Stefan Schuster

Last updated
Stefan Schuster
Prof Stefan Schuster.JPG
Born (1961-11-07) 7 November 1961 (age 62)
Meissen, Germany
Nationality German
Alma mater Humboldt University of Berlin
Known for metabolism, signal transduction, metabolic control analysis, evolutionary game theory
Scientific career
Institutions University of Jena
Thesis Theoretical studies on the interrelation between time hierarchy in enzymatic reaction systems and optimization principles
Academic advisors Reinhart Heinrich

Stefan Schuster (born 7 November 1961 in Meissen) is a German biophysicist. He is professor for bioinformatics at the University of Jena.

Contents

Life

Stefan Schuster studied biophysics at the Humboldt University Berlin and wrote his PhD thesis under the supervision of Prof. Reinhart Heinrich at the Department of Theoretical Biophysics at Humboldt University, Berlin (Title: "Theoretical studies on the interrelation between time hierarchy in enzymatic reaction systems and optimization principles"). In 2003 he got a professorship at the Department of Bioinformatics at the Friedrich Schiller University, Jena.

Stefan Schuster is one of the spokesmen of the Jena Centre for Bioinformatics (JCB).

Stefan Schuster is currently editor of the Elsevier journal BioSystems .

His younger brother is the stage director Robert Schuster.

Research

The research by Stefan Schuster comprises a wide range of topics in bioinformatics and systems biology. These include, among others:

Stefan Schuster has significantly contributed to the development of elementary mode analysis. [6] [7] [8] That method has amply been used ever since for determining metabolic pathways and diverse applications in biotechnology such as calculating optimal molar yields. Schuster and his coworkers used the method, for example, for analyzing penicillin production [9] and NAD+ metabolism [10] as well as for predicting the viability of Escherichia coli mutants. [11] He contributed to the development of software for metabolic pathway analysis. [12]

An application of intense biochemical interest is the question whether humans and other higher animals could convert fatty acids into sugar. While biochemical textbook knowledge says that this would be infeasible, in silico analyses by Christoph Kaleta, Stefan Schuster and coworkers showed that there are, in principle, several entangled routes on which gluconeogenesis from fatty acid is feasible. This theoretical prediction found considerable attention in online articles. [13] [14]

Research on metabolic pathways includes flux balance analysis, which is used, for example, for explaining the Warburg effect. [15]

The book of Reinhard Heinrich and Stefan Schuster "The Regulation of Cellular Systems" [16] was reviewed by Athel Cornish-Bowden. [17] He wrote: "For general readers, it would be a major advance if books like this one could help to overthrow the ideas of rate-limiting steps that have bedevilled the biochemical conception of metabolism for so long, preventing biotechnology from realizing many of the objectives that were promised when genetic engineering first became possible. For specialists already concerned with the kinetic behaviour of multi-enzyme systems, this is a book they need to have".

Related Research Articles

<span class="mw-page-title-main">Metabolism</span> Set of chemical reactions in organisms

Metabolism is the set of life-sustaining chemical reactions in organisms. The three main functions of metabolism are: the conversion of the energy in food to energy available to run cellular processes; the conversion of food to building blocks of proteins, lipids, nucleic acids, and some carbohydrates; and the elimination of metabolic wastes. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. The word metabolism can also refer to the sum of all chemical reactions that occur in living organisms, including digestion and the transportation of substances into and between different cells, in which case the above described set of reactions within the cells is called intermediary metabolism.

<span class="mw-page-title-main">Gene regulatory network</span> Collection of molecular regulators

A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).

<span class="mw-page-title-main">Systems biology</span> Computational and mathematical modeling of complex biological systems

Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.

Modelling biological systems is a significant task of systems biology and mathematical biology. Computational systems biology aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems. It involves the use of computer simulations of biological systems, including cellular subsystems, to both analyze and visualize the complex connections of these cellular processes.

<span class="mw-page-title-main">Metabolic engineering</span>

Metabolic engineering is the practice of optimizing genetic and regulatory processes within cells to increase the cell's production of a certain substance. These processes are chemical networks that use a series of biochemical reactions and enzymes that allow cells to convert raw materials into molecules necessary for the cell's survival. Metabolic engineering specifically seeks to mathematically model these networks, calculate a yield of useful products, and pin point parts of the network that constrain the production of these products. Genetic engineering techniques can then be used to modify the network in order to relieve these constraints. Once again this modified network can be modeled to calculate the new product yield.

<span class="mw-page-title-main">Henrik Kacser</span> Hungarian biochemist and geneticist

Henrik Kacser FRSE was a Romanian-born biochemist and geneticist who worked in Britain in the 20th century. Kacser's achievements have been recognised by his election to the Royal Society of Edinburgh in 1990, by an honorary doctorate of the University of Bordeaux II in 1993.

<span class="mw-page-title-main">Metabolic network modelling</span> Form of biological modelling

Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.

<span class="mw-page-title-main">Metabolic control analysis</span> Metabolic control

Metabolic control analysis (MCA) is a mathematical framework for describing metabolic, signaling, and genetic pathways. MCA quantifies how variables, such as fluxes and species concentrations, depend on network parameters. In particular, it is able to describe how network-dependent properties, called control coefficients, depend on local properties called elasticities or Elasticity Coefficients.

A cell-free system is an in vitro tool widely used to study biological reactions that happen within cells apart from a full cell system, thus reducing the complex interactions typically found when working in a whole cell. Subcellular fractions can be isolated by ultracentrifugation to provide molecular machinery that can be used in reactions in the absence of many of the other cellular components. Eukaryotic and prokaryotic cell internals have been used for creation of these simplified environments. These systems have enabled cell-free synthetic biology to emerge, providing control over what reaction is being examined, as well as its yield, and lessening the considerations otherwise invoked when working with more sensitive live cells.

<span class="mw-page-title-main">Metabolic flux analysis</span> Experimental fluxomics technique

Metabolic flux analysis (MFA) is an experimental fluxomics technique used to examine production and consumption rates of metabolites in a biological system. At an intracellular level, it allows for the quantification of metabolic fluxes, thereby elucidating the central metabolism of the cell. Various methods of MFA, including isotopically stationary metabolic flux analysis, isotopically non-stationary metabolic flux analysis, and thermodynamics-based metabolic flux analysis, can be coupled with stoichiometric models of metabolism and mass spectrometry methods with isotopic mass resolution to elucidate the transfer of moieties containing isotopic tracers from one metabolite into another and derive information about the metabolic network. Metabolic flux analysis (MFA) has many applications such as determining the limits on the ability of a biological system to produce a biochemical such as ethanol, predicting the response to gene knockout, and guiding the identification of bottleneck enzymes in metabolic networks for metabolic engineering efforts.

Fluxomics describes the various approaches that seek to determine the rates of metabolic reactions within a biological entity. While metabolomics can provide instantaneous information on the metabolites in a biological sample, metabolism is a dynamic process. The significance of fluxomics is that metabolic fluxes determine the cellular phenotype. It has the added advantage of being based on the metabolome which has fewer components than the genome or proteome.

<span class="mw-page-title-main">Reinhart Heinrich</span> German biophysicist

Reinhart Heinrich was a German biophysicist.

David A. Fell is a British biochemist and professor of systems biology at Oxford Brookes University. He has published over 200 publications, including a textbook on Understanding the control of metabolism in 1996.

<span class="mw-page-title-main">Athel Cornish-Bowden</span> British biochemist

Athel Cornish-Bowden is a British biochemist known for his numerous textbooks, particularly those on enzyme kinetics and his work on metabolic control analysis.

Herbert M. Sauro works in the field of metabolic control analysis and systems biology.

In biochemistry, a rate-limiting step is a step that controls the rate of a series of biochemical reactions. The statement is, however, a misunderstanding of how a sequence of enzyme catalyzed reaction steps operate. Rather than a single step controlling the rate, it has been discovered that multiple steps control the rate. Moreover, each controlling step controls the rate to varying degrees.

<span class="mw-page-title-main">Markus Ralser</span> Italian biologist

Markus Ralser is an Italian biologist. His main research interest is metabolism of microorganisms. He is also known for his work on the origin of metabolism during the origin of life, and proteomics.

libRoadRunner is a C/C++ software library that supports simulation of SBML based models.. It uses LLVM to generate extremely high-performance code and is the fastest SBML-based simulator currently available. Its main purpose is for use as a reusable library that can be hosted by other applications, particularly on large compute clusters for doing parameter optimization where performance is critical. It also has a set of Python bindings that allow it to be easily used from Python as well as a set of bindings for Julia.

In metabolic control analysis, a variety of theorems have been discovered and discussed in the literature. The most well known of these are flux and concentration control coefficient summation relationships. These theorems are the result of the stoichiometric structure and mass conservation properties of biochemical networks. Equivalent theorems have not been found, for example, in electrical or economic systems.

References

  1. ↑ Pfeiffer, T.; Schuster, S. (2005). "Game-theoretical approaches to studying the evolution of biochemical systems". Trends in Biochemical Sciences. 30 (1): 20–25. doi:10.1016/j.tibs.2004.11.006. PMID   15653322.
  2. ↑ Schuster, S. (1996). "Control Analysis in Terms of Generalized Variables Characterizing Metabolic Systems". Journal of Theoretical Biology. 182 (3): 259–268. Bibcode:1996JThBi.182..259S. doi:10.1006/jtbi.1996.0163. PMID   8944157.
  3. ↑ Schuster, S.; Marhl, M.; Höfer, T. (2002). "Modelling of simple and complex calcium oscillations: From single-cell responses to intercellular signalling". European Journal of Biochemistry. 269 (5): 1333–1355. doi:10.1046/j.0014-2956.2001.02720.x. PMID   11874447.
  4. ↑ Bodenstein, C.; Heiland, I.; Schuster, S. (2012). "Temperature compensation and entrainment in circadian rhythms". Physical Biology. 9 (3): 036011. Bibcode:2012PhBio...9c6011B. doi:10.1088/1478-3975/9/3/036011. ISSN   1478-3967. PMID   22683844. S2CID   22888493.
  5. ↑ Schuster, S.; de Figueiredo, L.F.; Schroeter, A.; Kaleta, C. (2011). "Combining Metabolic Pathway Analysis with Evolutionary Game Theory. Explaining the occurrence of low-yield pathways by an analytic optimization approach". Biosystems. 105 (2): 147–153. Bibcode:2011BiSys.105..147S. doi:10.1016/j.biosystems.2011.05.007. PMID   21620931.
  6. ↑ Schuster, S.; Hilgetag, C.; Woods, J.H.; Fell, D.A. (2002). "Reaction routes in biochemical reaction systems: Algebraic properties, validated calculation procedure and example from nucleotide metabolism". Journal of Mathematical Biology. 45 (2): 153–181. doi:10.1007/s002850200143. ISSN   0303-6812. PMID   12181603. S2CID   18109186.
  7. ↑ Schuster, S; Dandekar, T; Fell, D.A. (1999). "Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering". Trends in Biotechnology. 17 (2): 53–60. doi:10.1016/S0167-7799(98)01290-6. PMID   10087604.
  8. ↑ Schuster, S.; Fell, D.A.; Dandekar, T. (2000). "A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks". Nature Biotechnology. 18 (3): 326–332. doi:10.1038/73786. ISSN   1087-0156. PMID   10700151. S2CID   7742485.
  9. ↑ Prauße, M.T.E.; Schäuble, S.; Guthke, R.; Schuster, S. (2016). "Computing the various pathways of penicillin synthesis and their molar yields". Biotechnology and Bioengineering. 113 (1): 173–181. doi:10.1002/bit.25694. PMID   26134880. S2CID   31216001.
  10. ↑ de Figueiredo, L.F.; Gossmann, T.I.; Ziegler, M.; Schuster, S. (2011). "Pathway analysis of NAD + metabolism" (PDF). Biochemical Journal. 439 (2): 341–348. doi:10.1042/BJ20110320. ISSN   0264-6021. PMID   21729004.
  11. ↑ Stelling, J.; Klamt, S.; Bettenbrock, K.; Schuster, S.; Gilles, E.D. (2002). "Metabolic network structure determines key aspects of functionality and regulation". Nature. 420 (6912): 190–193. Bibcode:2002Natur.420..190S. doi:10.1038/nature01166. ISSN   0028-0836. PMID   12432396. S2CID   4301741.
  12. ↑ Kamp, A. von; Schuster, S. (2006). "Metatool 5.0: fast and flexible elementary modes analysis". Bioinformatics. 22 (15): 1930–1931. doi: 10.1093/bioinformatics/btl267 . ISSN   1367-4803. PMID   16731697.
  13. ↑ Informationsdienst Wissenschaft
  14. ↑ Ärztezeitung 2011
  15. ↑ Schuster, S.; Boley, D.; Moller, P.; Stark, H.; Kaleta, C. (2015). "Mathematical models for explaining the Warburg effect: a review focussed on ATP and biomass production". Biochemical Society Transactions. 43 (6): 1187–1194. doi:10.1042/BST20150153. ISSN   0300-5127. PMID   26614659.
  16. ↑ Heinrich, R.; Schuster, S. (1996). The Regulation of Cellular Systems. Boston, MA: Springer US. ISBN   9781461311614. OCLC   840281317.
  17. ↑ Cornish-Bowden, A. (1998). "The Regulation of Cellular Systems, by Reinhart Heinrich and Stefan Schuster, Chapman and Hall, New York, 1996. 372 pp". Bulletin of Mathematical Biology. 59 (5): 1027–1028. doi:10.1016/S0092-8240(97)00050-5.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.