Cluster compartmentalization may provide resistance to parasites for catalytic networks

Cronhjort, Mikael; Blomberg, Clas

doi:10.1016/s0167-2789(97)87469-6

Cited by 46 publications

(44 citation statements)

References 9 publications

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“…The structural perturbations that may lead to dynamical instabilities in hypercycles are given by ''shortcuts'' and parasites (e.g. selfish replicators) (Cronhjort and Blomberg, 1997;Eigen and Schuster, 1979;Stadler and Stadler, 2003). We underline that the two-member hypercycle can act as a switch making such ''shortcuts'' in larger hypercycles.…”

Section: Article In Pressmentioning

confidence: 94%

Bifurcations and phase transitions in spatially extended two-member hypercycles

Sardanyés¹,

Solé²

2006

Journal of Theoretical Biology

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Mounting theoretical and experimental evidence indicates that the success of molecular replicators is strongly tied to the local nature of their interactions. Local dispersal in a given spatial domain, particularly on surfaces, might strongly enhance the growth and selection of fit molecules and their resistance to parasites. In this work the spatial dynamics of a simple hypercycle model consisting of two molecular species is analysed. In order to characterize it, both mean field models and stochastic, spatially explicit approaches are considered. The mean field approach predicts the presence of a saddle-node bifurcation separating a phase involving stable hypercycles from extinction, consistently with spatially explicit models, where an absorbing first-order phase transition is shown to exist and diffusion is explicitly introduced. The saddle-node bifurcation is shown to leave a ghost in the phase plane. A metapopulation-based model is also developed in order to account for the observed phases when both diffusion and reaction are considered. The role of information and diffusion as well as the relevance of these phases and the underlying spatial structures are discussed, and their potential implications for the evolution of early replicators are outlined. r

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Section: Article In Pressmentioning

confidence: 94%

Bifurcations and phase transitions in spatially extended two-member hypercycles

Sardanyés¹,

Solé²

2006

Journal of Theoretical Biology

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“…7.) A significantly simpler route to stable evolutionary multimolecular self-organization has been recognized (8,9). It involves the self-replicating spot patterns that emerge (10) in systems that incorporate the Scott-Gray mechanism.…”

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confidence: 99%

Evolutionary self-organization of cell-free genetic coding

Füchslin

McCaskill

2001

Proc. Natl. Acad. Sci. U.S.A.

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Genetic encoding provides a generic construction scheme for biomolecular functions. This paper addresses the key problem of coevolution and exploitation of the multiple components necessary to implement a replicable genetic encoding scheme. Extending earlier results on multicomponent replication, the necessity of spatial structure for the evolutionary stabilization of the genetic coding system is established. An individual-based stochastic model of interacting molecules in three-dimensional space is presented that allows the evolution of genetic coding to be analyzed explicitly. A massively parallel configurable computer (NGEN) is used to implement the model, on the time scale of millions of generations, directly in electronic hardware. The spatial correlations between components of the genetic coding system are analyzed and found to be essential for evolutionary stability.T he genetic code, as a wonder of ancient biological engineering, has excited much recent study (1). Its structure, origin, uniqueness, and information-processing capabilities have been the focus of biochemical and theoretical analysis. Its emergence has often been identified (2) with the origin of life itself in the long-standing debate as to whether genes or proteins came first. Genetic coding of function remains a central issue in an RNA world, in which RNA catalysts could act singly or jointly as polymerases to replicate other RNA as genes. It has long been realized that some form of effective compartmentation is necessary to allow the selection of the functional multicomponent replicable systems needed to implement a genetic code, and this has added an additional complexity to models of the evolution of such systems. Possibly for this reason, there have been few explicit models of the evolution of genetic coding. In this paper, we show that a multicomponent replication-translation system can evolve a stable genetic code in a continuous medium with no explicit compartmentation or control thereof.The model framework is simple and abstract, compared with our detailed understanding of the cellular translation process. However, it appears to capture the essential organizational problem associated with the simultaneous evolution of a generic coding mechanism and self-replicating entities. The basic assumptions of the model are:(i) Two distinct combinatorial families of chain molecules (catalysts and templates) are formed at a slow rate by random synthesis. (ii) Catalytic molecules (like proteins) are mostly not capable of self replication, whereas template molecules (like RNA) can be transcribed (or replicated) and translated generically. (iii) At least one rare polymer sequence exists that catalyzes the replication of templates; no recognition of specific template sequences is assumed. (iv) Rare polymer sequences exist that catalyze the translation of templates to potentially catalytic molecules according to one of a number of ''conflicting'' codes; no specific recognition of template sequences is assumed. (v) Molecular reactions are limited b...

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“…This evolutionary system, suffering from a lack of robustness, was designed as a single-level Artificial Chemistry (AC) where only molecules were competing with each other. Further developments of the system included the introduction of a second level of selection [3,7], where molecules were now contained in compartments (analogous to cells). Similarly to competing molecules, cells were subjected to artificial selection: There was a fixed number of cells, when a cell divides, another cell is picked at random and removed from the cellular population.…”

Section: A Bottom-up Approachmentioning

confidence: 99%

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

Decraene¹,

Mitchell²,

McMullin³

2008

China-Ireland International Conference on Information and Communications Technologies (CIICT 2008)

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This paper is concerned with the modeling and evolving of Cell Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first provided, in which we describe the potential applications of modeling and evolving these biochemical networks in silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the ESIGNET project 1 . Results obtained with these methods are summarized and discussed.

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Cluster compartmentalization may provide resistance to parasites for catalytic networks

Cited by 46 publications

References 9 publications

Bifurcations and phase transitions in spatially extended two-member hypercycles

Bifurcations and phase transitions in spatially extended two-member hypercycles

Evolutionary self-organization of cell-free genetic coding

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

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