User:Lance Martin: Difference between revisions
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[[Image:Inversion.jpg|thumb|right|A biological bit]] | [[Image:Inversion.jpg|thumb|right|A biological bit]] | ||
===Basic requirements=== | ===Biological computation=== | ||
*Reliably holds state | *Where are the parallels? | ||
*Controllable state change | **DNA=memory | ||
* | **RNA=machine code | ||
**RNAp=CPU | |||
**So what? | |||
*[http://www.citeulike.org/user/dylan77/article/1281370 Arkin: computation in biochemical networks] | |||
*[http://www.pnas.org/content/89/1/383.abstract Chemical implementation of finite state machine] | |||
*[http://www.pnas.org/content/88/24/10983.abstract Chemical implementation of Turing machine] | |||
===Basic requirements for memory & logic=== | |||
*Big picture | |||
**Reliably holds state | |||
**Controllable state change | |||
*Then, degenerates into many application-specific requirements | |||
**What are the applications for memory and logic in biological systems? | |||
**How do naturally evolved mechanisms break down between combinatorial and sequential logic? | |||
**Need a chart listing all mechanisms with associated cellular applications, requirements, timescale ... | |||
===Systems=== | ===Systems=== | ||
Revision as of 18:46, 23 March 2009
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Electronic memory & logic devices
Devices
- Transistors
- Logic gates
- Latches
- D-type flip-flops
- JK flip-flops
Summary presentation
Media: Electronic_Memory_&_Logic_Devices.ppt
References
Horowitz & Hill Electronics tree Very strong overview of circuits: DC, AC, Semi-conductors, Digital
Electronic counters & system architecture
Architectures
- Cascade
- Asynchronous
- Synchronous
Summary presentation
References
Native biological memory & logic
Biological computation
- Where are the parallels?
- DNA=memory
- RNA=machine code
- RNAp=CPU
- So what?
- Arkin: computation in biochemical networks
- Chemical implementation of finite state machine
- Chemical implementation of Turing machine
Basic requirements for memory & logic
- Big picture
- Reliably holds state
- Controllable state change
- Then, degenerates into many application-specific requirements
- What are the applications for memory and logic in biological systems?
- How do naturally evolved mechanisms break down between combinatorial and sequential logic?
- Need a chart listing all mechanisms with associated cellular applications, requirements, timescale ...
Systems
- Recombination
- Types of enzymes
- Kinetic modeling recombinase mechanism
- DNA methylation
- Others?
- Feedback loops
- Bi-stable networks
Design of engineered biological systems
Basic construction / design principles
- Summary of reviews by
- Voight
- Endy
- Arkin
Computational modeling to aid design
- Review of
- Collins toggle switch
- Elowitz repressilator
Past engineered biological memory & logic devices/systems
Of particular interest to us
- Ham & Arkin inversion switch
- Harvard/BU 2004 iGem Int/Xis inversion switch & counter
- Their final presentation
- My notes on this work
- DNA methylation switch
My projects
Gemini
- Summary
- synBERC poster
- Current focus
- What is the unique application for compact (LacZa-GFP) dual reporter?
- What is the dynamic range (transfer function) for the LacZa-GFP fusion construct?
- Sequence: understand what we have.
- Determine method to modulate PoPS input (use the existing, different promoters or build with inducible promoter).
- Set up assays (plate reader for GFP and beta-gal)
- How does this compare to full length LacZ-GFP fusion, GFP, and LacZ?
- With information from the above in hand, determine additional work necessary to make genetically identical constructs (same promoter, RBS, reporters – from Meagan)
Modeling recombinase-driven genetic counters
- What are the key questions that we want a model to help us answer?
- What is counter's dynamic behavior across a range of parameter settings within both asynchronous and synchronous system architectures?
- Which architecture is more reliable (exhibits "robust" counting) across the range of parameters?
- What do we need to know in order to build a model that answers our questions?
- Desired dynamic behavior of our system (e.g. counting within cell division timescale, etc)
- How much do we need to know about flipee performance (e.g latency, transfer function, etc)?
- Defined state variables (e.g. recombinase mRNA/protein, excisionase mRNA/protein, the bits, etc)
- Defined parameters that describe dynamic behavior of the state variable
- (e.g. gene expression rate, recombinase-DNA association and dissociation rates, etc)
- Lay out model architecture and build it
- parameters from mathematical model for recombinase kinetics provide foundation
- iGem 2004 model serves as an example and provides additional foundation
