Multi-Agent Based Modeling and Simulation of Stem Cell Behaviour BIOMABS – Biomedical Multi-Agent Based Simulation April, 2007.

Multi-Agent Based Modeling and Simulation of Stem Cell Behaviour

BIOMABS – Biomedical Multi-Agent Based Simulation

April, 2007

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Outline

• Motivation

– The Problem Description

• The Research Hypothesis

• The Objectives

• The Expected Contributions

• The Methodology

• On Going Work

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Motivation

• How do stem cells behave in the human body?

• How to predict about how and why stem cells behave either individually or collectively?

– formal models

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Motivation

• Medicine point of view

– The model serves as a reference, a guide for interpreting experimental results

– The models serves as a powerful means of suggesting new hypotheses

– The simulation lets us test experimentally unfeasible scenarios and can potentially reduce experimental costs

• Software engineering

– How can we model such a large and complex system?

– How to minimize the complexity of the design and implementation of simulation ?

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Motivation

• Agent-Oriented Software Engineering (AOSE) and Multi-Agent-Based Simulation (MABS) provides the best of both worlds:

– clean design in the modeling phase

– efficient numerical routines in the simulation phase.

• Current Drawback

– Model’s semantics

– Model and program reuse is limited

• Such dynamic structures can be intuitively represented and efficiently implemented in agent-oriented simulators.

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The Problem Description:Definition of Stem Cells

• Stem cells are:

– a potentially heterogeneous population of functionally undifferentiated cells,

– composed of multi-cellular organisms.

• capable of:

– homing to an appropriate growth heterogeneous environment;

– proliferation;

– production of a large number of differentiated progeny;

– self-renewing or self-maintaining their population;

– regenerating the functional tissue after injury with

– flexibility and reversibility in the use of these options.

- Stem Cell Definitions from Wikipedia. http://en.wikipedia.org/wiki/Stem_cell, accessed in March 2007.

- M. Loeffler and I. Roeder. Cells Tissues Organs, 171(1):8-26, 2002.

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The Problem Description:Simulating Stem Cells - Motivation

• Self-organizing system: The way to understand how stem cells organize themselves

• Emergent global behavior

• The agent-based simulation

– suggests how tiny changes in individual stem cell behavior might lead to disease at the global

– allows temporal analysis

– reduce costs and risks

– avoid ethical issues

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The Problem Description:The niche – Stem Cells Environment

• The niche

– a specialized cellular environment

– provides stem cells with the support needed for self-renewal

– contains the cells and proteins that constitute the extra cellular environment

• The niche has regulatory mechanisms:

– It saves stem cells from depletion

– It protects the host from over-exuberant stem-cell proliferation

David T. Scadden. The stem-cell niche as an entity of action. Nature 441, 1075-1079 (29 June 2006) | doi:10.1038/nature04957; Published online 28 June 2006.

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Niche

• Example: The Epithelial Stem Cell Niche in Skin

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Stem Cells

• To ensure self-renewal, stem cells undergo two types of cell division:

– Symmetric division

– Asymmetric division

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The Problem Description:Stem cell division and differentiation

• A - stem cell;

• B - progenitor cell;

• C - differentiated cell;

• 1 - symmetric stem cell division;

• 2 - asymmetric stem cell division;

• 3 - progenitor division;

• 4 - terminal differentiation

NicheSelf-renew

Limited Self-renew

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The Research Hypothesis

• The multi-agent system approach is appropriated for the modeling and simulation of stem cell behavior

• The multi-agent system approach is more suitable for the modeling of stem cell behavior then– Mathematical models (ODE)

– Cellular automata

– Petri nets

– Other agent-based related works that uses formal methods

• And MAS allows– functional view on stem cell population as self-organising

systems, necessary to

– describe plasticity* and flexibility observed in experimental work*plasticity phenomena: capacity to change tissue-specific differentiation program

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The Research Hypothesis

Main Organization

Multi-Agent System

Environment

agentagent agent

Main Irganization

object

Sub-Organization

Agent RoleObject Role

+ +

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Objectives

• To build an agent-based stem cells model in which current and new theories of stem cell behaviour can be modelled.

• To investigate appropriated frameworks to run the stem cells simulation.

• To investigate how to better present the simulation results and process to the users/physicians

• To compare the agent-based results with the Petri nets approache (at the first moment)

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The Expected Contributions

• Medicine point of view

– A tool that allows the understanding of how the behaviour of a system of stem cells is related to the local cell-cell and cell-environment interactions.

– A first step in order to

• challenge current modes of conceptualising stem cell behaviour.

• predict properties of systems of stem cells that can be tested experimental that will provide insights into what behaviour is happening at the cellular level

• Agent-Oriented Software Engineering point of view

– To provide:

• Methods and techniques for modeling stem cell behavior

• a framework for the stem cells simulation

– To illustrate the advantages of using agent-oriented software engineering instead of other technique

– Better understanding of the process of self-organisation multi-agent systems

• HCI – Human-Computer Interaction point of view

– To identify appropriated ways to present the results

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The Methodology

• Work Team

– PUC-Rio, LES

• Prof. Carlos Lucena

• Maíra Gatti

• José Eurico

• Renato Raposo

– UFRJ, LANDIC – Cellular Differentiation and Neurogenesis Lab

• Prof. Stevens Rehen

• 4 Students

– 1 PhD. student, 3 Ms. student, 1 undergraduate student

• Mini-workshops

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The Methodology

Iteratively

– Domain Analysis

– Modeling

– Simulation

– Results Analysis

LES Team+

LANDIC Team

LES Team

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The Methodology

First Phase– Domain Analysis

• Cell Life-cycle

• Stem cell process– Self-renew

– Differentiation

– Modeling• MAS-ML

• Petri nets

– Simulation• Jade

• Java

– Agent-based Simulation FW evaluation

Third Phase

– Comparisons

– Advantages

– Disadvantages

– Papers

Second Phase

– Domain Analysis

• Refine the Stem Cell

process

• Differentiation

problem

– Modeling

• MAS-ML

• Petri nets

– Simulation

• ??

• Java

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On Going Work

First Phase– Domain Analysis

• Cell Life-cycle

• Stem cell process– Self-renew

– Differentiation

– Modeling• MAS-ML

• Petri nets

– Simulation• Jade

• Java

– Agent-based Simulation FW evaluation

Second Phase

– Domain Analysis

• Refine the Stem Cell

process

• Differentiation

problem

– Modeling

• MAS-ML

• Petri nets

– Simulation

• ??

• Java

Third Phase

– Comparisons

– Advantages

– Disadvantages

– Papers

June 05

July 03 July 31

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The Cell Lyfecicle

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MAS-ML Modeling

• Structural Elements– Environment

– Main Organization: Niche

– Organizations: Cell, StemCell, ProgenitorCell, DifferentiatedCell, Centrosome

– Agents: Chromatin, Chromosome, Microtubules

– Objects: CellMembrane, NuclearMembrane, Nuclei, Chromatids, Centriole, Substances, Protein, Organelles, CellsStructures, Centromere, Kinetochore, MolecularMotorProtein

• Static Diagram– Organization Diagram

– Role Diagram

– Agent Diagram*

– Class Diagram

– Dependence Diagram** (Tropos)

• Dynamic Diagram– Sequence Diagram

• Agent interactions

• Goals, plans and actions

– Activity Diagram

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DifferentiatedCell

Organization Diagram

Object role

Agent roleObject / Environment

Agent

Organization

Legend:

Niche

Environment

StemCell

ProgenitorCell

<<main-organization>>

Cell<<organization>>

StemCell<<organization>>

ProgenitorCell<<organization>>

<<organization>>

Cell

Centrosome<<organization>>

Centrosome

Cell

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Organization Diagram

Object role

Agent roleObject / Environment

Agent

Organization

Legend:

Environment

Cell<<organization>>

Chromosome<<agent>>

CellMembrane

Chromatin<<agent>>

Chromatin

Microtubule<<agent>>

Microtubule

CellMembrane

Chromosome

NuclearMembrane

NuclearMembrane

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Main Organization

Niche <main-organization>

<<goal>> boolean : regulateStemCellDifferentiation = true regulatingCellsNumber

<<belief>> Ontology : ontology<<belief>> String :type<<belief>> int : id<<belief>> Collection : cells = null<<belief>> int: bestCellsNumber = 0<<belief>> int: time

{} preventFromDepletion {}{} preventFromOverExuberantStemCellProliferation {}{} acceptCell {}regulatingCellsNumber { preventFromDepletion, preventFromOverExuberantStemCellProliferation } regulateStemCellDifferentiation

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Cell <organization>

<<goal>> boolean : metabolize = true {<<sub-goal>> boolean : accomplishInterphaseG1 = true runningInterphaseG1<<sub-goal>> boolean : accomplishInterphaseS = true runningInterphaseS<<sub-goal>> boolean : accomplishInterphaseG2 = true runningInterphaseG2<<sub-goal>> boolean : accomplishProphase = true runningProphase <<sub-goal>> boolean : accomplishPrometaphase = true runningPrometaphase <<sub-goal>> boolean : accomplishMetaphase = true runningMetaphase <<sub-goal>> boolean : accomplishAnaphase = true runningAnaphase <<sub-goal>> boolean : accomplishTelophase = true runningTelophase }

<<belief>> Status : status<<belief>> int : maturityPromoterFactor #MPF<<belief>> int : selfRenewPotentialy<<belief>> int : differentiationPotentialy<<belief>> Collection<Substance> : substances

{} synthesizeSubstances {}{} increaseCellMetabolicRate {}{} startChromatidReplication {}{} finishChromatidReplication {}{} replicateCentrosome {}{} condenseChromatin {}{} createRepulsiveForces {}{} disassembleNuclearMembrane {}{} increaseMPF {}{} executeControlCheckpoint {}runningInterphaseG1 {synthesizeSubstances, increaseCellMetabolicRate } accomplishInterphaseG1runningInterphaseS {startChromatidReplication, replicateCentrosome } accomplishInterphaseSrunningInterphaseG2 {finishChromatidReplication } accomplishInterphaseG2runningProphase { condenseChromatin, createRepulsiveForces, disolveNuclearMembrane, increaseMPF,

executeControlCheckpoint } accomplishProphase

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Class Diagram

Environment

Substance

Protein

MolecularMotorProtein

Chromatid

Centromere

Kinetochore

Organelle

CellStructure

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Role Diagram

Cell

Environment

Substance

Centriole

Centrosome

MolecularMotorProtein

Chromosome 2 Chromatid

CellMembrane1

*

2*

Chromatin

Nuclei

NuclearMembrane

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References• M. d’Inverno, and J. Prophet. Modelling, simulation and visualisation of adult stem

cells. In P. Gonzalez, E. Merelli, and A. Omicini, editors, Fourth International Workshop on Network Tools and Applications, NETTAB, pages 105–116, 2004.

• M. d’Inverno and R. Saunders. Agent-based modelling of stem cell organisation in a niche. In Engineering Self-Organising Systems, volume 3464 of LNAI. Springer, 2005.

• M. d’Inverno, N. D. Theise, and J. Prophet. Mathematical modelling of stem cells: a complexity primer for the stem cell biologist. In Christopher Potten, Jim Watson, Robert Clarke, and Andrew Renehan, editors, Tissue Stem Cells: Biology and Applications. Marcel Dekker, 2005.

• Instituto Virtual de Células-Tronco. http://www.ivct.org• Project: Modelling and Simulating the Behaviour of Adult Stem Cells using Agent-

Based Systems - http://doc.gold.ac.uk/~mas02md/cell/index.htm• David T. Scadden. The stem-cell niche as an entity of action. Nature 441, 1075-1079

(29 June 2006) | doi:10.1038/nature04957; Published online 28 June 2006.• Preece, J.;Rogers, Y.; Sharp, H. Design de Interação: Além da interação homem-

computador, Porto Alegre, Brasil: Bookman, 2005.• Gatti, M.; Lucena, C. An Agent-Based Approach for Building Biological Systems:

Improving The Software Engineering for Complex and Adaptative Multi-Agent Systems. To be published in Monografias de Ciências da Computação, PUC-Rio, Rio de Janeiro, 2007.

• Stem Cell Definitions from Wikipedia. http://en.wikipedia.org/wiki/Stem_cell, accessed in March 2007.

• M. Loeffler and I. Roeder. Cells Tissues Organs, 171(1):8-26, 2002.

Multi-Agent Based Modeling and Simulation of Stem Cell Behaviour

BIOMABS – Biomedical Multi-Agent Based Simulation

April, 2007