![4 THEORETICAL AND ENGINEERING PRINCIPLES
possible by the powerful computational tools available to them. Bioinformatics
has emerged as the science of the 21st century, requiring the contributions of
truly interdisciplinary scientists who are equally at home at the lab bench or
writing software at the computer.
However,theseedsoftherelationshipbetweenbiologyandcomputerscience
were sown long ago, when the latter discipline did not even exist. When, in
the 17th century, the French mathematician and philosopher René Descartes
declared to Queen Christina of Sweden that animals could be considered a class
of machines, she challenged him to demonstrate how a clock could reproduce.
Three centuries later, with the publication of The General and Logical Theory
of Automata [19] John von Neumann showed how a machine could indeed
construct a copy of itself. Von Neumann believed that the behavior of natural
organisms, although orders of magnitude more complex, was similar to that of
the most intricate machines of the day. He believed that life was based on logic.
In 1970, the Nobel laureate Jacques Monod identified specific natural pro-
cesses that could be viewed as behaving according to logical principles: “The
logic of biological regulatory systems abides not by Hegelian laws but, like
the workings of computers, by the propositional algebra of George Boole” [16,
p. 76; see also 15].
The concept of molecular complexes forming computational components
was first proposed by Richard Feynman in his famous talk “There’s Plenty
of Room at the Bottom” [11]. The idea was further developed by Bennett [6]
and Conrad and Liberman [9], and since then there has been an explosion of
interest in performing computations at a molecular level. In 1994, Adleman
showed how a massively parallel random search may be implemented using
standard operations on strands of DNA [1; see also 2]. Several authors have
proposed simulations of Boolean circuits in DNA [3, 17], and recently the
regulation of gene expression in bacteria has been proposed as a potential in
vivo computational framework. We now discuss this last development in more
detail.
BACKGROUND
Although proposed by Feynman [11] as long ago as 1959, the realization of per-
forming computations at a molecular level has had to wait for the development
of the necessary methods and materials. However, a rich body of theoretical
work existed prior to Adleman’s experiment. In 1982, Bennett [6] proposed
the concept of a “Brownian computer” based around the principle of reactant
molecules touching, reacting, and effecting state transitions due to their random
Brownian motion. Bennett developed this idea by suggesting that a Brownian
Turing machine could be built from a macromolecule such as RNA. “Hypo-
thetical enzymes,” one for each transition rule, catalyze reactions between the](https://image.slidesharecdn.com/6687-250310073949-417c5885/85/Cellular-Computing-Genomics-and-Bioinformatics-1st-Edition-Martyn-Amos-22-320.jpg)
![INTRODUCTION TO CELLULAR COMPUTING 5
RNA and chemicals in its environment, transforming the RNA into its logical
successor.
Conrad and Liberman [9] developed this idea further, describing parallels
between physical and computational processes (e.g., biochemical reactions be-
ing used to implement basic switching circuits). They introduced the concept of
molecular level “word-processing” by describing it in terms of transcription and
translation of DNA, RNA processing, and genetic regulation. However, their
article lacks a detailed description of the biological mechanisms highlighted and
their relationship with “traditional” computing. As the authors acknowledge,
“Our aspiration is not to provide definitive answers . . . but rather to show that
a number of seemingly disparate questions must be connected to each other in
a fundamental way” [9, p. 240].
Conrad [8] expanded on this work, showing how the information process-
ing capabilities of organic molecules may, in theory, be used in place of digital
switchingcomponents(Figure1.1a).Enzymesmaycleavespecificsubstratesby
severing covalent bonds within the target molecule. For example, restriction en-
donucleases cleave strands of DNA at specific points known as restriction sites.
Substrate
Enzyme
Ligand
(a)
(b)
(c)
Figure 1.1 (a) Components of enzy-
matic switch. (b) Enzyme recognizes
substrate and cleaves it. (c) Ligand
binds to enzyme, changing its confor-
mation. Enzyme no longer recognizes
substrate.](https://image.slidesharecdn.com/6687-250310073949-417c5885/85/Cellular-Computing-Genomics-and-Bioinformatics-1st-Edition-Martyn-Amos-23-320.jpg)
![6 THEORETICAL AND ENGINEERING PRINCIPLES
In doing so, the enzyme switches the state of the substrate from one to another.
Before this process can occur, a recognition process must take place, where the
enzyme distinguishes the substrate from other, possibly similar molecules. This
is achieved by virtue of what Conrad refers to as the “lock-key” mechanism,
whereby the complementary structures of the enzyme and substrate fit together
and the two molecules bind strongly (Figure 1.1b). This process may, in turn, be
affected by the presence or absence of ligands. Allosteric enzymes can exist in
more than one conformation (or “state”), depending on the presence or absence
of a ligand. Therefore, in addition to the active site of an allosteric enzyme
(the site where the substrate reaction takes place), there is a ligand binding
site, which, when occupied, changes the conformation and hence the properties
of the enzyme. This gives an additional degree of control over the switching
behavior of the entire molecular complex.
In 1991, Hjelmfelt et al. [14] highlighted the computational capabilities of
certain biochemical systems, as did Arkin and Ross in 1994 [4]. In 1995, Bray
[7] discussed how the primary function of many proteins in the living cell
appears to be the transfer and processing of information rather than metabolic
processing or cellular construction. Bray observed that these proteins are linked
into circuits that perform computational tasks such as amplification, integration,
and intermediate storage.
GENETIC REGULATORY NETWORKS
In this section we develop the notion of circuits being encoded in genetic regu-
latory networks rather than simply being used as a useful metaphor. The central
dogma of molecular biology is that DNA produces RNA, which in turn produces
proteins. The basic building blocks of genetic information are known as genes.
Each gene codes for a specific protein, and these genes may (simplistically)
be turned on (expressed) or off (repressed). For the DNA sequence to be con-
verted into a protein molecule, it must be read (transcribed) and the transcript
converted (translated) into a protein (Figure 1.2).
Transcription of a gene produces a messenger RNA (mRNA) copy, which
can then be translated into a protein. This results in the DNA containing the
information for a vast range of proteins (effector molecules), but only those that
are being expressed are present as mRNA copies. Each step of the conversion,
from stored information (DNA) through mRNA (messenger) to protein synthe-
sis (effector), is catalyzed by effector molecules. These effector molecules may
be enzymes or other factors that are required for a process to continue. Conse-
quently, a loop is formed, where products of one gene are required to produce
further gene products, which may even influence that gene’s own expression.
Genes are composed of a number of distinct regions that control and encode
the desired product. These regions are generally of the form promoter–gene–](https://image.slidesharecdn.com/6687-250310073949-417c5885/85/Cellular-Computing-Genomics-and-Bioinformatics-1st-Edition-Martyn-Amos-24-320.jpg)
Cellular Computing Genomics and Bioinformatics 1st Edition Martyn Amos
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- 5. Cellular Computing Genomics and Bioinformatics 1st Edition Martyn Amos Digital Instant Download Author(s): Martyn Amos ISBN(s): 9781423720218, 1423720210 Edition: 1 File Details: PDF, 6.17 MB Year: 2004 Language: english
- 6. Cellular Computing Martyn Amos, Editor OXFORD UNIVERSITY PRESS
- 8. Series in Systems Biology Edited by Dennis Shasha, New York University Editorial Board Michael Ashburner, University of Cambridge Amos Bairoch, Swiss Institute of Bioinformatics David Botstein, Princeton University Charles Cantor, Sequenom, Inc. Leroy Hood, Institute for Systems Biology Minoru Kanehisa, Kyoto University Raju Kucherlapati, Harvard Medical School Systems Biology describes the discipline that seeks to understand biological phenomena on a large scale: the association of gene with function, the detailed modeling of the interaction among proteins and metabolites, and the function of cells. Systems Biology has wide-ranging application, as it is informed by sev- eral underlying disciplines, including biology, computer science, mathematics, physics, chemistry, and the social sciences. The goal of the series is to help practitioners and researchers understand the ideas and technologies underly- ing Systems Biology. The series volumes will combine biological insight with principles and methods of computational data analysis. Cellular Computing Martyn Amos, University of Exeter
- 9. Cellular Computing Edited by Martyn Amos 2004
- 10. Oxford New York Auckland Bangkok Buenos Aires Cape Town Chennai Dar es Salaam Delhi Hong Kong Istanbul Karachi Kolkata Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi São Paulo Shanghai Taipei Tokyo Toronto Copyright © 2004 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York, 10016 www.oup.com Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Cellular computing / edited by Martyn Amos. p. cm. — (Series in Systems Biology) Includes bibliographical references and index. ISBN 0-19-515539-4; 0-19-515540-8 (pbk) 1. Bioinformatics. 2. Cellular automata. 3. Molecular computers. 4. Nanotechnology. I. Amos, Martyn. II. Series. QH324.2.C35 2004 571.6—dc22 2003058013 Series logo concept by Cloe L. Shasha. 9 8 7 6 5 4 3 2 1 Printed in the United States of America on acid-free paper
- 11. Preface The field of cellular computing is a novel and exciting development at the intersection of biology, computer science, mathematics, and engineering. Prac- titioners in this emerging discipline are concerned with the analysis, modeling, and engineering of inter- and intra-cellular processes for the purposes of com- putation. Developments in the field have potentially huge significance, ranging from new biological sensors and methods for interfacing living material with silicon substrates, through intelligent drug delivery and nanotechnology and on toward a deeper understanding of the fundamental mechanisms of life itself. This book provides both an introduction to some of the early fundamental work and a description of ongoing cutting-edge research in the field. This volume is organized into three parts. PART I: THEORETICAL AND ENGINEERING PRINCIPLES Part I is concerned with the theoretical foundations of the field; in it we define the engineering principles upon which the second part is founded. Chapter 1 (Amos and Owenson) introduces the field of cellular computing, placing it in historical context and highlighting some of the more significant early papers in the field. A brief introduction to some underlying biological principles is supplied for the benefit of the nonspecialist biologist. Chapter 2 (Paton, Fisher, Malcolm, and Matsuno) reviews computational methods that may be useful to biologists seek- ing to model the interaction of proteins in spatially heterogenous and changing
- 12. vi PREFACE environments. Chapter 3 (Lones and Tyrell) describes enzyme genetic pro- gramming, a new optimization method that draws inspiration from biological representations of information. In Chapter 4 (Weiss, Knight, and Sussman) the idea of genetic process engineering is introduced. This is a methodology for mapping digital circuitry onto genetic elements in a rigorous and robust fashion. The fundamental engineering principles are established, and software to sup- port development is described. Part I closes with Chapter 5 (Simpson et al.), in which whole cells are considered as analogous to semiconductor components. Communication between cells, and between cells and synthetic devices, is dis- cussed, as well as their integration with nano- and micro-structured substrates and the modeling and simulation of relevant cellular processes. PART II: LABORATORY EXPERIMENTS Part II is concerned with reporting the results of laboratory experiments in cellular computing. In Chapter 6 (Wakabayashi and Yamamura), the construc- tion of a bacterial logical inverter is described. This is developed further in Chapter 7, where Weiss, Knight, and Sussman describe the feasibility of digital computation in cells by building several in vivo digital logic circuits. Engineered intercellular communication, which may be crucial to the large-scale scalability of cellular computing, is also described. Chapter 8 (Simpson et al.) describes exciting work on the integration of living cells with micro- and nano-scale sys- tems. This chapter includes state-of-the-art results concerning the incorporation of nanofibers into living cells. PART III: COMPUTATION IN CILIATES Part III covers an intriguing subfield of cellular computing concerned with the class of organisms known as ciliates. These organisms are particularly interest- ing because they “encrypt” their genomic information. Although studies of this phenomenon have not yet suggested obvious potential for human engineering of ciliates, elucidation of the underlying cellular processes will be of great use in the future. In Chapter 9, Prescott and Rozenberg both describe the assembly of ciliate genes from a biological perspective and consider its computational implications. The latter theme is developed further in Chapter 10, where Kari and Landweber study the decryption of ciliate DNA from a computational perspective. ACKNOWLEDGMENTS Firstthanksmustgotothecontributors.Theirtimeliness,flexibility,andhelpful- ness made editing this volume an enjoyable and rewarding experience. Thanks
- 13. PREFACE vii go also to Dennis Shasha, the series editor, and to Kirk Jensen at OUP for their advice and careful stewardship of the project. I am also grateful to Robert Heller for invaluable typesetting advice. Martyn Amos Exeter
- 14. This page intentionally left blank
- 15. Contents Contributors xi Part I Theoretical and Engineering Principles 1 An Introduction to Cellular Computing 3 Martyn Amos and Gerald Owenson 2 Proteins and Information Processing 11 Ray Paton, Michael Fisher, Grant Malcolm, and Koichiro Matsuno 3 Enzyme Genetic Programming 19 Michael A. Lones and Andy M. Tyrell 4 Genetic Process Engineering 43 Ron Weiss, Thomas F. Knight Jr., and Gerald Sussman 5 The Device Science of Whole Cells as Components in Microscale and Nanoscale Systems 74 Michael L. Simpson, Gary S. Sayler, James T. Fleming, John Sanseverino, and Chris D. Cox
- 16. x CONTENTS Part II Laboratory Experiments 6 The Enterococcus faecalis Information Gate 109 Kenichi Wakabayashi and Masayuki Yamamura 7 Cellular Computation and Communication Using Engineered Genetic Regulatory Networks 120 Ron Weiss, Thomas F. Knight Jr., and Gerald Sussman 8 The Biology of Integration of Cells into Microscale and Nanoscale Systems 148 Michael L. Simpson, Timothy E. McKnight, Michael A. Guillorn, Vladimir I. Merkulov, Gary S. Sayler, and Anatoli Melechko Part III Computation in Ciliates 9 Encrypted Genes and Their Assembly in Ciliates 171 David M. Prescott and Grzegorz Rozenberg 10 Biocomputation in Ciliates 202 Lila Kari and Laura F. Landweber Index 217
- 17. Contributors Martyn Amos Department of Computer Science School of Engineering, Computer Science and Mathematics University of Exeter UK Chris D. Cox Center for Environmental Biotechnology University of Tennessee USA Michael Fisher School of Biological Sciences University of Liverpool UK James T. Fleming Center for Environmental Biotechnology University of Tennessee USA Michael A. Guillorn Molecular-Scale Engineering and Nanoscale Technologies Research Group Oak Ridge National Laboratory USA Lila Kari Department of Computer Science University of Western Ontario Canada Thomas F. Knight Jr. Artificial Intelligence Laboratory and Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology USA Laura F. Landweber Department of Ecology and Evolutionary Biology Princeton University USA Michael A. Lones Bio-Inspired Electronics Laboratory Department of Electronics University of York UK Grant Malcolm Department of Computer Science University of Liverpool UK
- 18. xii CONTRIBUTORS Koichiro Matsuno Department of BioEngineering Nagaoka University of Technology Japan Timothy E. McKnight Molecular-Scale Engineering and Nanoscale Technologies Research Group Oak Ridge National Laboratory USA Anatoli Melechko Center for Environmental Biotechnology University of Tennessee USA Vladimir I. Merkulov Molecular-Scale Engineering and Nanoscale Technologies Research Group Oak Ridge National Laboratory USA Gerald Owenson Biotechnology and Biological Sciences Research Council (formerly Department of Biological Sciences, University of Warwick) UK Ray Paton Department of Computer Science University of Liverpool UK David M. Prescott Department of Molecular, Cellular and Developmental Biology University of Colorado USA Grzegorz Rozenberg Department of Computer Science University of Colorado USA, and Leiden Institute of Advanced Computer Science Leiden University The Netherlands John Sanseverino Center for Environmental Biotechnology University of Tennessee USA Gary S. Sayler Center for Environmental Biotechnology University of Tennessee USA Michael L. Simpson Molecular-Scale Engineering and Nanoscale Technologies Research Group Oak Ridge National Laboratory USA Gerald Sussman Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology USA Andy M. Tyrell Bio-Inspired Architectures Laboratory Department of Electronics University of York UK Kenichi Wakabayashi Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Japan Ron Weiss Department of Electrical Engineering Princeton University USA Masayuki Yamamura Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Japan
- 19. Part I Theoretical and Engineering Principles
- 20. This page intentionally left blank
- 21. 1 An Introduction to Cellular Computing Martyn Amos and Gerald Owenson The abstract operation of complex natural processes is often expressed in terms of networks of computational components such as Boolean logic gates or ar- tificial neurons. The interaction of biological molecules and the flow of infor- mation controlling the development and behavior of organisms is particularly amenable to this approach, and these models are well established in the bio- logical community. However, only relatively recently have papers appeared proposing the use of such systems to perform useful, human-defined tasks. Rather than merely using the network analogy as a convenient technique for clarifying our understanding of complex systems, it is now possible to harness the power of such systems for the purposes of computation. The purpose of this volume is to discuss such work. In this introductory chapter we place this work in historical context and provide an introduction to some of the underly- ing molecular biology. We then introduce recent developments in the field of cellular computing. INTRODUCTION Despite the relatively recent emergence of molecular computing as a distinct research area, the link between biology and computer science is not a new one. Of course, for years biologists have used computers to store and analyze experimental data. Indeed, it is widely accepted that the huge advances of the Human Genome Project (as well as other genome projects) were only made 3
- 22. 4 THEORETICAL AND ENGINEERING PRINCIPLES possible by the powerful computational tools available to them. Bioinformatics has emerged as the science of the 21st century, requiring the contributions of truly interdisciplinary scientists who are equally at home at the lab bench or writing software at the computer. However,theseedsoftherelationshipbetweenbiologyandcomputerscience were sown long ago, when the latter discipline did not even exist. When, in the 17th century, the French mathematician and philosopher René Descartes declared to Queen Christina of Sweden that animals could be considered a class of machines, she challenged him to demonstrate how a clock could reproduce. Three centuries later, with the publication of The General and Logical Theory of Automata [19] John von Neumann showed how a machine could indeed construct a copy of itself. Von Neumann believed that the behavior of natural organisms, although orders of magnitude more complex, was similar to that of the most intricate machines of the day. He believed that life was based on logic. In 1970, the Nobel laureate Jacques Monod identified specific natural pro- cesses that could be viewed as behaving according to logical principles: “The logic of biological regulatory systems abides not by Hegelian laws but, like the workings of computers, by the propositional algebra of George Boole” [16, p. 76; see also 15]. The concept of molecular complexes forming computational components was first proposed by Richard Feynman in his famous talk “There’s Plenty of Room at the Bottom” [11]. The idea was further developed by Bennett [6] and Conrad and Liberman [9], and since then there has been an explosion of interest in performing computations at a molecular level. In 1994, Adleman showed how a massively parallel random search may be implemented using standard operations on strands of DNA [1; see also 2]. Several authors have proposed simulations of Boolean circuits in DNA [3, 17], and recently the regulation of gene expression in bacteria has been proposed as a potential in vivo computational framework. We now discuss this last development in more detail. BACKGROUND Although proposed by Feynman [11] as long ago as 1959, the realization of per- forming computations at a molecular level has had to wait for the development of the necessary methods and materials. However, a rich body of theoretical work existed prior to Adleman’s experiment. In 1982, Bennett [6] proposed the concept of a “Brownian computer” based around the principle of reactant molecules touching, reacting, and effecting state transitions due to their random Brownian motion. Bennett developed this idea by suggesting that a Brownian Turing machine could be built from a macromolecule such as RNA. “Hypo- thetical enzymes,” one for each transition rule, catalyze reactions between the
- 23. INTRODUCTION TO CELLULAR COMPUTING 5 RNA and chemicals in its environment, transforming the RNA into its logical successor. Conrad and Liberman [9] developed this idea further, describing parallels between physical and computational processes (e.g., biochemical reactions be- ing used to implement basic switching circuits). They introduced the concept of molecular level “word-processing” by describing it in terms of transcription and translation of DNA, RNA processing, and genetic regulation. However, their article lacks a detailed description of the biological mechanisms highlighted and their relationship with “traditional” computing. As the authors acknowledge, “Our aspiration is not to provide definitive answers . . . but rather to show that a number of seemingly disparate questions must be connected to each other in a fundamental way” [9, p. 240]. Conrad [8] expanded on this work, showing how the information process- ing capabilities of organic molecules may, in theory, be used in place of digital switchingcomponents(Figure1.1a).Enzymesmaycleavespecificsubstratesby severing covalent bonds within the target molecule. For example, restriction en- donucleases cleave strands of DNA at specific points known as restriction sites. Substrate Enzyme Ligand (a) (b) (c) Figure 1.1 (a) Components of enzy- matic switch. (b) Enzyme recognizes substrate and cleaves it. (c) Ligand binds to enzyme, changing its confor- mation. Enzyme no longer recognizes substrate.
- 24. 6 THEORETICAL AND ENGINEERING PRINCIPLES In doing so, the enzyme switches the state of the substrate from one to another. Before this process can occur, a recognition process must take place, where the enzyme distinguishes the substrate from other, possibly similar molecules. This is achieved by virtue of what Conrad refers to as the “lock-key” mechanism, whereby the complementary structures of the enzyme and substrate fit together and the two molecules bind strongly (Figure 1.1b). This process may, in turn, be affected by the presence or absence of ligands. Allosteric enzymes can exist in more than one conformation (or “state”), depending on the presence or absence of a ligand. Therefore, in addition to the active site of an allosteric enzyme (the site where the substrate reaction takes place), there is a ligand binding site, which, when occupied, changes the conformation and hence the properties of the enzyme. This gives an additional degree of control over the switching behavior of the entire molecular complex. In 1991, Hjelmfelt et al. [14] highlighted the computational capabilities of certain biochemical systems, as did Arkin and Ross in 1994 [4]. In 1995, Bray [7] discussed how the primary function of many proteins in the living cell appears to be the transfer and processing of information rather than metabolic processing or cellular construction. Bray observed that these proteins are linked into circuits that perform computational tasks such as amplification, integration, and intermediate storage. GENETIC REGULATORY NETWORKS In this section we develop the notion of circuits being encoded in genetic regu- latory networks rather than simply being used as a useful metaphor. The central dogma of molecular biology is that DNA produces RNA, which in turn produces proteins. The basic building blocks of genetic information are known as genes. Each gene codes for a specific protein, and these genes may (simplistically) be turned on (expressed) or off (repressed). For the DNA sequence to be con- verted into a protein molecule, it must be read (transcribed) and the transcript converted (translated) into a protein (Figure 1.2). Transcription of a gene produces a messenger RNA (mRNA) copy, which can then be translated into a protein. This results in the DNA containing the information for a vast range of proteins (effector molecules), but only those that are being expressed are present as mRNA copies. Each step of the conversion, from stored information (DNA) through mRNA (messenger) to protein synthe- sis (effector), is catalyzed by effector molecules. These effector molecules may be enzymes or other factors that are required for a process to continue. Conse- quently, a loop is formed, where products of one gene are required to produce further gene products, which may even influence that gene’s own expression. Genes are composed of a number of distinct regions that control and encode the desired product. These regions are generally of the form promoter–gene–
- 25. INTRODUCTION TO CELLULAR COMPUTING 7 Information Storage Information Transfer Effector Molecule DNA mRNA PROTEIN Transcription Translation Replication Figure 1.2 The central dogma of molecular biology. terminator (Figure 1.3). Transcription may be regulated by effector molecules known as activators and repressors, which interact with the promoter and in- crease or decrease the level of transcription. This allows effective control over the expression of proteins, avoiding the production of unnecessary compounds. An operon is a set of functionally related genes with a common promoter. An example of this is the lac operon, which contains three structural genes that allow E. coli to utilize the sugar lactose. When E. coli is grown on the common carbon source glucose, the product of the lacI gene represses the transcription of the lacZYA operon (Figure 1.4). However, if lactose is supplied together with glucose, a lactose by-product is produced that interacts with the repressor molecule, preventing it from repressing the lacZYA operon. This de-repression does not initiate transcription, since it would be inefficient to utilize lactose if the more common sugar glucose were still available. The operon is positively regulated by the CAP–cAMP (catabolite activator protein: cyclic adenosine monophosphate) complex, whose level increases as the amount of available glucose decreases. Therefore, if lactose were present as the sole carbon source, the lacI repression would be relaxed and the high CAP–cAMP levels would activate transcription, leading to the synthesis of the lacZYA gene products (Figure 1.5). Thus, the promoter is under the control of two sugars, and the lacZYA operon is only transcribed when lactose is present and glucose is absent. It is clear, therefore, that we may view the lac operon in terms of a genetic switch that is under the control of two sugars, lactose and glucose. PROMOTER TERMINATOR STRUCTURAL GENE(S) Figure 1.3 Major regions found within a bacterial operon.
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