Adaptive Modularization of the MAPK Signaling Pathway Using the Multiagent Paradigm

Shirazi, Abbas Sarraf; Mammen, Sebastian von; Jacob, Christian

doi:10.1007/978-3-642-15871-1_41

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…In several publications, Sarraf Shirazi et. al present the exploration and extension of SOMO implementations in the context of biological simulations [Sarraf Shirazi et al (2010, 2011a,b, 2012; Jacob et al (2012);von Mammen et al (2012);Sarraf Shirazi et al (2013 (submitted)]. Therein, the application domain slightly shifted from protein-interaction networks (in context of the MAPK signalling pathway) towards cell-cell/cell-membrane interaction systems (in context of blood coagulation processes).…”

Section: Current Implementationsmentioning

confidence: 99%

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Mammen

Steghöfer

2014

The Computer After Me

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Section: Current Implementationsmentioning

confidence: 99%

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Mammen

Steghöfer

2014

The Computer After Me

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“…The authors of the present article have previously demonstrated that high-level agent substitution indeed results in a reduction of computational cost. 31,32 In particular, we deployed ANNs and GP, two established inductive learning methods, to learn agent abstractions in a model of a biological signaling pathway. Clusters of biological substrates and their corresponding activation patterns were substituted by meta-agents.…”

Section: Related Workmentioning

confidence: 99%

“…Results for the MAPK pathway model of Figure 1: (a), (c) number of agents (solid line: our hierarchical approach; dashed line: non-hierarchical approach proposed by Sarraf Shirazi 31 ); (b), (d) concentration of MAPK-PP.…”

Section: Abstraction In the Mapk Signaling Pathwaysmentioning

confidence: 99%

Abstraction of agent interaction processes: Towards large-scale multi-agent models

2013

Self Cite

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The typically large numbers of interactions in agent-based simulations come at considerable computational costs. In this article, we present an approach to reduce the number of interactions based on behavioural patterns that recur during runtime. We employ machine learning techniques to abstract the behaviour of groups of agents to cut down computational complexity while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents’ interactions, are constantly tested for their validity: after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed again from the system. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed approach can successfully reduce the computational complexity during the simulation while maintaining the freedom of arbitrary interactions.

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Self-organized Middle-Out Abstraction

Mammen

Steghöfer

Denzinger

et al. 2011

Self-Organizing Systems

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Adaptive Modularization of the MAPK Signaling Pathway Using the Multiagent Paradigm

Cited by 4 publications

References 19 publications

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Abstraction of agent interaction processes: Towards large-scale multi-agent models

Self-organized Middle-Out Abstraction

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