Affinity Based Model of Multicellular Development Oisn Mac Aodha 4ECE 02/04/2007

Affinity Based Model of Multicellular Development

Oisín Mac Aodha 4ECE

02/04/2007

This project investigates if it is possible to model the development of multicellular organisms.

Presentation Structure: - Background - Brief introduction to genetic algorithms (GAs) - Introduction to development

- Modelling development - Experimental results

- Conclusions

Introduction


As the drive towards nano-scale fabrication continues we may soon reach a point where physical manipulation of atomic scale components will become impossible.

A possible alternative to the current top-down lithographic process would be a bottom up manufacturing process where the pre-existing components of a system can automatically self assemble to produce the desired behavior.

Background


Genetic Algorithms


Generation

0 5 10 15 20 25 30 35 40 45 50

Ave

rage

Fitn

ess

5

6

7

8

9

10

11

The goal is to evolve an a population whose individuals comprise of just 1s.

Ones Max Problem


Development


http://www.microscopy-uk.org.uk/

Development is the process by which organisms grow and develop.

As the problem becomes more complex so will the processing time required by a GA to find the best solution.

Development allows a more complex genotype to phenotype mapping compared to the one to one mapping provided by a GA.

May contribute to achieving the levels of complexity as seen in nature.

Why Use Development?


Development was modelled using Java by creating a gene regulatory network.

Corresponding classes were created for the objects found in nature including: Protein, Gene, Chromosome, Cell and Phenotype. GA was used to evolve the genome (configuration) used data for development.

Modelling Development


10.5

20.8

30.3

Cell

Genome

Protein

In a GRN, a gene produces a protein. The protein produced subsequently affects other genes and ultimately cell behavior.

Gene Regulatory Network


1 3 2 2 4 2 1 3 2 3 4 1

Gene 1 Gene 2 Gene 3

Cis – Reg Site Coding Region

The affinity matrix contains the protein-gene interaction

strengths.

The gene transcribes a new protein when the evolved threshold for the gene is exceeded.

Affinity Matrix


ON

OFF

OutputInputs

0

0.3 X 0.45

1.2 X -0.25

0.4 X 0.1

The following results were obtained using the configuration data below.

Single Cell Results


Development Step

0 2 4 6 8 10 12 14 16 18 20

Con

cent

ratio

n

0

1

2

3

4

5

Protein 0Protein 1Protein 2Protein 3Protein 4

Flat Concentration


Development Step

0 2 4 6 8 10 12 14 16 18 20

Con

cent

ratio

n

0

1

2

3


Linear Concentration


Development Step

0 2 4 6 8 10 12 14 16 18 20

Con

cent

ratio

n

0

1

2

3


Development Step

0 2 4 6 8 10 12 14 16 18 20

Con

cent

ratio

n

0

1

2

3


Heterochrony


Next stage was to expand the current single cell modelto allow for multicellular development.

Cells have the ability to: - Divide - Die - Communicate (via signalling proteins)

Multicellular Development


Cell division is the process by which a parent cell divides into two cells.

The genome of the parent cell is transferred is copied and transferred to the new cell.

Protein concentrations are distributed between the cells.

Cell Division


10.6

20.8

30.2

10.3

20.4

30.1

10.3

20.4

30.1

Genome Replicates

Proteins Divide

10.6

20.8

30.2

Cell

Genome

Protein

The direction a cell divides in is based on the concentration of the direction protein.

The direction around the unitcircle corresponds to the eightpossible neighbouring locations acell has in a 2D grid.

Cell Direction


0

12

3

74

5 6

0

π/4

π/23π/4

π

5π/4

3π/27π/4

2π

3 2 1

4 cell 0

5 6 7

The multicellular model has nine proteins with the following functions:

Protein Functions


For the multicellular experiments the fitness was based on the position of the cells.

Multicellular Results


Line


Step 1 Step 10 Step 20

fitness = (1/cellsInside) + (cellsOutside/desiredLength)

Square


Step 7 Step 15 Step 20

fitness = (size*size) – cellsInside + cellsOutside

A morphogen is a substance which governs thepositions of the various specialized cell types within anorganism.

It spreads from a localised source and forms a concentration gradient across a developing organism.

Based on Wolpert’s French flag model.

Morphogens


Blue Threshold

Red Threshold

Source

Sink

Cells

Distance

0 5 10 15 20

Mor

phog

en C

once

ntra

tion

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

morphogenConc = sourceConc x (1 / (sourceConc + distanceFromSource))

Morphogen Gradients


French Flag


Research: - Inter cell communication - More complex development model - 3D organisms - Environmental effects during development

Possible Applications: - Self developing systems - Fault repairing circuits

Future Research


Questions


http://ohm.nuigalway.ie/0607/03macaodha

Documents

Affinity Based Model of Multicellular Development Oisn Mac Aodha 4ECE 02/04/2007