Modelling overflow metabolism in Escherichia coli by acetate cycling

Abstract

A new set of mathematical equations describing overflow metabolism and acetate accumulation in E. coli cultivation is presented. The model is a significant improvement of already existing models in the literature, with modifications based on the more recent concept of acetate cycling in E. coli, as revealed by proteomic studies of overflow routes. This concept opens up new questions regarding the speed of response of the acetate production and its consumption mechanisms in E. coli. The model is formulated as a set of continuous differentiable equations, which significantly improves model tractability and facilitates the computation of dynamic sensitivities in all relevant stages of fermentation (batch, fed-batch, starvation). The model is fitted to data from a simple 2 L fed-batch cultivation of E. coli W3110M, where twelve (12) out of the sixteen (16) parameters were exclusively identified with relative standard deviation less than 10%. The framework presented gives valuable insight into the acetate dilemma in industrial fermentation processes, and serves as a tool for the development, optimization and control of E. coli fermentation processes.

A model‐based framework for parallel scale‐down fed‐batch cultivations in mini‐bioreactors for accelerated phenotyping

“…Therefore, although there was some amount of acetate produced in these cultivations, the higher reassimilation rate did not allow acetate accumulation in the medium. According to the acetate cycling concept (Anane et al, ), the difference in acetate reassimilation rates, relative to its production rates, is what actually leads to different acetate concentrations in the extracellular medium in the cultivations.…”

“…A platform of 21 parallel mini‐bioreactors was used, that was coupled to a robotic liquid handling station (LHS) for automated operation of the cultivations. The operation of the LHS was based on mechanistic model outputs which describe both the dynamic physiological behavior of the strain (Anane, López C, Neubauer, & Cruz Bournazou, ) and gradient profiles of scale‐down bioreactors determined from typical mixing times of large‐scale bioreactors (Anane, Sawatzki, Neubauer, & Cruz‐Bournazou, ). As a demonstration, the platform was used to study the effects of the oscillating glucose/dissolved oxygen concentrations on the amount and quality of recombinant proinsulin expressed in E. coli as inclusion bodies.…”

Modelling concentration gradients in fed‐batch cultivations of E. coli – towards the flexible design of scale‐down experiments

“…Using the ratio of the volumes of the PFR:STR and the feed and recycling rates, it is estimated that a cell, on average, spends about 5 min in the STR before going through one cycle in the PFR, if the residence time in the PFR is set to 30 s . The mathematical model used to describe the macro‐kinetic dynamics of the culture has been presented elsewhere, but a summarized version is presented in the Appendix. This model is a nonlinear ordinary differential equation (ODE) system given as: where t ∈ [ t o , t end ] ⊆ ℝ is the time, are dependent state variables, are the time‐varying inputs or experimental design variables, the unknown parameter vector, and initial conditions are given by x 0 .…”

“…(a)). To formulate the mechanistic model of the 2CR system, the transient solution of the partial differential equation (PDE) system was solved by finite differences with discretization in space (dividing the 3.6 m long PFR into 100 elements), and solving the biomass, glucose, acetate and dissolved oxygen (DOT) concentration profiles of the E. coli model over each finite element. The cumulative time of the transient solution is equal to the desired residence time in the PFR.…”

Identification of cadmium resistance and adsorption gene from Escherichia coli BL21 (DE3)

“…[14][15][16][17][18][19][20][21] Currently, most studies focus on the overexpression of cadmium resistant genes from Gram-positive bacteria to reverse cadmium pollution or to isolate strains in heavy metal contaminated areas: 2 cadA from S. aureus, 22 czc system from Alcaligenes eutrophus, 23 and MTS 16,23,24 and phsABC 25 from Salmonella typhimurium. In fact, E. coli is intrinsically tolerant to high levels of cadmium up to 0.9-1.0 mM.…”