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Co-Expression Network Models Suggest that Stress Increases Tolerance to Mutations.

Lehtinen S, Bähler J, Orengo C - Sci Rep (2015)

Bottom Line: Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks.In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness.We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Infectious Disease Epidemiology, Imperial College, St Mary's Campus, Norfolk Place, London W2 1PG, UK.

ABSTRACT
Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks. Past work has concentrated on average damage caused by random node removal, with little attention to the shape of the damage distribution. In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness. We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations. The shape of the damage distribution is also changed upon stress, with a greater incidence of extreme damage after exposure to stress. We demonstrate that the change in shape of the damage distribution can have considerable functional consequences, highlighting the need to consider the damage distribution in addition to average behaviour.

No MeSH data available.


Related in: MedlinePlus

Damage to the network (measured as decrease in efficiency) in response to removal of an increasing proportion of the nodes in a SF and ER network.Each dotted line represents one realisation of random node removal, with a total of 500 realisation for each network. The solid lines represent average behaviour across realisations. The difference in average damage (ER minus SF) is shown in green, corresponding to the y-axis on the right.
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f1: Damage to the network (measured as decrease in efficiency) in response to removal of an increasing proportion of the nodes in a SF and ER network.Each dotted line represents one realisation of random node removal, with a total of 500 realisation for each network. The solid lines represent average behaviour across realisations. The difference in average damage (ER minus SF) is shown in green, corresponding to the y-axis on the right.

Mentions: First we compared the damage to scale-free (SF) Barabasi-Albert graphs23 and Erdős-Rényi (ER) random graphs under random node removal. We examined damage to the network, measured in terms of decrease in efficiency (a network property capturing the global connectivity of the network - see Methods), after removal of an increasing proportion of the nodes in random order. To produce an estimate of the damage probability density function (or ‘damage distribution’), we repeated this procedure 500 times (500 ‘realisations’) (Fig. 1). As reported previously, SF networks are, on average, less damaged by node removal. Importantly, however, the shape of the damage distribution is different, with greater variance and skewness in the SF network. (Skewness is a measure of the asymmetry of a distribution where a positive value indicates the data is spread to the left of the mean, and is given by s = E(x − μ)3/σ3 where μ and σ are the mean and standard deviation of x and E(t) represents the expected value of t).


Co-Expression Network Models Suggest that Stress Increases Tolerance to Mutations.

Lehtinen S, Bähler J, Orengo C - Sci Rep (2015)

Damage to the network (measured as decrease in efficiency) in response to removal of an increasing proportion of the nodes in a SF and ER network.Each dotted line represents one realisation of random node removal, with a total of 500 realisation for each network. The solid lines represent average behaviour across realisations. The difference in average damage (ER minus SF) is shown in green, corresponding to the y-axis on the right.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644955&req=5

f1: Damage to the network (measured as decrease in efficiency) in response to removal of an increasing proportion of the nodes in a SF and ER network.Each dotted line represents one realisation of random node removal, with a total of 500 realisation for each network. The solid lines represent average behaviour across realisations. The difference in average damage (ER minus SF) is shown in green, corresponding to the y-axis on the right.
Mentions: First we compared the damage to scale-free (SF) Barabasi-Albert graphs23 and Erdős-Rényi (ER) random graphs under random node removal. We examined damage to the network, measured in terms of decrease in efficiency (a network property capturing the global connectivity of the network - see Methods), after removal of an increasing proportion of the nodes in random order. To produce an estimate of the damage probability density function (or ‘damage distribution’), we repeated this procedure 500 times (500 ‘realisations’) (Fig. 1). As reported previously, SF networks are, on average, less damaged by node removal. Importantly, however, the shape of the damage distribution is different, with greater variance and skewness in the SF network. (Skewness is a measure of the asymmetry of a distribution where a positive value indicates the data is spread to the left of the mean, and is given by s = E(x − μ)3/σ3 where μ and σ are the mean and standard deviation of x and E(t) represents the expected value of t).

Bottom Line: Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks.In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness.We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Infectious Disease Epidemiology, Imperial College, St Mary's Campus, Norfolk Place, London W2 1PG, UK.

ABSTRACT
Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks. Past work has concentrated on average damage caused by random node removal, with little attention to the shape of the damage distribution. In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness. We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations. The shape of the damage distribution is also changed upon stress, with a greater incidence of extreme damage after exposure to stress. We demonstrate that the change in shape of the damage distribution can have considerable functional consequences, highlighting the need to consider the damage distribution in addition to average behaviour.

No MeSH data available.


Related in: MedlinePlus