Abstract
BackgroundStreptomyces lividans is an appealing host for the production of proteins of biotechnological interest due to its relaxed exogenous DNA restriction system and its ability to secrete proteins directly to the medium through the major Sec or the minor Tat routes. Often, protein secretion displays non-uniform time-dependent patterns. Understanding the associated metabolic changes is a crucial step to engineer protein production. Dynamic Flux Balance Analysis (DFBA) allows the study of the interactions between a modelled organism and its environment over time. Existing methods allow the specification of initial model and environment conditions, but do not allow introducing arbitrary modifications in the course of the simulation. Living organisms, however, display unexpected adaptive metabolic behaviours in response to unpredictable changes in their environment. Engineering the secretion of products of biotechnological interest has systematically proven especially difficult to model using DFBA. Accurate time-dependent modelling of complex and/or arbitrary, adaptive metabolic processes demands an extended approach to DFBA.ResultsIn this work, we introduce Adaptive DFBA, a novel, versatile simulation approach that permits inclusion of changes in the organism or the environment at any time in the simulation, either arbitrary or interactively responsive to environmental changes. This approach extends traditional DFBA to allow steering arbitrarily complex simulations of metabolic dynamics. When applied to Sec- or Tat-dependent secretion of overproduced proteins in S. lividans, Adaptive DFBA can overcome the limitations of traditional DFBA to reproduce experimental data on plasmid-free, plasmid bearing and secretory protein overproducing S. lividans TK24, and can yield useful insights on the behaviour of systems with limited experimental knowledge such as agarase or amylase overproduction in S. lividans TK21.ConclusionsAdaptive DFBA has allowed us to overcome DFBA limitations and to generate more accurate models of the metabolism during the overproduction of secretory proteins in S. lividans, improving our understanding of the underlying processes. Adaptive DFBA is versatile enough to permit dynamical metabolic simulations of arbitrarily complex biotechnological processes.
Highlights
Streptomyces lividans is an appealing host for the production of proteins of biotechnological interest due to its relaxed exogenous Deoxyribonucleic acid (DNA) restriction system and its ability to secrete proteins directly to the medium through the major Sec or the minor Tat routes
In our experience, we found that LP-SOLVE gave different results in several of the calculated simulations, GLPK failed to run in only one specific combination of simulation parameters, operating system and architecture, CLP worked correctly in all cases but was significantly slower than GLPK, and CPLEX was tested only in the free, academic version, which accepts a limited number of equations: for systems within this limit, it was the fastest method, but we could not check it with larger systems
Descriptive modelling The systems modelled correspond to Streptomyces lividans TK24 strains growing on a complex medium (NMMP, supplemented with glucose and casamino acids)
Summary
Streptomyces lividans is an appealing host for the production of proteins of biotechnological interest due to its relaxed exogenous DNA restriction system and its ability to secrete proteins directly to the medium through the major Sec or the minor Tat routes. Streptomycetes possess distinctive characteristics that make them an appealing model system for engineering overproduction of biotechnologically interesting products: they are capable of producing a large array of antibiotics and other compounds of interest, and interestingly, large amounts of extracellular proteins [1]. This is coupled with a relaxed DNA restriction system, which facilitates employment of functional plasmids and cloning and propagating heterologous DNA sequences [1, 2]. Relevant differences have been identified in the cellular response to Sec- and Tatdependent protein secretion, as well as in the secretion patterns and preference for growth phases [5, 6]
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