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STI-GMaS: An open-source environment for simulation of sexually-transmitted infections

Nelson, MR; Sutton, KJ; Brook, BS; Mallet, DG; Simpson, Daniel Peter; Rank, RG
Journal article, Peer reviewed
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URI
http://hdl.handle.net/11250/2358262
Date
2014
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  • Institutt for matematiske fag [1432]
  • Publikasjoner fra CRIStin - NTNU [20842]
Original version
BMC Systems Biology 2014, 8(1)   10.1186/1752-0509-8-66
Abstract
Background: Sexually-transmitted pathogens often have severe reproductive health implications if treatment is

delayed or absent, especially in females. The complex processes of disease progression, namely replication and

ascension of the infection through the genital tract, span both extracellular and intracellular physiological scales, and

in females can vary over the distinct phases of the menstrual cycle. The complexity of these processes, coupled with

the common impossibility of obtaining comprehensive and sequential clinical data from individual human patients,

makes mathematical and computational modelling valuable tools in developing our understanding of the infection,

with a view to identifying new interventions. While many within-host models of sexually-transmitted infections (STIs)

are available in existing literature, these models are difficult to deploy in clinical/experimental settings since

simulations often require complex computational approaches.

Results: We present STI-GMaS (Sexually-Transmitted Infections – Graphical Modelling and Simulation), an

environment for simulation of STI models, with a view to stimulating the uptake of these models within the laboratory

or clinic. The software currently focuses upon the representative case-study of Chlamydia trachomatis, themost

common sexually-transmitted bacterial pathogen of humans. Here, we demonstrate the use of a hybrid PDE–cellular

automata model for simulation of a hypothetical Chlamydia vaccination, demonstrating the effect of a

vaccine-induced antibody in preventing the infection from ascending to above the cervix. This example illustrates the

ease with which existing models can be adapted to describe new studies, and its careful parameterisation within

STI-GMaS facilitates future tuning to experimental data as they arise.

Conclusions: STI-GMaS represents the first software designed explicitly for in-silico simulation of STI models by nontheoreticians,

thus presenting a novel route to bridging the gap between computational and clinical/experimental

disciplines. With the propensity for model reuse and extension, there is much scope within STI-GMaS to allow clinical

and experimental studies to inform model inputs and drive future model development. Many of the modelling

paradigms and software design principles deployed to date transfer readily to other STIs, both bacterial and viral;

forthcoming releases of STI-GMaS will extend the software to incorporate a more diverse range of infections.
Publisher
BioMed Central
Journal
BMC Systems Biology

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