The multiagent systems approach has become recognized as a useful approach for mod- elling and simulating biological complex systems. In this chapter we provide an example of such an approach, which concerns the modelling and simulation of the Hematopoietic Stem Cell (HSC) system in adults. We are specifically interested in how local cell interactions give rise to well understood properties of systems of stem cells, such as the ability to maintain their own population and to maintain a population of fully differentiated functional cells. There is a need to establish key cell mechanisms that can produce self-regulating behaviour of HSC systems using different theoretical techniques. It is our belief that modelling the behaviour of HSCs in the adult human body as an agent-based system is the most appropriate way of understanding these mechanisms and the consequent process of self-organisation. In recent years there has been a growing debate about how stem cells behave in the human body; whether the fate of stem cells is pre-determined or stochastic, and whether the fate of cells relies on their internal state, or on extra-cellular micro-environmental factors. However, current experimental limitations mean that stem cells cannot be tracked in the adult human body. There is no way of “observing” micro-level behaviour. Models and simulations have a crucial role therefore in explaining the relationship of micro-behaviour to macro-behaviour and it now seems that the importance of computational modelling and simulation for understanding stem cells is beginning to be realised in many wet-labs. There have been several attempts to build formal models of these theories, so that predictions can be made about how and why stem cells behave, both individually or collectively. In this chapter we propose an agent based model which describes at the same time the intracellular behaviour of the cell (i.e., intra-cellular networks) and the cellular level where all the systemic interactions are developed. This enables us to build a multi-level model.

Agent-based Modelling of Stem Cells

MONTAGNA, SARA;
2009

Abstract

The multiagent systems approach has become recognized as a useful approach for mod- elling and simulating biological complex systems. In this chapter we provide an example of such an approach, which concerns the modelling and simulation of the Hematopoietic Stem Cell (HSC) system in adults. We are specifically interested in how local cell interactions give rise to well understood properties of systems of stem cells, such as the ability to maintain their own population and to maintain a population of fully differentiated functional cells. There is a need to establish key cell mechanisms that can produce self-regulating behaviour of HSC systems using different theoretical techniques. It is our belief that modelling the behaviour of HSCs in the adult human body as an agent-based system is the most appropriate way of understanding these mechanisms and the consequent process of self-organisation. In recent years there has been a growing debate about how stem cells behave in the human body; whether the fate of stem cells is pre-determined or stochastic, and whether the fate of cells relies on their internal state, or on extra-cellular micro-environmental factors. However, current experimental limitations mean that stem cells cannot be tracked in the adult human body. There is no way of “observing” micro-level behaviour. Models and simulations have a crucial role therefore in explaining the relationship of micro-behaviour to macro-behaviour and it now seems that the importance of computational modelling and simulation for understanding stem cells is beginning to be realised in many wet-labs. There have been several attempts to build formal models of these theories, so that predictions can be made about how and why stem cells behave, both individually or collectively. In this chapter we propose an agent based model which describes at the same time the intracellular behaviour of the cell (i.e., intra-cellular networks) and the cellular level where all the systemic interactions are developed. This enables us to build a multi-level model.
Multi-Agent Systems: Simulation and Applications
389
418
Mark d'Inverno; Paul Howells; Sara Montagna; Ingo Roeder; Rob Saunders
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/85923
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