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  • Synthetic Biology

    Molecular Mechanisms

    Concepts

    Applications

    09.05.07

  • The central dogma - genetic machinery

    http://old.mb.au.dk/graphics/dogma.jpg

  • Transcription (in E. Coli)

    DNA ➞ RNA

  • Translation

    RNA ➞ Protein

  • (Alberts, MB of the cell)

    Transcriptional regulation

  • The lac operon

    Jacob Monod

    Nobel prize (Physiology/Medicine) 1965

  • Nomenclature

    promotor: DNA region participating in binding of RNA polymerase to start transcription of a gene

    operator: DNA region where a repressor can bind and inhibit binding of RNAp to the neighboring promotor

    regulatory protein: protein controlling the transcription of another gene

    transcription factor: regulatory protein binding to promotor sequence or other regulatory site and initiating transcription by RNAp

    repressor: protein binding to an operator sequence and inhibiting binding of RNAp

  • inducer: low molecular weight molecule which induces/reduces transcription of a gene by binding to a regulatory protein

    enhancer: cis-active sequence enhancing the activity of some eucaryotic promotors; can be far from promotor, upstream, downstream or even in the transcribed gene

    activator proteins: proteins (transcription factors) bound to enhancer sequences stimulating transcription of genes

    operon: in bacteria, unit of gene expression containing several genes and accompanying regulatory sequences

    cis-regulatory protein: protein which only interacts with the DNA region it was synthesized from

    trans-acting element: effect of element insensitive to its position

  • Biochemical "circuits"

    Biophys Journal Volume 67 August 1994 560-578

    Computational Functions in Biochemical Reaction Networks

    Adam Arkin** and John Ross* *Departent of Chenistry and 1Departrent of Neurobiooy, Sdhod of Medicine, Stanford University, Stanford, CA 94305 USA

    ABSTRACT In pnor work we demonstrated Fte implementation of logic gates, sequential computers (universal Turing ma- chines), and parallel computers by means of the kinetics of chemical reaction mechanisms. In the present arficle we develop this subject further by first investigating the computational properties of several enzymatic (single and multiple) reaction mecha- nisms: we show their steady states are analogous to either Boolean or fuzzy logic gates. Nearly perfect digital function is obtained only in the regime in which the enzymes are saturated with their substrates. With these enzymatic gates, we constru com- binational chemical networks that execute a given trut-table. The dynamic range of a network's output is strongly affected by "input/output matching conditions among the intemal gate elements. We find a simple mechanism, similarto the interconversion of fructose-ctposphate between its two bisphWphate forns (fructose-1,bisphophate and fructose-2,6bposphate), that functons analogously to an AND gate. When the simple model is supplanted with one in which the enzyme rate laws are derived from experimental data, the steady state of the mechanism furnions as an asymmetric fuzzy aggregation operator with prop- erties akin to a fuzzy AND gate. The qualitative behavior of the mechanism does not change when sitated within a large model of glycolysis/gluconeogenesis and the TCA cycle. The mehanism, in this case, switches the pathway's mode from glycolysis to gluconeogenesis in response to chemical signals of low blood glucose (cAMP) and abundant fuel for the TCA cycle (acetyl coenzyme A).

    INTRODUCTION

    Biochemical reaction networks (BRNs), such as glycolysis and the tricarboxylic acid cycle, are an integral part of the machinery by which an organism maintains itself and adapts to its environment. These networks are responsible for nu- merous cellular tasks including the maintenance of ho- meostasis and the creation and propagation of chemical sig- nals such as those indicating hunger or satiation. It is often very difficult to determine the underlying logic of the regu- lation of even relatively small portions of a BRN. First, the sub-network may be highly interconnected and contain many feedback loops, branching pathways, etc. Second, it is dif- ficult to determine all the kinetic parameters that determine the behavior of a BRN in vitro let alone in vivo (Fersht, 1985). Third, the great range of temporal and spatial scales over which a large BRN can react to the perturbation of its variables makes it difficult to deduce the laws of biological control and signal processing from examination of models of the dynamic equations of motion (Acerenza, Sauro, and Kacser, 1989). Therefore, it is desirable to develop additional techniques for the investigation of reaction mechanisms, their control and signal processing.

    In previous papers we have demonstrated the implemen- tation of formal logical computations and functions such as logic gates, neural networks, and universal Turing machines (Hjehmfelt and Ross, 1992, 1993, 1994; Hjelmfelt et al., 1991, 1992, 1993) by means of macroscopic kinetics of chemical reaction networks. (This work is briefly reviewed

    Received for publication 16 March 1994 and in final form 18 May 1994.

    Address reprint requests to Dr. John Ross, Deprtment of Chemistry, Stan- ford University School of Medicine, Stanford, CA 94305. TeL: 415-723- 9203; Fax: 415-723-4817; E-mail: [email protected] istanford.edu.

    1994 by the Biophysical Society

    0006-3495/94/0W560/19 $2.00

    under Implementation of Computation with Macroscopic Chemical Kinetics.) The simplest chemical kinetic mecha- nism capable of computation discussed in these networks bears a striking resemblance to parts of many important multi-enzymatic pathways found in metabolism. It is natural, therefore, to look for logical computation performed by these structures within the known BRNs.

    Abbreviations used: GCP, glucose carrier protein; G Dg, glucose degrada- ton; GK, ghicokinase; HK, hexokinase; G6Pase, glucose-6-phosphatase; PHI, phosphohexose isomerase; PFK1, phosphofiuctokinase-1; F16BPase, frcose-1,6-bisphosphatase; PFK2, phosphofructokinase-2, F26BPase, fructose-2,6-bisphosphatase; a-OP DH, a-glycerol phosphate dehydroge- nase; a-OP Dg, a-glycerol phosphate degradation; TPI, tiose phosphate isomerase; GAPDH, glyceraldehyde phosphate dehydrogenase; PGK, phos- phoglycerate kinase; PGM, phosphoglycerate mutase; PyrK, pyruvate ki- nase; PyrC, pyruvate carboxylase; PEPCK phospho enolpyruvate car- boxykinase; LacDH, lactose dehydrogenase; Lac Dg, lactose degradation; CarbA, carbonic anhydrase; Citl Dg, cytosolic citrate degradation; PyrDHC, pyruvate dehydrogenase complex; CitSyn, citrate synthase; ICDH, isocitrate dehydrogenase; GiuDH, glutamate dehydrogenase; 2-KGDHC, 2-ketoglutarate dehydrogenase complex; SucDH, succinate dehydrogenase; MalE, malic enzyme; MaIDH, malate dehydyrogenase; As- pTA, aspartate tansaminase; AlaTA, alanine transaminase; AK, adenylate kinase; OAA, oxalacetate;Glu, glutamate; Ala, alanine; Suc, succinate; Cit, cirate; AsP, aspartate; Pyr, pyruvate; PEP, phosphoenolpyruvate; CoA, co- enzyme a; ACoA, acetyl-coenzyme A; Gluc, ghlcse; G6P, glucose-6-phos- phate; F6P, fructose-6phosphate; F16BP, fructose-1,6-bisphosphate; F26BP, fructose-2,6-bisphosphate; K catalytic subunit ofcAMP-dependent protein kinase; DHP, dihydroxyacetone phosphate; GAP, glyceraldehyde phosphate; 3PGA, 3-phosphoglyceraldehyde; 13DPGA, 1,3-diphospho- glyceraldehyde; 23DPGA, 2,3- diphosphoglyceraldehyde; 2PGA, 2-phos- phoglyceraldehyde; a-GP, a-glycerol phosphate; [AC, lactate; HIP, Hexose-phosphate Interconversion Pathway; sHIP, simplified HIP. In these models, when a chemical species, such as citrate, may take on different concentrations in different cellular spaces (i.e., the extracellular space, cy- tosol, and mitochondrion) its abbreviation is postfixed with a number des- ignating the compartment to which it belongs (0, 1, and 2 respectively).

    560

  • A biochemical "AND gate"

  • An "AND gate" in the glycolytic

    pathway

  • © 1999 Macmillan Magazines Ltd

    Although living systems obey the laws ofphysics and chemistry, the notion offunction or purpose differentiates biol- ogy from other natural sciences. Organisms exist to reproduce, whereas, outside religious belief, rocks and stars have no purpose. Selection for function has produced the liv- ing cell, with a unique set of properties that distinguish it from inanimate systems of interacting molecules. Cells exist far from thermal equilibrium by harvesting energy from their environment. They are composed of thousands of different types of molecule. They contain information for their survival and reproduction, in the form of their DNA. Their interactions with the environment depend in a byzantine fashion on this infor- mation, and the information and the machinery that interprets it are replicated by reproducing the cell. How do these proper- ties emerge from the interactions between the molecules that make up cells and how are they shaped by evolutionary competition with other cells?

    Much of twentieth-century biology has been an attempt to reduce biological phenomena to the behaviour of molecules. This approach is particularly clear in genet- ics, which began as an investigation into the inheritance of variation, such as differences in the colour of pea seeds and fly eyes. From these studies, geneticists inferred the exis- tence of genes and many of their properties, such as their linear arrangement along the length of a chromosome. Further analysis led to the prin