in from photoperiodism (Salazar et al. Cell 2009) to the field is urgently required to respond to global carbon metabolism. Our mathematical models challenges. 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 Mark Isalan understanding of broad principles of biological Centre for Genomic Regulation (CRG), regulation (Rand et al. Interface 2004, Akman et Barcelona, Spain al. Mol Syst. Biol. 2008). Why, for example, do The Propagation of Perturbations in Rewired the clock mechanisms in all organisms comprise Gene Networks many interlocked feedback loops (cf. work by Carl There are many gene knock-out and Troein and Ozgur Akman)? Why, in Arabidopsis, overexpression studies available in different is one family of clock proteins represented by five organisms, but no one previously explored members, which are expressed successively, in the effect of systematically adding new links to a wave from dawn to dusk? A new clock model biological networks. We therefore constructed separates the members to reveal the functional 598 different combinations of promoters and implications (Pokhilko et al., Mol. Syst. Biol. 2010, transcription- or sigma-factor coding regions, in press). in Escherichia coli, and added these shuffled combinations over the genetic background of The Systems Biology Software Infrastructure, SBSI the wild type. In a way, this is reminiscent of the (www.sbsi.ed.ac.uk, led by Steven Gilmore) is a gene duplication and regulatory drift processes set of open-source, modular software that aims to thought to shape gene networks during evolution. streamline the process of modelling such dynamic The study showed that most perturbations were biological systems. Global parameter searching active and yet tolerated by the bacteria. We have on HPC platforms is a particular emphasis: SBSI recently extended our initial analysis by selecting is now available as a service on HECToR. A new 57 shuffled network constructs for microarray entry-point has recently been provided to lower transcriptome analysis. The resulting view allows the technical barriers to this complex area: model us to explore the extent to which rewiring optimisation via SBSI has recently been provided perturbations propagate across the network. 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

17 Mark Goulian a range of systems including circadian clocks, the University of Pennsylvania, USA NF-B signalling system, and prolactin transcription. Cell-to-Cell Variability in the E. Coli TorS/TorR Signaling System In the absence of oxygen, E. coli can respire by Michal Komorowski using trimethylamine-N-oxide (TMAO) as an Imperial College London, UK electron acceptor. Expression of the torCAD Co-authors: Barbel Finkenstadt ¹, David Rand ¹, operon, which encodes the proteins required for Michael Stumpf ², (¹ University of Warwick; TMAO reduction, is controlled by the TorS/TorR ² Imperial College London, UK) two-component signaling system. TMAO signaling An Integrated Framework for Inference, occurs through TorS, a hybrid sensor kinase that Identifiability, Sensitivity and Robustness in phosphorylates the response regulator TorR. Stochastic Models of Biochemical Reactions

In an effort to explore the properties of hybrid The aim of the presentation is to introduce a kinases, we have been studying the behavior novel, integrated theoretical framework for of this system in single cells using fluorescent the analysis of stochastic biochemical kinetics transcriptional reporters. Surprisingly, transcription models. Our framework includes efficient of the torC promoter shows considerable cell-to- methods for statistical parameter estimation cell variability, even when the system is maximally from experimental data, as well as tools to study induced with TMAO. I will describe our efforts to parameter identifiability, sensitivity and robustness. understand this behaviour and present evidence Our methods provide novel conclusions about that the variability arises from a step upstream of functionality and statistical properties of stochastic TorR in the phosphorelay. 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 David Rand to write explicit expressions for the likelihood University of Warwick, UK of experimental data, which lead to an efficient Measuring Dynamics, Noise and Heterogeneity inference algorithm and a quick method for in Genes and Networks calculation of the Fisher Information Matrices. I will describe some new mathematical approaches We present a number of experimental and to measure dynamics and noise and probe theoretical examples that show how our system design principles both in individual genes techniques can be used to extract information from and in signalling and regulatory networks. These the noise structure inherent to experimental data. approaches and techniques allow one to probe Examples include a model of gene expression, fundamental aspects such as uncertainty and Bayesian hierarchical model for estimation of heterogeneity in single cell behaviour, the variation degradation and transcription rates and a study of between cells and the interaction between them the p53 system. Novel insights into the causes and that have not been investigated with conventional effects of stochasticity in biochemical systems are methods. To illustrate these ideas I will consider obtained by the analysis of the Fisher Information

18 Matrices. Our methodology is the first which, not Max Reuter only allows parameter estimation, but can also be University College London, UK used to study sensitivity and to guide the design of Co-authors: Alexander Stewart, University College experiments probing stochastic systems without London, UK the need for extensive Monte Carlo simulations. Transcriptional Noise Reduction and the Evolution of Negative Auto-Regulation Gene transcription is a key step in linking genotype Laurence Hurst to phenotype. Gaining insight into how gene University of Bath, UK regulation functions and evolves is therefore crucial Co-authors: Balazs Papp ¹, Karoly Kovacs ¹, Martin to our understanding of the genotype-phenotype J. Lercher ², Guang-Zhong Wang ², (¹ Biological map. Transcriptional noise poses a significant Research Center of the Hungarian Academy challenge to maintaining optimal gene expression of Sciences, Hungary; ² Group, and therefore an organism’s phenotype. Heinrich-Heine-University Düsseldorf, Germany) Transcriptional noise arises due to both fluctuations Looking After the Neighbourhood: Noise in the environment and the stochastic nature of Abatement and Genome Evolution transcription itself. Minimising the effect of noise Noise in gene expression is both inevitable and on gene expression is an important function likely to be selectively important, especially for of gene regulatory networks and selection on more dose sensitive genes, such as those that the capacity to buffer noise is thought to be an result in inviability when knocked out (essential important driver of regulatory evolution. Negative genes). Here I ask whether gene order and auto-regulation has been proposed as a powerful architecture adapt to modify expression in noise. mechanism for noise-reduction. Stochastic models In particular I show how bidirectional promoter of gene expression have shown that negative auto- architecture in yeast and co-linearity of metabolic regulation reduces both the response time and genes in bacterial operons both appear to be noise in gene expression. Empirical support for the noise abatement mechanisms. The noise model role of auto-regulation, however, is contradictory. uniquely explains why co-linearity is strongest for In prokaryotes, the motif is very prevalent and lowly expressed operons. The bidirectional model about half of the transcription factors in Escherichia correctly predicts the dearth of sub-telomeric coli negatively auto-regulate. In eukaryotes, bipromoter genes, the enrichment of essential the situation is very different, for example only genes associated with bidirectional promoters about ten percent of Saccharomyces cerevisiae and explains why genes associated with cryptic transcription factors show this type of regulation. unstable transcripts tend both to be essential and We propose that the difference in prevalence of to have low noise levels. 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

19 that when selection acts to reduce noise in gene motor. How do bacteria balance the signals from expression, an increase in the strength of negatively apparently homologous pathways to bias the auto-regulating binding sites is frequently subject swimming towards an optimum environment to under-dominance. This results in a barrier to for growth? All chemosensory pathways include the de novo evolution of negatively auto-regulating receptors that regulate the activity of a histidine binding sites and explains the relative scarcity of protein kinase (CheA), and this in turn regulates, negative auto-regulation in yeast. 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 Peter Swain sphaeroides expressed two chemosensory University of Edinburgh, UK pathways under normal laboratory conditions, and ltrasensitivity and Cellular Decision-Making both are essential for a chemosensory response. Many genetic and signalling networks respond The components of one pathway localise ultrasensitively as the concentration of the with transmembrane chemoreceptors in large input to the network increases. We argue that quaternary complexes close to the cell poles, while ultrasensitive responses could occur because cells the components of the second pathway localise must infer changes in the state of the extracellular with soluble chemoreceptors in a large complex environment only from intracellular changes close to the middle of a newly divided cell. A or from local changes at the membrane. Using localisation system related to the ParA system of synthetic biology to study gene expression in plasmid organisation ensures each daughter cell bacteria and systems biology to study signal has a cytoplasmic cluster on division, emphasising transduction in yeast, we show that ultrasensitive the importance of each cell having both pathways. responses are convergent in that they can be While each cluster has all the components of generated by very different biochemistry: through a chemosensory pathway, they are unique cooperative interactions between transcription homologues in each location and even when factors or through a competition between a kinase overexpressed the components never localise to and a phosphatase for multiple phosphorylation the alternative site. Swapping the targeting domains sites on a protein scaffold. We show that both of the CheAs in each pathway causes the kinases responses may be understood as inference in a to localise to the “wrong” cluster. Biochemical two-state environment. studies have identified patterns of phosphotransfer from the CheAs to the CheYs and CheBs. Even when a kinase able to phosphotransfer to all Judy Armitage CheYs and CheBs is localised to both cluster, it Oxford University, UK is unable to support chemotaxis, showing the need for specific localisation. Using a wide range Decision Making in Bacterial Chemotaxis of experimental data, from structural, through The majority of swimming bacterial species have biochemical to in vivo localisation mathematical more than one chemotactic pathway regulating models have been developed to attempt to the switching behaviour of the rotary flagellar understand possible signalling pathways that

20 could result in chemosensory responses in Problems in biology are intrinsically multi-scale, R.sphaeroides. These will be discussed, along with with processes occurring on many disparate possible mechanisms for the controlled localisation spatial and temporal scales. We present a multi- of the cytoplasmic chemosensory proteins. scale framework for computational modelling of biological systems. Utilising the natural structural unit of the cell, the framework consists of three Ralf Steuer main scales: the tissue level (macro-scale); the Humboldt University zu Berlin, Germany cell level (meso-scale); and the sub-cellular level Co-authors: V. Sourjik ¹, M. Kollmann ², (micro-scale). Cells are modelled as discrete (¹ Universität Heidelberg, Germany; ² Humboldt interacting entities using either an off-lattice University zu Berlin, Germany) tessellation, or a vertex-based model. The behaviour at the tissue level is represented by Robust Signal Processing in Living Cells field equations for nutrient or messenger diffusion, Cellular signaling has to operate reliably under with cells functioning as sinks and sources. The conditions of uncertainty and in the face of sub-cellular level concerns numerous metabolic constant perturbations. In this respect, a particular processes and models interaction networks challenge for living cells is the necessity to keep and signalling pathways by ordinary differential the concentrations of certain active signaling equations or rule-based models. The modular molecules within narrowly defined ranges, despite approach of the framework enables much a multitude of detrimental influences. Here, we more complicated sub-cellular behaviour to be present a novel formalism that pinpoints the considered. Interactions may occur between necessary architecture for perfect concentration all spatial scales. The multi-scale framework is robustness in living cells. We show, supported implemented in an open source software library by conclusive experimental evidence, that any called Chaste (http://web.comlab.ox.ac.uk/chaste), signaling network can be constructed such that which is written in object-oriented C++ and a set of possibly detrimental fluctuations has no developed using an agile approach. All software effect on the active concentrations of signaling is tested, robust, reliable and extensible. We compounds, hence the function, of the network. introduce Chaste and discuss both its functionality Our mathematical framework accounts for diverse and development. manifestations of cellular robustness and enables the predictive design of perfectly robust synthetic network topologies. Michael Stumpf Imperial College London, UK ...The Rest are Details: Model Selection in Alexander Fletcher Systems and Evolutionary Biology Oxford University, UK Co-authors: J.M. Osborne ¹, D.J. Gavaghan ¹, For the vast majority of biological processes and P.K. Maini ¹, (¹ University of Oxford, UK) systems we still lack suitable mechanistic models. Inference or reverse engineering of such models, Chaste: A Computational Framework for however, remains a statistical challenge. In a Multi-scale Modelling in Systems Biology

21 recent landmark paper Sydney Brenner used behaviour of a complex system from a microscopic the notorious difficulty of this so-called inverse description. We apply these techniques to analyse problem to question the rationale underlying much collective-decision making in social insect colonies, of modern systems biology. In this talk I will argue allowing us to derive the colony-level behaviour that this is perhaps a slightly pessimistic assessment. from an individual-level model. This contrasts In particular I will show that if we are ready, not to with the traditional approach where a differential worry too much about details of such models – equation model, with or without arbitrary noise such as the precise values of rate constants – we terms, is assumed. can indeed learn a lot about biological systems from suitable high-throughput data. Social insect colonies vary in size from on the order 100 to 10,000,000 individuals, and such a I will discuss this in the context of analyses into statistical physics approach allows us explicitly to the dynamics and evolutionary history of signal derive equations for both the average behaviour transduction and protein interaction networks, and the noise in the system, across this entire respectively. In particular so-called approximate scale. We develop such a framework by building Bayesian computation (ABC) techniques emerge upon an existing stochastic model of opinion as powerful tools for robust inference in systems, formation to model the decision-making processes synthetic, population and evolutionary biology, in emigrating ant colonies. This new model is and their power and flexibility will be illustrated. both driven by and evaluated against results from ABC (as well as other Bayesian approaches) experiments with the rock ant Temnothorax allows us to identify the structure and dynamics albipennis. We begin with a microscopic master of biological systems; more generally, and quite equation description of relevant individual-level unlike conventional e.g. optimisation approaches, interactions in the colony. For biologically realistic the Bayesian formalism gives much more detailed colony sizes, we derive equations describing insights into what can be inferred about biological the emergent macroscopic behaviour of the systems from data. 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 Patrick Hogan direct switching in the colony-level decision-making University of Bristol, UK process, which optimality theory has predicted to Co-authors: Thomas Schlegel ¹, Nigel R. be of crucial importance, and which we compare Franks ¹, James A. R. Marshall ², (¹ School of with our experimental results. This illustrates the Biological Sciences, University of Bristol; power of stochastic methods in statistical physics ² Department of Computer Science, University for understanding social insect colonies as complex of Bristol) systems. The Statistical Physics of Decision-Making in Insect Colonies The stochastic methods of statistical physics provide tools to derive the emergent macroscopic

22 Kevin Foster Sam Brown Harvard University, USA Oxford University, UK Social Evolution in Microbes Horizontal Gene Transfer of the Secretome Drives the Evolution of Bacterial Cooperation “If it could be proved that any part of the structure and Virulence of any one species had been formed for the exclusive good of another species, it would Microbes engage in a remarkable array of annihilate my theory, for such could not have cooperative behaviours, secreting shared proteins been produced through natural selection” Darwin that are essential for foraging, shelter, microbial (1859). Since Darwin, evolutionary biologists have warfare and virulence. These proteins are costly, been troubled by cooperative behaviour. Why do rendering populations of cooperators vulnerable organisms frequently evolve social behaviours that to exploitation by non-producing cheaters arising promote others at an apparent cost to their own by gene loss or migration. In such conditions, reproduction? For example, honeybee workers how can cooperation persist? Our model predicts labour their whole life without reproducing; birds that differential gene mobility drives intragenomic make alarm calls; and humans often help one variation in investment in cooperative traits. another. More mobile loci generate stronger among- individual genetic correlations at these loci (higher This fundamental question has received relatedness) and thereby allow the maintenance of considerable attention over the last 50 years with more cooperative traits via kin selection. the development of the field of sociobiology. This has resulted in a solid base of theory, centered By analyzing 21 Escherichia genomes, we on principles like inclusive-fitness, and a myriad confirm that genes coding for secreted proteins of empirical tests. It is now widely accepted that (the secretome) are very frequently lost and cooperative behaviours evolve because they gained and are associated with mobile elements. directly help the actor alongside any recipients, We show that homologs of the secretome are or they help individuals who share more alleles overrepresented among human gut metagenomics with the actor than predicted by chance (genetic samples, consistent with increased relatedness relatedness), or both. at secretome loci across multiple species. The biosynthetic cost of secreted proteins is shown to One major group that remains relatively be under intense selective pressure, even more unexplored, however, is the microbes, whose full than for highly expressed proteins, consistent with spectrum of sociality only recently came to light. a cost of cooperation driving social dilemmas. We ask how social environment and relatedness affects microbial behaviour in a number of model Finally, we demonstrate that mobile elements are systems, including biofilm-forming bacteria in conflict with their chromosomal hosts over the and budding yeast. We find that microbes are chimeric ensemble’s social strategy, with mobile extremely sensitive to social context – both in real elements enforcing cooperation on their otherwise time and over evolutionary time – and use them selfish hosts via the cotransfer of secretome to better understand the genetic and genomics of genes with ‘mafia strategy’ addictive systems. social traits; a pursuit that is difficult in the more We conclude that horizontal transfer promoted classical model organisms for social behaviour. by agents such as plasmids, phages or integrons

23 shapes population genetic structure and drives Sebastian Bonhoeffer microbial cooperation and virulence. ETH Zurich, Switzerland The Evolutionary Systems Biology of HIV-1 Drug Resistance Ivana Gudelj The development of a quantitative understanding Imperial College London of HIV-1 drug resistance represents a formidable Microbial Evolution in Theory and Practice challenge given the large number of available drugs and drug resistance mutations. We employ Microbes are ubiquitous in nature and occupy ridge regression based models to estimate virtually every environmental niche on earth. main fitness effects and epistatic interactions of Contributing to this evolutionary success are 1,857 mutations in HIV-1 protease and reverse diverse metabolic strategies as well as the ability transcriptase, using a data set of 60,000 virus to adapt to changing environments. Experimental samples assayed for in vitro replicative capacity evolution has provided an ideal setting for studying in the absence of drugs as well as the presence microbial diversification in action: experiments of 15 individual drugs. The model predicts an are conducted in controlled environments using average of 45.4% of the variance in replicative culturable strains that are easily manipulated capacity across the 16 different environments and due to their known genetic structure. However, substantially outperform models based on main this simplified approach to evolution poses the effects only. The model thus represents a realistic following questions: how do we know whether approximation of the fitness landscape underlying a given experimental outcome is particular to HIV-1 protease and reverse transcriptase. the laboratory system? What can we learn from laboratory experiments about microorganisms in We use our model of the HIV fitness landscape the wild? to determine generic properties that have long remained elusive in the absence of realistic fitness In this talk I argue that through the use of systems landscapes. We find that the fitness landscape is mathematical models we can begin to bridge the characterised by the presence of a large number gap between laboratory and nature. I will present of local optima and large neutral networks. a series of mathematical models of microbial Moreover, sequences that differ only by few evolution reflecting different types of selection mutations initially can evolve to optima that differ pressures that microbes repeatedly encounter greatly both genetically and phenotypically. Thus in nature: 1) Evolution of cooperative metabolic our explorations of the HIV fitness landscape strategies and 2) Evolution of resistance to support the view that fitness landscapes are highly pathogens. I will demonstrate that such models complex and that evolutionary trajectories depend can make good quantitative predictions of a given sensitively on the initial conditions. laboratory experimental setup and discuss which of these predictions can be generalised to other microbial systems.

24 Participants

Özgür Akman Declan Bates University of Exeter, UK University of Exeter, UK [email protected] [email protected]

Munia Amin Travis Bayer University of Exeter, UK Imperial College London, UK [email protected] [email protected]

Svetlana Amirova Manuel G. Bedia University of Exeter, UK University of Zaragoza, Spain [email protected] [email protected]

Judy Armitage Sebastian Bonhoeffer University of Oxford, UK ETH Zurich, Switzerland [email protected] [email protected]

Peter Arndt Sam Brown Max Planck Institute for Molecular Genetics, University of Oxford, UK Germany [email protected] [email protected] Nicolas Buchler Peter Ashwin Duke University, USA University of Exeter, UK [email protected] [email protected] Luca Cardelli Ruth Baker Microsoft Research, UK University of Oxford, UK [email protected] [email protected] Riccardo Cipelli Ruth Bastow University of Exeter, UK University of Warwick, UK [email protected] [email protected] Carlo Cosentino University of Catanzaro, Italy [email protected]

25 Neil Dalchau Nicholas Harmer Microsoft Research, UK University of Exeter, UK [email protected] [email protected]

Anup Das Ken Haynes University of Exeter, UK University of Exeter, UK [email protected] [email protected]

Emma Denham Patrick Hogan University Medical Center Groningen, University of Bristol, UK Netherlands [email protected] [email protected] Martin Howard Alexander Fletcher John Innes Centre, UK University of Oxford, UK [email protected] [email protected] Laurence Hurst Kevin Foster University of Bath Harvard University, USA [email protected] [email protected] Mark Isalan Luca Gerosa Centre for Genomic Regulation (CRG), ETH Zurich, Switzerland Barcelona, Spain [email protected] [email protected]

Murray Grant Siddharth Jayaraman University of Exeter, UK University of Exeter, UK [email protected] [email protected]

Mark Goulian Karen Kastenhofer University of Pennsylvania, USA Austrian Academy of Sciences, Austria [email protected] [email protected]

Ivana Gudelj Chris Knight Imperial College London, UK University of Manchester [email protected] [email protected]

26 Karl Kochanowski Steve Porter ETH Zurich, Switzerland University of Exeter, UK [email protected] [email protected]

Michal Komorowski Avinoam Rabinovitch Imperial College London Ben-Gurion University, Israel [email protected] [email protected]

Karoly Kovacs David Rand Institute of Biochemistry, Hungary University of Warwick, UK [email protected] [email protected]

Congpin Lin Max Reuter University of Exeter, UK University College London, UK [email protected] [email protected]

Bruno Martins David Richards University of Edinburgh, UK John Innes Centre, UK [email protected] [email protected]

Colin Miles Gabriel Rosser BBSRC, UK University of Oxford, UK [email protected] [email protected]

Andrew Millar Susan Rosser University of Edinburgh University of Glasgow, UK [email protected] [email protected]

Claudia Mueller Yasushi Saka University of Exeter University of Aberdeen, UK [email protected] [email protected]

Maureen O’Malley Uwe Sauer University of Exeter, UK ETH Zurich, Switzerland [email protected] [email protected]

Antonis Papachristodoulou Michael Savageu University of Oxford, UK University of California, USA [email protected] [email protected]

27 Jean-Marc Schwartz Michael Stumpf University of Manchester, UK Imperial College London, UK [email protected] [email protected]

Yishay Shoval Peter Swain Weizmann Institute of Science, Israel University of Edinburgh [email protected] [email protected]

Nick Smirnoff Nick Talbot University of Exeter, UK University of Exeter [email protected] [email protected]

Ralf Sommer Stuart Townley Max Planck Institute for Developmental Biology, University of Exeter, UK Germany [email protected] [email protected] Carl Troein Orkun Soyer University of Edinburgh, UK University of Exeter, UK [email protected] [email protected] Jannis Uhlendorf Andrea Splendiani INRIA Paris – Rocquencourt, France Rothamsted Research, UK [email protected] [email protected] Andreas Wagner Gero Steinberg University of Zurich, Switzerland University of Exeter, UK [email protected] [email protected]

Ralf Steuer Humboldt-Universitaet zu Berlin, Germany [email protected]

David Studholme University of Exeter, UK [email protected]

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