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CS4030: Biological Appications of Computing Science (BioComputing): Introduction & Overview George M. Coghill

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Presentation on theme: "CS4030: Biological Appications of Computing Science (BioComputing): Introduction & Overview George M. Coghill"— Presentation transcript:

1 CS4030: Biological Appications of Computing Science (BioComputing): Introduction & Overview George M. Coghill

2 Structure of Course Lectures –(Wednesday @ 9 & Friday @ 9): –Weds: Taylor A21; Fri: Kings NK14 Practicals (Friday @ 13:00): –in Room Meston 204, 2 hours per week –Attendance mandatory –Only CS4030 work to be done during this time: attendance credited only in this case.

3 Assessment 75% from a 2 hour examination in January; the paper will consist of three questions - candidates have a free choice of two from three. 25% from continuous assessment

4 Reading List Bioinformatics and Model-based Technology Recommended: Krane D E & Raymer M. L. Fundamental Concepts of Bioinformatics. Benjamin Cummings, 2002. (Library) May also be consulted: Kuipers B. J. Qualitative Reasoning, MIT Press, 1994 Evolutionary Computing May be consulted: Mitchell T. Machine Learning (ch 4 & 9) plus web based material.

5 Attendance You are expected to attend all the lectures. The lecture notes (see below) cover all the topics in the course, but these notes are concise, and do not contain much in the way of discussion, motivation or examples. The lectures will consist of slides (Powerpoint and possibly OHP transparencies), spoken material, and additional examples given on the blackboard. In order to understand the subject and the reasons for studying the material, you will need to attend the lectures and take notes to supplement lecture slides. This is your responsibility. If there is anything you do not understand during the lectures, then ask, either during or after the lecture. If the lectures are covering the material too quickly, then say so. If there is anything you do not understand in the slides, then ask. In addition you are expected to supplement the lecture material by reading around the subject; particularly the course text.

6 What is BioComputing? For the purposes of this course: 1.The use of computational methods to solve biological problems (bioinformatics and systems biology). 2.The development of novel compuational methods inspired by biological processes.

7 Breakdown of the Course Bioinformatics: –Including: data searches and pairwise allignment Model-based Technology: –Including: constraint based reasoning and model learning Biologically Inspired Computing: –Including: neural nets, genetic algorithms and artificial immune systems.

8 What is Bioinformatics? Computational Biology Bioinformatics Genomics Proteomics Functional genomics Structural bioinformatics

9 What is Bioinformatics? DNA (and RNA)Proteins

10 Over time, genes accumulate mutations Environmental factors Radiation Oxidation Mistakes in replication or repair Deletions, Duplications Insertions Inversions Point mutations

11 Protein Folding

12 Why is Bioinformatics Important? Applications areas include –Medicine –Pharmaceutical drug design –Toxicology –Molecular evolution –Biosensors –Biomaterials –Biological computing models –DNA & RNA computing

13 Biologically Inspired Computing Neural Nets Evolutionary Computing –Genetic Algorithms, Genetics Programming etc. Artificial Immune Systems Particle Swarm Optimisation Ant Colony Optimisation


15 xnxn x1x1 x2x2 Input (visual input) Output (Motor output) Four-layer networks Hidden layers

16 Genetic algorithms Variant of local beam search with sexual recombination.

17 Genetic algorithms Variant of local beam search with sexual recombination.

18 Lecture 1CBA - Artificial Immune Systems Multiple layers of the immune system

19 Lecture 1CBA - Artificial Immune Systems Clonal Selection

20 QML-CSA: Clonal Selection Algorithm selection Antibody repertoire Selected Antibodies proliferation cloned Antibodies matured Antibodies Affinity Mature Hyper-mutation Selected Antibodies Reselection Random Antibodies Update Repertoire Memory cell An Evolutionary Algorithm Inspired by the clonal selection principle of immune system Using hyper-mutation and re-selection instead of crossover and mutation.

21 Model-based Technology Qualitative Reasoning –Symbolic, using no numbers –Structural though incomplete –Synonyms: Naive physics, Qualitative modelling, Qualitative simulation, Commonsense reasoning, Deep knowledge. Developments –Use of any models in the domain reasoning process –Numerical, Interval, Semi-quantitative, Fuzzy, Qualitative, Rule-based, Procedural

22 Motivations Problems with RBS –Reasoning from First Principles –Dangers with nearest approximation Second Generation Expert Systems –Use deep knowledge –Provide explanations of reasoning process Commonsense reasoning –Capture how humans reason –Enable use of appropriate causality Model reuse –Improved ease of ES maintenance

23 Models and Inference Learning Engine Input Data BehaviourModel Inference Engine Input Data BehaviourModel

24 24 Qualitative Modelling Behavioural Abstraction

25 25 Qualitative Analysis ?? x 1 f 10 Δx = u – f 10 Time x1x1 {+,0} {+,+} {+,-} {0,+} u is steady & positive, how will x and f 10 change? Qualitative Prediction Quantitative Prediction {+,-} magnitude Rate of change 1 u f 10 =k 10.x 1 x 1 = u – f 10 f 10 = M + (x 1 )

26 PL models of genetic regulatory networks Genetic networks modeled by class of differential equations using step functions to describe regulatory interactions b - B a - A - - x a a s - (x a, a2 ) s - (x b, b1 ) – a x a. x b b s - (x a, a1 ) s - (x b, b2 ) – b x b. x : protein concentration, : rate constants : threshold concentration Differential equation models of regulatory networks are piecewise-linear (PL) de Jong et al 2003

27 State transition graph Closure of qualitative states and transitions between qualitative states results in state transition graph Transition graph contains qualitative equilibrium states and/or cycles a1 max a 0 max b a6 b1 b2 D2D2 D3D3 D4D4 D7D7 D5D5 D6D6 D1D1 D8D8 D9D9 D 10 D 11 D 12 D 13 D 14 D 15 D 16 D 17 D 18 D 24 D 20 D 21 D 22 D 23 D 19 D 25 QS 3 QS 2 QS 1 QS 4 QS 5 QS 10 QS 15 QS 20 QS 25 QS 24 QS 23 QS 22 QS 21 QS 16 QS 11 QS 6 QS 7 QS 12 QS 17 QS 18 QS 19 QS 13 QS 14 QS 8 QS 9 de Jong et al 2003

28 Model Learning - compartmental Robust to Noise! 1 2 u k.x1 k.x2 ko.x2

29 Glycolysis

30 30

31 The Diagnostic Process Biological System (Plant) Predictor Candidate Generator Discrepency Detector Input Output Fault Identification Fault Isolation Fault Detection

32 Cascaded Solution Space x 1 =0 x2x2 x1x1 x 2 =0 1 11 12 6 2 0 10 13 7 5 3 9 8 4 1 2 u k12.x1 k20.x2 8 4

33 The End? A Machine with a Mind of its Own Ross King wanted a research assistant who would work 24/7 without sleep or food. So he built one. Wired 12/8/04 obot&topic_set= The Robot Scientist /n6971/n6971/abs/nature02236_fs.html&dynoptions=doi1096277730 Discovery Net

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