SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology

Adams, Richard R.; Clark, Allan; Yamaguchi, Akinobu; Hanlon, Neil; Tsorman, N.; Ali, Shakir; Lebedeva, Galina; Goltsov, Alexey; Sorokin, Anatoly; Akman, Ozgur E.; Troein, Carl; Millar, Andrew J.; Goryanin, Igor; Gilmore, Stephen

doi:10.1093/bioinformatics/btt023

Cited by 22 publications

(23 citation statements)

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“…Resources: A comprehensive guide to the functionality of Copasi has been published by the developers and interested readers are referred to it [81]. Users should also access the supporting documentation and video clips on the Copasi web site (http://www.copasi.org/tiki-view_articles.php)…”

Section: Copasimentioning

confidence: 99%

“…This was followed by double clicking on the downloaded Microsoft Windows executable file (Copasi-35-Win32.exe). An icon was then displayed on the desktop of the computer, which was double clicked each time access to the software was needed.Resources: A comprehensive guide to the functionality of Copasi has been published by the developers and interested readers are referred to it [81]. Users should also access the supporting documentation and video clips on the Copasi web site (http://www.copasi.org/tiki-view_articles.php)…”

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Nutrition Research and the Impact of Computational Systems Biology

Auley¹

2013

J Comput Sci Syst Biol

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The value of computational modelling in improving our understanding of complex nutrient-based pathways is becoming increasingly recognised. This is due to the integral role that computer modelling is playing within the multidisciplinary field of systems biology, where in silico quantitative simulations are being used to compliment more traditional wet-laboratory investigations. A large number of computational models are accessible via the Biomodels database, an archive of openly available peer reviewed models of biological systems. Moreover, there has been an explosion in the availability of free modelling software tools that can be used to assemble and simulate the dynamic behaviour of nutrient mediated systems. Computational modelling will continue to play an increasingly significant role in nutrition research. Thus, it is important that freely accessible models and resources relevant to nutrition research are highlighted. In response to these needs, we firstly examined the Biomodels database, to identify and categorise nutrition themed models. The outcome of the analysis revealed 163 nutrition themed models. These models are mainly cellular in nature, with intracellular representations of calcium oscillations the most common. Secondly, a generic nutrition centred modelling framework was used, to explore recent advances, data repositories and software relevant to model building. We conclude this paper by using our review findings to discuss areas of nutrition that could further exploit the potential of computational modelling in the future. As a result of the progress in nutrient centred systems modelling it is important that freely available models, approaches, software and data resources relevant to nutrient focused modelling are highlighted. As a result of these needs, the aim of this paper is to discuss the progress in systems modelling and its relevance to nutrition research. Firstly, we will briefly introduce the main modelling approaches. We then highlight openly accessible nutrient themed models archived within the Biomodels database http://www.ebi.ac.uk/biomodels-main/ a repository for published peer reviewed models [22]. These models have been encased within the System Biology Markup Language (SBML) http://sbml.org/Main_Page a format used for model exchange [23]. We recognise that several formats exist for exchanging computational models, including the Cell Mark-up language (Cell-ML) [24] and the recently developed simulation experiment description markup language (SED-ML) [25], which also supports SBML. However, the rationale for focusing on SBML is because it is currently the leading exchange format in Systems Biology. This is emphasized by the results of a search of PubMed and Biomodels for models or tools published in each year since SBML was launched in 2003. The search terms SBML/ systems biology markup language were used to search for publications archived in PubMed. Biomodels was then used to crosscheck for models not revealed by the PubMed search. Figure 1 provides a summary of this ...

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Section: Copasimentioning

confidence: 99%

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confidence: 99%

Nutrition Research and the Impact of Computational Systems Biology

Auley¹

2013

J Comput Sci Syst Biol

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“…The use of network biology in predicting fluxes (and how to change them), as well as in parameter optimisation (Mendes and Kell 1998; Moles et al 2003; Adams et al 2013), has enjoyed particular success in biotechnology where it is usually the fluxes to external products that are of interest (Park et al 2007; Lee et al 2012; Park et al 2010; Becker et al 2011). In this area, the ongoing development of a systems biology toolkit for Chinese Hamster Ovary (CHO) cells, which will be increasingly utilised for biotechnological production of pharmaceutical proteins (Kildegaard et al 2013), will be aided by the development of Recon 2, which can act as a template for development of a detailed metabolic reconstruction of CHO.…”

Section: What the Recon 2 Network Will Allow Us To Do Or To Do Bettermentioning

confidence: 99%

An analysis of a ‘community-driven’ reconstruction of the human metabolic network

2013

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Following a strategy similar to that used in baker’s yeast (Herrgård et al. Nat Biotechnol 26:1155–1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonellatyphimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419–425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved ‘community consensus’ reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at http://humanmetabolism.org/ and in SBML format at Biomodels (http://identifiers.org/biomodels.db/MODEL1109130000). This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.

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“…Формат SBML является стандартом представления моделей метаболических процессов в клетке и осуществляет их построение в виде блок-схем с указанием внутренних связей. В рамках этого формата разрабатываются и развиваются системы конструирования и анализа математических моделей [93][94][95], в том числе, программные средства для решения одной из важнейших теоретических проблем в области моделирования живых систем -параметрической обратной задачи [96][97]. Несмотря на наличие большого числа разработанных компьютерных систем (более 250), отметим ряд ограничений, снижающие их индивидуальные возможности по моделированию молекулярно-генетических систем.…”

Section: перспективыunclassified

In Silico Cell: Challenges and Perspectives

Akberdin¹

2013

Math.Biol.Bioinf.

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Аннотация. В статье приведен обзор исследований по созданию «электронной клетки» -информационно-компьютерного ресурса, позволяющего моделировать полный метаболизм клетки с учетом процессов генетической регуляции и механизмов клеточного роста и деления; освящены основные проблемы для ее создания и существующие подходы к ее решению, в том числе, разработанные и развиваемые в Институте цитологии и генетики СО РАН.Ключевые слова: электронная клетка, элементарная подсистема, моделирование. ВВЕДЕНИЕС начала 50-60-х годов двадцатого столетия, после открытия структуры ДНК [1], как основного носителя генетической информации в живых системах, и фундаментальных принципов регуляции экспрессии генов [2], началось интенсивное изучение законов функционирования молекулярно-генетических систем. Открытие структуры ДНК послужило толчком для развития методов молекулярной биологии, направленных на выявление генов и функций кодируемых ими белков. Вплоть до создания высокопроизводительных экспериментальных подходов (конец 90-х годов 20-го столетия -начало 21 века), в силу существующих возможностей, любая живая система рассматривалась как набор элементарных компонентов, которые исследовались независимо от остальных. В научной литературе такой подход позиционируется как редукционистский, в противоположность системному (интегративному) подходу (рис. 1), однако широко используется и в настоящее время. Системный подход получил совершенно новые возможности с появлением современных экспериментальных технологий, компьютерных систем, предназначенных для анализа огромного массива генерируемых экспериментальных данных, и методов высокопроизводительных вычислений [3]. Появилась возможность реконструкции на качественном уровне молекулярно-генетических систем, обеспечивающих функционирование, как отдельных клеток, так и тканей, органов и организмов в целом в условиях гомеостаза, стрессовых ответов на факторы окружающей среды или в ходе индивидуального развития организмов. В результате, изменилась парадигма исследования живой системы, как единого целого [4].Однако, ни один, даже самый современный экспериментальный подход сам по себе не может дать истинного представления об изучаемой биологической системе в силу * akberdin@bionet.nsc.ru

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SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology

Cited by 22 publications

References 8 publications

Nutrition Research and the Impact of Computational Systems Biology

Nutrition Research and the Impact of Computational Systems Biology

An analysis of a ‘community-driven’ reconstruction of the human metabolic network

In Silico Cell: Challenges and Perspectives

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