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

Outline Motivation The Research Hypothesis The Objectives The Problem Description The Research Hypothesis The Objectives The Expected Contributions The Methodology On Going Work © LES/PUC-Rio

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 © LES/PUC-Rio

Motivation Medicine point of view Software engineering 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 ? © LES/PUC-Rio

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. © LES/PUC-Rio

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. © LES/PUC-Rio

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 © LES/PUC-Rio

The Problem Description: The niche – Stem Cells Environment 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. © LES/PUC-Rio

Niche Example: The Epithelial Stem Cell Niche in Skin Illustration of the epidermal stem cells. Stem cells are located in the bulge region of the hair follicle beneath the sebaceous gland. Upon activation, stem cells undergo division; the daughter cells retained in the bulge remain as stem cells while other daughter cells migrate down to become hair-matrix progenitors responsible for hair regeneration. In neonatal mice or in damaged skin, stem cells can also migrate upward and convert to epidermal progenitors that replenish lost or damaged epidermis. The bulge area is an environment that restricts cell growth and differentiation by expressing Wnt inhibitors, including DKK,Wif, and sFRP as well as BMPs. During the early anagen phase, Wnts from dermal papilla (DP) and Noggin, which is derived from both DP and bulge (J. Zhang & L. Li, unpublished data), coordinate to overcome the restriction signals imposed by both BMPs and Wnt inhibitors; this leads to stem cell activation and subsequent hair regeneration. The FGF and Notch pathways are also involved in DP function for hair-matrix cell proliferation and lineage fate determination, but their in.uence on stem cells is not clear. Annu. Rev. Cell Dev. Biol. 2005.21:605-631. Downloaded from arjournals.annualreviews.org by CAPES on 04/11/07. For personal use only. © LES/PUC-Rio

Stem Cells To ensure self-renewal, stem cells undergo two types of cell division: Symmetric division Asymmetric division © LES/PUC-Rio

The Problem Description: Stem cell division and differentiation Self-renew Niche 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 Limited Self-renew © LES/PUC-Rio

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 © LES/PUC-Rio

The Research Hypothesis + + Main Organization Multi-Agent System Environment agent Main Irganization object Sub-Organization Agent Role Object Role © LES/PUC-Rio

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) © LES/PUC-Rio

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 © LES/PUC-Rio

The Methodology Work Team Mini-workshops 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 © LES/PUC-Rio

The Methodology Iteratively LES Team + LANDIC Team Domain Analysis Modeling Simulation Results Analysis LES Team © LES/PUC-Rio

The Methodology First Phase Second Phase Third 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 © LES/PUC-Rio

On Going Work  First Phase Second Phase Third 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 July 03 July 31 June 05 Second Phase Domain Analysis Refine the Stem Cell process Differentiation problem Modeling MAS-ML Petri nets Simulation ?? Java Third Phase Comparisons Advantages Disadvantages Papers  © LES/PUC-Rio

The Cell Lyfecicle © LES/PUC-Rio

MAS-ML Modeling Structural Elements Static Diagram Dynamic Diagram 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 © LES/PUC-Rio

Organization Diagram Environment Cell StemCell DifferentiatedCell <<main-organization>> Niche <<organization>> StemCell Cell Centrosome ProgenitorCell <<organization>> Cell <<organization>> <<organization>> ProgenitorCell Centrosome Object role Agent role Object / Environment Agent Organization Legend: © LES/PUC-Rio

Organization Diagram Environment Chromosome Chromatin Chromatin <<agent>> Chromosome Chromatin Chromatin <<organization>> <<agent>> Chromosome Cell Microtubule NuclearMembrane CellMembrane NuclearMembrane <<agent>> CellMembrane Microtubule Object role Agent role Object / Environment Agent Organization Legend: © LES/PUC-Rio

<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 © LES/PUC-Rio

<organization> 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 } accomplishInterphaseG1 runningInterphaseS {startChromatidReplication, replicateCentrosome }  accomplishInterphaseS runningInterphaseG2 {finishChromatidReplication }  accomplishInterphaseG2 runningProphase { condenseChromatin, createRepulsiveForces, disolveNuclearMembrane, increaseMPF, executeControlCheckpoint }  accomplishProphase © LES/PUC-Rio

Class Diagram Environment Chromatid CellStructure Substance Centromere Organelle Protein Kinetochore MolecularMotorProtein © LES/PUC-Rio

Role Diagram Environment Cell Chromosome Centrosome Chromatin 1 CellMembrane * Substance Cell Chromosome 2 Chromatid Nuclei Centrosome NuclearMembrane Chromatin 2 Centriole * MolecularMotorProtein © LES/PUC-Rio

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. © LES/PUC-Rio

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