Modeling Metabolic Networks for Mammalian Cell Systems: General Considerations, Modeling Strategies, and Available Tools

Abstract

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives

“…Both approaches rely on advanced computational methods to model the behaviour of the system, which will be discussed in section_5. For a mode extensive and detailed discussion of the techniques, the reader is directed to excellent reviews of statistical/physiochemical modelling [ 109 ] and modelling from top-down and bottom-up approaches [ 108 ].…”

“…Statistical modelling is employed when difficult to manage, abundant data is available, but the insight into the structure of system is sparse i.e., components of the system, their interaction and regulation. As no assumption can be made about the underlying mechanism of the system due to the lack of structure, the objective of modelling is to identify relevant variables or dimensions in the system [ 109 ]. This type of modelling employs statistical tools to connect different layer of data and gap filling.…”

Quality by control: Towards model predictive control of mammalian cell culture bioprocesses

“…This idea is not new and has been used for the modeling of other bioprocesses [28][29][30][31][32]. Metabolic flux networks of mammalian cells have been investigated by a number of researchers [33,34]. For hybridoma cells a genome scale metabolic network model has been described [35], but no Chinese hamster ovary (CHO) cell specific genome scale metabolic network was reported until recently [36,37].…”

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

“…Mammalian cells are distinct from microbes in their metabolic pathways, auxotrophic requirements, sub-compartmentalization and regulation 56 , and therefore require parameterization that capture such differences. A few studies have investigated mammalian-specific parameters describing cell biomass , novel metabolic pathways 12 and cellular objectives 58,14 .…”