Newcastle University Drylab/22 May 2008

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(Difference between revisions)
(New page: {{:Team:Newcastle_University/DrylabCalendarHeader}} == 22 May 2008 == ===Mark=== Began this morning by finishing off my java program which makes noisy data. The program now takes a file ...)
 
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===Nina===
===Nina===
 +
iGem meeting: Spent today at the meeting, pub and writing up the minutes.. (see minutes).
 +
My personal tasks this week include;
 +
 +
# Google “evolving logic gates for neural network”
 +
# Write very basic java doc for database structure (for record collection)
 +
# Write pseudo code for constraints database.
 +
# Add a “types of parts” compatibility matrix to the “parts” one.
 +
# Meet with Matt (10:00 Tuesday) to go over RDF
 +
# Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus.
 +
 +
Now I will do tasks 1. and 6.
 +
 +
'''Logic gates'''
 +
 +
[http://www.generation5.org/content/2005/NeuralArchitecture1.asp neuralArchitecture]
 +
 +
Logic gates are defined by working out two things:
 +
 +
* the weight values
 +
* the threshold
 +
 +
Weights can be positive or negative and be in the range between -1 and 1.
 +
The threshold will also be assumed to be in the range -1 to 1.
 +
 +
<pre>
 +
A Neural AND Gate
 +
input= [0, 0] output= 0.0
 +
input= [0, 1] output= 0.0
 +
input= [1, 0] output= 0.0
 +
input= [1, 1] output= 1.0
 +
 +
 +
A Neural OR gate
 +
input= [0, 0] output= 0.0
 +
input= [0, 1] output= 1.0
 +
input= [1, 0] output= 1.0
 +
input= [1, 1] output= 1.0
 +
 +
 +
A Neural NAND gate
 +
input= [0, 0] output= 1.0
 +
input= [0, 1] output= 1.0
 +
input= [1, 0] output= 1.0
 +
input= [1, 1] output= 0.0
 +
 +
 +
A Neural NOR gate
 +
input= [0, 0] output= 1.0
 +
input= [0, 1] output= 0.0
 +
input= [1, 0] output= 0.0
 +
input= [1, 1] output= 0.0
 +
</pre>
 +
 +
In fact, it can be shown that all the logic functions can be implemented with one type of gate: either all NAND gates or NOR gates. As an example, consider our first example, the simple AND gate. This could be used to digitally perform an operation on bits, but it can also be used to perform a logical AND operation on two rules, for example: IF rule X AND rule Y THEN fire (output is active). Furthermore, by using neurons, the inputs are not at all limited to binary signals, they could be analog signals (rule X is sort of true) or even the output of an entire neural network.
 +
 +
The neural architecture approach can be seen to have the following key advantages or possibilities:
 +
 +
* Can develop structures built on a universal neuron building block.
 +
* The same basic building block can be considered as a neuron (standard perceptron) or a logic gate.
 +
* The approach allows crisp structures to interface with fuzzy ones, combining the nondeterministic and evolutionary properties of neural networks along with the advantages of explicit, structured design.
 +
* Can develop recognizable higher level building blocks, a important requirement for realizing continuously more complex architectures.
 +
 +
Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus
 +
 +
“The expression of various chromosomal genes encoding extracellular factors (i.e., phospholipases C, proteases, cell wall proteins, enterotoxins and hemolysins) is activated by a pleiotropic regulator, PlcR, that is specific to the B. cereus group. The activity of PlcR depends on the presence of PapR, a small signaling peptide that acts as a quorum-sensing effector. PapR is exported by the bacterial cell, processed, presumably as a pentapeptide, and then reimported into the cell, where it interacts with PlcR to facilitate its binding to its DNA targets. This activating mechanism is strain specific, and this specificity is determined by the first residue of the pentapeptide.”
 +
 +
(Slamti et al., 2005) See references from 19/05 entry.
 +
 +
 +
# Google “evolving logic gates for neural network
 +
# Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus.

Latest revision as of 15:55, 13 October 2008

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22 May 2008

Mark

Began this morning by finishing off my java program which makes noisy data. The program now takes a file in containing the relevant perfect data, and produces some nice noisy data to prevent underfitting of the network. This is given in an output file. The exercise has been useful both for proof of concept, and helping me with my Java skills and has some good value for using neural network software. Have also decided to add to this week's agenda by adding a discussion on what programs to practice Java coding, providing that they are relevant to the topic.

Matt came in at 11 and helped me finish off my program. Note to self - there is a programmer in there somewhere, its just actually learning how to get the stuff down on paper that is the problem at the moment. Need more practice. Matt went through the svn system with everyone (including some non-iGEM intruders) and we used my system that we got running yesterday as an example of how the svn system actually works. It was developed for open source code sharing.

At 12 Marcus Kaiser came in and went through with each of us where we are with out projects. I gave him a working demo of my neural net and new java doc software, and oulined my plan for the coming weeks as well as talked about the Biological concept.

Just going on again to looking through the Sourceforge database for relevant programs and structures. Prior to the meeting at 2 30 we should also have all our biological concept stuff nailed down and ready to discuss.

We are also just about to do a bit of last minute Marketing brainstorming.

Had the meeting at 2. 30 and was very productive. Aims for the week are to look in greater depth at the proteins involved in the Listeria and Staphylococcus sytems, and to start writeing an evolutionary algorithm that maps input nodes to output nodes, and evaluates fitness based on the highest number of connections between those nodes.


Megan

Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus

The expression of various chromosomal genes encoding extracellular factors (i.e., phospholipases C, proteases, cell wall proteins, enterotoxins and hemolysins) is activated by a pleiotropic regulator, PlcR, that is specific to the B. cereus group. The activity of PlcR depends on the presence of PapR, a small signaling peptide that acts as a quorum-sensing effector. PapR is exported by the bacterial cell, processed, presumably as a pentapeptide, and then reimported into the cell, where it interacts with PlcR to facilitate its binding to its DNA targets. This activating mechanism is strain specific, and this specificity is determined by the first residue of the pentapeptide.

(Slamti et al., 2005)


Morgan

Thinking about the whitespace legend thing in more detail, I think I have some coordinates skewed, with an x and y switched or something. Going to look for that now.

Aha, it was indeed just the width and the height switched. Fixed and commiting now.

Kind of made a cursory README file for the project. I have old libraries in there that I really should delete, but there you are.

Neil said that the workbench should be able to infer connections between parts. I thought maybe that when a part was dropped, connections could be suggested, which the user can confirm or deny.


Nina

iGem meeting: Spent today at the meeting, pub and writing up the minutes.. (see minutes). My personal tasks this week include;

  1. Google “evolving logic gates for neural network”
  2. Write very basic java doc for database structure (for record collection)
  3. Write pseudo code for constraints database.
  4. Add a “types of parts” compatibility matrix to the “parts” one.
  5. Meet with Matt (10:00 Tuesday) to go over RDF
  6. Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus.

Now I will do tasks 1. and 6.

Logic gates

neuralArchitecture

Logic gates are defined by working out two things:

  • the weight values
  • the threshold

Weights can be positive or negative and be in the range between -1 and 1. The threshold will also be assumed to be in the range -1 to 1. 

A Neural AND Gate
input= [0, 0] output= 0.0
input= [0, 1] output= 0.0
input= [1, 0] output= 0.0
input= [1, 1] output= 1.0


A Neural OR gate
input= [0, 0] output= 0.0
input= [0, 1] output= 1.0
input= [1, 0] output= 1.0
input= [1, 1] output= 1.0


A Neural NAND gate
input= [0, 0] output= 1.0
input= [0, 1] output= 1.0
input= [1, 0] output= 1.0
input= [1, 1] output= 0.0


A Neural NOR gate
input= [0, 0] output= 1.0
input= [0, 1] output= 0.0
input= [1, 0] output= 0.0
input= [1, 1] output= 0.0

In fact, it can be shown that all the logic functions can be implemented with one type of gate: either all NAND gates or NOR gates. As an example, consider our first example, the simple AND gate. This could be used to digitally perform an operation on bits, but it can also be used to perform a logical AND operation on two rules, for example: IF rule X AND rule Y THEN fire (output is active). Furthermore, by using neurons, the inputs are not at all limited to binary signals, they could be analog signals (rule X is sort of true) or even the output of an entire neural network.

The neural architecture approach can be seen to have the following key advantages or possibilities:

  • Can develop structures built on a universal neuron building block.
  • The same basic building block can be considered as a neuron (standard perceptron) or a logic gate.
  • The approach allows crisp structures to interface with fuzzy ones, combining the nondeterministic and evolutionary properties of neural networks along with the advantages of explicit, structured design.
  • Can develop recognizable higher level building blocks, a important requirement for realizing continuously more complex architectures.

Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus

“The expression of various chromosomal genes encoding extracellular factors (i.e., phospholipases C, proteases, cell wall proteins, enterotoxins and hemolysins) is activated by a pleiotropic regulator, PlcR, that is specific to the B. cereus group. The activity of PlcR depends on the presence of PapR, a small signaling peptide that acts as a quorum-sensing effector. PapR is exported by the bacterial cell, processed, presumably as a pentapeptide, and then reimported into the cell, where it interacts with PlcR to facilitate its binding to its DNA targets. This activating mechanism is strain specific, and this specificity is determined by the first residue of the pentapeptide.”

(Slamti et al., 2005) See references from 19/05 entry.


  1. Google “evolving logic gates for neural network
  2. Find out for sure whether B. subtilis has the PapR/PlcR system like B. cereus.
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