Models of the Ecological Hierarchy: From Molecules to the Ecosphere

By Elsevier Science

In the application of statistics to ecological inference problems, hierarchical models combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are applied in this book to a wide range of problems ranging from the molecular level, through populations, ecosystems, landscapes, networks,  through to the global ecosphere.

• Provides an excellent introduction to modelling
• Collects together in one source a wide range of modelling techniques
• Covers a wide range of topics, from the molecular level to the global ecosphere

• Language: English
• Publisher: Elsevier Science
• Release date: Dec 31, 2012
• ISBN: 9780444594051

Book preview

1973:1–27.

Quantum Chemical Modeling in the Molecular Ecology

Sergey Ovchinnikov*,†, Felix Tomilin*,†, Polina Artushenko*, Vladislav Sukhovol’sky**, Tamara Ovchinnikova**, Palina Volkova**, Yurii Baranchikov**, Evgenii Vysotskii$

---

* L.V. Kirensky Institute of Physics, SB RAS, Krasnoyarsk, 660036, Russia

† Siberian Federal University, Krasnoyarsk 660041, Russia

** V.N. Sukachev Institute of Forest, SB RAS, Krasnoyarsk, 660036, Russia

$ Institute of Biophysics, SB RAS, Krasnoyarsk, 660036, Russia

Abstract

Quantum chemical modeling of the electronic structure and evaluation of the total energy of several biological molecules important for the ecology have been carried out. The first example is represented by pheromone molecules of the gypsy moth and the Siberian silk moth and their responses to substances present in the forest and to electromagnetic radiation. The second example is concerned with the molecular mechanism of bioluminescence. Here we report the quantum chemical modeling of the excited states of coelenteramide embedded in the active site of the protein. We demonstrate that the fluorescence wavelength extremely depends on the proton position between coelenteramide and histidine.

Keywords

• Forest insect • Pheromones • Bioluminescence • Obelin • Quantum chemical modeling

1.1 Introduction

During their life, plants and animals synthesize and secrete into the environment volatile substances of different chemical natures. Flows of the molecules of these substances form information fields in the terrestrial ecosystems. It is possible for individuals in ecosystems to use these fields for searching habitats, food, sexual partners, etc. The air medium is a channel through which the information is transmitted. The study of the information flows and properties of the information channels in ecosystems opens up the possibilities for understanding the behavior of individuals in the populations and controls the ecological processes in the ecosystems. In the framework of the information theory, common tasks assess the reliability of the information transfer and the noise in the information channels. Reliability and noise level in the chemical communication channel are largely dependent on the characteristic lifetime of the molecules (the carriers of information) and on their stability to the modifying factors (electromagnetic radiation in the ultraviolet (UV), visible, and infrared ranges; air temperature and precipitation; and content of various substances in the air).

Convenient objects used to study the information flows in terrestrial ecosystems and to assess the impact of environmental factors are insects. The chemical composition of sex pheromones is known for several thousand species of insects (http://www.pherobase.com). However, the experimental study of the pheromones molecular stability to the effects of the modifying factors and the assessment of the impact of the environmental factors on the sexual behavior of insects are associated with technical difficulties. In addition, it is a time-consuming process that requires considerable financial resources.

Bioluminescence is the emission of visible light by an organism. This phenomenon is very widespread in the biosphere. However, the vast majority of bioluminescent organisms reside in the ocean; 80% of the more than 700 genera known to contain luminous species are marine (Shimomura, 2006). As a result of its prevalence, bioluminescence plays an important role in the ecology of the ocean. Its importance is clearer in the context of the essentially dark environment below 1000 m because a large number of organisms retain functional eyes to detect the bioluminescence at depths where sunlight never penetrates (Warrant and Locket, 2004). The bioluminescence can serve animals for the defense against predators, as an aid in locating food, for communication, and for camouflage, as well as for attracting a mate by means of species-specific spatial or temporal patterns of the light emission. Thus, in fact, the bioluminescence and the pheromone molecules implement similar functions in ecological systems.

Modern quantum chemistry provides a powerful tool to study complex molecules, their atomic and electronic structures in the ground and excited states, as well as unstable intermediates and transition states. The approach based on the density functional theory (DFT) incorporates the electron–electron Coulomb and exchange interactions beyond the well-known single-electron Hartree–Fock approximation (Schmidt et al., 1993). In this paper we propose to use quantum theoretical calculations for addressing the above-mentioned problems in molecular ecology and as an alternative to laboratory experiments. We consider two case studies. The first is represented by the pheromone molecules of different forest moths and their response to the substances present in the forest air and to the electromagnetic radiation. The second case study deals with the molecular mechanism of bioluminescence in Ca²+-regulated photoproteins, which are mainly responsible for the luminescence of marine coelenterates.

1.2 Pheromone Molecules and Their Interaction With the Environment

The pheromones of the gypsy moth, Limantria dispar L., and the Siberian silk moth, Dendrolimus sibiricus superans Tschetv, have been studied. The gypsy moth pheromone has only one pheromone component, the disparlure, (7R,8S)-cis-7,8-epoxy-2-methyloctadecane, that belongs to the epoxy family (Bierl et al., 1970). The Siberian silk moth pheromone has two major components: the (Z,E)-5,7-dodecadienal (aldehyde) and the (Z,E)-5,7-dodecadien-1-ol (spirit) (Khrimian et al., 2002; Klun et al., 2000; Pletnev et al., 2000).

The quantum chemical computations of the electronic structure and analysis of the atomic–electronic structure for these compounds in the ground and excited states have been carried out. We used the configuration interaction method (configurations were constructed from 10 occupied and 6 unoccupied orbitals) of the semiempirical Hartree–Fock method PM3 (Parameterization Method 3) (Stewart, 1989) with the OpenMopac2007 (http://openmopac.net) and the HyperChem 7.52 (http://www.hyper.com) programs. The most advanced method of density functional theory (DFT) B3LYP/6-31(p,d) (Becke, 1993; Lee et al., 1988) has been incorporated in the GAMESS package (Schmidt et al., 1993).

For each pheromone molecule studied the following characteristics were calculated:

• The molecule total energy Etotal.

• The electric dipole moment D, forming due to shifting of the electron cloud toward one of the atoms. This dipole moment shows how polar the molecule is.

• The oscillator strength (OS); this dimensionless quantity gives the information on the probability of the excitation, i.e., the intensity of transitions between the ground and excited states. The more the OS, the larger the intensity of the transition.

• The wavelength λ and the absorption energy Eabs, which correspond to the absorption edge of molecule (E = hc/λ, where E is the energy, h is the Planck constant, and c is the light velocity).

Three types of the conformers were chosen for each pheromone molecule: the linear structure (K1), the most curved structure (K2) and the armchair structure (K3). These conformers differ by the disposition of their carbon skeleton relative to the functional group of the molecule. In Fig. 1.1, conformers of the (7R,8S)-cis-7,8-epoxy-2-methyloctadecane (disparlure) are presented as an example.

FIGURE 1.1 Conformers of the (7R,8S)-cis-7,8-epoxy-2-methyloctadecane (disparlure). (a) Conformer K1, (b) conformer K2, and (c) conformer K3.

Pheromone molecules of the Siberian silk moth, Dendrolimus superans, have the sp²-hybridized molecular orbital of carbon atoms, which form bonds in the same plane at an angle of 120°. This leads to the formation of the linear section in all the conformers and make structures K1 and K3 similar.

The calculated characteristics for the studied pheromone molecules are presented in Table 1.1.

TABLE 1.1 The Characteristics of the Studied Pheromone Molecules’ Conformers

As it is seen from Table 1.1, there is no large difference in the total energy value, Etotal, for the different conformers of individual pheromone molecules. Maximal difference in the energy (6 kcal mol−1) between the structure K2 and the rest of the conformers is found for the disparlure molecule. Similarity in the K1 and K3 structures for the pheromone molecules causes similarity in the conformers’ properties. The K2 conformer’s dipole moment is different from the dipole moments of the K1 and K3 structures by 0.1–0.4 D. Conformers’ dipole moments range from 1.2 to 2.7 D. For the considered pheromone molecules, the dipole moment provides the ability to interact with polar molecules, for example, with water molecules, H2O (dipole moment 1.8 D), from the air.

In Table 1.1 the wavelengths, λ, and the absorption energies, Eabs, that correspond to the maximum of the pheromone molecules’ electron transitions are listed. The absorbing power, Eabs, for the considered transitions depends on the quantity and relative position of the multiple bonds between the carbon atoms. These compounds absorb in the UV spectrum.

For the pheromone molecules of D. sibiricus, which have the conjugated double bonds, the absorbing wavelengths lie in the range of 260–265 nm. The other functional groups have less influence on the energy of the transition to the excited state.

Unlike the pheromone molecules of D. sibiricus, the pheromone molecule of L. dispar does not have multiple bonds, and as it can be observed from Table 1.1, the energy Eabs for this molecule differs greatly from the values for other studied pheromone molecules (by approximately 2.5 eV). Therefore, more energy is needed to excite the disparlure molecule than to excite all the other molecules (Fig. 1.2).

FIGURE 1.2 The pheromone molecules’ absorption edge.

Minimization of the molecule’s energy in the excited state has revealed the change in the atomic geometry; the largest ones are shown in Fig. 1.3. These changes result in the sharp reduction of the activation energy of the pheromone chemical reaction with a water molecule. Notice that after reaction with water the pheromone molecule becomes deactivated. For example, the activation energy is equal to 290 kJ mol−1 in the ground state (H2O + (Z,E)-5,7-dodecadien-1-ol) and no activation barrier in the excited state (H2O + *(Z,E)-5,7-dodecadien-1-ol) of the Siberian silk moth.

FIGURE 1.3 Configurations of the diene group in aldehydes and alcohols of the pheromone components of the Siberian silk moth and the gypsy moth and its changes in the excited states. The bond length is in Angstroms.

Thereby our calculations show that the solar radiation can change the configuration of the D. sibiricus pheromone molecules. During the morning hours, when the solar radiation is rather strong, the atom spacing and the bond angles can be changed in these pheromone molecules. This causes chemical reactivity of the molecule to increase and results in the deactivation of the D. sibiricus pheromone. In contrast, the gypsy moth pheromone (7R,8S)-cis-7,8-epoxy-2-methyloctadecane requires the high-energy UV radiation (e.g., lightning) to be deactivated.

We can compare our theoretical conclusions with the experimental data (Fig. 1.4) on the pheromone activity. The Siberian silk moth is active only during the night hours, whereas the gypsy moth is active in both periods.

FIGURE 1.4 Mate search activity during the day for the insect species resistant (gypsy moth) and nonresistant (Siberian silk moth) to the external factor pheromone molecules.

Thus, the information losses in the pheromone communication channel of the Siberian silk moth are minimized during evening and night hours because the pheromone molecules do not get deactivated by light. The pheromone molecule components emitted by the female insects during the previous evening and night do not transmit information anymore because during that time the insects could have moved to a different location when the molecules were randomly carried around by turbulent airflows. However, during the following daylight hours these molecules can get deactivated, which will decrease the noise level in the pheromone channel by the next evening. Then influence of the external environmental factors can be seen as a mechanism assisting breakdown of the pheromone molecules that do not transmit information about the location of the female insect anymore.

The absorption band for the L. dispar pheromone molecules lies in the zone of the hard UV light (∼200 nm), and for the L. dispar pheromone molecules to be affected by light, a greater energy has to be applied than that applied in the case of the Siberian silk moth pheromones. Usually the solar radiation in the zone of the hard UV is rather low because it is absorbed by the oxygen present in the air; therefore, the risk of information loss due to disparlure molecule breakdown is low at any time of the day. These conclusions correlate with the fact that the gypsy moth’s mate search takes place throughout the day.

1.3 Structure of the Active Center and Luminescence in the Photoprotein Obelin

The systems named preliminary charged occupy a special place among numerous bioluminescence systems. The most well-known and studied representatives of such bioluminescence systems are Ca²+-regulated photoproteins, which are mainly responsible for the luminescence of the marine coelenterates (Vysotskii et al., 2006). The photoprotein molecule is a stable enzyme–substrate complex composed of a monosubunit polypeptide and an oxygen-preactivated substrate, 2-hydroperoxycoelenterazine, which is noncovalently bound to the protein. Bioluminescence is initiated by the calcium ions and emerges due to the oxidative decarboxylation of the substrate bound to the protein. This causes formation of the reaction product, the coelenteramide (CLM), in an excited state. The transition of the CLM from the excited to the ground state is accompanied by light emission. The bioluminescence of the photoproteins is observed in the range of 465–495 nm and depends on the particular organism from which the photoprotein is isolated. Since the moment of discovery, the Ca²+-regulated photoproteins have been studied intensively. The structures of the substrate and the reaction product have been determined, and the chemical mechanism of the bioluminescence reaction has been proposed. The spatial structures of several ligand-dependent conformational states of the photoproteins have been recently determined. However, the spatial structure of the protein provides information only about the static state of the protein molecule and the amino acids in the active center. It is still unknown what changes take place during the reaction. The state-of-the-art quantum chemical modeling allows this gap to be filled. In this section we used the quantum chemical methods to model the molecular mechanisms underlying the fluorescence of the Ca²+-discharged photoprotein obelin from the hydroid Obelia longissima.

In a previous study, the fluorescence of the CLM and its analogs in solvents with various polarities was studied and it was found that the excited CLM can form five different ionic forms depending on the solvent (Shimomura and Teranishi, 2000; Mori et al., 2006). Theoretical models were constructed for all five CLM ionic forms using the quantum chemical semiempirical method PM3 (Becke, 1993; Stewart, 1989). Because, when modeling the absorption spectra and luminescence, it is also necessary to take into account the electron–electron Coulomb interaction, the calculations were conducted using the configuration interaction method (121 configurations). The calculated CLM emission wavelength in vacuum without any environment differs considerably from the experimental values. Therefore, the nearest amino acid environment of the cavity containing the CLM was included into the cluster model in the assumption that it could strongly influence the CLM excited state. The CLM and the amino acid environment do not interact chemically, and the effect of the environment is electrostatic. Thus, we computed the CLM molecule (about 50 atoms) and its nearest amino acid environment at a distance of about 4 Å from the CLM atoms, namely, His22, Gly143, Tyr190, Met171, Trp114, Phe72, Trp179, Phe28, Ala46, Phe88, Val118, Trp92, Ile50, Met25, Leu29, Gly115, Thr172, His175, Ile144, Asp49, Ser142, Lis53, Lis45, Cys51, Leu54, His64, Phe122, and 5H2O (overall, about 500 atoms; Fig. 1.5).

FIGURE 1.5 The obelin and the model cluster for computations.

To model the fluorescence process, it is necessary to test all possible CLM forms that could be the candidate emitters. Correspondingly, we have computed the absorption and fluorescence spectra for all ionic forms (Tomilin et al., 2008). However, all known static forms do not allow to reproduce the experimental spectra. Two static forms are related by the possible proton transfer from the CLM phenolic group to His22. We assume that the proton exchange may be a dynamical process.

Therefore, we modeled the position of the hydrogen atom between the CLM phenolic group and His22. For this purpose, the distance between the outermost hydrogen positions, 2.42 Å (Fig. 1.6), was divided into 30 equal parts to calculate the emission wavelength at each position (Table 1.2).

FIGURE 1.6 Modeling of the hydrogen transfer between the central molecule and His22.

TABLE 1.2 The Fluorescence Wavelengths Depending on the Proton Position

The position of the hydrogen atom has a strong influence on the emission wavelength. The experimental value is obtained for the intermediate proton distance 1.60–1.64 Å from the CLM. The modeling of the proton transfer in the system His22–CLM has demonstrated that an energy of approximately 3.6 eV is required to move the proton of the CLM phenolic group to a distance of up to 1.5 Å; this energy fits with the fluorescence excitation energy. When the proton returns to the initial state, the energy of 2.3–2.5 eV is emitted. As the energy during the transition from the excited to the ground states is emitted spontaneously, the proton can return to the CLM from various distances; correspondingly, this broadens the fluorescence peak. In essence, this model reflects the formation of an ion-pair proton transfer complex (Mori et al., 2006; Shimomura and Teranishi, 2000).

1.4 Conclusions

The analysis on these two case studies showed that the existing quantum mechanics methods and computer software are quite suitable for calculating characteristics of such small molecules as lepidopterous pheromone molecules. A suggested approach allows estimating how resistant the pheromone molecules of some dangerous taiga pests are to the external environmental factors. The connection between the behavioral activity of an insect and the time of the day when the pheromone molecules are less influenced by the external factors is of considerable interest. Such a connection was found for D. sibiricus and L. dispar, and we believe that further juxtaposition of the pheromone molecules’ properties, environmental conditions and sexual activity times can shed light on the evolutionary mechanisms underlying pheromone communication formation. It can also give clues on why the populations of certain insect species attend to occupy forest biomes, with certain biogeographic characteristics.

The methods applied to study the molecular mechanism of fluorescence have revealed that the fluorescence of the Ca²+-discharged obelin (and possibly other Ca²+-discharged photoproteins) is well modeled by moving the proton from the oxygen of the CLM phenolic group to the nitrogen of the amino acid His22.

Acknowledgments

The authors would like to acknowledge the RFBR (grant no. 12-04-00119), FCP program Kadry GK P333, Government of Russian Federation’s grant no. 11. G34.31.058, President of Russian Federation’s grant NSh no. 1044.2012.2, Joint Super Computer Center (Moscow) (MBC-100K), and HPC IKIT of the Siberian Federal University (Krasnoyarsk) for financial support of this research.

References

A.D. Becke. Density-functional thermochemistry. III. The role of exact exchange. J. Chem. Phys.. 1993;98:5648–5652.

B.A. Bierl, M. Beroza, C.W. Collier. Potent sex attractant of the gypsy moth: its isolation, identification, and synthesis. Science. 1970;170:87–89.

A. Khrimian, J.A. Klun, Y. Hijji, et al. Syntheses of (Z,E)-5,7-dodecadienol and (E,Z)-10,12-hexadecadienol, Lepidoptera pheromone components, via zinc reduction of enyne precursors. Test of pheromone efficacy against the Siberian moth. J. Agric. Food Chem.. 2002;50(22):6366–6370.

J.A. Klun, Y.N. Baranchikov, V.C. Mastro, Yo Hijji, J. Nicholson, I. Ragenovich, T.A. Vshivkova. A sex attractant for the Siberian moth—Dendrolimus superans sibiricus (Lepidoptera: Lasiocampidae). J. Entomol. Sci.. 2000;36:84–92.

C. Lee, W. Yang, R.G. Parr. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B. 1988;37:785–789.

K. Mori, S. Maki, H. Niwa, H. Ikeda, T. Hirano. Real light emitter in the bioluminescence of the calcium-activated photoproteins aequorin and obelin: light emission from the singlet-excited state of coelenteramide phenolate anion in a contact ion pair. Tetrahedron. 2006;62:6272–6288.

V.A. Pletnev, V.L. Ponomarev, N.V. Vendilo, S.A. Kurbatov, K.V. Lebedeva. Pheromone search of Siberian silk moth Dendrolimus superans sibiricus (Lepidoptera: Lasiocampidae). Agrochemistry. 2000;6:67–72. (in Russian)

M.W. Schmidt, K.K. Baldridge, J.A. Boatz, S.T. Elbert, M.S. Gordon, J.H. Hensen, S. Koseki, N. Matsunaga, K.A. Nguen, S. Su, T.L. Windus, M. Dupius, J.A. Montgomery. General atomic and molecular electronic structure system. J. Comput. Chem.. 1993;14:1347–1363.

O. Shimomura, K. Teranishi. Light-emitters involved in the luminescence of coelenterazine. Luminescence. 2000;15:51–58.

O. Shimomura. Bioluminescence: Chemical Principles and Methods. New Jersey: World Scientific; 2006.

J.J.P. Stewart. Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem.. 1989;10:209–220.

F.N. Tomilin, LYu Antipina, E.S. Vysotski, S.G. Ovchinnikov, Gitelzon, II. Fluorescence of Ca-discharged obeline: structure and molecular mechanism of emitter formation. Dokl. Biochem. Biophys.. 2008;422:279.

E.S. Vysotskii, S.V. Markova, L.A. Frank. Calcium-regulated photoproteins of marine coelenterates. Mol. Biol.. 2006;40(3):404–417. (Moscow)

E.J. Warrant, N.A. Locket. Vision in the deep sea. Biol. Rev. Camb. Philos. Soc.. 2004;79(3):671–712.

Evolution before Life

Vera Vasas*, Chrisantha Fernando†

---

* Departament de Genètica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain

† School of Electronic Engineering and Computer Science (EECS), Queen Mary, University of London, Mile End Road, London E1 4NS, UK

Abstract

All life, as we know it, springs from already existing life. But how did it all begin? We argue that evolution preceded life, and we must understand the origin of evolvable chemical systems as their gradual increase of complexity lead to the appearance of the first living entity. In this chapter, we discuss various theoretical models that have been proposed to explain the origin of evolvable systems (including template replicators, autocatalytic cycles, polymer autocatalytic sets, and the GARD model) and translate the critical properties of evolution to features of chemical networks.

Keywords

• Origin of life • Early evolution • Autocatalysis • Replication

2.1 Introduction

If you want to make mice, the recipe is very easy; take a male and a female mouse, give them food, wait, and soon you will have more of them anyone would ever ask for. Try, however, to assemble a Frankenstein mouse in an organic chemistry laboratory, and you are up to certain disappointment. All life, as we know it, is autocatalytic; its creation requires already existing life. But how did it all begin?

Speaking in the most general terms, we expect that an autocatalytic chemical (Fig. 2.1) must have spontaneously appeared from the random chemistry of prebiotic Earth (Lifson, 1997). An autocatalyst can be any molecule or set of molecules that catalyze their production from environmentally available compounds, as A in the following reaction:

FIGURE 2.1 Three basic motifs for direct and indirect autocatalysis. White circles: food molecules available in the environment; grey circles: different nonfood molecules. Solid lines: reactions; dotted lines: catalytic activities. (a) A molecular autocatalyst is a molecule that catalyses its own formation from food. (b) An autocatalytic cycle comprises several stoichiometric reactions and can be regarded as one composite autocatalytic entity. Here, each turn of the cycle doubles its mass. (c) Indirect autocatalysis can arise from heterocatalysis if the catalytic dependencies form a closed circular path, called an autocatalytic loop.

     (1)

As opposed to heterocatalysis, as H in the following reaction:

     (2)

The hypothetical autocatalyst grew to macroscopic levels while exhausting its reactants, and competition appeared among the slightly different autocatalysts that were produced by random chemical changes (Lifson, 1997). Thus, natural selection of chemical compounds, along with the continuous interaction between autocatalysts and their changing environment, kicked off the evolution leading to the wondrous diversity of current life.

A spontaneous synthesis of an autocatalyst initiates an explosion-like phenomenon. For a single catalyst molecule, with the velocity of one catalyzed reaction per microsecond, it would take more time than the estimated age of the universe to produce a mole of products—in contrast, for an autocatalyst with the same catalytic properties, this task would require 79 μs (Lifson, 1997). This exponential growth allows the increase of replicating molecules in a geometrical progression that makes the survival of the fittest possible (Szathmáry, 1991). As Eigen has shown in his seminal paper (Eigen, 1971), if autocatalysts (with certain specified properties) are reliably supplied with energy, their evolution becomes inevitable.

However, the certain specified properties passage is important—not all autocatalysts can evolve. A fire is certainly autocatalytic, but it will always remain the very same fire; its properties entirely dependent on the material it is consuming. For natural selection to happen, we need a population of units that is capable of multiplication (one entity can give rise to many), variation (entities are not all alike, and some kinds are more likely to survive and multiply than others), and heredity (like begets like) (Fig. 2.2) (Maynard Smith, 1986). No matter how fascinating behavior the autocatalysts can show, they must pass on hereditary variation to be relevant for the origin of life. In other words, they must be informational replicators (Szathmáry, 2000). The aim of this chapter is to review the various theoretical models that have been proposed to explain the origin of evolvable systems and to provide a unified theory of chemical organizations that allow for their selectability. Doing so, we hope to highlight yet another possible use of network models.

FIGURE 2.2 Requirements for evolution. Any population of units can evolve if the entities have the properties of multiplication (one entity can give rise to many), variation (entities are not all alike, and some kinds are more likely to survive and multiply than others), and heredity (like begets like).

2.2 Template-Based Replicators

All present life uses polynucleotide strings as the basis of storing and transmitting information. DNA and RNA replication is template based, meaning that the sequence of molecules on the parent strand is replicated (with mutation) to the child strand by a topographic mapping between the parent and child strand. Let us imagine a hypothetical polymer, held together with strong bonds, whose different building blocks can weakly bind identical monomers (Fig. 2.3). Due to the proximity of the monomers a new strand can form, and if it manages to separate from the original strand, we have an autocatalytic self-replicator—a molecule that catalyses its own formation from the food