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toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map

In this pre-print article on arXiv the authors present a computational toolkit for simulating intra-cellular events and interactions from the level of gene transcription and protein folding to the level of the entire system of regulatory and metabolic networks.

toyLIFE: a computational framework to study the multi-level 
organization of the genotype-phenotype map.
Clemente F. Arias, Pablo Catalán, Susanna Manrubia, José A. Cuesta
http://arxiv.org/abs/1409.4904

The model they use (“toyLife”) is called a toy model because it reduces biological complexity to the level of 2D polymer representations known as the HP lattice model that has been used to study protein folding and the sequence-to-structure mapping. Proteins are short chains of either hydrophobic or polar (hence the name HP) building blocks that follow a very simple interaction scheme mimicking the hydrophobic effect that drives protein folding. This interaction scheme has been extended in toyLife to also represent any type of inter-chain binding to form dimers or protein-metabolite complexes. There also is a simple polymerase and transcription factor activity that allows for very complex regulatory networks that are captured in a Boolean fashion of genes being either on or off. The more genes are involved, the larger the number of distinct regulatory states (think of tissue specificity) a cell can produce with a given genome.

The paper focusses on the model itself and presents very little actual results. However, toyLife looks like a very useful tool to address fundamental questions about such things as the systems-level effects of single point mutations. In other words, toyLife can be used to study the genotype-phenotype link within a multi-level system ranging from protein stability and binding all the way to large scale regulatory effects.

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About Tobias Sikosek

Researcher in Computational Biophysics and Protein Evolution at the University of Toronto

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