Embed Size (px)
DESCRIPTION
Presentation summarising the 2013 ICSB conference in Copenhagen, a requirement of James Hutton Institute Visits Abroad funding. Presented at the Cellular and Molecular Sciences seminar series.
Citation preview
14th Interna+onal Conference on Systems Biology, Copenhagen (Visits Abroad)
Leighton Pritchard
ICSB2013 l Copenhagen, Denmark Friday 30th August-‐Tuesday 3rd September
l Website: hBp://www.icsb2013.dk/
l TwiBer hashtag: #ICSB13
l Three Parallel sessions (50 talks aBended): l Metagenomics; Industrial applicaPons; Temporal phenomena
across biological Pmescales
l Cancer and Stem cells; Physiology-‐based modelling of disease; Metabolomics
l Precision medicine; Health care data; Network engineering
ICSB2013 l Three parallel sessions:
l Strategies for modelling biological systems; Protein interacPon networks; Drug discovery
l GenePc networks; Complete cell modelling; Host-‐pathogen interac+ons
l Signalling networks; Cells to Pssues; Very large scale data visualisa+on
l GenePcs and imaging; Complex genePc traits; Synthe+c biology
l Two poster sessions: l Session 1: 191 posters
l Session 2: 195 posters
l My poster: hBp://shar.es/EQuFG and hBp://dx.doi.org/10.6084/m9.figshare.767275
ICSB2013: Keynotes l Included:
l Stuart Kauffmann: hBp://en.wikipedia.org/wiki/Stuart_Kauffman
� TheorePcal biologist, introduced Boolean networks to biology
l Marc Vidal: hBp://dms.hms.harvard.edu/BBS/fac/Vidal.php
� Pioneering invesPgaPon of protein-‐protein interacPon networks l Gene Myers: hBp://en.wikipedia.org/wiki/Eugene_Myers
� Comp. sci/bioinformaPcs, co-‐inventor of BLAST
l Wendell Lim: hBp://en.wikipedia.org/wiki/Wendell_Lim
� Signalling networks; optogenePcs l Peer Bork: hBp://en.wikipedia.org/wiki/Peer_Bork
� Human gut microbiome; Tree of Life etc.
l Ruedi Aebersold: hBp://en.wikipedia.org/wiki/Ruedi_Aebersold
� Pioneer of mass spectrometry in biology
l Chris Voigt: hBp://en.wikipedia.org/wiki/Christopher_Voigt
� Engineering logic circuits in bacteria
l Bernhard Palsson: hBp://en.wikipedia.org/wiki/Bernhard_Palsson
� Whole-‐cell modelling; introducPon of linear programming to biological cell modelling
ICSB2013: Impressions l Wet-‐lab/dry compuPng integraPon is leading biological
understanding – SysBio a lot further on than you might think
l Whole-‐system measurement (sequencing, proteomics, metabolomics etc.)
l Modelling and predicPon
l Cataloguing is not understanding l Biology is dynamic
l You cannot intuiPvely understand a cell at the molecular level
l Few speakers were working on plants or plant pathogens l More money (and acceptance…) in cancer and human health?
l Opportunity for us?
l Single-‐cell studies waiPng in the wings
l “Network state determines phenotype” – Ruedi Aebersold
ICSB2013: Human Gut (1) l Parallels with soil microbiome and interac+on with plants obvious!
l Bjorn Neilsen, CBS Lyngby l Sequenced 396 human faecal samples (MetaHit2 database)
l IdenPfied bacteria by coabundance gene groups (CAGs) – 741 ‘species’
l Found community-‐wide dependency networks
l Damian Plichta, CBS Lyngby
l FuncPonal expression (array) profiles in 233 stool samples consistent across variaPon in species mix; most transcribed funcPons unknown!
l DifferenPal acPvaPon/silencing depending on companion species.
l hBp://www.nature.com/nature/journal/v493/n7430/full/nature11711.html
l hBp://www.nature.com/nature/journal/v500/n7464/full/nature12506.html
ICSB2013: Human Gut (2) l Mani Arumugam, University of Copenhagen
l Microbiome ferments undigested carbohydrates to produce SCFA. l Microbiome richness and bacteroides/firmicute balance shired in
overweight/obese individuals.
l Yuri Kosinsky, NovarPs l Constructed semi-‐mechanisPc 4-‐compartment spaPal model of
metabolism in gut species. l Model predicted differenPal butyrate flux from gut to plasma as a
result of change in bacteroides/firmicute balance.
l Gijs den Besten, University of Groningen l 13C-‐labelled SCFA (acetate/propionate/butyrate) introduced into
mouse. l ODE modelling of SCFA/insulin in response to dietary fibre and fat;
SCFA flux – not concentraPon -‐ the significant influence on body weight.
ICSB2013: Human Gut (3) l Peer Bork, EMBL
l MetaHit database has over 10m genes idenPfied from human gut microbiome analysis (hBp://www.metahit.eu/)
l Have species-‐based ‘diagnosPcs’ for colorectal cancer, type II diabetes, Crohn’s etc. (AUC≈0.8, n>120).
l Need beBer funcPonal annotaPon. Some signal from anPbioPc resistance potenPal: most are resistances to veterinary anPbioPcs; geographical variaPon with anPbioPc use.
l Donate your own poo: hBp://microbes.eu/
l Microbiota and SCFA: hBp://onlinelibrary.wiley.com/doi/10.1038/oby.2009.167/full
l CompePPon in faBy acid oxidaPon: hBp://www.ploscompbiol.org/arPcle/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003186
l Human gut microbiome: hBp://www.nature.com/nature/journal/v464/n7285/full/nature08821.html
ICSB2013: Networks (1) l Ruedi Aebersold, University of Zurich
l mRNA is stochasPc (42k copies per cell), proteins are not (95m copies per cell). Focus on transcripts misses a lot of biology. hBp://www.cell.com/retrieve/pii/S0092867412011269
l Developing SWATH-‐MS: records complete, Pme-‐resolved, high accuracy fragment ion spectrum. Aiming for complete proteome measurement. hBp://www.imsb.ethz.ch/researchgroup/malars/research/openswath
l Measured phosphorylaPon states of 1015 proteins as consequence of co-‐sPmulaPon of two signalling pathways – validated against prior publicaPons. Over 1000 ‘response surfaces’ revealing pathway crosstalk (see hBp://onlinelibrary.wiley.com/doi/10.1111/j.1742-‐4658.2006.05359.x)
ICSB2013: Networks (2) l Anne-‐Claude Gavin, EMBL
l Over 1600 ‘omics datasets for M.pneumoniae, including phosphorylaPon dynamics: hBp://www.nature.com/msb/journal/v8/n1/full/msb20124.html
l Has developed a liposome array to study protein recruitment to biological membranes (patent applicaPon GB1212896.3)
l Pierre Millard, University of Manchester
l ThEcoli – a kinePc (ODE) model of E.coli central metabolism. 2 compartments, 52 metabolites, 76 reacPons, 56 allosteric interacPons, 490 parameters.
l Validated against experimental data obtained in the project (sugar pulses/substrate limitaPon) – see also hBp://www.sciencedirect.com/science/arPcle/pii/S1096717613000074 .
ICSB2013: Networks (3) l Jasmin Fisher, Microsor
l ‘Executable biology’: the logic and calculus of biological molecular decision-‐making
l BioModelAnalyzer to make models accessible to wet-‐lab biologists (hBp://biomodelanalyzer.research.microsor.com/)
l Julio Saez-‐Rodriguez, EBI l Building large-‐scale models of signalling in disease from
pathway/phosphoproteomic data.
l ExhausPvely characterising feasible logic models of a signalling network:hBp://bioinformaPcs.oxfordjournals.org/content/29/18/2320.long
ICSB2013: Highlight (1) l Rama Ranganathan, SouthWestern University
l Protein residue coevoluPon (my PhD topic) associaPon with thermodynamic coupling: hBp://www.sciencemag.org/content/286/5438/295
l Coevolving protein residues define ‘sectors’ of structure with independent divergence: hBp://www.cell.com/retrieve/pii/S0092867409009635
l ‘Sectors’ define spaPal architecture of protein funcPon and adaptaPon – all residues subsPtuted to all other residues in combinaPon to define funcPonal sequence constraints: hBp://www.nature.com/nature/journal/vaop/ncurrent/full/nature11500.html
l EpistaPc posiPons provide opportuniPes for adaptaPon to novel funcPons (novel ligand binding)
l Applica+on to effector/resistance protein func+on obvious! l (See also Pritchard & Duron, 2000…
hBp://www.sciencedirect.com/science/arPcle/pii/S0022519399910433)
ICSB2013: Highlight (2) l Wendell Lim, University of California San Franciso
l OptogenePcs: use light sPmulus to acPvate/inacPvate a network component. Enables Pme-‐variant signals to control network intervenPon.
l Based on phytochrome. Demonstrated light-‐gated YFP membrane recruitment, and applicaPon to Ras signalling. Also light-‐dependent BFP-‐Erk transfer to the nucleus.
l OptogenePcs enables live cell modificaPon and readout
l IdenPfied ‘gated signalling’ – differenPal response to signal frequency, not on/off
l Obvious applica+ons to plant pathology! l hBp://www.nature.com/ncb/journal/v9/n3/abs/ncb1543.html
l hBp://www.cell.com/molecular-‐cell/retrieve/pii/S1097276511009506
ICSB2013: Highlight (3a) l Chris Voigt, MIT
l “…biology is a mess…constantly peeling the onion”
l Took Klebsiella N-‐fixaPon nif cluster as basis to engineer N-‐fixaPon into crop plants.
l “Refactored” the cluster – removed ncDNA, non-‐essenPal genes, TFs etc.; randomised all codons. Reorganised into arPficial operons under control of known (BioBrick: hBp://biobricks.org/) regulatory systems.
l That part took seven years.
l Cluster had 7.3% of WT efficiency
l Then undertook massively-‐parallel design tesPng with tens of thousands of modified constructs of the cluster in an automated fashion.
ICSB: Highlight (3b) l Developed a novel rule-‐based design language to direct evoluPon of the cluster by direcPng rounds of permutaPon of components.
l Used machine learning to infer what worked
l IniPal cluster had 7.3% of WT N-‐fixaPon efficiency
l Final cluster recovered 100% of WT N-‐fixaPon efficiency
l Surprises: l Nearly all the original/WT operon structure is unnecessary
l CrypPc RNAs in the WT genes interact with porins, making some genes appear lethal
l CollaboraPon with John Innes Centre (Giles Oldroyd)
ICSB2013: Personal ego bonus l MASS Toolbox
l Mass AcPon Stoichiometric SimulaPon by Nik Sonnenschein (Bernhard Palsson’s group), UCSD
l Uses one of my published models as its tutorial example (as do MathWorks for their SimBiology training)
� Pritchard & Kell (2002) doi: 10.1046/j.1432-‐1033.2002.03055.x
Acknowledgements
l Visits Abroad Fund
l Seedcorn Funding
