Origin of Life: Emergence, Self-organization and Evolution
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A system-centered perspective on the origin and evolution of Life on Earth
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Testing the co-evolution theory

Testing the co-evolution theory | Origin of Life: Emergence, Self-organization and Evolution | Scoop.it

The coevolution theory (CET) of the genetic code postulates that the genetic code and codon assignment to amino acid coevolved with amino acid biosynthesis. In particular, the coevolution theory proposes that primordial proteins consisted only of those amino acids readily obtainable from the prebiotic environment, representing about half the twenty encoded amino acids of today, and the missing amino acids entered the system as the code expanded along with pathways of amino acid biosynthesis. Namely, as primordial organisms succeeded to synthetize new amino acids, codon were re-assigned and genetic was reshaped accordingly. The isolation of genetic code mutants, and the antiquity of pretran synthesis revealed by the comparative genomics of tRNAs and aminoacyl-tRNA synthetases, have combined to provide a rigorous proof of the fundamental tenets of the theory.

Recently, Fournier and co-workers casted new doubts on CET investigating Aminoacyl-tRNA synthetases (aaRS) which are universally distributed across cellular life. In particular, sequence reconstructions of the paralog ancestors of isoleucyl- and valyl- RS provide strong empirical evidence that at least for this divergence, the genetic code did not co-evolve with the aaRSs; rather, both amino acids were already part of the genetic code before their cognate aaRSs diverged from their common ancestor.

 

Source:

Wong JT

A co-evolution theory of the genetic code.

Proc Natl Acad Sci USA 1975, 72:1909-1912.

 

Gregory P. Fournier, Cheryl P. Andam, Eric J. Alm and J. Peter Gogarten

Molecular Evolution of Aminoacyl tRNA Synthetase Proteins in the Early History of Life

Origins of Life and Evolution of Biospheres. Volume 41, Number 6 (2011), 621-632

 

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On stochastic simulation of minimal cell models

On stochastic simulation of minimal cell models | Origin of Life: Emergence, Self-organization and Evolution | Scoop.it

If stochastic simulation of minimal cell models intrigues you, see may be interested in:

 

Carletti T, Filisetti A.

The stochastic evolution of a protocell: the gillespie algorithm in a dynamically varying volume.
Comput Math Methods Med. 2012;2012:423627.

 

Lazzerini-Ospri L, Stano P, Luisi P, Marangoni R.

Characterization of the emergent properties of a synthetic quasi-cellular system.

BMC Bioinformatics. 2012 Mar 28;13 Suppl 4:S9.

 

Zachar I, Fedor A, Szathmáry E.

Two different template replicators coexisting in the same protocell: stochastic simulation of an extended chemoton model.

PLoS One. 2011;6(7):e21380. Epub 2011 Jul 19.

 

Van Segbroeck S, Nowé A, Lenaerts T.

Stochastic simulation of the chemoton.

Artif Life. 2009 Spring;15(2):213-26.

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Noise, Stochasticity and Evolvability

Noise, Stochasticity and Evolvability | Origin of Life: Emergence, Self-organization and Evolution | Scoop.it

Kuwahara and Soyer report about stochastic fluctuations in gene regulation that can lead to increased evolvability of biological systems by pushing "system parameters toward a nonlinear regime where phenotypic diversity is increased and small changes in genotype cause large changes in expression level".

These findings strongly suggest that stochasticity and noise are used by biosystems to increase plasticity and access new phenotypes.

 

Source:

Hiroyuki Kuwahara & Orkun S Soyer. Bistability in feedback circuits as a byproduct of evolution of evolvability. Molecular Systems Biology 8:564

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Stochastic simulations of minimal cells: the Ribocell model

Stochastic simulations of minimal cells: the Ribocell model | Origin of Life: Emergence, Self-organization and Evolution | Scoop.it

Lipid compartments (liposomes, lipid-coated droplets) have been extensively used as in vitro "minimal" cell models to shed new light on the origin of life and the basic feature of living system as well. In this paper, Mavelli and Mirazo develop a comprehensive platform called ENVIRONMENT suitable for studying the stochastic time evolution of reacting lipid compartments. The software will allow the investigation of random fluctuations and stochastic events tha may lead an open system towards unexpected time evolutions.

 

Source:

Mavelli F.

Stochastic simulations of minimal cells: the Ribocell model.

BMC Bioinformatics. 2012 Mar 28;13 Suppl 4:S10.

 

Mavelli F, Ruiz-Mirazo K.

ENVIRONMENT: a computational platform to stochastically simulate reacting and self-reproducing lipid compartments.

Phys Biol. 2010 Aug 11;7(3):036002.

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