Workshop 2: Understanding the Control of Metabolism

Total Page:16

File Type:pdf, Size:1020Kb

Workshop 2: Understanding the Control of Metabolism Understanding the Control of Metabolism C1net Workshop 2; Day 2 David Fell [email protected] http://mudshark.brookes.ac.uk C1netW2 2015 L3: - p. 1 Outline Introduction ● Outline ● The context: manipulating ■ Elements of Metabolic Control Analysis (MCA) metabolism ● The rate–limiting step concept ◆ The flux control coefficient ● Quotes ● Critique of ‘rate–limiting’ steps Control Coefficients ◆ Control coefficients and enzyme kinetics Control coefficients and enzyme kinetics ◆ Flux control coefficients in context Elasticities Connectivity theorem Relevance of flux control coefficients Problems C1netW2 2015 L3: - p. 2 The context: manipulating metabolism Introduction Two different problems: ● Outline ● The context: manipulating ■ Easy: stopping flux to a product through a pathway. (Pick an metabolism ● The rate–limiting step concept essential enzyme; knock out by mutation or inhibition.) ● Quotes ● Critique of ‘rate–limiting’ steps Control Coefficients ■ Hard: increasing flux to a product through a pathway. Control coefficients and enzyme kinetics Elasticities Connectivity theorem Why isn’t the solution to the hard problem: Relevance of flux control 1. find the rate–limiting enzyme, and coefficients Problems 2. increase the amount of this enzyme? C1netW2 2015 L3: - p. 3 The rate–limiting step concept Introduction ● Outline ● The context: manipulating metabolism ● The rate–limiting step concept When a process is conditioned as to its rapidity by a number of ● Quotes ● Critique of ‘rate–limiting’ steps separate factors, the rate of the process is limited by the pace Control Coefficients of the slowest factor. Blackman (1905). Control coefficients and enzyme kinetics Elasticities Connectivity theorem Relevance of flux control coefficients Problems C1netW2 2015 L3: - p. 4 Quotes Introduction ● Outline ■ The first committed step being irreversible . most metabolic ● The context: manipulating metabolism pathways are controlled by regulating . their first committed ● The rate–limiting step concept ● Quotes steps (Voet & Voet, 1990). ● Critique of ‘rate–limiting’ steps Control Coefficients ■ ...an entire pathway can be controlled by regulating only the Control coefficients and enzyme enzyme that catalyzes the first step in the pathway (Zubay et kinetics al, 1995). Elasticities Connectivity theorem ■ The first enzyme of a pathway is usually a strategic place for Relevance of flux control control (Elliott & Elliott, 1997). coefficients Problems ■ In a multistep pathway the first enzyme is ususally regulated and the others are not (Zubay, 1998). C1netW2 2015 L3: - p. 5 Critique of ‘rate–limiting’ steps Introduction ● Outline ● The context: manipulating ■ It is not possible for one step in a pathway to be ‘slower’ than metabolism ● The rate–limiting step concept the others. ● Quotes ● Critique of ‘rate–limiting’ steps Control Coefficients ■ The flux through multistep pathways, even with simple Control coefficients and enzyme kinetics kinetics, has long been known to depend in principle on all Elasticities the steps. Connectivity theorem Relevance of flux control ■ The experimental evidence is that the genuinely rate–limiting coefficients step is rare. Problems C1netW2 2015 L3: - p. 6 Introduction Control Coefficients ● Metabolic Control Analysis ● The flux–enzyme relationship ● The flux–enzyme relationship ● A specimen pathway ● Definition of the flux control coefficient Control Coefficients ● Definition of the flux control coefficient ● Values of the flux control coefficient ● Definition of the flux control coefficient ● Experimental effect of reduced SBPase. ● The flux summation theorem ● Flux control is a system property ● The Concentration Control Coefficent ● Concentration Control Summation Control coefficients and enzyme kinetics Elasticities Connectivity theorem Relevance of flux control coefficients Problems C1netW2 2015 L3: - p. 7 Metabolic Control Analysis Introduction originated with: Control Coefficients ■ Henrik Kacser & Jim Burns (Edinburgh) and ● Metabolic Control Analysis ● The flux–enzyme relationship ● The flux–enzyme relationship ● A specimen pathway ■ Reinhart Heinrich & Tom Rapoport (Berlin) ● Definition of the flux control coefficient ● Definition of the flux control independently in 1973 (based in part on earlier work by Joe coefficient ● Values of the flux control Higgins). coefficient ● Definition of the flux control coefficient ● Experimental effect of Kacser, H. and Burns, J. A. (1973) Symp. Soc. Exp. Biol. 27, 65–104. Reprinted in Biochem. Soc. Trans. 23, 341–366, reduced SBPase. ● The flux summation theorem (1995). ● Flux control is a system property Heinrich, R. and Rapoport, T. A. (1974) Eur. J. Biochem. 42, 89–95, 97–105. ● The Concentration Control Coefficent ● Concentration Control Summation Control coefficients and enzyme kinetics Elasticities Connectivity theorem Relevance of flux control coefficients Problems C1netW2 2015 L3: - p. 8 The flux–enzyme relationship 10 Introduction Control Coefficients ● Metabolic Control Analysis 8 ● The flux–enzyme relationship ● The flux–enzyme relationship ● A specimen pathway ● Definition of the flux control 6 coefficient ● Definition of the flux control coefficient ● Values of the flux control coefficient 4 ● Definition of the flux control coefficient ● Experimental effect of Tryptophan flux reduced SBPase. ● The flux summation theorem 2 ● Flux control is a system property ● The Concentration Control Coefficent 0 ● Concentration Control Summation 0 20 40 60 80 100 120 Control coefficients and enzyme kinetics Try 2,3-dioxygenase Elasticities A typical example: Tryptophan 2,3–dioxygenase was adjusted Connectivity theorem by various dietary and hormonal treatments Relevance of flux control Results of Salter et al (1986). coefficients Problems C1netW2 2015 L3: - p. 9 The flux–enzyme relationship Introduction Control Coefficients ● Metabolic Control Analysis ● The flux–enzyme relationship ● The flux–enzyme relationship ● A specimen pathway ● Definition of the flux control coefficient ● Definition of the flux control coefficient ● Values of the flux control coefficient ● Definition of the flux control coefficient ● Experimental effect of reduced SBPase. ● The flux summation theorem ● Flux control is a system property ● The Concentration Control Coefficent ● Concentration Control Summation Control coefficients and enzyme kinetics Elasticities A typical example: dependence of carbon assimilation flux on Connectivity theorem rubisco levels in transgenic tobacco plants. Relevance of flux control Results of Laurer et al, Planta 190 332-345 (1993). coefficients Problems C1netW2 2015 L3: - p. 10 A specimen pathway Introduction Control Coefficients ● Metabolic Control Analysis ● The flux–enzyme relationship xase ydh zase ● The flux–enzyme relationship ● A specimen pathway X0 −→ Y −→ Z −→ X1 ● Definition of the flux control coefficient ● Definition of the flux control coefficient ● Values of the flux control coefficient ● Definition of the flux control coefficient ● Experimental effect of X0 is termed the source reduced SBPase. ● The flux summation theorem ● Flux control is a system property X1 is the sink ● The Concentration Control Coefficent ● Concentration Control Summation Y and Z are the variable metabolites that reach constant levels Control coefficients and enzyme at steady state, when their rates of formation equal their rates kinetics of utilization. Elasticities Connectivity theorem Relevance of flux control coefficients Problems C1netW2 2015 L3: - p. 11 Definition of the flux control coefficient Introduction Control Coefficients Suppose a small change, δE , is made in the amount of ● Metabolic Control Analysis xase ● The flux–enzyme relationship enzyme Exase, and that this produces a small change in the ● The flux–enzyme relationship ● A specimen pathway flux through the step catalyzed by ydh. ● Definition of the flux control coefficient ● Definition of the flux control coefficient Jydh ● Values of the flux control The flux control coefficient Cxase is approximately the % coefficient ● Definition of the flux control change in Jydh produced by a 1% change in Exase. coefficient ● Experimental effect of reduced SBPase. ● The flux summation theorem ● Flux control is a system property ● The Concentration Control Coefficent ● Concentration Control Summation Control coefficients and enzyme kinetics Elasticities Connectivity theorem Relevance of flux control coefficients Problems C1netW2 2015 L3: - p. 12 Definition of the flux control coefficient Introduction @ J ydh . ... ... ... ... .. ... ... .. @ E . ... ... .. .. ... ... .. xase . ... .. .. .. Control Coefficients . ... .. .. ... .. .. ... .. .. .. .. ... .. .. .. .. .. .. ... .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ● . .. .. .. .. .. Metabolic Control Analysis . .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ● .. .. .... .. ..... ..... .. The flux–enzyme relationship .... ... .. .... ... .. ... .. .... .. .. ... .. .... .. ... .. .. .... .. .. ... ... .. ... ... ● ... ... ... The flux–enzyme relationship . ... ... ... ... ... ... j . ... ... ... .. .. .. ... ● ... .. .. .. A specimen pathway ... .. .. .. .. ... ... .. .. .. ... .. .. .. .. ... .. .. .. .. ● .. Definition of the flux control . .. .. .. .. .. .. .. coefficient . .. .. .. .. ● .. Definition of the flux control . .. .. coefficient . ydh . ● . Values of the flux control . J . @ J J . ydh . ydh . e . coefficient . C . xase . ● . Definition of the flux control . @ E j . xase . coefficient . ● . Flux . Experimental effect of . reduced SBPase. ● . The flux summation theorem . ● . Flux control is a system . property . ● . The Concentration Control . Coefficent . ● . Concentration
Recommended publications
  • Reinhart Heinrich (1946–2006) Pioneer in Systems Biology
    NEWS & VIEWS NATURE|Vol 444|7 December 2006 OBITUARY Reinhart Heinrich (1946–2006) Pioneer in systems biology. In biology, mathematical systems analysis where he showed that the flux of was until recently nearly invisible in the reaction was shared by several the dazzling light of twentieth-century enzymes. Much later, he extended discoveries. But it has emerged from the his ideas to signal-transduction shadows in the field of systems biology, pathways, introducing control a subject buoyed by immense data sets, coefficients to dynamic processes. conveyed by heavy computing power, and Sticking to real examples, such as addressing seemingly incomprehensible the Wnt signalling and MAP kinase forms of complexity. If systems biology has pathways, he again demonstrated heroes, one of them is Reinhart Heinrich, a that new properties and constraints former professor at the Humboldt University emerge when the individual steps in Berlin, who died on 23 October, aged are combined into a complete 60. His most famous accomplishment was pathway. metabolic control theory, published in Heinrich also pointed the way to 1974 with Tom Rapoport and formulated considerations of optimality theory independently by Henrik Kacser and James and evolution that will confront A. Burns in Edinburgh, UK. systems biology for the next From the 1930s to the 1960s, biochemists century. The question of evolution were busy describing metabolic pathways, lies just beneath any effort to just as molecular biologists today are understand biology. Yet in most feverishly trying to inventory the cell’s cases, physiological function and gene-transcription and signalling circuits. evolutionary change are considered The basic kinetic features of the enzymes in distinct and are investigated by the major pathways were studied in great different people.
    [Show full text]
  • Implications for Optimal Distributions of Enzyme Concentrations and for the Distribution of flux Control
    BioSystems 54 (1999) 1–14 www.elsevier.com/locate/biosystems Competition for enzymes in metabolic pathways: Implications for optimal distributions of enzyme concentrations and for the distribution of flux control Edda Klipp *, Reinhart Heinrich Humboldt Uni6ersity Berlin, Institute of Biology, Theoretical Biophysics, In6alidenstr. 42, D-10115, Berlin, Germany Received 1 February 1999; accepted 25 April 1999 Abstract The structures of biochemical pathways are assumed to be determined by evolutionary optimization processes. In the framework of mathematical models, these structures should be explained by the formulation of optimization principles. In the present work, the principle of minimal total enzyme concentration at fixed steady state fluxes is applied to metabolic networks. According to this principle there exists a competition of the reactions for the available amount of enzymes such that all biological functions are maintained. In states which fulfil these optimization criteria the enzyme concentrations are distributed in a non-uniform manner among the reactions. This result has conse- quences for the distribution of flux control. It is shown that the flux control matrix c, the elasticity matrix o, and the vector e of enzyme concentrations fulfil in optimal states the relations cTe=e and o Te=0. Starting from a well-balanced distribution of enzymes the minimization of total enzyme concentration leads to a lowering of the SD of the flux control coefficients. © 1999 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Metabolic control theory; Evolutionary optimization; Competition; Enzyme concentrations; Flux control coefficient; Metabolic pathway; Mathematical modeling 1. Introduction optimal manner. On this basis one can draw conclusions about the actual states of metabolic The structural properties of metabolic systems systems using optimization criteria.
    [Show full text]
  • Fitness As a Function of Β-Galactosidase Activity In
    Genet. Res., Camb. (1986), 48, pp. 1-8 With 3 text-figures Printed in Great Britain Fitness as a function of /?-galactosidase activity in Escherichia coli ANTONY M. DEAN, DANIEL E. DYKHUIZEN AND DANIEL L. HARTL Department of Genetics, Washington University School of Medicine, St Louis, Missouri USA 63110-1095 (Received 12 July 1985 and in revised form 13 January 1986) Summary Chemostat cultures in which the limiting nutrient was lactose have been used to study the relative growth rate of Escherichia coli in relation to the enzyme activity of /?-galactosidase. A novel genetic procedure was employed in order to obtain amino acid substitutions within the /acZ-encoded /?-galactosidase that result in differences in enzyme activity too small to be detected by ordinary mutant screens. The cryptic substitutions were obtained as spontaneous revertants of nonsense mutations within the lacZ gene, and the enzymes differing from wild type were identified by means of polyacrylamide gel electrophoresis or thermal denaturation studies. The relation between enzyme activity and growth rate of these and other mutants supports a model of intermediary metabolism in which the flux of substrate through a metabolic pathway is represented by a concave function of the activity of any enzyme in the pathway. The consequence is that small differences in enzyme activity from wild type result in even smaller changes in fitness. 1. Introduction sidase activity and growth rate in E. coli, together with a novel genetic technique for obtaining amino acid In the seminal paper, Kacser & Burns (1973) demon- strated that the net flux through a simple linear meta- substitutions that result in electrophoretic or thermo- bolic pathway should be a concave function of the lability differences from wild type.
    [Show full text]
  • Uva-DARE (Digital Academic Repository)
    UvA-DARE (Digital Academic Repository) Henrik Kacser 1918-1995: Metabolism of control. Westerhoff, H.V. DOI 10.1016/S0167-7799(00)88956-8 Publication date 1995 Published in Trends in Biotechnology Link to publication Citation for published version (APA): Westerhoff, H. V. (1995). Henrik Kacser 1918-1995: Metabolism of control. Trends in Biotechnology, 13, 245. https://doi.org/10.1016/S0167-7799(00)88956-8 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:25 Sep 2021 245 f orum Henrik Kacser (19184995): metabolism of control Obituary Henrik Kacser has been referred missed the rate-limiting step. He funding agency had allowed much to as the ‘pope’ of Metabolic much appreciated
    [Show full text]
  • Principles of Systems Biology and Dmitri Belyaev's Co-Selection of Traits
    Principles of systems biology and Dmitri Belyaev’s co-selection of traits Hans V. Westerhoff and friends MCISB, MIB, SCEAS, University of Manchester SysBA, Universities of Amsterdam Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity Bioinformatics: From biological data to information Systems Biology: From that information to understanding Systems Biology: From data to understanding: why is this such an issue? • Because the mapping from genome to function is extremely nonlinear • E.g.: – - – - The DNA in all our cells is the same, but: a heart cell is essentially different from a brain cell – - Self organization, bistability: Belousov, Zhabotinsky, Waddington, Ilya Prigogine, Boris Kholodenko Why systems biology? Cause 1 ~all functions are X network functions Multiple causality Cause 2 Cause 3 X Multifactorial X disease Impaired function 2006 Hornberg et al: ‘Cancer: a systems biology disease’. Now: ‘virtually all disease are Systems Biology diseases.’ This causes the ‘missing heritability problem (Baranov; Stepanov)’ Systems Biology= • The Science that • aims to understand • principles governing • how the biological functions • arise from the interactions = from the networking This leads to precision, personalized, 4P medicine, PPP4M And to precision biotechnology Systems Biology • What is it? • Principles – Lack of dominance (Kacser) – Co-selection (Belyaev) • Progress – Make Me My Model – The genome wide metabolic maps – Epigenetics and noise/cell diversity Henrik Kacser (Student of Waddell) Henrik Kacser Recessivity of most lack-of-function mutations Lack of dominance: No loss of function in heterozygote Lack of dominance: observation F0 F0’ F1 (knock out) X Function= 100% 0% 95% Flux J Lack of dominance: single molecule explanation fails F0 F0’ F1 AA 00 A0 X This is almost always incorre Function= 100% 100% 50% Flux J ct Cf.
    [Show full text]
  • Computational Modeling of Genetic and Biochemical Networks. Edited by James M. Bower and Hamid Bolouri Computational Molecular Biology, a Bradford Book, the MIT Press, Cambridge
    BRIEFINGS IN BIOINFORMATICS. VOL 7. NO 2. 204 ^206 doi:10.1093/bib/bbl001 Advance Access publication March 9, 2006 Book Review Computational Modeling of Genetic stochastic modelling are inevitable due to the small and Biochemical Networks number of molecules in a cell [1]. These intrinsic Edited by James M. Bower and noise effects have been measured in gene expression Hamid Bolouri using fluorescent probes [2, 3]. Chapter 3, ‘A Logical Model of cis-Regulatory Control in Eukaryotic Computational Molecular Biology, Downloaded from https://academic.oup.com/bib/article/7/2/204/304421 by guest on 23 September 2021 A Bradford Book, The MIT Press, Systems’ by Chiou-Hwa Yuh and others, builds Cambridge, Massachusetts; 2004; up on the theoretical framework introduced in ISBN: 0 262 52423 6; Paperback; 390pp.; Chapter 1 and presents a detailed characterization of £22.95/$35.00. a developmental biology gene network with a large number of regulatory factors. Chapters 1–3 deal with individual gene regulations. Eric Mjolsness intro- ‘Computational Modeling of Genetic and duces and reviews computational techniques, such as Biochemical Networks’ arose from a graduate neural network approaches, to study gene networks course taught by the editors in the California in Chapter 4 ‘Trainable Gene Regulation Networks Institute of Technology in 1998. The aim of the with Application to Drosophila Pattern Formation’. book is to provide instruction in the application of Special emphasis is made in the activity patterns modelling techniques in molecular and cell biology during the fruit-fly development. High-throughput to graduate students and postdoctoral researchers. experimental assays play a major role in the current It is also intended as a primer in the subject for shift from reductionist to systems biology approa- both theoretical and experimental biologists.
    [Show full text]
  • The Molecular Basis of Dominance
    THE MOLECULAR BASIS OF DOMINANCE HENRIK KACSER AND JAMES A. BURNS Deprrrtment of Genetics, Uniuersity of Edinburgh, Edinburgh Manuscript received September 3, 1980 ABSTRACT The best known genes of microbes, mice and men are those that specify enzymes. Wild type, mutant and heterozygote for variants of such genes differ in the catalytic activity at the step in the enzyme network specified by the gene in question. The effect on the respective phenotypes of such changes in catalytic activity, however, is not defined by the enzyme change as estimated by in vitro determination of the activities obtained from the extracts of the three types. In vivo enzymes do not act in isolation, but are kinetically linked to other enzymes uiu their substrates and products. These interactions modify the effect of enzyme variation on the phenotype, depending on the nature and quantity of the other enzymes present. An output of such a system, say a flux, is therefore a systemic property, and its response to variation at one locus must be measured in the whole system. This response is best described by the sensi- tivity coefficient, Z, which is defined by the fractional change in flux over the fractional change in enzyme activity. Its magnitude determines the extent to which a particular enzyme “controls” a particular flux or phenotype and, implicitly, determines the values that the three phenotypes will have. There are as many sensitivity coefficients for a given flux as there are enzymes in the system. It can be shown that the sum of all such coefficients equals unity. n Since n, the number of enzymes, is large, this summation property results in the individual coefficients being small.
    [Show full text]
  • Geoffrey Herbert Beale, MBE, FRS, FRSE 11 June 1913 - 16 October 2009
    Geoffrey Herbert Beale, MBE, FRS, FRSE 11 June 1913 - 16 October 2009 Geoffrey Beale was recognized internationally as a leading protozoan geneticist with an all-absorbing love of genetics, stimulated in the early part of his career by either working with or meeting many of the key figures who laid the foundations of modern genetics in the 1930s and 1940s. His work on the genetics of the surface antigens of Paramecium provided a conceptual breakthrough in our understanding of the role of the environment, the cytoplasm and the expression of genes, and he continued his interest in the role of cytoplasmic elements in heredity through studies on both the endosymbionts and mitochondria of Paramecium. He pioneered the genetic analysis of parasitic protozoa with his work on Plasmodium, and this stimulated many other scientists to take a genetic approach with these experimentally challenging organisms. Geoffrey was born in Wandsworth, London, on 11 June 1913, the son of Herbert Walter Beale and Elsie Beale (née Beaton). His family included an elder brother (Hugh) and two younger sisters (Margaret and Joan). When he was about five years old the family moved to Wallington, Surrey, where he spent the rest of his childhood as well as staying there during his university undergraduate and postgraduate studies. He attended Sutton County School, Surrey from 1923 until he obtained his higher school certificates in mathematics, physics and chemistry in 1931. His main interest at that time was music and he briefly considered the possibility that he might make music his career and he became an accomplished pianist and organist.
    [Show full text]
  • Histidinaemia in Mouse and Man
    Arch Dis Child: first published as 10.1136/adc.49.7.545 on 1 July 1974. Downloaded from Archives of Disease in Childhood, 1974, 49, 545. Histidinaemia in mouse and man GRAHAME BULFIELD and HENRIK KACSER From the Department of Genetics, University of Edinburgh Bulfield, G., and Kacser, H. (1974). Archives of Disease in Childhood, 49, 545. Histidinaemia in mouse and man. A recently discovered mutant in the mouse was found to have very low levels of histidase. It is an autosomal recessive. In its enzymic and metabolic properties it appears to be a homologue ofhuman histidinaemia. While the homozygous mouse mutants show no overt abnormalities, offspring of histidinaemic mothers display a balance defect resulting in circling behaviour. This is associated with vestibular damage during in utero development. Mental retardation caused by human maternal phenylketonuria may have a similar aetiology. There are well recognized advantages in studying histidine and its derivatives. The enzymic and animal models in the investigation of human metabolic changes in both mouse and human diseases. The increasing number of disorders in mutants appear to be the same. In addition, man which are reported to have a genetic basis maternal histidinaemia in the mouse has a terato- (McKusick, 1971; Raine, 1972) makes it desirable to genic effect on the developing inner ear which results find and investigate animal homologues with the in abnormal behaviour of the affected offspring. same genic lesions. The genetically most in- These findings may be relevant to the aetiology of vestigated laboratory mammal is the mouse, with human histidinaemia. over 500 known mutants (Green, 1966; Searle, 1973).
    [Show full text]
  • 2007 Econnect V02 N1.Pdf (946.0Kb)
    e_Connections feature technology in the news interview Vol. 2 No. 1 Spring 2007 Newsletter of the Virginia Bioinformatics Institute at Virginia Tech Why care about prokaryotes? On February 9, Professor William “Barny” Whitman of He continued: “We have the University of Georgia Department of Microbiology been able to estimate presented a seminar entitled, “The number and diversity of that prokaryotes bacteria on earth and why represent about 3.5 we care” at the Virginia to 5.5 x 1017 grams of Bioinformatics Institute carbon on the Earth In This Issue Conference Center. In as we know it today, the seminar, Dr. Whitman or about the same as Dr. William Whitman...............1 discussed the biological the biomass of plants. In terms of individuals, Technology Focus......................2 impact and contributions of prokaryotes, the dominant their numbers are 30 VBI in the News........................3 life form on earth whose about 4 to 6 x 10 . Dr.William Whitman evolution established the Carbon tracing studies VBI Scientific Publications.......3 central plan for the living have shown that 3.5 billion years ago, the level of organic cell. The presentation was carbon in the biosphere was more or less the same. Since Interview: Dr. Athel part of the Molecular Cell the eukaryotes evolved during the last two billion years, it’s Cornish-Bowden.......................4 Biology and Biotechnology apparent that the contribution of prokaryotes to the biomass (MCBB) seminar series. of the early earth was largely prokaryotic.” As a Ph.D. student at the University of Texas at Austin, The presence of an ancient and large population of prokaryotes Dr.
    [Show full text]
  • Memories of Reinhart Heinrich
    ARTICLE IN PRESS Journal of Theoretical Biology 252 (2008) 544–545 www.elsevier.com/locate/yjtbi In Memoriam Memories of Reinhart Heinrich I first met Reinhart at the symposium that Athel In April 2000, Reinhart secured lodging for me in the Cornish-Bowden organised at Il Ciocco in April 1989 that lovely house for at Humboldt University, where Marı ´ a brought together numerous researchers interested in the Rosa, my wife, came to spend a few delightful days with control and design of metabolism. We connected immedi- me, and spent time talking with Nana, Reinhart’s wife. We ately, because we had many things in common and were so spent a couple of days in Dresden at Reinhart’s mother’s like-minded. home and heard the concert in Dresden Cathedral for After much discussion of the evolution and optimisation Bach’s St. Matthew’s Passion, which Reinhart loved and of metabolism, our first work was on the optimisation of liked to play on his violin. This was a sensational glycolysis. Paco Montero had the first important idea that performance, but there was no applause at the end, the aim of evolution was to maximize the flux of ATP because, as he explained to us, there is never applause in production, and this has been instrumental for all a church in Germany. There are some things about subsequent work in the field. Germans that are hard for Spanish people to understand! I saw that Reinhart’s theory of the optimization of Reinhart was always worried about world peace. He was enzyme kinetic parameters was very important, but hard very sensitive to human suffering, was especially troubled for students.
    [Show full text]
  • Inferring the Shape of Global Epistasis
    bioRxiv preprint doi: https://doi.org/10.1101/278630; this version posted March 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Inferring the shape of global epistasis Jakub Otwinowski1, David M. McCandlish2, and Joshua B. Plotkin3 1University of Pennsylvania, Biology Department, [email protected] 2Cold Spring Harbor Laboratory, Simons Center for Quantitative Biology, [email protected] 3University of Pennsylvania, Biology Department, [email protected] March 7, 2018 Abstract Genotype-phenotype relationships are notoriously complicated. Idiosyncratic interactions between specific combinations of mutations occur, and are difficult to predict. Yet it is increasingly clear that many interactions can be understood in terms of global epistasis. That is, mutations may act additively on some underlying, unobserved trait, and this trait is then transformed via a nonlinear function to the observed phenotype as a result of subsequent biophysical and cellular processes. Here we infer the shape of such global epistasis in three proteins, based on published high-throughput mutagenesis data. To do so, we develop a maximum-likelihood inference procedure using a flexible family of monotonic nonlinear functions spanned by an I-spline basis. Our analysis uncovers dramatic nonlinearities in all three proteins; in some proteins a model with global epistasis accounts for virtually all the measured variation, whereas in others we find substantial local epistasis as well. This method allows us to test hypotheses about the form of global epistasis and to distinguish variance components attributable to global epistasis, local epistasis, and measurement error.
    [Show full text]