Simulating the decentralized processes of the human immune system in a virtual anatomy model

BACKGROUND: Many physiological processes within the human body can be perceived and modeled as large systems of interacting particles or swarming agents. The complex processes of the human immune system prove to be challenging to capture and illustrate without proper reference to the spacial distribution of immune-related organs and systems. Our work focuses on physical aspects of immune system processes, which we implement through swarms of agents. This is our first prototype for integrating different immune processes into one comprehensive virtual physiology simulation. RESULTS: Using agent-based methodology and a 3-dimensional modeling and visualization environment (LINDSAY Composer), we present an agent-based simulation of the decentralized processes in the human immune system. The agents in our model - such as immune cells, viruses and cytokines - interact through simulated physics in two different, compartmentalized and decentralized 3-dimensional environments namely, (1) within the tissue and (2) inside a lymph node. While the two environments are separated and perform their computations asynchronously, an abstract form of communication is allowed in order to replicate the exchange, transportation and interaction of immune system agents between these sites. The distribution of simulated processes, that can communicate across multiple, local CPUs or through a network of machines, provides a starting point to build decentralized systems that replicate larger-scale processes within the human body, thus creating integrated simulations with other physiological systems, such as the circulatory, endocrine, or nervous system. Ultimately, this system integration across scales is our goal for the LINDSAY Virtual Human project. CONCLUSIONS: Our current immune system simulations extend our previous work on agent-based simulations by introducing advanced visualizations within the context of a virtual human anatomy model. We also demonstrate how to distribute a collection of connected simulations over a network of computers. As a future endeavour, we plan to use parameter tuning techniques on our model to further enhance its biological credibility. We consider these in silico experiments and their associated modeling and optimization techniques as essential components in further enhancing our capabilities of simulating a whole-body, decentralized immune system, to be used both for medical education and research as well as for virtual studies in immunoinformatics

Semantically Linking In Silico Cancer Models

Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible

2000 - 2001: Postgraduate Prospectus

This booklet outlines the opportunities for post graduate studies offered by the Dublin Institute of Technology. Including both taught and programmes of research leading to higher level awards

Process Algebra with Layers: Multi-scale Integration Modelling applied to Cancer Therapy

We present a novel Process Algebra designed for multi-scale integration modelling: Process Algebra with Layers (PAL). The unique feature of PAL is the modularisation of scale into integrated layers: Object and Population. An Object can represent a molecule, organelle, cell, tissue, organ or any organism. Populations hold specific types of Object, for example, life stages, cell phases and infectious states. The syntax and semantics of this novel language are presented. A PAL model of the multi-scale system of cell growth and damage from cancer treatment is given. This model allows the analysis of different scales of the system. The Object and Population levels give insight into the length of a cell cycle and cell population growth respectively. The PAL model results are compared to wet laboratory survival fractions of cells given different doses of radiation treatment [1]. This comparison shows how PAL can be used to aid in investigations of cancer treatment in systems biology