title: In silico predicted structural and functional robustness of piscine steroidogenesis.
abstract: Assessments of metabolic robustness or susceptibility are inherently dependent on quantitative descriptions of network structure and associated function. In this paper a stoichiometric model of piscine steroidogenesis was constructed and constrained with productions of selected steroid hormones. Structural and flux metrics of this in silico model were quantified by calculating extreme pathways and optimal flux distributions (using linear programming). Extreme pathway analysis showed progestin and corticosteroid synthesis reactions to be highly participant in extreme pathways. Furthermore, reaction participation in extreme pathways also fitted a power law distribution (degree exponent γ=2.3), which suggested that progestin and corticosteroid reactions act as 'hubs' capable of generating other functionally relevant pathways required to maintain steady-state functionality of the network. Analysis of cofactor usage (O2 and NADPH) showed progestin synthesis reactions to exhibit high robustness, whereas estrogen productions showed highest energetic demands with low associated robustness to maintain such demands. Linear programming calculated optimal flux distributions showed high heterogeneity of flux values with a near-random power law distribution (degree exponent γ≥2.7). Subsequently, network robustness was tested by assessing maintenance of metabolite flux-sum subject to targeted deletions of rank-ordered (low to high metric) extreme pathway participant and optimal flux reactions. Network robustness was susceptible to deletions of extreme pathway participant reactions, whereas minimal impact of high flux reaction deletion was observed. This analysis shows that the steroid network is susceptible to perturbation of structurally relevant (extreme pathway) reactions rather than those carrying high flux.
authors: Institute of Applied Sciences, University of North Texas, Denton, TX 76203, USA. Electronic address: david.hala@unt.edu.
authors: D
authors: D
authors: Hala
authors: Institute of Applied Sciences, University of North Texas, Denton, TX 76203, USA. Electronic address: dbhuggett@unt.edu.
authors: D B
authors: DB
authors: Huggett
mesh_terms: D000728:Androgens / Q000096:biosynthesis; D000818:Animals; D003067:Coenzymes / Q000378:metabolism; D003198:Computer Simulation; D003350:Cortodoxone / Q000378:metabolism; D003530:Cyprinidae / Q000378:metabolism*; D005260:Female; D053858:Metabolic Networks and Pathways / Q000502:physiology; D008954:Models, Biological; D011372:Progestins / Q000096:biosynthesis; D013256:Steroids / Q000096:biosynthesis*
chemical_list: D000728:Androgens; D003067:Coenzymes; D011372:Progestins; D013256:Steroids; D003350:Cortodoxone
keywords: Extreme pathways; Linear programming; Metabolite flux-sum; Power law; Stoichiometric