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

CS4030: Biological Appications of Computing Science (BioComputing):Introduction & Overview

George M. Coghill

g.coghill@abdn.ac.uk

Structure of CourseStructure of Course

• Lectures – (Wednesday @ 9 & Friday @ 9):– Weds: Taylor A21; Fri: King’s 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.

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

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.

AttendanceYou 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.

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.

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.

What is Bioinformatics?

ComputationalBiology

Bioinformatics

Genomics

Proteomics

Functionalgenomics

Structuralbioinformatics

What is Bioinformatics?DNA (and RNA) Proteins

Over time, genes accumulate mutations Environmental factors

• Radiation

• Oxidation Mistakes in replication or

repair Deletions, Duplications Insertions Inversions Point mutations

Protein Folding

Why is Bioinformatics Important?

• Applications areas include– Medicine– Pharmaceutical drug design– Toxicology– Molecular evolution– Biosensors– Biomaterials– Biological computing models– DNA & RNA computing

Biologically Inspired Computing

• Neural Nets

• Evolutionary Computing – Genetic Algorithms, Genetics Programming

etc.

• Artificial Immune Systems

• Particle Swarm Optimisation

• Ant Colony Optimisation

xn

x1

x2

Input

(visual input)

Output

(Motor output)

Four-layer networks

Hidden layers

Genetic algorithms

• Variant of local beam search with sexual recombination.

Genetic algorithms

• Variant of local beam search with sexual recombination.

Lecture 1 CBA - Artificial Immune Systems

Multiple layers of the immune system

Phagocyte

Adaptive immune

response

Lymphocytes

Innate immune

response

Biochemical barriers

Skin

Pathogens

Lecture 1 CBA - Artificial Immune Systems

Clonal Selection

QML-CSA: Clonal Selection Algorithm

selection

Antibody repertoire

Selected Antibodies

proliferation

cloned Antibodies

matured Antibodies

AffinityMature

Hyper-mutation

Selected Antibodies

Reselection

Random Antibodies

Update Repertoire

Memorycell

An Evolutionary Algorithm Inspired by the clonal selection principle of immune system

Using hyper-mutation and re-selection instead of crossover and mutation.

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

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

Models and Inference

LearningEngine

Input Data

BehaviourModel

InferenceEngine

Input Data

BehaviourModel

24

Qualitative Modelling• Behavioural Abstraction

PhysicalSystem

ActualBehaviour

DifferentialEquation

Fi: R R

Qualitative Constraints

BehaviouralDescription

numerical or analytic solution

qualitative simulation

25

Qualitative Analysis

??x1↑ f10 ↑

Δx = u – f10

Time

x1 {+,0}

{+,+}

{+,-}

{0,+}

u is steady & positive, how will x and f10

change?

Qualitative Prediction

Quantitative Prediction{+,-}

magnitude

Rate of change

1

u

f10 =k10.x1

€

′ x 1 = u − k10x1

x1’ = u – f10

f10 = M+(x1)

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

- -

xa a s-(xa , a2) s-(xb , b1 ) – a xa .

xb b s-(xa , a1) s-(xb , b2 ) – b xb .

x : protein concentration

, : rate constants : threshold concentration

• Differential equation models of regulatory networks are piecewise-linear (PL)

de Jong et al 2003

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 maxa0

maxb

a6

b1

b2

D2 D3 D4

D7

D5

D6

D1

D8 D9 D10

D11 D12 D13 D14 D15

D16 D17 D18

D24

D20

D21 D22 D23

D19

D25

QS3QS2QS1 QS4 QS5

QS10

QS15

QS20

QS25QS24QS23QS22QS21

QS16

QS11

QS6

QS7

QS12

QS17 QS18

QS19

QS13

QS14

QS8 QS9

de Jong et al 2003

Model Learning - compartmental

• Robust to Noise! Learning with Noise

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 3 5 7 9 11 13

No states present

Accuracy

Clean

Inv' N

Rand' N1 2

u

k.x1

k.x2

ko.x2

Glycolysis

30

The Diagnostic ProcessBiologicalSystem (Plant)

Predictor

CandidateGenerator

DiscrepencyDetector

Input Output

Fault Identification

Fault Isolation

Fault Detection

Cascaded Solution Space

x1’=0x2

x1

x2 ’=0

111

12

6 2010

13

7

5

3

9

8

4

1 2

u

k12.x1

k20.x2

8

4

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 12/8/04http://www.wired.com/wired/archive/12.08/robot.html?

pg=2&topic=robot&topic_set=

• The Robot Scientisthttp://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v427/n6971/abs/nature02236_fs.html&dynoptions=doi1096277730

• Discovery Net

http://www.discovery-on-the.net/