title: A flexible state-space approach for the modeling of metabolic networks II: advanced interrogation of hybridoma metabolism.
abstract: Having previously introduced the mathematical framework of topological metabolic analysis (TMA) - a novel optimization-based technique for modeling metabolic networks of arbitrary size and complexity - we demonstrate how TMA facilitates unique methods of metabolic interrogation. With the aid of several hybridoma metabolic investigations as case-studies (Bonarius et al., 1995, 1996, 2001), we first establish that the TMA framework identifies biologically important aspects of the metabolic network under investigation. We also show that the use of a structured weighting approach within our objective provides a substantial modeling benefit over an unstructured, uniform, weighting approach. We then illustrate the strength of TAM as an advanced interrogation technique, first by using TMA to prove the existence of (and to quantitatively describe) multiple topologically distinct configurations of a metabolic network that each optimally model a given set of experimental observations. We further show that such alternate topologies are indistinguishable using existing stoichiometric modeling techniques, and we explain the biological significance of the topological variables appearing within our model. By leveraging the manner in which TMA implements metabolite inputs and outputs, we also show that metabolites whose possible metabolic fates are inadequately described by a given network reconstruction can be quickly identified. Lastly, we show how the use of the TMA aggregate objective function (AOF) permits the identification of modeling solutions that can simultaneously consider experimental observations, underlying biological motivations, or even purely engineering- or design-based goals.
authors.affiliation: Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Adam C
authors.initials: AC
authors.lastname: Baughman
authors.forename: Susan T
authors.initials: ST
authors.lastname: Sharfstein
authors.forename: Lealon L
authors.initials: LL
authors.lastname: Martin
mesh_terms: D000465:Algorithms; D000818:Animals; D002247:Carbon Isotopes / Q000378:metabolism; D006825:Hybridomas / Q000378:metabolism*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted
chemical_list: D002247:Carbon Isotopes