Jürgen Zanghellini

A Comparative Study of Shear Stresses in Collagen-Glycosaminoglycan and Calcium Phosphate Scaffolds in Bone Tissue-Engineering Bioreactors

The increasing demand for bone grafts, combined with their limited availability and potential risks, has led to much new research in bone tissue engineering. Current strategies of bone tissue engineering commonly use cell-seeded scaffolds and flow perfusion bioreactors to stimulate the cells to produce bone tissue suitable for implantation into the patients body. The aim of this study was to quantify and compare the wall shear stresses in two bone tissue engineering scaffold types (collagen-glycosaminoglycan (CG) and calcium phosphate) exposed to fluid flow in a perfusion bioreactor. Based on micro-computed tomography images, three-dimensional numerical computational fluid dynamics (CFD) models of the two scaffold types were developed to calculate the wall shear stresses within the scaffolds. For a given flow rate (normalized according to the cross-sectional area of the scaffolds), shear stress was 2.8 times as high in the CG as in the calcium-phosphate scaffold. This is due to the differences in scaffold geometry, particularly the pore size (CG pore size approximately 96 microm, calcium phosphate pore size approximately 350 microm). The numerically obtained results were compared with those from an analytical method that researchers use widely experimentalists to determine perfusion flow rates in bioreactors. Our CFD simulations revealed that the cells in both scaffold types were exposed to a wide range of wall shear stresses throughout the scaffolds and that the analytical method predicted shear stresses 12% to 21% greater than those predicted using the CFD method. This study demonstrated that the wall shear stresses in calcium phosphate scaffolds (745.2 mPa) are approximately 40 times as high as in CG scaffolds (19.4 mPa) when flow rates are applied that have been experimentally used to stimulate the release of prostaglandin E(2). These findings indicate the importance of using accurate computational models to estimate shear stress and determine experimental conditions in perfusion bioreactors for tissue engineering.

Testing the multi-configuration time-dependent Hartree–Fock method

We test the multi-configuration time-dependent Hartree–Fock method as a new approach towards the numerical calculation of dynamical processes in multi-electron systems using the harmonic quantum dot and one-dimensional helium in strong laser pulses as models. We find rapid convergence for quantities such as ground-state population, correlation coefficient and single ionization towards the exact results. The method converges, where the time-dependent Hartree–Fock method fails qualitatively.

Elementary flux modes in a nutshell: properties, calculation and applications.

Elementary flux mode (EFM) analysis allows the unbiased decomposition of a metabolic network into minimal functional units, making it a powerful tool for metabolic engineering. While the use of EFM analysis (EFMA) is still limited by the size of the models it can handle, EFMA has been successfully applied to solve real-world metabolic engineering problems. Here we provide a user-oriented introduction to EFMA, provide examples of recent applications, analyze current research strategies to overcome the computational restrictions and give an overview over current approaches, which aim to identify and calculate only biologically relevant EFMs.

Quantitative analysis of proteome and lipidome dynamics reveals functional regulation of global lipid metabolism

Elucidating how and to what extent lipid metabolism is remodeled under changing conditions is essential for understanding cellular physiology. Here, we analyzed proteome and lipidome dynamics to investigate how regulation of lipid metabolism at the global scale supports remodeling of cellular architecture and processes during physiological adaptations in yeast. Our results reveal that activation of cardiolipin synthesis and remodeling supports mitochondrial biogenesis in the transition from fermentative to respiratory metabolism, that down-regulation of de novo sterol synthesis machinery prompts differential turnover of lipid droplet-associated triacylglycerols and sterol esters during respiratory growth, that sphingolipid metabolism is regulated in a previously unrecognized growth stage-specific manner, and that endogenous synthesis of unsaturated fatty acids constitutes an in vivo upstream activator of peroxisomal biogenesis, via the heterodimeric Oaf1/Pip2 transcription factor. Our work demonstrates the pivotal role of lipid metabolism in adaptive processes and provides a resource to investigate its regulation at the cellular level.

Metabolomics integrated elementary flux mode analysis in large metabolic networks

Elementary flux modes (EFMs) are non-decomposable steady-state pathways in metabolic networks. They characterize phenotypes, quantify robustness or identify engineering targets. An EFM analysis (EFMA) is currently restricted to medium-scale models, as the number of EFMs explodes with the networks size. However, many topologically feasible EFMs are biologically irrelevant. We present thermodynamic EFMA (tEFMA), which calculates only the small(er) subset of thermodynamically feasible EFMs. We integrate network embedded thermodynamics into EFMA and show that we can use the metabolome to identify and remove thermodynamically infeasible EFMs during an EFMA without losing biologically relevant EFMs. Calculating only the thermodynamically feasible EFMs strongly reduces memory consumption and program runtime, allowing the analysis of larger networks. We apply tEFMA to study the central carbon metabolism of E. coli and find that up to 80% of its EFMs are thermodynamically infeasible. Moreover, we identify glutamate dehydrogenase as a bottleneck, when E. coli is grown on glucose and explain its inactivity as a consequence of network embedded thermodynamics. We implemented tEFMA as a Java package which is available for download at https://github.com/mpgerstl/tEFMA.

regEfmtool: speeding up elementary flux mode calculation using transcriptional regulatory rules in the form of three-state logic.

Despite the considerable progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We present regEfmtool which is an extension to efmtool that utilizes transcriptional regulatory networks for the computation of elementary flux modes. The implemented extension significantly decreases the computational costs for the calculation of elementary flux modes, such as runtime, memory usage and disk space by omitting biologically infeasible solutions. Hence, using the presented regEfmtool pushes the size of metabolic networks that can be studied by elementary flux modes to new limits.

Designing optimal cell factories: integer programming couples elementary mode analysis with regulation.

BackgroundElementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information.ResultsHere we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible.ConclusionsWe used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli.

tEFMA: computing thermodynamically feasible elementary flux modes in metabolic networks

UNLABELLED : Elementary flux modes (EFMs) are important structural tools for the analysis of metabolic networks. It is known that many topologically feasible EFMs are biologically irrelevant. Therefore, tools are needed to find the relevant ones. We present thermodynamic tEFM analysis (tEFMA) which uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible EFMs. Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared with standard approaches, tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks. AVAILABILITY AND IMPLEMENTATION https://github.com/mpgerstl/tEFMA CONTACT: : christian.jungreuthmayer@boku.ac.at or juergen.zanghellini@boku.ac.at SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Comparison and improvement of algorithms for computing minimal cut sets

BackgroundConstrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable.ResultsHere we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach.ConclusionsGenerally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations.

The 3D pore structure and fluid dynamics simulation of macroporous monoliths: High permeability due to alternating channel width.

Polymethacrylate-based monoliths have excellent flow properties. Flow in the wide channel interconnected with narrow channels is theoretically assumed to account for favorable permeability. Monoliths were cut into 898 slices in 50nm distances and visualized by serial block face scanning electron microscopy (SBEM). A 3D structure was reconstructed and used for the calculation of flow profiles within the monolith and for calculation of pressure drop and permeability by computational fluid dynamics (CFD). The calculated and measured permeabilities showed good agreement. Small channels clearly flowed into wide and wide into small channels in a repetitive manner which supported the hypothesis describing the favorable flow properties of these materials. This alternating property is also reflected in the streamline velocity which fluctuated. These findings were corroborated by artificial monoliths which were composed of regular (interconnected) cells where narrow cells followed wide cells. In the real monolith and the artificial monoliths with interconnected flow channels similar velocity fluctuations could be observed. A two phase flow simulation showed a lateral velocity component, which may contribute to the transport of molecules to the monolith wall. Our study showed that the interconnection of small and wide pores is responsible for the excellent pressure flow properties. This study is also a guide for further design of continuous porous materials to achieve good flow properties.