Team:Newcastle University/Original Aims

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{{:Team:Newcastle University/Template:UnderTheHome|page-title=[[Team:Newcastle University/Parallel Evolution|Approach]]}}
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== Parallel Evolution ==
== Parallel Evolution ==
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"An essential prerequisite for large-scale synthetic biology is accurate computational modelling of the biological system of interest.
 
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For small systems, involving few genes and gene products, in-depth knowledge of the biology of the organism may be a sufficient basis
 
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upon which to design new genetic circuits, but larger systems have emergent global behaviours, and usually involve genes and interactions whose biological functions are poorly-understood. Computational modelling is essential for exploring the likely behaviour of large systems, and understanding the likely effects of modification to their components.
 
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Modern high-throughput biology produces large amounts of data, often on a genome-wide scale, which should be valuable in constraining the initial design of computational models of large biological systems. However, such data is generated and stored in many formats in hundreds of large, diverse databases scattered around the world. Much of it does not immediately reach the literature, meaning that it is impractical for working biologists to access all of the data which could be valuable to them. Our current research involves large-scale data integration to automatically collate and update biological data into interactomes and integrated databases, putting large amounts of data in the biologist's hands in an easily accessed and manipulated format. We describe how we extending this research to support synthetic biology, by using data integration to build interactomes to act as a scaffold for constructing and parameterising dynamic models for large biological systems, using approaches based on evolutionary computation."
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There standard practice in biology today, synthetic or not, is to treat bioinformatics as any other tool to achieve an end. A problem is determined, and the biologist works out a possible avenue of exploration. Sometimes, this involves the use of bioinformatics tools, such as BLAST searches or phenotypic trees. Once the method of exploration is established, the biologist rarely goes back to bioinformatics approaches to analyze her results.
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The concept of parallel evolution treats bioinformatics not only as a tool in biology, but as a viable but limited method for exploring a problem. Wet-lab biology is expensive in terms of time, money, and manpower. A single bioinformatician can test the same situation with no equipment other than a computer, and run many iterations of the same experiment within seconds, rather than the weeks that the labs may take.
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However, the bioinformatics is only as good as its simulation. The field expands daily with new information, all of which must be incorporated into the simulation in order for it to give useful results. Much of bioinformatics is backed by hard data from wetlabs. Running an experiment 1000 times is only useful if you know what all the variable are, and what they should be.
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The most sensible approach to the problem of these different yet complementary methods is to play one off of the other, taking advantage of the strengths while minimizing the limitations.
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== Dry-Lab Approach ==
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The two techniques we decided to use in our bioinformatics approach were neural networks and genetic algorithms. Computer science has consistently looked to Mother Nature for inspiration, and the results are best exemplified with these two techniques.
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[[Image:Neuron.png|right|thumbnail|A quick sketch of the anatomy and connection of neurons]]
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Neural networks are a method of machine learning based on neurons in the brain. In a neuron, signals are received at the dendrites. If the combined triggering impulse is higher than a certain threshold, the neuron fires. The impulse travels down the cell membrane of the axon and is transmitted via the axon terminators to the dendrites of the next neuron. When the neuronic pathway is traveled often, the connections are reinforced. The axon of the preceeding neuron and the dendrites of the subsequent neuron grow to be more highly connected. It is in this way that we learn. and why we can change behaviour patterns once they are established. Forcing the impulse to take another route results in an altered behaviour or thought.
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'''Source:''' J. Hallinan, M. Pocock, M. Taschuk and A. Wipat. Data Integration to Constrain Computational Modelling in Synthetic Biology. [http://conferences.theiet.org/biosysbio/ BioSysBio IET 2008]. Poster presentation.
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This same approach is used to teach a computer how to 'think' about a problem. The neurons in the system become nodes. The nodes are arranged in several layers. "Impulses" begin at the input layer, travel through any number of hidden layers, and terminate at the output layer. The output layer gives the computer its learned behaviour.  
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Nodes can be highly interconnected. The connections themselves have a weight, which corresponds to the number of axon-dendrite connections in neurons. Nodes that are connected with a heigher weight are more likely to fire.
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Neural networks must be trained in order to function properly. Untrained neural networks are like newborn babies: cute, but unable to perform any higher functions. They must be taught.
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Revision as of 23:23, 26 August 2008

Bugbuster-logo-red.png
Ncl uni logo.jpg


Newcastle University

GOLD MEDAL WINNER 2008

Home Team Original Aims Software Modelling Proof of Concept Brick Wet Lab Conclusions


Home >> Approach

Parallel Evolution

There standard practice in biology today, synthetic or not, is to treat bioinformatics as any other tool to achieve an end. A problem is determined, and the biologist works out a possible avenue of exploration. Sometimes, this involves the use of bioinformatics tools, such as BLAST searches or phenotypic trees. Once the method of exploration is established, the biologist rarely goes back to bioinformatics approaches to analyze her results.

The concept of parallel evolution treats bioinformatics not only as a tool in biology, but as a viable but limited method for exploring a problem. Wet-lab biology is expensive in terms of time, money, and manpower. A single bioinformatician can test the same situation with no equipment other than a computer, and run many iterations of the same experiment within seconds, rather than the weeks that the labs may take.

However, the bioinformatics is only as good as its simulation. The field expands daily with new information, all of which must be incorporated into the simulation in order for it to give useful results. Much of bioinformatics is backed by hard data from wetlabs. Running an experiment 1000 times is only useful if you know what all the variable are, and what they should be.

The most sensible approach to the problem of these different yet complementary methods is to play one off of the other, taking advantage of the strengths while minimizing the limitations.


Dry-Lab Approach

The two techniques we decided to use in our bioinformatics approach were neural networks and genetic algorithms. Computer science has consistently looked to Mother Nature for inspiration, and the results are best exemplified with these two techniques.

A quick sketch of the anatomy and connection of neurons

Neural networks are a method of machine learning based on neurons in the brain. In a neuron, signals are received at the dendrites. If the combined triggering impulse is higher than a certain threshold, the neuron fires. The impulse travels down the cell membrane of the axon and is transmitted via the axon terminators to the dendrites of the next neuron. When the neuronic pathway is traveled often, the connections are reinforced. The axon of the preceeding neuron and the dendrites of the subsequent neuron grow to be more highly connected. It is in this way that we learn. and why we can change behaviour patterns once they are established. Forcing the impulse to take another route results in an altered behaviour or thought.

This same approach is used to teach a computer how to 'think' about a problem. The neurons in the system become nodes. The nodes are arranged in several layers. "Impulses" begin at the input layer, travel through any number of hidden layers, and terminate at the output layer. The output layer gives the computer its learned behaviour.

Nodes can be highly interconnected. The connections themselves have a weight, which corresponds to the number of axon-dendrite connections in neurons. Nodes that are connected with a heigher weight are more likely to fire.

Neural networks must be trained in order to function properly. Untrained neural networks are like newborn babies: cute, but unable to perform any higher functions. They must be taught.

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