BioControl:Week1

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What is your favorite example of a biological control ______ ? read the paper. think about the question. write down an answer to the question, or some initial thoughts here. Info meeting: Friday Nov 10 presenting: Eric Reading Bintu L, Buchler NE, Garcia HG, Gerland U, Hwa T, Kondev J, and Phillips R. Transcriptional regulation by the numbers: models. Curr Opin Genet Dev. 2005 Apr;15(2):116-24. DOI:10.1016/j.gde.2005.02.007 | Notes: I think for this reading, roughly review the mathematical modelling--I didn't recommend the paper for that purpose. Rather, I'd like to discuss transcription in general, and I think that a simple model is the place to start. think about the questions: why does transcription work? likewise, what other processes might have worked in it's place? which elements are necessay for cellular function and which might be artificts of evolution? what control strategies parallel this organization? how could we make transcption better? -Kelsic Take away equilibrium levels for many trancscriptional regulation schemes can be expressed in similar mathematical form would like to know: can we write down analogues for dynamic processes? transcription separates control over protein number from protein function implements computation with greater efficiency than other processes, by halting production of non-essential products not highly reconfigurable: protein<-->DNA specific - only so many independent signal carriers in natural systems + difficult to engineer new ones useful abstractions from steady-state analysis: identification of system states: multiple equilibria can retain 'cellular memory' network analysis: functional specification can be inferred from network structure via community finding algorithms discussion Eric: memory mechanisms in biology Elisa: transcription and translation based regulation Nathan: T-cells Mary: multi-cellular signaling and decision making (eg, Ron Weiss work) Katie: bacterial chemotaxis Milo: robustness limits in biological systems (ala ??? at Princeton) RMM: my favorite class of examples is biological motion control systems. This has lots of attractive features, including: A cool video Explanations of behavior based on integral gain It ranges from bacterial motion (chemotaxis) to insect motion (ala Michael Dickinson) - can we find a unified set of principles that holds across these scales (ala what we see in mechanical motion control systems, where you can go from MEMS devices to motors to airplanes using the same tools) Some level of separation between sensing, actuation and computation (which I think will be helpful in figuring out how to model interactions between biological components) Seemingly poor performance compared to what engineers like me think they can do