Hierarchical self-organized learning in agent-based modeling of the MAPK signaling pathway

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

doi:10.1109/cec.2011.5949893

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…See also [30] for a new efficient way to deal with stochastic biochemical systems. Finally, discrete approaches, mainly agent (or multi agent) based methods, are rules-based modeling approaches that provide easier ways of representing the interactions between entities [91,119]. Agent based modeling approaches have been used to study in silico how signaling molecules influence carcinogenesis.…”

Section: Signaling Modelsmentioning

confidence: 99%

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Oden

Lima

Almeida

et al. 2015

Arch Computat Methods Eng

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New directions in medical and biomedical sciences have gradually emerged over recent years that will change the way diseases are diagnosed and treated and are leading to the redirection of medicine toward patientspecific treatments. We refer to these new approaches for studying biomedical systems as predictive medicine, a new version of medical science that involves the use of advanced computer models of biomedical phenomena, high-performance computing, new experimental methods for model data calibration, modern imaging technologies, cutting-edge numerical algorithms for treating large stochastic systems, modern methods for model selection, calibration, validation, verification, and uncertainty quantification, and new approaches for drug design and delivery, all based on predictive models. The methodologies are designed to study events at multiple scales, from genetic data, to sub-cellular signaling mechanisms, to cell interactions, to tissue physics and chemistry, to organs in living human subjects. The present document surveys work on the development and implementation of predictive models of vascular tumor growth, covering aspects of what might be called modeling-and-experimentally based computational oncology. The work described is that of a multi-institutional team, centered at ICES with strong participation by members at M. D. Anderson Cancer Center and University of Texas at San Antonio. This exposition covers topics on signaling models, cell and cell-interaction models, tissue models based on multi-species mixture theories, models of angiogenesis, and beginning work of drug effects. A number of new parallel computer codes for implementing finite-element methods, multi-level Markov Chain Monte Carlo sampling methods, data classification methods, stochastic PDE solvers, statistical inverse algorithms for model calibration and validation, models of events at different spatial and temporal scales is presented. Importantly, new methods for model selection in the presence of uncertainties fundamental to predictive medical science, are described which are based on the notion of Bayesian model plausibilities. Also, as part of this general approach, new codes for determining the sensitivity of model outputs to variations in model parameters are described that provide a basis for assessing the importance of model parameters and controlling and reducing the number of relevant model parameters. Model specific data is to be accessible through careful and model-specific platforms in the Tumor Engineering Laboratory. We describe parallel computer platforms on which large-scale calculations are run as well as specific time-marching algorithms needed to treat stiff systems encountered in some phase-field mixture models. We also cover new non-invasive imaging and data classification methods that provide in vivo data for model validation. The study concludes with a brief discussion of future work and open challenges.

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Section: Signaling Modelsmentioning

confidence: 99%

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Oden

Lima

Almeida

et al. 2015

Arch Computat Methods Eng

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“…In agent-based modeling (ABM), each entity is considered to be an agent in a multi agent system (MAS) that interacts locally with its neighbors as well as the environment [44][45][46]. For example, Shirazi et.al., [46] presented a multi-agent approach to model the MAPK signaling pathway, which defines each substrate in a signaling pathway as an independent entity (agent). And Pogson et al, [43] employ ABM to demonstrate that it is a suitable to describe the cellular regulatory events such as the NF B  pathway.…”

Section: Agent-based Models(abm)mentioning

confidence: 99%

Review of the computational models for the signaling pathway

Kong

Wang

Zhang

2016

Proceedings of the 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016TOEC 2016)

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Abstract.In the past few years, systems biology methods have been widely used to explore the mechanism of cancer. Various signaling pathways play the important role in cancer. And a large number of computational methods are used to model the signaling pathway. Here, we reviewed many existing mechanisms and modeling techniques, which are used to model different signaling pathways. Then we introduced the systematic workflow of a system biology approach and some fundamental analysis methods. Finally we discuss some known challenges in the process of modeling signaling pathways and what we should handle in our future work.

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“…In our second set of experiments, we attempted to amend the particularly short lifespans of the abstractions seen in the periodic MAPK model in Figure 8(d). So we promoted a dynamic management of the learned meta-agent hierarchies [54]. Whenever a meta-agent became obsolete, it would restore the subsumed, previously active abstraction hierarchy.…”

Section: Towards Self-organizing Hierarchiesmentioning

confidence: 99%

Optimization of Swarm-Based Simulations

Mammen

Shirazi

Sarpe

et al. 2012

ISRN Artificial Intelligence

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is properly cited.In computational swarms, large numbers of reactive agents are simulated. The swarm individuals may coordinate their movements in a "search space" to create efficient routes, to occupy niches, or to find the highest peaks. From a more general perspective though, swarms are a means of representation and computation to bridge the gap between local, individual interactions, and global, emergent phenomena. Computational swarms bear great advantages over other numeric methods, for instance, regarding their extensibility, potential for real-time interaction, dynamic interaction topologies, close translation between natural science theory and the computational model, and the integration of multiscale and multiphysics aspects. However, the more comprehensive a swarm-based model becomes, the more demanding its configuration and the more costly its computation become. In this paper, we present an approach to effectively configure and efficiently compute swarm-based simulations by means of heuristic, populationbased optimization techniques. We emphasize the commonalities of several of our recent studies that shed light on top-down model optimization and bottom-up abstraction techniques, culminating in a postulation of a general concept of self-organized optimization in swarm-based simulations.

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Hierarchical self-organized learning in agent-based modeling of the MAPK signaling pathway

Cited by 6 publications

References 17 publications

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Toward Predictive Multiscale Modeling of Vascular Tumor Growth

Review of the computational models for the signaling pathway

Optimization of Swarm-Based Simulations

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