TY - JOUR
T1 - Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models
AU - Alden, Kieran
AU - Timmis, Jon
AU - Andrews, Paul S.
AU - Veiga-Fernandes, Henrique
AU - Coles, Mark
N1 - Funding Information:
This work was partly funded by the Wellcome Trust [ref: 097829] through the Centre for Chronic Diseases and Disorders (C2D2) at the University of York and the Medical Research Council (G0601156) to Mark Coles. Paul Andrews was funded by EPSRC grant EP/I005943/1 Resilient Futures. Jon Timmis is partly funded by the Royal Society and the Royal Academy of Engineering. Henrique Veiga-Fernandes was funded by the FCT Portugal (PTDC/SAU-MII/100016/ 2008), European Molecular Biology Organisation (Project 1648) and European Research Council (Project 207057).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve-hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur.
AB - Through integrating real time imaging, computational modelling, and statistical analysis approaches, previous work has suggested that the induction of and response to cell adhesion factors is the key initiating pathway in early lymphoid tissue development, in contrast to the previously accepted view that the process is triggered by chemokine mediated cell recruitment. These model derived hypotheses were developed using spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve-hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur.
KW - computational model
KW - lymphoid organs
KW - peyer's patches (PP)
KW - Sensitivity analysis
KW - spartan
KW - Models, Biological
KW - Computer Simulation
KW - Peyer's Patches/cytology
KW - Chemokines/metabolism
KW - Software
KW - Computational Biology/methods
UR - http://www.scopus.com/inward/record.url?scp=85017135851&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2016.2527654
DO - 10.1109/TCBB.2016.2527654
M3 - Article
C2 - 26887007
AN - SCOPUS:85017135851
SN - 1545-5963
VL - 14
SP - 431
EP - 442
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 2
M1 - 7403919
ER -