Photosynthesis and Phototrophic Growth: Modelling Life on Earth and elsewhere
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1 Photosynthesis and Phototrophic Growth: Modelling Life on Earth and elsewhere Ralf Steuer Humboldt-University Berlin, Germany Institute of Theoretical Biology (ITB) CzechGlobe Global Change Research Centre, Brno, CZ Fakulta informatiky MU pondělí , 14:00
2 outline Part I News and Events on May 14, 2012 Part II Cyanobacteria: understanding phototrophic growth Dynamics of large-scale networks Applications to biotechnology
3 outline May 14, 2012, 0am
4 outline May 14, 2012, 0am Water, water, everywhere,... Bacterial abundance in in stratified oligotrophic waters can be high (> 105 cells ml-1 )
5 outline May 14, 2012, 0am Water, water, everywhere,... Bacterial abundance in in stratified oligotrophic waters can be high (> 105 cells ml-1 ) But no primary productivity...
6 outline May 14, 2012, a few hours later...
7 outline May 14, 2012, a few hours later...
8 outline May 14, 2012, a few hours later... sunrise over pacific
9 outline May 14, 2012, and so Life begins...
10 outline May 14, 2012, and so Life begins...
11 the light reactions The light reactions
12 the light reactions The light reactions photosystem II
13 the light reactions The light reactions: photosystem II
14 the light reactions The light reactions: photosystem II
15 the light reactions The light reactions: photosystem II
16 the light reactions The light reactions: photosystem II 2 2
17 the light reactions The light reactions: photosystem II 2 2
18 the light reactions The light reactions: photosystem II 2 2 the waste product water splitting...
19 the light reactions The light reactions: eating the sun
20 the light reactions The light reactions: eating the sun NADPH ATP
21 the light reactions The light reactions: eating the sun NADPH ATP A view from theory/modelling: Very fast processes (sub-second) Combinatorial number of states Modelling: either ODE or transition matrices The system is inherently dynamic Several reasonable models are available
22 carbon fixation Fixation of atmospheric CO2 by RuBisCO ribulose-1,5-bisphosphate (RuBP, 5 carbon) CO2 3-phosphoglycerate (3 carbon)
23 carbon fixation Fixation of atmospheric CO2 by RuBisCO ribulose-1,5-bisphosphate (RuBP, 5 carbon) CO2A view from theory/modelling: RubisCo is slow and sloppy Only few interconversions per second A limiting factor in phototrophic growth. Low specificity to its substrate Modelling: usually ODE/enzyme kinetics 3-phosphoglycerate (3 carbon)
24 carbon fixation The Calvin-Benson-Bassham (CBB) cycle: Regeneration From: R. Steuer and B. H. Junker. (2009) Computational Models of Metabolism: Stability and Regulation in Metabolic Networks. Advances in Chemical Physics, Volume 142
25 carbon fixation The Calvin-Benson-Bassham (CBB) cycle: Regeneration From: R. Steuer and B. H. Junker. (2009) Computational Models of Metabolism: Stability and Regulation in Metabolic Networks. Advances in Chemical Physics, Volume 142
26 carbon fixation The Calvin-Benson-Bassham (CBB) cycle: Regeneration A view from theory/modelling: Timescale: seconds to minutes About 20 reactions with 100 parameters. Typically implemented as ODE model Q: are there alternative cycles? From: R. Steuer and B. H. Junker. (2009) Computational Models of Metabolism: Stability and Regulation in Metabolic Networks. Advances in Chemical Physics, Volume 142
27 carbon fixation ATP NADPH 3-phosphoglycerate biomass The Calvin-Benson-Bassham (CBB) cycle: Regeneration
28 cyanobacteria: phototrophic growth Cellular metabolism: facilitated by a network of reactions from: Knoop, Zilliges, Lockau, Steuer. Plant Physiology (2010)
29 cyanobacteria: phototrophic growth Cellular metabolism: facilitated by a network of reactions from: Knoop, Zilliges, Lockau, Steuer. Plant Physiology (2010)
30 cyanobacteria: phototrophic growth Cellular metabolism: facilitated by a network of reactions reconstruct validate analyze A view from theory/modelling: Large systems with hundreds of reactions Assumption of stationarity Flux-balance analysis (FBA) Linear programming (LP) optimization of objective functions More to come... from: Knoop, Zilliges, Lockau, Steuer. Plant Physiology (2010)
31 cyanobacteria: phototrophic growth Cellular metabolism: facilitated by a network of reactions from: Knoop, Zilliges, Lockau, Steuer. Plant Physiology (2010)
32 cyanobacteria: phototrophic growth Phototrophic growth and the environment:
33 cyanobacteria: phototrophic growth Phototrophic growth and the environment:
34 cyanobacteria: phototrophic growth May 14, 2012, 11:59pm, by the end of the day joule solar energy is absorbed by Earth's atmosphere, oceans and land masses per day tons of carbon fixed by photosynthesis 120 Gt carbon per year (land) 90 Gt carbon per year (ocean)
35 cyanobacteria: the CyanoNetwork Cyanobacteria: a hierarchy of processes We aim to understand the life and growth of cyanobacteria
36 cyanobacteria: the CyanoNetwork Cyanobacteria: a hierarchy of processes We aim to understand the life and growth of cyanobacteria The CyanoTeam and CyanoNetwork An association between several groups from EU, Israel, and USA to model and understand a cyanobacterial cell in silico. International team led by John Whitmarsh Coordinator local experimental team: Ladislav Nedbal Coordinator local modelling team: Ralf Steuer
37 cyanobacteria: the CyanoNetwork Cyanobacteria: a hierarchy of processes We aim to understand the life and growth of cyanobacteria The CyanoTeam and CyanoNetwork An association between several groups from EU, Israel, and USA to model and understand a cyanobacterial cell in silico. International team led by John Whitmarsh Coordinator local experimental team: Ladislav Nedbal Coordinator local modelling team: Ralf Steuer
38 cyanobacteria Cyanobacteria: understanding phototrophic growth phototrophic micro-organisms (prokaryotes) capable of oxygen-evolving photosynthesis
39 cyanobacteria Cyanobacteria: understanding phototrophic growth phototrophic micro-organisms (prokaryotes) capable of oxygen-evolving photosynthesis globally extremely abundant The cyanobacterium Prochlorococcus is the numerically dominant phototroph in some oceans (up to half of the photosynthetic biomass). Cyanobacterial abundance in in stratified oligotrophic waters can be high (> 105 cells ml-1 ) from: Sullivan et al. Nature (2003)
40 cyanobacteria Cyanobacteria: understanding phototrophic growth phototrophic micro-organisms (prokaryotes) capable of oxygen-evolving photosynthesis globally extremely abundant first mass-producers of free molecular oxygen responsible for the Great Oxygenation Event (GOE) around 2.4 billion years ago.
41 cyanobacteria Cyanobacteria: understanding phototrophic growth phototrophic micro-organisms (prokaryotes) capable of oxygen-evolving photosynthesis globally extremely abundant first mass-producers of free molecular oxygen ancestors of modern day chloroplasts relevant for the global carbon cycle
42 cyanobacteria Cyanobacteria: understanding phototrophic growth phototrophic micro-organisms (prokaryotes) capable of oxygen-evolving photosynthesis globally extremely abundant first mass-producers of free molecular oxygen ancestors of modern day chloroplasts relevant for the global carbon cycle live as symbionts and in communities relevance for biotechnology (biofuels)
43 cyanobacteria Cyanobacteria: understanding phototrophic growth An open pond Spirulina farm: relevance for biotechnology (biofuels)
44 cyanobacteria Cyanobacteria: understanding phototrophic growth relevance for biotechnology (biofuels)
45 cyanobacteria Cyanobacteria: understanding phototrophic growth relevance for biotechnology (biofuels)
46 cyanobacteria Cyanobacteria: a modular approach We aim to understand the life and growth of cyanobacteria
47 cyanobacteria Cyanobacteria: a modular approach Energy: ATP Redox: NADPH
48 cyanobacteria Cyanobacteria: a modular approach Energy: ATP Redox: NADPH
49 cyanobacteria Cyanobacteria: a modular approach Energy: ATP Redox: NADPH
50 cyanobacteria Cyanobacteria: a modular approach Energy: ATP Redox: NADPH
51 cyanobacteria Cyanobacteria: a modular approach Energy: ATP Redox: NADPH DNA topology
52 cyanobacteria Cyanobacteria: a modular approach Energy: ATP Redox: NADPH Transcriptome DNA topology
53 cyanobacteria Cyanobacteria: a modular approach THE ENVIRONMENT CCM Energy: ATP Redox: NADPH Transcriptome DNA topology
54 cyanobacteria: from biology to ecology Cyanobacteria: a hierarchy of processes Phototrophic growth and the environment
55 cyanobacteria: phototrophic growth Phototrophic growth and the environment:
56 cyanobacteria: photobioreactor Cyanobacteria: a hierarchy of processes Phototrophic growth and the environment
57 cyanobacteria: photobioreactor Cyanobacteria: a hierarchy of processes Phototrophic growth and the environment Traditional ODE model for gas-liquid mass transfer: plus carbonate chemistry and a light gradient. Stefan Mueller et al. An integrated model of photosynthetic growth in a bioreactor: gas-liquid mass transfer, carbonate chemistry, and cellular fluxes (to be completed soon).
58 cyanobacteria: a hierarchy of processes Cyanobacteria: a hierarchy of processes We aim to understand the life and growth of cyanobacteria Biophysics of photosynthesis and the light reactions ODE models of cellular metabolism and CCMs Integration of the cyanobacterial circadian clock Integration of gene expression and signalling Need to integrate diverse computational methodologies to describe sub-processes Necessitates a community approach: The international CyanoTeam
59 modelling metabolism Modelling cellular metabolism Understanding phototrophic growth in a complex environment
60 modelling metabolism Modelling cellular metabolism Understanding phototrophic growth in a complex environment Steuer, Knoop, Machne. Journal of Experimental Botany (2012)
61 modelling metabolism Modelling cellular metabolism Mechanistic versus teleological models Based on mechanistic details of the underlying processes (bottom-up)
62 modelling metabolism Modelling cellular metabolism Mechanistic versus teleological models Based on constraints and optimization principles (top-down). Widely applied to study flux distributions in metabolic network
63 modelling metabolism Modelling cellular metabolism Mechanistic versus teleological models All results are based on a high-quality reconstruction of the underlying network of biochemical interconversions.
64 modelling metabolism Modelling cellular metabolism Mechanistic versus teleological models All results are based on a high-quality reconstruction of the underlying network of biochemical interconversions. Metabolic reconstruction: a compendium of all biochemical interconversions of small molecules within a cell.
65 modelling metabolism: network reconstruction Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 [1] Start with databases and genome sequence: Initial draft network [2] Identify gaps and inconsistencies: manual curation and literature mining [3] Convert to mathematical model: Include pseudo-reactions for cellular maintenance [4] Analyse the model using contraint-based optimization The whole process is iterative and is repeated several times!
66 modelling metabolism: network reconstruction Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 From: Steuer et al. JXB (2012)
67 modelling metabolism: network reconstruction Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 Plot by H. Knoop (HU Berlin), see also Knoop et al. Plant Physiology (2010)
68 modelling metabolism: network reconstruction Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 Plot by H. Knoop (HU Berlin), see also Knoop et al. Plant Physiology (2010)
69 modelling metabolism: FBA Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 Analyse the model using contraint-based optimization v1 v2 Assuming stationary conditions: v1 v2 v3 = 0 v3
70 modelling metabolism: FBA Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 Analyse the model using contraint-based optimization More general: 2nd assumptions: metabolic fluxes are organized such that a given (usually linear) objective function Z is maximized.
71 modelling metabolism: FBA Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 Analyse the model using contraint-based optimization See: Steuer and Junker. Advances in Chemical Physics (2009)
72 modelling metabolism: FBA Modelling cellular metabolism A stoichiometric model of Synechocystis sp. PCC6803 From: Steuer et al. JXB (2012)
73 modelling metabolism: FBA A stoichiometric model of Synechocystis 6803 Applications of constraint-based optimization Optimal flux patterns (maximal biomass yield) Flux-variability analysis Gene essentiality analysis Reaction coupling (with A. Bockmayr, FU Berlin)
74 modelling metabolism: FBA A stoichiometric model of Synechocystis 6803 Optimal flux patterns (maximal biomass yield) flux distribution: growth rate/yield:
75 modelling metabolism: FBA A stoichiometric model of Synechocystis 6803 Gene essentiality analysis: network validation still viable? 126 (of 337) genes are classified as essential for biomass formation: Comparison with CyanoMutants new hypotheses/questions!
76 cyanobacteria: biofuels A stoichiometric model of Synechocystis 6803 Applications of constraint-based optimization: Biofuels
77 cyanobacteria: biofuels A stoichiometric model of Synechocystis 6803 Atsumi et al., 2007 The model as a platform for strain improvement
78 cyanobacteria: biofuels A stoichiometric model of Synechocystis 6803 The model as a platform for strain improvement product CO2 ATP NAD(P)H photons ethanol ethylene isobutanol isoprene
79 cyanobacteria: biofuels From lab to applications: large-scale cultivation of Synechocystis sp. PCC 6803
80 cyanobacteria: biofuels From lab to applications: large-scale cultivation of Synechocystis sp. PCC 6803 Culture Duration: 79 days/final EtOH Conc. : 0.15 %(v/v)
81 cyanobacteria: biofuels A stoichiometric model of Synechocystis 6803 Applications of constraint-based optimization: Biofuels Introduce fuel pathways into the stoichiometric reconstruction contributions to host optimization and metabolic streamlining identify main routes of synthesis for precursor metabolites prediction of optimal knockout targets for product formation
82 modelling metabolism: beyond FBA A stoichiometric model of Synechocystis 6803 CHALLENGES AND EXTENSIONS OF FBA Thermodynamic consistency The costs of pathways: minimum-cost flow problems Temporal coordination of metabolism
83 modelling metabolism: temporal coordination A stoichiometric model of Synechocystis 6803 Temporal coordination of metabolism
84 modelling metabolism: temporal coordination A stoichiometric model of Synechocystis 6803 Temporal coordination of metabolism Light metabolism Storage Dark metabolism
85 modelling metabolism: temporal coordination A stoichiometric model of Synechocystis 6803 Temporal coordination of metabolism Expression of metabolic genes Circadian time Indeed, cyanobacterial metabolism follows a complex circadian program Most genes expressed during light period Data: group of I. Axmann (ITB, Berlin) Clustering/Data analysis: Rob Lehmann, Rainer Machne submitted
86 modelling metabolism: temporal coordination A stoichiometric model of Synechocystis 6803 Temporal coordination of metabolism A time-dependent objective function:
87 modelling metabolism: summary Modelling cellular metabolism: summary Understanding phototrophic growth in a complex environment Biological systems typically involve multiple temporal and spatial scales: need for different methodologies. It is a conceptual and computational challenge to integrate diverse systems into a coherent whole. Of most interest are intermediate methods that allow to deal with incomplete and uncertain data. Large-scale predictive models of cells are possible: computational biology needs to integrate parts into a coherent whole
88 The end Thanks for your attention! And thanks to the group in Berlin: Henning Knoop Sabrina Hoffmann Stefan Mueller Natalie Stanford Raik Otto Robert Lehmann (with I. Axmann) And other people involed Ilka Axmann (ITB) Rainer Machne (ITB, Wien) Wolfgang Lockau (HU) Ettore Murabito (Manchester) Hans Westerhoff (Manchester) Lada Nedbal (Brno, CZ) Wolfgang Hess (ALU-FR) Patrik Jones (Turku)
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