Introduction to whole-cell modeling lecture | Whole-cell modeling summer school | March 3-9, 2015 @ University of Rostock
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Lecture from whole-cell modeling summer school March 3-9, 2015 at the University of Rostock. See http://sites.google.com/site/vwwholecellsummerschool/ for more information.
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Introduction to whole-cell modeling lecture | Whole-cell modeling summer school | March 3-9, 2015 @ University of Rostock
- 1. Toward whole-cell models for science and engineering Jonathan Karr March 9, 2015 Positions available research.mssm.edu/karr karr@mssm.edu
- 3. Chemicals & fuels Optimize yield Minimize cost Food Optimize yield Resist drought Prevent infection Medicine Predict prognoses Optimize therapy Maximize quality of life Central challenge: predict phenotype from genotype
- 11. Example: drug biosynthesis Predicting phenotype from genotype requires “whole-cell” models
- 13. Biological data is readily available
- 14. ? Data Knowledge Whole-cell model goals
- 15. Whole-cell modeling A grand challenge of the 21st century – Masaru Tomita Biology urgently needs a theoretical basis to unify it – Sydney Brenner The ultimate test of understanding a simple cell, more than being able to build one, would be to build a computer model of the cell – Clyde Hutchison
- 16. Single-cell variation Microscopy Transcription RNA-seq Protein expression Mass-spec, Western blot Modeling challenge: heterogeneous data
- 17. Modeling challenge: sparse data
- 18. MetabolicSignaling Transcriptional regulatory Modelling challenge: heterogenous networks
- 19. Time Length Replication Growth Transcription Metabolism Modeling challenge: multiple time and length scales
- 20. 0 25 50 75 100 1970's Coarse-grained ODEs 1990's FBA 2000's Boolean models 2008 iFBA 2012 Whole-cell model %annotatedgenes Whole-cell modeling progress vv v v v
- 22. Uptake FBA Composition Metabolism FBA Composition Transcription Stochastic binding Gene expression Translation Stochastic binding Gene expression Replication Chemical kinetics DNA sequence Solution: integrated models
- 23. 0 25 50 75 100 1970's Coarse-grained ODEs 1990's FBA 2000's Boolean models 2008 iFBA 2012 Whole-cell model %annotatedgenes Whole-cell modeling progress vv
- 26. Model construction 1. Define system 2. Define scope 3. Curate data 4. Choose representation 5. Identify parameters 6. Test predictions
- 27. E. coli M. genitalium Genome 4700 kb 580 kb Genes 4461 525 Size 2 μm × 0.5 μm 0.2-0.3 μm 1. Select a tractable model organism
- 28. Comparative genomics Fraiser et. al, 1995 Genome-wide essentiality Glass et. al, 1999 M. genitalium is well-characterized
- 29. Genomic-scale data Kühner et. al, 2009 M. genitalium is well-characterized
- 30. Genomic transplantation Lartigue et. al, 2009 Genomic synthesis Gibson et. al, 2009 M. genitalium has unique engineering tools
- 31. 2. Choose model scope
- 32. 2. Choose model scope • Explicitly represent each metabolite, gene, RNA, and protein species • Explicitly model the function of every characterized gene product • Account for the metabolic cost of every uncharacterized gene product • Represent important, well-characterized molecules individually
- 33. 3. Broadly curate experimental data Karr et al., 2013
- 34. Uptake FBA Composition Metabolism FBA Composition Transcription Stochastic events Gene expression Translation Stochastic events Gene expression Replication Chemical kinetics DNA sequence Sub-modelsStates 4. Select a flexible mathematical representation Mass, shape Metabolite, RNA, protein counts Mammalian host Transcript, polypeptide sequences DNA polymerization, proteins, modifications FtsZ ring
- 36. DNA RNA Protein Other Replication RepInitiation Supercoiling Condensation Segregation Damage Repair TransReg Transcription Processing Modification Aminoacylation Degradation Translation ProcessingI Translocation ProcessingII Folding Modification Complexation Ribosome TermOrg Activation Degradation Metabolism Shape FtsZ Cytokinesis DNA Replication Rep Initiation Supercoiling Condensation Segregation Damage Repair Trans Reg RNA Transcription Processing Modification Aminoacylation Degradation l, Protein Translation Processing I Translocation Processing II Folding Modification Complexation Ribosome Term Org # Activation Degradation l, # Other Metabolism Shape FtsZ Cytokinesis Many resources are shared
- 37. Many resources are shared
- 39. Mycoplasma model contains 28 sub-models Karr et al., 2012
- 40. Course expertise Modeling • Frank Bergmann • Marcus Krantz • Wolfgang Liebermeister • Pedro Mendes • Chris Myers • Pnar Pir • Kieran Smallbone Curation • Vijayalakshmi Chelliah Standards • Michael Hucka • Falk Schreiber • Dagmar Waltemath
- 41. Karr et al., 2012 Example sub-model: Transcription
- 42. Example sub-model: Transcription Karr et al., 2012
- 43. Free Bound Promoter Bound Active 1. Update RNA polymerase states 3. Bind RNA polymerase 2. Calculate promoter affinities 4. Elongate and terminate transcripts AUGAUCCGUCUCUAAUGUCUAC UTCAACGUGAGGUAAUAAAGUC UCCACGAUGCUACUGUAUC GCCUCAUACUGCGGAU UUACGUAUCAGUGAUCAGUACU Sequence Transcript HcrA SpxFur GntR LuxR glpF dnaJ dnaK gntR trxB polC Example sub-model: Transcription
- 44. •Compare the model’s predictions to data, 𝑦𝑖 •Define an error metric ∑ 𝐸 𝑓𝑖(𝑥; 𝑝) cells,time − 𝑦𝑖 2 •Numerically minimize error • Gradient descent • Scatter search • Simulated annealing • Genetic algorithms 5. Identify parameters
- 45. •Large parameter space •Stochastic model •Large computational cost •Heterogeneous data •Little dynamic, single cell data 5. Identify parameters
- 46. Model reduction enables parameter identification 3. Manually tune parameters using full model 1. Reduce model Time ModelExperiment Molecule Molecule 2. Identify reduced model parameters using traditional methods
- 48. • ODE models • COPASI: copasi.org • V-Cell: nrcam.uchc.edu • Systems biology toolbox • Boolean models • CellNOpt • Flux-balance analysis • openCOBRA: opencobra.sourceforge.net • RAVEN • Integrative models • E-Cell: e-cell.org • Whole-cell: wholecell.org • Standards • SBML: sbml.org • CellML: cellml.org Software
- 52. mRNA, protein copy numbers
- 54. Karr et al., 2012 DNA binding protein collisions
- 55. Karr et al., 2012 DNA binding
- 56. Replication
- 57. Translation
- 58. 60 m mol ATP / gDCW 80 a mol ATP / cell Energy consumption
- 59. v v Karr et al., 2012 Energy consumption
- 61. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 62. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 63. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 64. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 65. Model reproduces observed metabolomics Karr et al., 2012
- 66. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 67. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Model validated by experiments and theoryValidate model against experiments and theory
- 68. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Model validated by experiments and theoryValidate model against experiments and theory
- 69. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 70. Colorimetric growth assay Model predictions Model reproduces measured growth rate Karr et al., 2012
- 71. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Model validated by experiments and theoryValidate model against experiments and theory
- 72. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 73. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 74. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 75. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 76. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 77. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 78. Matches training data Cell mass, volume Biomass composition RNA, protein expression, half-lives Superhelicity Matches published data Metabolite concentrations DNA-bound protein density Gene essentiality Matches new data Wild-type growth rate Disruption strain growth rates Matches theory Mass conservation Central dogma Cell theory Evolution No obvious errors Plot model predictions Manually inspect data Compare to known biology Software stable Simulation code is stable Tests passing Validate model against experiments and theory
- 80. What genomic modifications maximize growth? Time Mass Example: growth optimization
- 81. M. genitalium M. mycoides M. pneumoniae Optimal gene expression
- 82. Optimal architecture retains robustnessOptimal gene expression retains robustness
- 83. Graphical design tool Clotho, TinkerCell, GenoCAD High-level language BioCompiler Biophysical model Whole-cell models, SCHEMA, MD Physical implementation Gibson assembly, TALENs, ZFNs, CRISPR Transplantation Transplantation (if (nutrients) (grow) (sporulate)) Directed evolutionMutate Select Synthetic design landscape
- 84. Karr lab: expanding whole-cell models M. pneumoniae • Expand scope: regulation • Improve accuracy: species-specific data • Enable rational genome engineering • Cell-based drug therapy Human cancer • Colorectal cancer • Personalized models • Precision medicine
- 85. Karr lab: solving important problems Biological discovery Synthetic networks Biological design Drug repositioning Drug toxicity
- 86. Karr lab: developing modeling tools Reconstruction: WholeCellKB Parallelized simulator Parameter estimation Simulation storage: WholeCellSimDB Visualization: WholeCellViz wholecell.org ??
- 87. •How can we model more complex physiology? • Transcriptional regulation • Translational regulation • Stochastic death, failure modes • Higher-order meta-stable states • Resource distribution • Aging • Evolution • Populations •How can we model more complex organisms? • Larger bacteria • Eukaryotes • Multicellularity • Humans •How can we use models to direct engineering? Open challenges
- 88. Whole-cell modeling course 1. Teach whole-cell modeling • Model biological systems • Construct dynamical models • Integrate models 2. Improve implementation • Reusable • Standard • Open 3. Improve methodology
- 89. Data ? Whole-cell models Broadly predicts cell physiology Integrates heterogeneous data and models Guides bioengineering and medicine Knowledge
- 90. • Karr JR et al. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell, 150, 389-401. • Macklin DN, Ruggero NA, Covert MW (2014) The future of whole-cell modeling. Curr Opin Biotechnol, 28C, 111-115. • Shuler ML, Foley P, Atlas J (2012). Modeling a minimal cell. Methods Mol Biol, 881, 573-610. • Joyce AR, Palsson BØ (2007). Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach. Prog Drug Res 64, 267-309. • Tomita M (2001). Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol 6, 205-10. • Surovtsev IV et al. (2009) Mathematical modeling of a minimal protocell with coordinated growth and division. J Theor Biol, 260, 422-9. Recommended reading
- 91. • Thiele I et al. (2009). Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput Biol. 5, e1000312. • Orth JD, Thiele I, Palsson BØ (2010). What is flux balance analysis? Nat Biotechnol, 28, 245-8. • Covert MW et al (2008). Integrated Flux Balance Analysis Model of Escherichia coli. Bioinformatics 24, 2044–50. • Covert MW et al (2004). Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429, 92-6. Recommended reading: FBA
Notas del editor
- Toward this goal we have built a gene-complete, computational model of a single bacterial cell which Integrates all cellular processes into a single computational model, providing a unified understanding of cellular physiology, which Predicts the dynamics of every molecule and process, Can be used to guide experimental design and inform data analysis Hopefully in the future, in concert with emerging genome-scale DNA synthesis techniques, can be used to guide rational engineering of biological systems
- Protein expression: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2778844/figure/F4/
- A central challenge in cell biology is to understand the molecular basis of cellular behaviors, that is: to understand how cellular behaviors arise from the interactions of biomolecules, to understand how cellular processes are controlled and coordinated at the molecular level, to understand how cells allocate limited molecular resources for growth and maintainence, and more.
- Lack of data Time scales Heterogeneity of experimental and computational methods across cell biology subfields Lack of correlated measurements (cell properties measured independtely Measurements are bulk (not single cells) (also obscures correlations, causation)
- Lack of data Time scales Heterogeneity of experimental and computational methods across cell biology subfields Lack of correlated measurements (cell properties measured independtely Measurements are bulk (not single cells) (also obscures correlations, causation)
- Havugimana et al., 2012; Yan et al., 2010; Sachs et al., 2005; Orth et al., 2010
- Lack of data Time scales Heterogeneity of experimental and computational methods across cell biology subfields Lack of correlated measurements (cell properties measured independtely Measurements are bulk (not single cells) (also obscures correlations, causation)
- M. genitalium is a tractable model organism Modular architecture integrates 28 processes Model broadly predicts single-cell physiology Model reproduces previously observed data Model provides insights into complex phenotypes
- M. genitalium is a tractable model organism Modular architecture integrates 28 processes Model broadly predicts single-cell physiology Model reproduces previously observed data Model provides insights into complex phenotypes
- http://www.pbs.org/wgbh/nova/sciencenow/dispatches/images/050707-mgenitalium.jpg Tractable genome 75% annotated Little overlapping function Genomic synthesis Genome-scale datasets
- To maximize the tractability of our model we made a few simplifying assumptions. First, we chose to model Mycoplasma genitalium, the smallest-known freely living organism which is believed to have evolved by a massive degenerative evolution from gram positive bacteria to tractably sized genome of 580 kb containing just 525 genes, 75% of which are functionally annotated.
- To maximize the tractability of our model we made a few simplifying assumptions. First, we chose to model Mycoplasma genitalium, the smallest-known freely living organism which is believed to have evolved by a massive degenerative evolution from gram positive bacteria to tractably sized genome of 580 kb containing just 525 genes, 75% of which are functionally annotated.
- Second, recognizing the modularity of biology and the separation of time scales of biological processes, we built our model by composition enabling us at short time scales to model each cellular process independently, using the most appropriate mathematical representation and experimental data for each cellular process. - Separation of time scales - Choose appropriate representations and parameterizations - Creatively decouple representations Computational reconcile and decouple parameters A module is: Independent physiologic function Independent enzyme complement Internal time scale faster than time scales of interactions with other modules Factorization of state space and transfer functions Module methods: Time evolution Interface to core simulation -- references to metabolites, enyzmes; RNAs, proteins, etc. Resource (energy) requirement during simulation Initial conditions Fit growth rate Expected resource requirements (contribution metabolism objective) Pre-processing, memory allocation Options, parameters, indices, pre-processed data, predicted time courses Plotting, printing, saving, loading
- Our model is then executed by first executing 28 individual models of cellular processes at a 1 s time scale, second integrating the inputs and outputs of the individual models, for example, and finally repeating this process tens of thousands of times across the length of the Mycoplasma genitalium cell cycle.
- Our model is then executed by first executing 28 individual models of cellular processes at a 1 s time scale, second integrating the inputs and outputs of the individual models, for example, and finally repeating this process tens of thousands of times across the length of the Mycoplasma genitalium cell cycle.
- Our 27 models are each based on extensive curation of the literature and are implemented separately using different mathematical representations. For example, we constructed the replication module by first considering the molecular mechanism of replication, interactions between replication and DNA, the enzymes involved in catalyzing replication, and the metabolic resources required for replication. This motivated us to build a model of replication which would account for: The formation of the replication bubble and DnaA complex disassembly at the oriC DNA unwinding, replication bubble progression, and leading strand polymerization toward the terC Discontinuous lagging strand primer and DNA polymerization Okazaki fragment ligation Interactions between DNA polymerase and other DNA-bound proteins and DNA damage Next we built data structures which enable us to represent the specific location and size of all of the replication machinery and other DNA-bound proteins and DNA modifications and damages at each point in the cell cycle. Finally we modeled the initiation, progression, and termination of replication as a set of rules governing the evolution of the state of the DNA, proteins, and metabolites.
- 50 million ATP / cell 80 atto mol ATP / cell 60 m mol ATP / gDCW
- M. genitalium is a tractable model organism Modular architecture integrates 28 processes Model broadly predicts single-cell physiology Model reproduces previously observed data Model provides insights into complex phenotypes
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- 3151 simulations (192 wt, 2959 deletions)
- M. genitalium is a tractable model organism Modular architecture integrates 28 processes Model broadly predicts single-cell physiology Model reproduces previously observed data Model provides insights into complex phenotypes
- A central challenge in cell biology is to understand the molecular basis of cellular behaviors, that is: to understand how cellular behaviors arise from the interactions of biomolecules, to understand how cellular processes are controlled and coordinated at the molecular level, to understand how cells allocate limited molecular resources for growth and maintainence, and more.
- A central challenge in cell biology is to understand the molecular basis of cellular behaviors, that is: to understand how cellular behaviors arise from the interactions of biomolecules, to understand how cellular processes are controlled and coordinated at the molecular level, to understand how cells allocate limited molecular resources for growth and maintainence, and more.