Stable production of streptavidin, a biotechnological tool

Stable production of streptavidin, a biotechnological tool

New Biotechnology · Volume 29S · September 2012 Poster 4.2.13 Stable production of streptavidin, a biotechnological tool Markus Jeschek 1,∗ , Livia K...

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New Biotechnology · Volume 29S · September 2012

Poster 4.2.13 Stable production of streptavidin, a biotechnological tool Markus Jeschek 1,∗ , Livia Knörr 2 , Thomas R. Ward 2 , Sven Panke 1 1

Bioprocess Laboratory, ETH Zurich, Switzerland Department of Chemistry, University of Basel, Switzerland E-mail address: [email protected] (M. Jeschek). 2

Due to the extraordinary strength of its non-covalent bond to biotin (KD < 10−13 M) streptavidin can be used as a handy and flexible tool in synthetic biology including the use as a protein scaffold for the creation of artificial metalloenzymes that are able to perform bio-orthogonal reactions including transfer hydrogenation, allylic alkylation or enantioselective sulfoxidation[1] . In this case a small molecule organometallic catalyst is incorporated into the streptavidin binding pocket and the protein scaffold can then function as the active pocket of a metallo-enzyme and improve binding, catalytic rate and stereocontrol. To enable streptavidin to fulfill this role, it needs to be evolved, which in turn requires an efficient expression platform. Due to its high affinity for biotin, streptavidin production in E. coli is not straightforward and severe toxic effects ensue upon expression and the resulting biotin depletion. We are currently investigating methods to circumvent this problem by bypassing the enzymatic reaction of the critical acetyl–CoA carboxylase which represents the only biotin dependent reaction in the central metabolism of E. coli. This can be accomplished by the expression of two heterologous proteins, that facilitate the uptake of external malonate and the subsequent conversion to malonyl–CoA[2] . The resulting strain should overcome the toxicity of streptavidin and allow its efficient intracellular production. Initial experiments point towards the importance of a controlled conversion of malonate in order to not unbalance the cell’s coenzyme A equilibrium. This limitation should be overcome by using a low level expression system combined with a malonatelimiting feeding strategy in a fed-batch process. [1] Ward TR. Acc Chem Res. 2011. 44(1):47-57. [2] Lombó F, Pfeifer B, Leaf T, Ou S, Kim YS, Cane DE, Licari P, Khosla C. Biotechnol Prog. 2001. 17(4):612-7 Poster 4.2.14 Network Inference Metabolome Data





glu 1 , Tunahan C ¸ akir 2,∗ Melik Öksüz 1,2 , Hasan Sadiko˘ 1

Gebze Institute of Technology, Department of Chemical Engineering, Turkey 2 Gebze Institute of Technology,Department of Bioengineering, Turkey E-mail address: [email protected] (T. C ¸ akir). Network Inference is a top–down systems biology approach that uses experimental data to uncover the structure of underlying networks. The aim of this study is the determination of biological objectives which gives shape to metabolic networks by S148

using ‘top–down’ computational systems biology methods. For this purpose, two different microorganisms (Escherichia coli and Saccharomyces cerevisiae) were used. In silico, experimental data were generated by simulating kinetic models from literature for different biological variability. Two different approaches were employed to infer metabolic networks from in silico generated experimental data: statistics-based and optimization-based. In statistical approach, Graphical Gaussian Model (GGM) was used since it is known to result in acceptable predictions for inferring biological networks. GGM method generates a sparse network, and hence implies a cellular objective of minimized number of interactions. Several alternative trials were conducted in this study to further improve the results of GGM approach. For instance, a refinement based on correlation coefficient value and their sign gave better results. In optimization approach, Lyapunov equation based jacobian matrix was used as a basis and formulations employing Mixed Integer Linear Programming, Quadratic Programming, and Genetic Algorithm were developed. Furthermore, the integrative use of the two approaches (GGM and optimization) were tested. This research presents a comprehensive analysis of metabolic network inference. Poster 4.2.15 Low stress weekends promote adaptation to stressful weeks: The design principles of the biological response to stress Nilgun Yilmaz 1,∗ , Alexey Kolodkin 2 , Nick Plant 3 , Hans Westerhoff 4 1

Amsterdam, The Netherlands Luxembourg, EU 3 Surrey, UK 4 Manchester, UK 2

Robustness is a fundamental and essential property of evolvable biological systems. It allows system to conserve its functionalities against internal/external perturbations and uncertainties. Product inhibition, feed-forward and feed-back inhibition and stimulation, and regulatory loops within signal transduction networks are a few of the approaches generated by biological systems to maintain both their robustness and adaptability. In this study, we described the interactions of the stress hormone cortisol with its two nuclear receptors, the high affinity glucocorticoid receptor (GR) and the low affinity pregnane X-receptor (PXR) by using a mathematical model based on realistic kinetic parameters. We demonstrate the importance of regulatory loops within this network, in terms of both pharmacodynamic and pharmacokinetic responses. Next, we demonstrate the network response following cortisol challenge; both a single peak in cortisol concentration, reminiscent of a single stress event and a repeated cortisol challenge, reminiscent of repeat stress events with differing frequencies and time frames. As a conclusion, we reveal that the network is robust towards low frequency perturbations, shows adaptation at moderate stress frequencies, but shifts to an altered steady state at high frequency stimulation. The latter