The Multiscale Systems Immunology project: software for cell-based immunological simulation

Mitha, Faheem; Lucas, Timothy A.; Feng, Feng; Kepler, Thomas B.; Chan, CP

doi:10.1186/1751-0473-3-6

Cited by 21 publications

(14 citation statements)

References 10 publications

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“…The additional computational cost in calculating both 4 th and 5 th order solutions, estimating error, and determining the correct time step could be outweighed by significantly larger time steps. Implicit algorithms for solving ODEs can also be used 16,32,41,58,59,61,91 . Similar to PDE algorithms, implicit algorithms require determining a Jacobian matrix and implementation of linear algebra solvers.…”

Section: Numerical Methods For Ode Sub-modelsmentioning

confidence: 99%

“…2 into simpler and more tractable systems for numerical solution 16,38,54,59,79,81 . Here, we split Eqn.…”

Section: Central Concepts For Hybrid Multi-scale Abmsmentioning

confidence: 99%

“…As shown in Figure 4, the solution to each individual equation is estimated in sequence using the same time step ( Δt ), with the solution of each operator as the input to the next operator 26,38,59 :

Θ_{1} false(normalΔ t false) \to Θ_{2} false(normalΔ t false) \to Θ_{3} false(normalΔ t false) \approx normalΘ false(normalΔ t false)

…”

Section: Central Concepts For Hybrid Multi-scale Abmsmentioning

confidence: 99%

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Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems

2014

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Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level.

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Section: Numerical Methods For Ode Sub-modelsmentioning

confidence: 99%

“…2 into simpler and more tractable systems for numerical solution 16,38,54,59,79,81 . Here, we split Eqn.…”

Section: Central Concepts For Hybrid Multi-scale Abmsmentioning

confidence: 99%

Θ_{1} false(normalΔ t false) \to Θ_{2} false(normalΔ t false) \to Θ_{3} false(normalΔ t false) \approx normalΘ false(normalΔ t false)

…”

Section: Central Concepts For Hybrid Multi-scale Abmsmentioning

confidence: 99%

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Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems

2014

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“…It is worth remarking the centrality of the scale problem, as choosing a proper model scale have recently allowed for important improvements in the IS simulation [17,18,19,20]. Much like the nervous system, the IS performs pattern recognition tasks and, additionally, it retains memory of the antigens to which it has been exposed.…”

Section: Agent-based Modelingmentioning

confidence: 99%

Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response

Stracquadanio

Umeton

Costanza

et al. 2011

Lecture Notes in Computer Science

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Abstract. The Immune System is, together with Central Nervous System, one of the most important and complex unit of our organism. Despite great advances in recent years that shed light on its understanding and in the unraveling of key mechanisms behind its functions, there are still many areas of the Immune System that remain object of active research. The development of in-silico models, bridged with proper biological considerations, have recently improved the understanding of important complex systems [1,2]. In this paper, after introducing major role players and principal functions of the mammalian Immune System, we present two computational approaches to its modeling; i.e., two insilico Immune Systems. (i) A large-scale model, with a complexity of representation of 10 6 − 10 8 cells (e.g., APC, T, B and Plasma cells) and molecules (e.g., immunocomplexes), is here presented, and its evolution in time is shown to be mimicking an important region of a real immune response. (ii) Additionally, a viral infection model, stochastic and light-weight, is here presented as well: its seamless design from biological considerations, its modularity and its fast simulation times are strength points when compared to (i). Finally we report, with the intent of moving towards the virtual lymph note, a cost-benefits comparison among Immune System models presented in this paper.G. Stracquadanio, R. Umeton, et al.

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“…By creating a connected multi-scale system, changes in a process (such as an infection) can ripple across the different levels of abstraction, to inform the whole system efficiently. The Multiscale Systems Immunology (MSI) project attempts to model the immune response over many different levels of temporal and spatial scales [27] and has been inspirational for our multi-scale models.…”

Section: Introductionmentioning

confidence: 99%

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

Sarpe

Jacob

2013

BMC Bioinformatics

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BackgroundMany 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.ResultsUsing 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.ConclusionsOur 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.

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The Multiscale Systems Immunology project: software for cell-based immunological simulation

Cited by 21 publications

References 10 publications

Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems

Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems

Large Scale Agent-Based Modeling of the Humoral and Cellular Immune Response

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

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