Meetings & Conferences Semantics and Ontologies

RKB: a SW knowledge base for RNA (ISMB Bio-Ont SIG 2009)

Michael Dumontier et al.

Wants to capture the structural features and interactions of RNA. Capture types and their relations, represent dynamic / context-specific knowledge, populate the KB with PDB structural data and MC-Annotate interactions, and answer questions about RNA structure. Looked at textbooks, review articles, book chapters, expert knowledge. Their upper-level ontology (ULO) was NULO, based on BFO/RO.

Contextual modelling of nucleic acids: base stacking varies in different NMR etc models. Then he described the Leontis-Westhof Nomenclature, where you describe the edges of the base as participating in the reaction. So a more sophisticated nomenclature was developed that was based on this called LW+ Nomenclature, where they divvied up the edges into a set of faces.

They want to capture information about residues, edges/faces, cis/trans nucleotide orientations, and across parallel/antiparallel strands. Base stacking involves inter-nucleobase interactions that involve London forces. Wanted to capture both numbers and a description of what is going on. They’ve used two roles: FacingAwayRole and FacingTowardsRole. There is both an endo and exo role for sugar puckering. Situational modeling assures that objects are represented by a single entity throught their lifetime.

RKB is popoulated with PDB and MC-Annotate and it is all represented in RDF. The population involved 3 steps: assigningnames, asserting class membership, and ?. So they can then ask the database things using DL queries. RKB is also accessible via SPARQL.

They’re now working with the RNA Ontology Consortium. They want to publish as part of the Bio2RDF netowkr, and extend the structure description with backbone angles.

NULO: there is a logic mapping bewteen NULO and BFO. They’ve relaxed restrictions where it is unclear what BFO’s stance is. It was unclear if you made certain statements you would still fit in with the idea in BFO.

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Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!

Meetings & Conferences

Programming RNA Devices to Control Cellular Information Processing (BioSysBio 2009)

C Smolke

This talk is more focused on synbio. There are many natural chemicals and materials with useful properties, and it would be great to be able to do things with them. Examples are taxol from pacific yew, codeine and morphine from opium poppies, and butanol from clostridium, spider silk and abalone shell, and the rubber tree. It is much more efficient to get these useful chemicals grown inside a bacterium rather than its natural source. These microbial factories are a useful application area for synbio. Similarly, intelligent therapeutics is another application area for synbio. In IT, two biomarkers together would (via other steps) produce a programmed output. You could link these programs to biosensors, or perform metabolic reprogramming, performed programmed growth and more. The ultimate goal is to be able to engineer systems. These systems generally need to interface with their environment.

Synbio *also* has circuitry, sensors and actuators, just like more traditional forms of engineering has. Foundational technologies (synthesis) -> Engineering Frameworks (standardization and composition) -> Engineered Biological Systems (environment, health and medicine). An information processing control (IPC) molecule would have three functions, as mentioned earlier: sensor, computation (process information from sensor and regulate activity of the actuator), and actuator. There are variety of inputs for sensor (small molecules, proteins, RNA, DNA, metal ions, temperature, pH, etc). The actuator could link to various mechanisms like transcription, translation, degradation, splicing, enzyme activity, complex formation, etc. Key engineering properties to think about are scalability, portability, utility, composability, and reliability.

What type of substrate should we build this IPC systems on? What about RNA synthetic biology? You'd go from RNA parts -> RNA devices -> engineered systems. Experimental frameworks provide general rules for assembling the parts into higher order devices. Then you organize devices into systems, which use in silico design frameworks for programming quantitative device performance. Why RNA? The biology of functional RNAs is one reason: noncoding regulatory RNA pathways are very useful. You can also have RNA sensor elements (aptamers), which bind a wide range of ligands with high specificity and affinity. Thirdly, RNA is a very programmable molecule.

They've developed a number of modular frameworks for assembling RNA devices, and she then gave a good explanation of one of them. In this explanation, she mentions that the transmitter can be modified to achieve desired gate function. The remaining nodes (or points of integration) can be used to assemble devices that exhibit desired information processing operations. A sensor + transmitter + actuator = device. The transmitter component for a buffer gate works via competitive binding between two strands. As the input increases in the cell a particular conformation is favored and gene expression is turned on. An inverter gate is the exact opposite. They wanted to make sure these sorts of frameworks are modular. They can do this by using a different receptor for the sensor to make it responsive to a different molecule.

You can also build higher-order information processing devices using these simpler modular devices. For instance, you might want to separate a gradient of an input signal into discrete parts. Another example would be the processing of multiple inputs, or cooperativity of the inputs.

The first architecture they proposed (SI 1): signal integration within the 3' UTR – multiple devices in series. They can build AND and NOR gates, as well as bandpass signal filters and others. In the output signal filter device, devices result in shifts in basal expression levels and output swing. Independent function is supported by matches to predicted values – the two devices linked in tandem are acting independently.

SI 2: a different type of architecture where signal integration is being performed at a single ribozyme core through both stems. You can make a NAND gate by coupling two inverter gates.

SI 3: Two sensor transmitter components are coupled onto a single ribozyme stem. This allows them to work in series. You can perform signal gain (cooperativity) as well as some gate types. With cooperativity, input A will modulate the second component which allows a second input A to bind to the second component.

Modularity of the actuator domain: using an shRNA switch – this exhibits similar properties to the ribozyme device.

How do we take these components and put them into real applications? One application is immune system therapies, where RNA-based systems offer teh potential for tight, programmable regulation over target protein levels. She had a really nice example of how she used a series of ribozymes to tune t-cell proliferation with RNA signal filters. After you get the right response, you need to create stable cell lines. Showed this working in mice.

Personal Comments: A very clear, very interesting talk on her work. Thanks very much!

Wednesday Session 1

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. I'm happy to correct any errors you may spot – just let me know!

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