Constructing kinetic models of metabolism at genome‐scales: A review

Abstract

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.

Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

“…Our approach of fitting the model with different sets of initial conditions to generate multiple parameter sets is akin to ensemble modeling for metabolic systems (Tran et al, 2008; Srinivasan et al, 2015; Saa and Nielsen, 2016). The ensemble modeling approach, which has been applied to build dynamic genome-scale models, generates multiple parameter sets (an ensemble of models) that produce the same steady state conditions.…”

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

“…Even metabolic pathways of moderate size, such as the Calvin–Benson cycle and adjacent reaction, typically consist of 20–30 enzymatic reactions. Therefore, the construction of larger kinetic models, while feasible from a computational point of view, is primarily limited by data availability and data reliability (Srinivasan et al, 2015). To some extent, the scarcity of information about kinetic parameters can be alleviated by explicitly accounting for uncertainty in kinetic models of metabolism—suitable approaches have been proposed recently (Wang et al, 2004; Steuer et al, 2006; Tran et al, 2008; Steuer and Junker, 2009; Murabito et al, 2014) but are not yet widely applied in models of phototrophic growth.…”

“…Due to the specific computational methodology, however, a direct integration of large-scale stoichiometric models into kinetic models of metabolism remains challenging. Various extensions toward incorporating dynamics have been proposed (Mahadevan et al, 2002; Kim et al, 2008; Feng et al, 2012; Antoniewicz, 2013), and extensive efforts are undertaken to bridge the gap between kinetic and stoichiometric models (Steuer, 2007; Steuer and Junker, 2009; Chakrabarti et al, 2013; Srinivasan et al, 2015). …”

Benchmarking optimization methods for parameter estimation in large kinetic models

“…Ultimately, the goal is the model-based prediction of cellular functions under new experimental conditions [1,32,34,53]. During the last decade, many efforts have been devoted to developing increasingly detailed and, therefore, larger systems biology models [29,49,51]. Such models are often formulated as nonlinear ordinary differential equations (ODEs) with unknown parameters.…”