JSim, an open-source modeling system for data analysis

Butterworth, Erik; Jardine, Bartholomew; Raymond, Gary M.; Neal, Maxwell Lewis; Bassingthwaighte, James B.

doi:10.12688/f1000research.2-288.v1

Cited by 35 publications

(16 citation statements)

References 46 publications

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“…The DROs had six K trans values ranging from 0.01 min −1 to 0.35 min −1 that were constant across the rows and five v e values ranging from 0.01 to 0.5 that were constant down the columns, resulting in 30 different K trans -v e pairs, each encompassing 10 × 10 pixels. The K trans and v e values were used to generate synthetic image data using the Tofts-Kermode two-parameter model run in JSim, an open-source modeling system 23 , 43 . One DRO without noise 44 and 28 DROs with noise (SNR 0.18–1.8) 45 simulated by varying the sampling interval, timing offset, S 0 , and sigma value were used to evaluate algorithm performance.…”

Section: Methodsmentioning

confidence: 99%

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Ger

Mohamed

Awan

et al. 2017

Sci Rep

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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

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Section: Methodsmentioning

confidence: 99%

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Ger

Mohamed

Awan

et al. 2017

Sci Rep

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“…The Java-based tool JSim has been designed for building quantitative numeric models as well as the analysis of these models based on given experimental data (Butterworth et al, 2014 ). It supports ODEs and PDEs, discrete events, and implicit methods.…”

Section: Resultsmentioning

confidence: 99%

Improving Collaboration by Standardization Efforts in Systems Biology

Dräger

Palsson

2014

Front. Bioeng. Biotechnol.

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Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.

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“…We assume a well-mixed scenario for our simulations. Kinetic parameters have been either taken from published data together with the underlying experimental conditions, or have been fitted using measured RNA concentrations and JSim’s Simplex non-linear steepest-descent algorithm (Butterworth et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

Transcriptional Circuit Fragility Influences HIV Proviral Fate

et al. 2019

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SUMMARY Transcriptional circuit architectures in several organisms have been evolutionarily selected to dictate precise given responses. Unlike these cellular systems, HIV is regulated through a complex circuit composed of two successive phases (host and viral), which create a positive feedback loop facilitating viral replication. However, it has long remained unclear whether both phases operate identically and to what extent the host phase influences the entire circuit. Here, we report that, although the host phase is regulated by a checkpoint whereby KAP1 mediates transcription activation, the virus evolved a mini-malist system bypassing KAP1. Given the complex circuit’s architecture, cell-to-cell KAP1 fluctuations impart heterogeneity in the host transcriptional responses, thus affecting the feedback loop. Mathematical modeling of a complete circuit reveals how these oscillations ultimately influence homogeneous reactivation potential of a latent virus. Thus, although HIV drives molecular innovation to fuel robust gene activation, it experiences transcriptional fragility, thereby influencing viral fate and cure efforts.

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JSim, an open-source modeling system for data analysis

Cited by 35 publications

References 46 publications

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Improving Collaboration by Standardization Efforts in Systems Biology

Transcriptional Circuit Fragility Influences HIV Proviral Fate

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