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Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

ABSTRACT: Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis RToolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.

SUBMITTER: Alden K

PROVIDER: S-EPMC3585389 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

Alden Kieran K Read Mark M Timmis Jon J Andrews Paul S PS Veiga-Fernandes Henrique H Coles Mark M

PLoS computational biology 20130228 2

Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis RToolkit ...[more]

PMID: 23468606

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Project description:HIGHLIGHTS Computational techniques provide accurate descriptions of the structure and dynamics of biological systems, contributing to their understanding at an atomic level.Classical MD simulations are a precious computational tool for the processes where no chemical reactions take place.QM calculations provide valuable information about the enzyme activity, being able to distinguish among several mechanistic pathways, provided a carefully selected cluster model of the enzyme is considered.Multiscale QM/MM simulation is the method of choice for the computational treatment of enzyme reactions offering quantitative agreement with experimentally determined reaction parameters.Molecular simulation provide insight into the mechanism of both the catalytic activity and inhibition of monoamine oxidases, thus aiding in the rational design of their inhibitors that are all employed and antidepressants and antiparkinsonian drugs. Aging society and therewith associated neurodegenerative and neuropsychiatric diseases, including depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease, urgently require novel drug candidates. Targets include monoamine oxidases A and B (MAOs), acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and various receptors and transporters. For rational drug design it is particularly important to combine experimental synthetic, kinetic, toxicological, and pharmacological information with structural and computational work. This paper describes the application of various modern computational biochemistry methods in order to improve the understanding of a relationship between the structure and function of large biological systems including ion channels, transporters, receptors, and metabolic enzymes. The methods covered stem from classical molecular dynamics simulations to understand the physical basis and the time evolution of the structures, to combined QM, and QM/MM approaches to probe the chemical mechanisms of enzymatic activities and their inhibition. As an illustrative example, the later will focus on the monoamine oxidase family of enzymes, which catalyze the degradation of amine neurotransmitters in various parts of the brain, the imbalance of which is associated with the development and progression of a range of neurodegenerative disorders. Inhibitors that act mainly on MAO A are used in the treatment of depression, due to their ability to raise serotonin concentrations, while MAO B inhibitors decrease dopamine degradation and improve motor control in patients with Parkinson disease. Our results give strong support that both MAO isoforms, A and B, operate through the hydride transfer mechanism. Relevance of MAO catalyzed reactions and MAO inhibition in the context of neurodegeneration will be discussed.

Project description:BackgroundMeasuring the performance of a health system is an essential requirement in creating systems that generate efficient, equitable, patient-focused, accessible and sustainable results. A fundamental requirement for a performance measurement system is the development of an assessment framework within which specific performance measures could be defined and applied regularly. This paper examines the comprehensiveness of Ghana's health system assessment framework called the Holistic Assessment Tool in relation to some of the internationally recognized frameworks. The paper also analyzes trends in the performance of the health system to understand whether or not an improvement has been recorded following the adoption and implementation of the Holistic Assessment Tool.MethodsMainly secondary data were used in this analysis. Searches were conducted on Google Scholar, PubMed, Scopus and Science Direct between May and July, 2019 for published documents on health system performance assessment. We also obtained unpublished documents from Ghana's Ministry of Health, Ghana Health Service website, and Ghana Statistical Service database. Descriptive statistics were used to examine trends in the performance of the Ghanaian health system.ResultsWhile the tool provides a national framework for evaluating the performance of the Ghana Health system in several domains, the Holistic Assessment Tool does not cover key health system domains such as information systems for health, access to essential medicines, and patient-centeredness. Also, the scope of the assessment program seems limited to the evaluation of the Ministry of Health's annual plans, programs and projects. However, the health system has recorded improvements in population health indicators, such as life expectancy at birth, infant mortality, under-5 mortality, HIV prevalence and disease burden (in terms of disability adjusted life years).ConclusionsThe Holistic Assessment Tool is a useful framework, but needs further refinement, both in scope and in conceptual robustness. Future studies should consider exploring factors influencing performance of the Ghanaian health system. Such information will help in strategizing for better and more improvements.

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Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

Publications

Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

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