Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (12)
Similar to Formal languages to map Genotype to Phenotype in Natural Genomes
Similar to Formal languages to map Genotype to Phenotype in Natural Genomes (20)
Recently uploaded
Recently uploaded (20)
Formal languages to map Genotype to Phenotype in Natural Genomes
- 1. Formal languages to map Genotype to Phenotype in Natural Genomes Laura Adam GBCB student
- 2. Outline 1. The Genotype to Phenotype (G2P) mapping problem 2. Using formal languages to formalize G2P mapping 3. Implementation in synthetic/systems biology design software to study mutants
- 3. 1. THE GENOTYPE TO PHENOTYPE (G2P) MAPPING PROBLEM G2P= Genotype to Phenotype
- 4. Genotype to Phenotype mapping Genotype • genetic makeup of a cell, an organism, or an individual • specific alleles • inherited Phenotype • observable characteristics or traits – morphology, development, biochemical or physiological properties, behavior, and products of behavior (such as a bird's nest). Definitions from Wikipedia Phenotypes result from: • the expression of an organism's genes • the influence of environmental factors and developmental conditions • the interactions between the two Mapping ?
- 5. The ultimate goal of a G2P map G2P map The Phenotype is X
- 6. Historical perspectives: Mendelian genetics Law of Segregation: “Mendelian genetics, considers traits that are determined completely by individual genes.”
- 7. Traditional G2P mapping is linear • Sui Huang, Rational drug discovery: what can we learn from regulatory networks?, Drug Discovery Today, Volume 7, Issue 20, 15 October 2002 • Peccoud, J., Velden, K. V., Podlich, D., Winkler, C., Arthur, L., & Cooper, M. (2004). The selective values of alleles in a molecular network model are context dependent. Genetics, 166(4), 1715–25. Phenotypes Central dogma
- 8. Current Formalisms Databases: genetic mapping, genome annotation, genotype, mutant, transcripteome, proteome and metabolomic data. Ontologies: Controlled vocabulary for annotation of genes and their products (cellular component, molecular function, biological process) Actually, G2P maps are nonlinear: Gene Networks • Priest, N. K., Rudkin, J. K., Feil, E. J., van den Elsen, J. M. H., Cheung, A., Peacock, S. J., Laabei, M., et al. (2012). From genotype to phenotype: can systems biology be used to predict Staphylococcus aureus virulence? Nature reviews. Microbiology, 10(11), 791–7. doi:10.1038/nrmicro2880 • Benfey, P. N., & Mitchell-Olds, T. (2008). From genotype to phenotype: systems biology meets natural variation. Science. “replacing the linear pathways with interconnected networks.”
- 9. Gene expression mechanisms also matter “The current understanding of the mechanisms of gene expression indicates the importance of nonlinear effects resulting from gene interactions. “ – Peccoud, J., Velden, K. V., Podlich, D., Winkler, C., Arthur, L., & Cooper, M. (2004). The selective values of alleles in a molecular network model are context dependent. Genetics. Trans-regulatory element = gene which may modify (or regulate) the expression of distant genes – Phosphorylation, protein complex, transcription inhibition, etc. Cis-regulatory element = a region of DNA or RNA that regulates the expression of genes located on the same section of DNA – Translation rate depends on RBS and CDS, etc. – Folding alters function and dynamics
- 10. What is missing in current G2P maps? Gene expression mechanisms: the dynamics trans and cis interactions
- 11. 2. HOW TO FORMALIZE G2P MAPPING TO MAKE PREDICTIONS? What formal languages can bring.
- 12. Is “language of life” just a metaphor? Or what insights can we get from computational studies of natural language?
- 13. Natural Language Processing How about a computational linguistics approach to the G2P mapping problem? Like a text, in biology we have a support for information (Genotype), and a meaning (Phenotype) Anaphora as trans-interactions: • “type of expression whose reference depends upon another referential element” • eg: relation noun/pronoun
- 14. Anaphora as trans-interactions • “type of expression whose reference depends upon another referential element”
- 15. Natural Language Processing How about a computational linguistics approach to the G2P mapping problem? Like a text, we have a support for information (Genotype), and a meaning (Phenotype) Anaphora as trans-interactions: • eg: relation noun/pronoun (Mary – she) Inflectional morphology as cis-interactions • eg: subject+verb (+tense)
- 16. Inflectional morphology as cis- interactions
- 17. Natural Language Processing How about a computational linguistics approach to the G2P mapping problem? Like a text, we have a support for information (Genotype), and a meaning (Phenotype) Anaphora as trans-interactions: • eg: relation noun/pronoun (Mary – she) Inflectional morphology as cis-interactions • eg: subject+verb (+tense) Handle context: • Wittgenstein - language-game • He went there.
- 18. We are learning about formal languages Nous apprenons les languages formels (Nosotros) estamos estudiando los lenguajes formales Natural languages and Computers? >> Linguistic universal
- 19. Intuition: Formal languages • <subject> <verb> <object> = (SVO) – A linguistic typology – Could be <subject> <object> <verb> = (SOV) We are learning about formal languages Nous apprenons les languages formels (Nosotros) estamos estudiando los lenguajes formales
- 20. Intuition: Formal language • SVO_sentence Subject, Verb, Object • Object Noun phrase | Relative_clause • Subject “I” | “You” | “He” | “She” | “We’ | “They” • Verb “are learning” | “is learning” • Noun phrase “about formal languages” • Relative_clause “that formal languages are awesome” A grammar is a: Set of rules describing how to form sentences from a language’s vocabulary
- 21. Example: Formal language Object Noun Phrase SVO_sentence Subject Verb Object SVO_sentence Subject We Verb are learning Object Noun phrase about formal languages. A parse tree represents the syntactic structure of a string according to some formal grammar.
- 22. Context free grammar • Terminals =words • Non Terminals = intermediary steps • Rules: – Non-terminals {Terminals and Non terminals} • Start >> The language is the set of all sentences that can be produced Noam Chomsky "father of modern linguistics"
- 23. The repressilator Elowitz, M. B., & Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature, 403(6767), 335-8. doi:10.1038/35002125
- 24. The toggle switch Gardner, T. S., Cantor, C. R., & Collins, J. J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature, 403(6767), 339-42. doi:10.1038/35002131 lacI tetR
- 25. Grammar and Biology? • Pattern to express protein (typology): – <promoter> <rbs> <coding_seq> <ter> <ter> >> Some underlying rules that must govern biology !
- 26. What would a CFG for Biology be like? • “Sentence” to express proteins – Transcription: promoter, terminator – Translation: ribosome biding site • Central dogma: – Cassette: Promoter + RBS + CDS + Terminator
- 27. Example: SynBio CFG Rules • CONSTRUCT CAS | 2CAS | 2CASREV | 3CAS • 2CAS CAS, CAS • 2CASREV CAS, [, CAS, ] • 3CAS CAS, CAS, CAS • CAS PROMOTER, CIX, TERMINATOR • CIX CISTRON | CISTRON, CISTRON • CISTRON RBS, CDS • TERMINATOR TERMINATOR, TERMINATOR
- 28. Example: SynBio CFG Terminals Genetic parts: • PROMOTER placi | ptetr | pci • RBS rbsA | rbsB • CDS laci | tetr |ci | gfp • TERMINATOR t1 | t2
- 29. CONSTRUCT CAS PROMOTER ptetr CIX CISTRON RBS rbsA CDS laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: SynBio CFG Express a gene 1. CONSTRUCT CAS 2. CAS PROMOTER, CIX, TERMINATOR 3. CIX CISTRON 4. CISTRON RBS, CDS 5. TERMINATOR TERMINATOR, TERMINATOR
- 30. CONSTRUCT CAS PROMOTER ptetr CIX CISTRON RBS rbsA CDS laci TERMINATOR TERMINATOR t1 TERMINATOR t2 [ CAS PROMOTER placi CIX CISTRON RBS rbsB CDS tetr CISTRON RBS rbsB CDS gfp TERMINATOR t1 ] Example: SynBio CFG A toggle switch lacI tetR GFP
- 31. And the Phenotype? The meaning • Use of Attribute Grammars • It is a CFG plus: – Terminals and Non-Terminals have attributes – Rules have semantic actions to compute attributes values >> While going through the parse tree, we now also evaluate the semantics (meaning)
- 32. And the Phenotype? The meaning – Transcription: • dna dna + mrna – Translation: • mrna mrna + protein – Degradation mrna: • mrna [] – Degradation protein: • protein [] – Interaction promoter protein: • dna + repressor <-> dna_repressor_x
- 33. Example - Attributes • Promoter: transcription rate, repressor – Promoter(transcription_rate, repressor) ptetr (50, tetr) – Promoter(transcription_rate, repressor) placi (10, laci) • RBS: translation rate – RBS(translation_rate) rbsA (25) – RBS(translation_rate) rbsB (50) • CDS: degradation rate for the protein and the mRNA – CDS(protein_deg,mrna_deg) laci(1,1) – CDS(protein_deg,mrna_deg) tetr(1,1) • Terminator – Terminator t1
- 34. Example: Semantic Actions • CAS PROMOTER(transcription_rate, repressor), CIX, TERMINATOR – Transcription: dna dna + mrna, [transcription_rate] – Interaction: if repressor in construct then dna + repressor dna_repressor_X • CISTRON RBS(translation_rate), CDS(protein_deg,mrna_deg) – Translation: mrna mrna + protein, [translation_rate] – Degradation_mrna: mrna φ, [mrna_deg] – Degradation_protein: protein φ, [protein_deg]
- 35. Semantic DNA Compilation Genetic Design A Get Chemical Equations for A Attribute Grammar
- 36. Semantic DNA Compilation Genetic Design B Get Chemical Equations for B Attribute Grammar
- 43. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS rbsA CDS laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna mrna + protein, [translation_rate] • Degradation_mrna: mrna φ, [mrna_deg] • Degradation_protein: protein φ, [protein_deg]
- 44. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna mrna + protein, [translation_rate] • Degradation_mrna: mrna φ, [mrna_deg] • Degradation_protein: protein φ, [protein_deg]
- 45. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna mrna + protein, [translation_rate] • Degradation_mrna: mrna φ, [mrna_deg] • Degradation_protein: protein φ, [protein_deg]
- 46. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1]
- 47. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1]
- 48. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1]
- 49. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1]
- 50. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1] • Transcription: dna dna + mrna, [transcription_rate] • Interaction: if repressor in construct then dna + repressor dna_repressor_X
- 51. CAS PROMOTER: ptetr ptetr CIX CISTRON RBS: rbsA rbsA CDS: laci laci TERMINATOR TERMINATOR t1 TERMINATOR t2 Example: Toggle switch • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1] • Transcription: dna_ptetr_rbsA_laci dna_ptetr_rbsA_laci + mrna_rbsA_laci , [50] • Interaction: if tetr in construct dna_ptetr_rbsA_laci + protein_tetr dna_ptetr_rbsA_laci _tetr_X
- 53. Toggle switch laci/tetr Get Chemical Equations for A CONST RUCT CAS PROMO TER ptetr CIX CISTRO N RBS rbsA CDS laci TERMIN ATOR TERMIN ATOR t1 TERMIN ATOR t2 [ CAS PROMO TER placi CIX CISTRO N RBS rbsB CDS tetr CISTRO N RBS rbsB CDS gfp TERMIN ATOR t1 ] Example: Toggle switch • Transcription: dna_ptetr_rbsA_laci dna_ptetr_rbsA_laci + mrna_rbsA_laci , [50] • Interaction: if tetr in construct dna_ptetr_rbsA_laci + protein_tetr dna_ptetr_rbsA_laci _tetr_X • Translation: mrna_rbsA_laci mrna_rbsA_laci + protein_laci, [25] • Degradation_mrna: mrna_rbsA_laci φ, [1] • Degradation_protein: protein_laci φ, [1] • Transcription: dna_placi_rbsB_tetr dna_placi_rbsB_tetr + mrna_rbsB_tetr , [10] • Interaction: if laci in construct dna_placi_rbsB_tetr + protein_tetr dna_placi_rbsB_tetr_laci_X • Translation: mrna_rbsB_tetr mrna_rbsB_tetr + protein_tetr, [50] • Degradation_mrna: mrna_rbsB_tetr φ, [1] • Degradation_protein: protein_tetr φ, [1] Attribute Grammar
- 54. Natural language Natural genomes Formal languages Synthetic biology Scaling up to Natural Genomes Building a Yeast Cell Cycle Attribute Grammar: A projection of the Cell Cycle model onto the Genome
- 55. AG ? 1. The syntax 2. The chemical equations
- 56. Genome database – Wild-type 56
- 57. 57 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 I II III IV V VI VII VIII IX X XI XII XIII XIV XV XVI < CLN3 < LTE1 < CDC15 CDC28 > < PDS1 < SWI5 BCK2 > < CDC14 CDC20 > < CDH1 < ESP1 CDC6 > NET1 > MAD2 > SBF > SIC1 > TEM1 > MCM1 > BUB2> < CLN2 < CLB2 < CLB5 #chromosome # bp 22 genes
- 58. Syntax of the yeast cell cycle grammar GENOME MODEL ( CHRI ) ( CHRII ) ( CHRIII ) ( CHRIV ) ( CHRV ) ( CHRVI ) ( CHRVII ) ( CHRVIII ) ( CHRIX ) ( CHRX ) ( CHRXI ) ( CHRXII ) ( CHRXIII ) ( CHRXIV ) ( CHRXV ) ( CHRXVI ) CHRI CHRI_L [CLN3 ] CHRI_M1 [LTE1 ] CHRI_M2 [CDC15 ] CHRI_R CHRII CHRII_L CDC28 CHRII_R CHRIV CHRIV_L [PDS1 ] CHRIV_M [SWI5 ] CHRIV_R CHRV CHRV_L BCK2 CHRV_R CHRVI CHRVI_L [CDC14 ] CHRVI_R CHRVII CHRVII_L CDC20 CHRVII_M1 [CDH1 ] CHRVII_M2 [ESP1 ] CHRVII_R CHRX CHRX_L CDC6 CHRX_M1 NET1 CHRX_M2 MAD2 CHRX_R CHRXI CHRXI_L SBF CHRXI_R CHRXII CHRXII_L SIC1 CHRXII_R CHRXIII CHRXIII_L [TEM1 ] CHRXIII_M1 MCM1 CHRXIII_M2 [BUB2 ] CHRXIII_R CHRXVI CHRXVI_L [CLN2 ] CHRXVI_M1 CLB2 CHRXVI_M2 [CLB5 ] CHRXVI_R
- 59. AG ? 1. The syntax 2. The chemical equations
- 60. Chen’s Model Cell Cycle •150 parameters •>100 mutants •59 ODEs •4 events Chen, K. C., Calzone, L., Csikasz-Nagy, A., Cross, F. R., Novak, B., & Tyson, J. J. (2004). Integrative analysis of cell cycle control in budding yeast. Molecular biology of the cell, 15(8), 3841-62. doi:10.1091/mbc.E03-11-0794
- 62. Rules’ Semantic Actions Trans interactions: • Synthesis of {proteinX} by {proteinY} • synthesis (X, Y, background_synthesis, Y_dependant_synthesis) • Degradation of {protein} • Phosphorylation of {protein} • Dephosphorylation of {protein} • Association of {proteinA} and {proteinB} • Dissociation of {proteinA} and {proteinB} • Degradation of {proteinA) in {proteinB} • {proteinA}/{proteinB} complex formation • {proteinA}/{proteinB} dissociation • … • Growth Events: • Reset ORI • Start DNA synthesis • Spindle checkpoint • Cell division Kinetic laws/functions: • BB • Michaelis-Menten • Mass action1 (1 element) • Mass action 2 (2elements) • Goldbeter-Koshland function
- 63. Semantic – BCK2 example • Definitions: Chemical Equation “ Protein, mass [rate]” – synthesis(Name, Rate):-Write(Name.” = [mass] . ” .Rate). • Rules – ChrV ChrV_L Bck2(B0) ChrV_R, {synthesis (‘Bck2’, B0)} • Parts = alleles – Bck2(0.054)[part_bck2_wt]. – Bck2(0)[part_bck2_ Δ]. 63
- 64. Compile and Get wild type SBML file
- 65. Future: Mutant design • Consider: – What genes are modified? New parts – How biologists make the mutant? New grammar rules – How it relates to the mathematical model? New semantic actions >> We can compute what would be the behavior of new mutants according to the model Phenotype: • Inviable (phase blocked?) • Viable (size at onset of DNA synthesis, size at bud emergence, size at division, and duration of G1 phase?)
- 66. 3. IMPLEMENTATION IN SYNTHETIC/SYSTEMS BIOLOGY DESIGN SOFTWARE TO STUDY MUTANTS GenoCAD.org
- 67. 67
- 68. 68
- 72. 72
- 76. Download the SBML file
- 77. To Switches and Oscillators, Yeast Cell Cycle…and beyond! Working on a workflow for users to define their OWN Attribute Grammar: • Define the syntax • Define your template equations (regular, trans and cis), choose kinetic laws >>parameters 1. Link equations to grammar rules as semantic actions 2. Link parameters to categories 3. Add any cis interaction Attribute Grammars can be a formalism for G2P maps
- 78. Use generated compiler to analyze your designs in GenoCAD Design1 Your project’s grammar AG editor Database Design2 Design mutants Prolog compiler Java (libSBML) SBML Java (libSBML) SBML
- 79. CONCLUSIONS
- 80. Conclusions Semantic models of DNA sequences: – formalize G2P mapping and confer predictive powers with Attribute Grammars: • translate DNA sequences into mathematical models • predicting the phenotype they encode – fill a gap in annotating genetic information by integrating gene expression mechanisms Attribute grammar for the yeast cell cycle: – in a logical and structured fashion, information from genomic databases and mathematical models will be utilized in the exploration of novel mutants – semantic models for natural genomes Genetic design tools user-friendly to the majority and still adaptable to specific projects. – GenoCAD: create libraries of parts, rule-based design and simulation, generation of SBML files – Define your own project’s Attribute Grammars: GUI editor Design mutants in minutes and simulate them!
- 81. Acknowledgements • VBI SynBio Group – J. Peccoud (P.I.) – N. Adames – D. Ball – M. Lux – C. Overend – M. Wilson – and Patrick (Yizhi) Cai – and R. Hertzberg Cai, Y., Lux, M. W., Adam, L., & Peccoud, J. (2009). Modeling structure-function relationships in synthetic DNA sequences using attribute grammars. PLoS computational biology • My PhD committee: Dr. Bevan Dr. Garner Dr. Kepes Dr. Peccoud Dr. Ramakrishnan Dr. Tyson And Dennie Munson!
- 83. Syntactic Limitation 83 The Chomsky hierachy Searls, D.B. “Linguistic approaches to biological sequences.” Bioinformatics 13, no. 4 (1997): 333. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/13/4/333.
- 84. Parsing 84 Left to Right Top-Down Parse The Parse Tree of the Sentence "The boy went home“ Right to Left Top-Down Parse Left to Right Bottom-Up Parse Right to Left Bottom-Up Parse
- 85. Use of attribute grammar in synthetic biology 85 Formal definition Semantic In the synthetic biology context V, a finite set of non- terminals Attributes Parts categories Σ, a finite set of terminals Attributes values Genetic Parts R, a finite relation from V to (VUΣ)* Semantic actions Design Rules S∈V, the start symbol Hard-coded declarations Start