Sunday 09/15/13

Shvartsman

• Bomyi lim (grad student)

Intro

• Michaelis Menten / invertase. disrupted last bastion of vitalism
• did not know what enzyme was, could not obseve ES complex, proof was purely kinetic
• enzymes modyifing proteins may have less specificity — region bound in active site occurs in mulitple proteins.
• crowding, compartments etc, too much too reconstitute in vitro. use in vivo prep.

ERK

• MEK only acts on ERK
• ERK acts on 100s of substrates
• pERK used repeatedly

early embryo ERK cc13 end through cc14 (terminal to ind pattern)

• local ligand activates ERK, represses Cic, repressing hkb and ind.
• Sokac calibration curve for membrane length to time
• only one ligand (RHo is important (average profile is the same. Can you compare the variation in Vn mutants)
• space time kinetics should be cubic. Fits data. Receptors (t) * ligand (t) * integral
• use snail mutant to expand vn pattern, no change in slope of regular pattern –> not much diffusion
• could have ordered in time by amplitude of leading mode, to reconstruct dynamics from snapshots without having direct time calibration.

Elisa Franco

• control theorist
• in vitro synthetic biology

synthetic oscillator in microdroplets

• in vitro synthetic oscillators
• why DNA? quantitive models go from sequence to structure to binding specificity. can do this ways.
• Toehold replacement (9 bp displacement)
• use toehold replacement to remove nicked short strand compliment to T7 promoter (making in double stranded)
• use RNase H to degrade the RNA release ‘activator’
• fluorophore-quencher pairs to measure ON vs OFF promoters. RNA is the gene product of the transcription

what is the source of variability

• Gillespie (want noise). Does not recapitulate period vs. radius variation.
• enzyme partitioning noise? use Poisson (is this appropriate for partioning?, not binomial)
• assume gamma distribution
• not measurement noise
• rise time is slower in vortexted solutoin, not strong ffect of radius
• In trying to fit the variation ignore the degeneration of the system. Might imagine that systems that get more enzyme fade faster and

Jane Knodev

• few mm of DNA in yeast, somehow folded
• students do experiments, Jane does theory
• what are the rules for folding? (for later). Does folded state affect function?
• yeast involves very simple rules of folding — random walk polymers. Probability of contact as a function of distance scales between -1.5 and -1.
• polymer linker length = Lk~100nm N steps separation <r^ = N Lk^2. length scales with L^1/2, L^3/2 for volume contact data
• Experiment setup: lacO repeats + centromere marker.

chromatin organization

• telomeres attached to nuclear perifery at random
• all chromosomes thethered at centromere by SPB (16 chromosome). Tethering points at middle and ends, random walk chromatin in between.
• nucleolus (ribosome RNA) is volume excluded region.
• only free parameter is 200 nm (z-precision is 100nm). Can you do astigmatism?
• 10 nm fiber — “definetely no 30 nm fiber in a yeast).
• can’t have fixed telomere location.

experiments

• Effect of untethering — see expected tightening of distribution of inter-spot distance.
• why yeast are unique random walk? Human chromosomes too large to equilibrate.
• diffusion scaling (.5 instead of 1 for MSD um^2) or random polymer.

mating switching

• Program cut in the MAT locus (specifies mating locus)
• homologous recombination with silenced genes on either side of the mating locus
• 90% of the time use the new one (which is further away) 200 kb aay
• should be some tether 20 kb away. Tether needs to appear after the break.
• recombinant enhancer “targeting element — binds something near double stranded break
• measure distance between loci.
• can estimate the ‘stickiness’ of the data.
• can you estimate the time for the leash to shorten and how does this compete with the shorter distance.

Zimmer

• deterministic inference for stochastic models
• deterministic model can not independently estimate birth and degradation rate from observations at steady state. But with stochastic data can ID a qualitative difference
  can we use this for improved parameter estimation.

setup

• observed and unobserved species. Use likelihood function
• chemical master equation of states
• complete system of with of order 12,000 protein copy molecules becomes very computationally inhibitory
• Mean is approximated by the solution of the ODE on the last measurement. Assume variance is constant
  inaccuracy in estimating the variance only effects the uncertainty of parameter estimation, it does not bias the value.
• test with simulation studies. Additive Gaussian measurement noise, show uncertainty increases
• test partial observation also reasonable
• resolves identifiability problems (requires sufficient data).
• how robust is this to the structure of underlying model?

Georg Seelig

• ‘trained as a physist’. now a renaissance man
• what is a miRNA? — folds as hairpin, interacts with protein machinery as negatie regulatory of complementary RNA (6-7bp compliment)
  play a role in stress responses, providing robustness to gene expression.

setup

• RFP with intronic miRNA that targets its own UTR (incoherent feed-forward motif. Capable of biochemical adaptation and noise supression, See Belris et al MSB 2011 and Bosia et al BMC Sys Bio 2011).
• is this a good system for stable transgene expression?
• can we better understand these biological mechanisms

Experimental results

• mRNA does show perfect adaptation.
• is the adaptive behavior robust to competitive targets
• self targeting system has different initial responses but response level quickly converges to give the same output despite different strengths of input. —
• A nice way to get flat expression response without saturation