Modeling formalisms in Systems Biology

Machado, Daniel; Costa, Rafael S.; Rocha, Miguel; Ferreira, Eugénio C.; Tidor, Bruce; Rocha, Isabel

doi:10.1186/2191-0855-1-45

Cited by 147 publications

(122 citation statements)

References 200 publications

(196 reference statements)

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“…In the final part of this review, a generic modelling framework is used to explore some recent advances, data repositories and software tools, relevant to this area. This contrasts with previous reviews in this field that discuss systems biology/modelling/ simulation approaches more broadly [1,[26][27][28][29]. Our overarching goal is to emphasise the utility, accessibility and exchangeability of the computer models and resources to the nutrition community.…”

Section: Introductionmentioning

confidence: 41%

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: Introductionmentioning

confidence: 41%

Nutrition Research and the Impact of Computational Systems Biology

Auley¹

2013

J Comput Sci Syst Biol

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“…This isoften due to the difficulty ofobtaining accurate experimental measurements ofvital kineticconstants [6,10,17,21,32,37]. This is currently the case for biochemical reactions occurring in complex environments in vivo that cannot be approximated through more simple experimentsin vitro.Complex gene regulatory networks are a prime example.Modelling these systems using aCA approach allows researchers to easily change kineticparameter values (individuallyand in combination) to study their effects on system function [4,7,8,25,38,51]. Since the overall system function (i.e., the output molecule or action) is something that can be measured easily, CA modelling provides a method for determining "difficult-tomeasure" parameters using "easy-to-measure" observations of the system being studied.However, the real challenge after conducting a CA study with combinatorial parameter variation is interpreting the results.…”

Section: Introductionmentioning

confidence: 99%

Using Cellular Automata and Global Sensitivity Analysis to Study the Regulatory Network of the L-Arabinose Operon

Apte¹,

Fong²,

Senger³

2013

Emerging Applications of Cellular Automata

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“…Many special issues in journals [27,34] or reviews have been written on the topic of hybrid modelling in biology, either explicitly or implicitly by focusing on related topics such as multiscale modelling [3,28,43], complexity in biology [9], systems and integrative biology [19,22,23]. Reviews dedicated to some specific applications of hybrid models can also be found, for example in biological networks modelling [12], in cancer modelling [10,21,30,40], and more specifically tumour growth [24,29], tumour immunology [41], brain cancer [42] or angiogenesis [16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modelling in Biology: a Classification Review

Stéphanou

Volpert

2015

Math. Model. Nat. Phenom.

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Abstract. This paper presents a general review on hybrid modelling which is about to become ubiquitous in biological and medical modelling. Hybrid modelling is classically defined as the coupling of a continuous approach with a discrete one, in order to model a complex phenomenon that cannot be described in a standard homogeneous way mainly due to its inherent multiscale nature. In fact, hybrid modelling can be more than that since any types of coupled formalisms qualify as being hybrid. This review first presents the evolution and current context of this modelling approach. It then proposes a classification of the models through three different types that relate to the nature and level of coupling of the formalisms used.

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Modeling formalisms in Systems Biology

Cited by 147 publications

References 200 publications

Nutrition Research and the Impact of Computational Systems Biology

Nutrition Research and the Impact of Computational Systems Biology

Using Cellular Automata and Global Sensitivity Analysis to Study the Regulatory Network of the L-Arabinose Operon

Hybrid Modelling in Biology: a Classification Review

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