1. Lingchong You Modeling T7 life cycle BME 265-05. March 31, 2005

2. Individual appointments (1hr/group) next week Monday: 1pm-6pm Tuesday: 9:30am-11:30am & 1:30- 5:30pm Project report due today!

4. Bacteriophages: landmarks in molecular biology 1939 one-step growth of viruses 1946 Genetic recombination 1947 Mutation & DNA repair 1952 DNA found to be genetic material, restriction & modification of DNA 1955 Definition of a gene 1958 Gene regulation, definition of episome 1961 Discovery of mRNA, elucidation of triplet genetic code, definition of stop codon 1964 Colinearity of gene and polypeptide chain 1966 Pathways of macromolecular assembly 1974 Vectors for recombination DNA technology Source: Principles of Virology. Flint et al, 2000.

5. Applications –Phage therapy (kills bacteria, not animal cells) For review: http://www.evergreen.edu/phage/phagetherapy/phagetherapy.htm http://www.evergreen.edu/phage/phagetherapy/phagetherapy.htm & http://www.phagetherapy.com/ptcompanies.html http://www.phagetherapy.com/ptcompanies.html –Phage display (high-throughput selection of proteins with desired function –Expression systems based phage elements E.g. T7 RNA polymerase (very high efficiency)

6. Phage T7  A lytic virus; infects E. coli  Life cycle ~ 30 min at 30°C  Genome (40kbp), 55 genes, 3 classes (Source: Novagen) RNAse splicing sites T7 RNAP promoters E. coli RNAP promoters

7. Phage T7 life cycle Source: http://icb.usp.br/~mlracz/animations/kaiser/kaiser.htm 1 cycle ~ 30 min at 30 °C

8. T7 genome programs a dynamic infection process Class I Class II Class III Genome T7 RNAP expression, host interference Gene functions host DNA digestion, T7 DNA replication T7 particle formation, DNA maturation and host lysis

9. Example: modeling transcription gene i 1. Compute the number of RNAPs allocated to gene i RNAP 2. Track the level of mRNA for gene i pipi RNAP elongation rate mRNA decay rate constant

10. Transcription (II) Density of EcRNAP allocated to the mRNA Density of T7RNAP allocated to the mRNA Elongation rates of EcRNAP and T7RNAP Decay rate constant of the mRNA

11. Translation Density of ribosome on mRNAs Ribosome elongation rate Decay rate constant of the protein

12.  92 coupled ordinary differential equations and 3 algebraic equations.  50 parameters from literature  host cell treated as a bag of resources. Endy et al, Biotech. Bioeng. 1997 Endy et al, PNAS, 2000 You et al, J. Bact., 2002

14. Simulated versus measured T7 growth (host growth rate = 1.5 doublings per hour) Experimental  Grow E. coli in a rich medium at 30C  Use chloroform to break open cells  Determine intracellular progeny over time

15. Applications of the T7 model – a “digital virus” Effects of host physiology on T7 growth (You et al, 2002 J. Bact.) Quantifying genetic interactions (You & Yin, 2002, Genetics) Design features of T7 genome (Endy et al. 2000. PNAS, You & Yin. 2001, Pac. Symp. Biocomput.) Methods to infer gene functions from expression data (You & Yin, 2000, Metabolic Eng.) Generating data sets for evaluating reverse engineering algorithms?

16. Effects of host physiology on T7 growth — A nature-nurture question Nature (Genome) Nurture (E. coli host) You, Suthers & Yin (2002) J. Bact.

17. How does T7 growth depends on the overall physiology of the host? What host factors contribute most to T7 development?

18. Measuring the dependence of T7 growth on E. coli growth rate (experimental) Cell growth rate  Feed rate Fresh medium Overflow Chemostat  Start infection  Measure T7 growth  Extract rise rate & eclipse time

19. Phage grows faster in faster-growing host cells host growth rate = 0.7 doublings/hr 1.0 1.2 1.7 minutes post infection T7 particles /bacterium Experiments by Suthers

20. Phage grows faster in faster-growing host cells host growth rate (doublings/hour) T7 particles/min minutes rise rate eclipse time Experiments by Suthers simulation simulation with one-parameter adjustment

21. What’s the most important host factor contributing to T7 growth? E. coli growth rate T7 growth  rise rate  eclipse time Bremer & Dennis, 1996 Donachie & Robinson, 1987 host growth rate (hr -1 ) RNAP number RNAP elongation rate Ribosome number Ribosome elongation rate DNA content Amino acid pool size NTP pool size Cell volume correlates determine

22. T7 growth is most sensitive to the host translation machinery Default setting: host growth rate = 1.5 hr -1

23. Summary: effects of host physiology Phage grow faster in faster growing host cells (experiment & simulation) Phage growth depends most strongly on the translation machinery (simulation)

24. Probing T7 “design” in silico (You & Yin, manuscript in preparation) purifying plasmid DNA (http://www.drm.ch/pages/aml.htm) Nature’s “solution” for T7 survival (by evolution) Engineers’ solutions for (by design) producing H 2 SO 4 (http://www.enviro-chem.com)

25. Probing T7 “design” in silico purifying plasmid DNA (http://www.drm.ch/pages/aml.htm) Nature’s “solution” for T7 survival (by evolution) Engineers’ solutions for (by design) producing H 2 SO 4 (http://www.enviro-chem.com) Ideal features: Efficiency Efficiency Productivity Productivity Robustness Robustness

26. Learning from Nature: What’s the rationale of T7 design? How will T7 respond to changes in its parameters or genomic structure? Does the environment play a role?

27. Hypothesis T7 has evolved to maximize its fitness in environments having limited resources minutes post infection T7 particles/cell Fitness definition

28. Two contrasting host environments Unlimited RNAP =  Ribosome =  NTP =  Amino acid =  DNA =  Limited (Cell growth rate = 1.0 hr -1 ) RNAP = 503 Ribosome = 10800 NTP = 5.5e7 Amino acid = 8.7e8 DNA = 1.8 (genome equivalents)

29. Probing T7 design by perturbing… Parameters –Single parameter perturbations –Random perturbations on multiple parameters Genomic structure –Sliding mutations –Permuted genomes Expectation: Wild-type T7 is optimal for the limited environment but sub-optimal for the unlimited environment

30. T7 is robust to single parameter perturbations; the wild type is nearly optimal in the limited environment UnlimitedLimited normalized fitness normalized promoter strengths base case (wild type)

31. Unlimited normalized fitness number of mutants T7 is robust to random perturbations in multiple parameters; the wild type is nearly optimal in the limited environment Limited wt 24 % 5.3 % 50,000 mutants

32. Sliding mutations: move an element to every possible position Toy string: 12341234, 2134, 2314, 2341 T7: 72 variants for each element

33. Sliding gene 1 (T7RNAP gene): wild-type position is optimal in the limited environment gene 1 position (kb) Unlimited Limited normalized fitness 1 wt

34. In the unlimited environment: positive feedback  faster growth promoter T7RNAP Gene 1

35. Negative feedback  robustness T7RNAPgp3.5 + Unlimited environment -

36. Negative feedback  robustness T7RNAPgp3.5 + Limited environment - gp2 EcRNAP + + -

37. Genome permutations 1234 1234 1243 1324 1342 1432 1423 2134 2143 2314 2341 2413 2431 3124 3142 3214 3241 3412 3421 4123 4132 4213 4231 4312 4321 72! = 6x10 103 combinations 24 combinations

38. T7 is fragile to genomic perturbations; the wild type is optimal for the limited environment LimitedUnlimited normalized fitness number of mutants 5 % 100,000 mutants 82% dead83% dead

39. Features of T7 design Optimality –The wild-type T7 is nearly optimal for the limited environment –Optimality especially distinct in the genome structure Robustness and Fragility –Robust to perturbations in parameters, but very fragile to its genomic structure –Negative feedback loops  robustness

40. Quantifying genetic interactions using in silico mutagenesis

41. Genetic interaction between two deleterious mutations genotypewild typemutation amutation bmutations a & b fitness10.80.5? 0.4 = 0.8 × 0.5> 0.4 < 0.4 MultiplicativeAntagonisticSynergistic

42. Genetic interactions among multiple deleterious mutations Power model: log(fitness) = -  n  n: # deleterious mutations synergistic (  > 1) multiplicative (  = 1) antagonistic ( 0<  < 1)

43. Genetic interactions are important for diverse fields Robustness of biological systems (engineering) Evolution of sex (population biology & evolution) But difficult to study experimentally…

44. Difficulties in characterizing genetic interactions experimentally  Obtaining mutants with many deleterious mutations systematically.  Estimating the number of mutations  Accurately quantifying fitness and mutational effects Example: experimental test of synergistic interactions in E. coli: 225 mutants, three data points (too few). (Elena & Lenski, Nature, 1997)

45. Goal: to elucidate the nature of genetic interactions using the T7 model

46. In silico mutagenesis  Select mutation severity  For n (# mutations) = 1 to 30 1. Construct 500 T7 mutants, each carrying n random mutations 2. Compute the fitness (for poor or rich environments) of each mutant 3. Compute the average and the standard deviation of log(fitness) values  Plot log(fitness) ~ n, and fit with power model.

47. Nature of genetic interactions depends on environment poor rich synergistic antagonistic number of mild mutations log(fitness) average of 500 mutants standard deviation

48. number of mutationslog(fitness) poorrich increasing severity increasing severity Nature of genetic interactions depends on severity of mutations

49. Summary: the nature of genetic interactions Environment Severity of mutations Weak interaction Antagonistic interaction Synergistic interaction Weak interaction Mild Severe PoorRich

50. Take-home messages  Existing data & mechanisms at the molecular level can be integrated to create computer models  Such models can serve as “digital organisms”, and facilitate the study of fundamental and applied biological questions.