View source for BFS GOTChAs, April 2010

This page contains some [[GOTChA Chart|GOTChA charts]] for possible rotation projects in the lab this summer. __NOTOC__

== Device Projects ==

The following GOTChAs are for projects that involve building new device technologies that can be used to push biological circuit design forward.

=== Integrated Load and Context Compensation (RMM) ===
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==== Goals ====
* Design a transcriptional regulator that uses feedback to compensate for changes in load and changes in context and thus provides consistent performance independent of downstream loading or cellular context (cell strain, growth media)
* Example: <nowiki>A -|  B</nowiki>, designed so that transfer curve is maintained across changes in downstream reactions involving B, cell strain, growth media
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==== Technical Challenges ====
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==== Objectives ====
* Characterize the performance of existing transcriptional regulators across 3 cell strains, 3 growth media and 3 loading conditions.
* Design a compensation circuit that measures output level and regulates device performance to compensate for uncertainty
** Idea: use RNA-based feedback in 5' UTR sequences to measure transcription and compensate
** Note: feedback dynamics should be 5-10X regulatory dynamics to allow for use in non-equilibrium circuits
* Measure the performance of one or more compensation circuits and compare to existing regulators
* Regulation circuitry should be "internal" to device, so that inputs and outputs are the same as traditional regulator
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==== Approach ====
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=== Fast Mechanisms for Biomolecular Feedback (RMM) ===
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==== Goals ====
* Design a set of devices for implementing feedback circuits that have timescales measured in seconds instead of minutes
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==== Technical Challenges ====
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==== Objectives ====
* Identify the mechanisms and timescales of various feedback mechanisms in natural systems
** Example: feedback mechanisms involved in chemotaxis
* Use models to estimate the bandwidth of regulators based on transcriptional feedback, post-transcriptional feedback, translational feedback, post-translational feedback, and covalent modifications
* Formulate a test setup that can be used to characterize the speed of response of one or more circuits using different mechanisms
* Implement a fast-feedback mechanism using one of the following methods:
** RNA-based feedback using anti-sense interference
** RNA-based feedback using secondary structure
** Programmable scaffolds for phosphorylation cascades
* Measure the response speed of one or more regulation mechanisms and demonstrate seconds-timescale response
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==== Approach ====
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=== Cell Division Tracker (ES) ===
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==== Goals ====
* Design a genetic construct that changes state upon cell division (i.e. Cells start expressing GFP, after cellular division cells now express YFP)
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==== Technical Challenges ====
* Frequency of recombinases not well characterized
* Might need to use a combination between two irreversible recombinases or a reversible recombinase and an irreversible one
* If recognition sequences are not compatible may need to evolve recombinases to recognize different sequences that are compatible
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==== Objectives ====
* Characterize different recombinases
** (example: reversible vs. irreversible, frequency of recombinase activity)
* Find a cell-cycle dependent promoter to use
* Organize recognition sites and proteins of interest in an operon
* Test the operon in cells (E coli or Yeast)
* Measure system characteristics such as % errors 
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==== Approach ====
* Use recombinases
** The simplest approach would be to use a reversible recombinase (like fin and hin [http://jb.asm.org/cgi/content/full/188/3/950]) under a cell cycle dependent promoter
** If we see too many double inversions need to device a more complex mechanism using a combination of recombinases
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=== Stoichiometric Protein Expression (JTM) ===
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==== Goals ====
* Design a modular genetic construct that allows proteins to be maintained at a precise relative stoichiometry 
** Allowing variable absolute expression, perhaps?
** A faculty member here once said that this might be useful for membrane protein crystallography
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==== Technical Challenges ====
* Western blots/ELISA are time-consuming, require certain equipment
* Expression may vary too much for modularity to work
* Variable degradation rates may make this difficult
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==== Objectives ====
* Review existing literature
* Investigate gene fusions and degradation control
** Are there paths in degradation that could connect the rate of degradation to expression in a specific way?
** Could some signaling peptide be integrated into the protein such that it's released upon degradation via a non-processive protease?
** For a 1:1 stoichiometry, one could imagine simply fusing the two proteins and then also expressing a protease to separate them after translation
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==== Approach ====
* TBD
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=== Use of photoactive proteins to access multiple time-scale control (JTM) ===
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==== Goals ====
* Photoactive yellow protein (PYP) has multiple states, actuated by light
** Upon being hit by a photon of appropriate energy, a picosecond-scale transition occurs, followed by a millisecond-scale reversible photobleaching, then a second-scale reversion.
** There are fast-recovery and slow-recovery PYPs [ref:http://pubs.acs.org/doi/full/10.1021/bi020690e]
** PYP typically mediates negative phototaxis
* Want to take Ppr-PYP (a PYP attached to a histidine kinase from Rc. centenum) and...
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==== Technical Challenges ====
* TBD
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==== Objectives ====
* TBD
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==== Approach ====
* TBD
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=== Manipulation of the integral controller in photo/chemotaxis (JTM) ===
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==== Goals ====
* Substitute heterologous che-like signal transducers between organisms such as H. salinarum, E. coli, and others
* Observe preservation or destruction of integral controller after recombination and after evolution
* TBD
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==== Technical Challenges ====
* TBD
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==== Objectives ====
* Put che-like transducers in mutator plasmid and observe recovery of integral control if damaged after recombination
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==== Approach ====
* TBD
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== Robustness Projects ==

=== CAGEN (RMM) ===
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==== Goals ====
* Design, synthesize and characterize a circuit that provides robust transcriptional regulation
* Win the 2011 [[CAGEN]] competition
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==== Technical Challenges ====
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==== Objectives ====
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==== Approach ====
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=== Consistent Gene Expression Level (ES and JTM) ===
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==== Goals ====
* Design, synthesize and characterize an inducible gene circuit that will express a constant level of a protein regardless of inducer concentration
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==== Technical Challenges ====
* Might have issues designing an ACR circuit synthetically
* Getting an inducible circuit might be difficult, may start with the simpler problem of just expressing a protein at a constant level
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==== Objectives ====
* Design a circuit that exhibits a constant concentration of gene of interest (probably an xFP at steady state) we might be able to use ACR theory [http://www.sciencemag.org/cgi/content/abstract/327/5971/1389?ijkey=c777f236d838b71911625aae954a625090cf51ca&keytype2=tf_ipsecsha]
* Construct and characterize designed circuit
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==== Approach ====
* Incorporate feedback into current inducer systems, possibly tie into a gene that is known to be tightly controlled
* Design theoretical circuit, then use canonical examples of reactions
* Try the a fusion protein with the circuit that is tightly regulated in paper as a crutch
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=== State Transition Modeling in Chemical Reaction Networks (JTM) ===
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==== Goals ====
* Develop an enumeration algorithm to find distinct "states" for a chemical reaction network
* Look at transition criteria between states
* Test analysis on simple circuits and biological networks
* Use transition criteria to develop robustness metrics
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==== Technical Challenges ====
* Not immediately clear how to classify/cluster output
* Not sure how to deal with continuous relationships vs. more discrete relationships (thresholding?)
* May be useful to use analytical techniques () in addition to blind sampling
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==== Objectives ====
* Program a library of well-characterized reaction circuits (bi-stable flipflops, oscillators, etc)
* Demonstrate proper time-evolution of circuits via numerical analysis
* Run each circuit with a variety of initial conditions and mid-time-course perturbations
* Cluster end states, evaluate quality of clustering, and match to known circuit behavior
* Measure transition criteria between states
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==== Approach ====
* Attempt naive exhausive approach to evaluation - sweep through all plausible initial conditions and cluster results (fuzzy c-means?)
* Attempt theorem/network solver approach to evaluation - sweep through initial conditions and use Bayesian network analysis and/or theorem postulators (eureqa) to enumerate states
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