Progress WP4, WP7Memodyn – Application Scenario – Organizational Coarse-Graining

Jan Huwald

Richard Henze

Bashar Ibrahim

Peter Dittrich

Bio Systems Analysis Group, Institute of Computer Science, Friedrich-Schiller-University Jena

Application scenario in collaboration with: Diekmann Group and Hemmerich Group, FLI Jena

11.12.2014 Brussels, HIERATIC Henze / Dittrich et al. - FSU Jena 1

OverviewI. Memodyn (WP4)

Report in D4.2Software / source code in D4.2

II. Artificial chemistries and organizational coarse-graining (WP4, WP7)

Paper in D4.2: Keyssig et al., Bioinformatics, 2014

III. Application scenario – Mitotic checkpoint (WP7)

Paper in D7.1: Ibrahim&Henze, Int. J. Mol. Sci., 2014 Paper in D7.1: Henze et al., Biosystems, 2014

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Part I MEMODYN

WP 4

Jan Huwald

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Memodyn

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Memodyn „Learning Cycle“

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Continuous sample generation

1. Random unbiased sampling

2. Constraint propagation

3. Force-based search

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Unbiased random sampling

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Constraint propagation(using GECODE)

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Force-based approach

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Force-based approach

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Force-based approach

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Force-based approach

For all combination all solutions are found!

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Continuous simulation

1. Micro-simulation border

2. Energy guided distance quantization

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Micro-simulation border

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Micro-simulation ersatz field

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Micro-simulation border for three particles

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Software

Source code implementing the methods mentioned above attached to D4.2.

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Part II Organizational Coarse-Graining

WP 4 / WP 7

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1. Basic Idea

1

32

4

Chemical OrganizationTheory

OrganizationsReaction network

1

32

4

Organization

[P. Dittrich, P. Speroni di Fenizi, Chemical Organization Theory, Bull. Math. Biol., 2007]

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1. Basic Idea

{1}

{2, 3}

{1,2,3,4}

{ }

Hasse diagram of organizations

OrganizationsReaction network

Chemical OrganizationTheory

1

32

4

[P. Dittrich, P. Speroni di Fenizi, Chemical Organization Theory, Bull. Math. Biol., 2007]

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1. Basic Idea

1

32

4

Thoerie chemischerOrganization

{1}

{2, 3}

{1,2,3,4}

{ }

Dynamics

[2][3]

[4][1]

Chemical OrganizationTheory

Hasse diagram of organizations

OrganizationsReaction network

[P. Dittrich, P. Speroni di Fenizi, Chemical Organization Theory, Bull. Math. Biol., 2007]

Organisational Coarse-graining Results

1. Discrete organizations (Kreyssig et al. 2014)2. Measuring organizations3. Hierarchical dynamics

(Boolean / Neuronal networks example)

1. Discrete organization

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[Kreyssig et al., Bioinformatics, 2014]

1. Discrete Organizations

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[Kreyssig et al., Bioinformatics, 2014]

2 C

1. Discrete Organizations

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[Kreyssig et al., Bioinformatics, 2014]

{A, B, C} is a purely discrete organization

2 C

2. Measuring Organizations

So far, a reaction network was necessary.

Now, measuring organizations and the hierarchical organizational structure directly, without the need to identify individual species or reactions.

“natural” coarse-graining, since it is derived from (physical) measurements

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2. Measuring Organizations - Recipe

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2. Measuring Organizations - Status

- Basic theory ready- Prototypic software ready- 16 species artificial chemistry with 50

organizations ready for testing.

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3. Hierarchical dynamics Boolean and neural networks

Is there a hierarchy of attractors?

How is the hierarchy of attractors related to the hierarchy of organizations?

Attractors and organizations for coarse-graining

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[SORN Network by Triesch et al. ]

3. Hierarchy of attractors

[Lukas Klimmasch] - Example

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Subset of active neurons

Set of active neurons within one attractor =“brain region”

3. Another Hierarchy of Attractors

[Lukas Klimmasch] - Example

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Part III Application Scenario

WP 7Richard Henze, Bashar Ibrahim

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16.11.06, Asselsheim Peter Dittrich (FSU Jena) 33

Wait until all kinetochores are correctly attached

http://library.thinkquest.org/C004535/mitosis .html

WAIT!

http://library.thinkquest.org/C004535/mitosis%20.html

16.11.06, Asselsheim Peter Dittrich (FSU Jena) 34

Wait until all kinetochores are correctly attached

http://library.thinkquest.org/C004535/mitosis .html

GO!

http://library.thinkquest.org/C004535/mitosis%20.html

16.11.06, Asselsheim Peter Dittrich (FSU Jena) 35

Wait until all kinetochores are correctly attached

http://library.thinkquest.org/C004535/mitosis .html

GO!

http://library.thinkquest.org/C004535/mitosis%20.html

Application Scenario Results

1. Preliminary studies: a) Active transport of Mad2

(Ibrahim&Henze, Int. J. Mol. Sci., 2014)b) Rule-based modeling of kinetochore mutants.

(Henze et al, Biosystems, 2014)2. Checkpoint Scenario3. PML nuclear bodies (potentially another

application scenario)

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1. Preliminary Work and Results

Various models at different scales of coarse-graining now available.

From ODE to detailed rule-based spatial particle simulation.

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1. Example: 3-D Rule-Based Model of Full Kinetochore

38Henze / Dittrich et al. - FSU Jena11.12.2014 Brussels, HIERATICby R. Henze, B. Ibrahim, et al., FSU Jena, 2014

2. Mitotic Checkpoint Scenario

Microlevel: Simulate all kinetochores (92), in a realistic 3D space, and realistic particle numbers (1 Mio) of inhibitors and activators.

Why: To have a trustworthy model that is- Understandable by domain experts (biologists)- Takes directly the rules from domain expertsDrawback: Computation time Coarse graining need for efficient computation,

e.g., if predictions. Also getting additional understanding, by extracting

more general laws.

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2. Mitotic Checkpoint Scenario

Unatched-Kinetochor activates Inhibitor

No inhibitor around a kinetochore GO signal

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Start

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End

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Preliminary simulation of mitotic checkpoint

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SummaryI. Memodyn (WP4)

Report in D4Software / source code in D4.2

II. Artificial chemistries and organizational coarse-graining (WP4, WP7)

Paper in D4.2: Keyssig et al., Bioinformatics, 2014

III. Application scenario – Mitotic checkpoint (WP7)

Paper in D7.1: Ibrahim&Henze, Int. J. Mol. Sci., 2014 Paper in D7.1: Henze et al., Biosystems, 2014

11.12.2014 Brussels, HIERATIC Henze / Dittrich et al. - FSU Jena 44

Progress WP4, WP7�Memodyn – Application Scenario – Organizational Coarse-GrainingOverviewPart I �MEMODYNMemodynMemodyn „Learning Cycle“Continuous sample generationUnbiased random samplingConstraint propagation �(using GECODE)Force-based approachForce-based approachForce-based approachForce-based approachContinuous simulationMicro-simulation borderMicro-simulation ersatz fieldMicro-simulation border for three particlesSoftwarePart II �Organizational Coarse-Graining 1. Basic Idea1. Basic Idea1. Basic IdeaOrganisational Coarse-graining Results1. Discrete organization1. Discrete Organizations1. Discrete Organizations2. Measuring Organizations2. Measuring Organizations - Recipe2. Measuring Organizations - Status3. Hierarchical dynamics Boolean and neural networks 3. Hierarchy of attractors 3. Another Hierarchy of AttractorsPart III �Application ScenarioWait until all kinetochores are correctly attachedWait until all kinetochores are correctly attachedWait until all kinetochores are correctly attachedApplication Scenario Results1. Preliminary Work and Results1. Example: 3-D Rule-Based Model of Full Kinetochore 2. Mitotic Checkpoint Scenario2. Mitotic Checkpoint ScenarioStartEndPreliminary simulation of mitotic checkpointSummary