Do genome‐scale models need exact solvers or clearer standards?

Abstract

Constraint-based analysis of genomescale models (GEMs) arose shortly after the first genome sequences became available. As numerous reviews of the field show, this approach and methodology has proven to be successful in studying a wide range of biological phenomena (McCloskey et al, 2013;Bordbar et al, 2014). However, efforts to expand the user base are impeded by hurdles in correctly formulating these problems to obtain numerical solutions. In particular, in a study entitled "An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models" (Chindelevitch et al, 2014), the authors apply an exact solver to 88 genome-scale constraint-based models of metabolism. The authors claim that COBRA calculations (Orth et al, 2010) are inconsistent with their results and that many published and actively

Harnessing Big Data for Systems Pharmacology

“…Recent debates on the origins of inconsistency of constraint-based network modeling that describes a biological system by a set of constraints (e.g., mass balance, thermodynamics) highlight the difficulties in model validation (31, 45, 46). Constraint-based network modeling is a powerful tool in systems pharmacology.…”

“…It has been applied to predict drug side effect profiles resulting from off-target binding (47), elucidate mechanisms of antibiotics (48), predict personalized drug responses (20), and identify drug targets and biomarkers (49). However, the application and reproducibility of constraint-based modeling are hampered by the lack of standard formats for network models and associated tools to parse the models (31, 37, 45, 46). A more fundamental issue is whether an exact arithmetic solver is required to achieve consistent results (31, 45, 46).…”

“…However, the application and reproducibility of constraint-based modeling are hampered by the lack of standard formats for network models and associated tools to parse the models (31, 37, 45, 46). A more fundamental issue is whether an exact arithmetic solver is required to achieve consistent results (31, 45, 46). The standardization efforts in systems biology discussed above may facilitate addressing these problems.…”

Community standards to facilitate development and address challenges in metabolic modeling

“…Here, we use the term 'standard' to represent a framework or resource to facilitate interpretation and evaluate quality, particularly involving data formatting and presentation in computational modeling. Beyond merely evaluating quality (Mbacham et al 2014), Manuscript compiled: Friday 12 th July, 2019 † Correspondence to: papin@virginia.edu or james.yurkovich@systemsbiology.org standardization can also improve efficiency (Rose et al 2010), content (Waltemath et al 2016), reproducibility (Ebrahim et al 2015), code and model sharing (Yurkovich et al 2017), and the ease of entering a field (Thiele and Palsson 2010). Much of the domainspecific knowledge embedded into a computational model can be uninterpretable to scientists other than the developer or inaccessible to those outside the field if unstandardized.…”

“…Standardizing how these phenomena are represented is thus required to organize these complexities and make these formalizations interpretable and accessible. Many resources-including databases, algorithms, file formats, software, and compiled 'best practices'-exist to facilitate standardization (Dräger and Palsson 2014;Le Novère et al 2005;Waltemath et al 2011;Hucka et al 2003;Brazma et al 2006;Ravikrishnan and Raman 2015;Stanford et al 2015), but the consistent use and application of these standards can pose a significant challenge (Ebrahim et al 2015).…”