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CBCComputing with Biology & Chemistry
MSc Natural ComputationDepartment of Computer ScienceUniversiy of York
Module description
To give a foundation in computational systems that are inspired by biological and chemical systems,
or that are themselves bio-chemical systems, or that include a bio-chemical part as an essential
constituent.
This goes from well-understood areas like DNA computing to less mature areas like reaction-diffusion computers, P-systems, H-systems…Unifying theme is their differences from conventional computational systems, both in the approach taken to the computation and to the physical substrate used for the computation.
Who is it aimed at?
Basic computer science experience of algorithms and complexity will be assumedNo biological/chemical background will be necessary – but we will cover some genetics and chemistry very quickly, so be prepared to read a lot!
Level of mathematics required
Ability to follow formal definitions of machines But this is not essential for very much
of the module
Basic complexity theorySome statistics/probability
Content 1: DNA Computing
Starting point is the use of biological macromolecules for computing
How do we represent data?
How do we manipulate it?
Is it efficient?
Image from
http://www.blc.arizona.edu/Molecular_Graphics/DNA_Structure/DNA_Tutorial.HTML
C A T A T A G G C A A T A T C C G T A G
V1,left V1,right V2,left V2,right
Ex,left =Vc1,right Ex,right= Vc2,left
V1,left V1,right
Representing data structures
Each node represented by a 20-mer strandEach possible edge represented by a complementary 20-mer
Chemical logic gates: 3 input OR gate
Controlled inputs are xyz1-input (“clock”) is required to get output in the 000 case (shown)Graph shows response to three patterns (000, 111 and 010). In the latter two cases, there is a single intensity peak in the output.Gradually increasing response is due to “ageing” of the systemMuch more complicated functions can be implemented…
Practical elements
As yet, no lab work with DAN or chemical systemsThere will be seminars Discussions on topics from the module based
around a specified piece of reading
Other possibilities include Simulation of DNA systems Analysis of complex dynamic systems (GRNs) Use of P-system simulators
Assessment
The assessment for the module is openThe assessment will consist of some or all of the following: Demonstration of understanding of lecture
material Selection and application of algorithms to
given datasets Analysis of the output of specific algorithms Review of the literature on a particular topic.