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Indeed, in previous studies, high agreement between co-evolutionary signal and cellular interaction data was assumed, and hence the cellular interaction data were used as proxy for co-evolution signal (Cordero et al., 2008; Tuller et al., 2010).
Here, we took a different approach, in which the agreement between cellular interaction data and co-evolutionary signal was not assumed but rather, measured.
Unfortunately very little is known about the effect of horizontal gene transfer on the global protein interaction networks in this aspect, mostly due to the lack of cellular interaction data in bacteria until recently.
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Moreover, several researchers have introduced protein interaction network based methods [19], [20], and Brun et al. [21] [24] clustered the Saccaromyces cerevisiae proteome into several groups to predict cellular functions using protein interaction data.
The ideal technology would provide cellular maps of binding and interaction data at diffraction-limited resolution and over a wide range of dynamics using image sequences of migrating cells.
To this end, we compiled and analyzed >600 experimental protein datasets from S. cerevisiae and S. pombe, including discrete sub-cellular localization and protein-protein interaction data, as well as quantitative protein half-life and abundance data.
We found that in KB cells, over-expression of HA-NTD brought a rise in p53 level and there was cellular interaction between NTD125 and endogenous p53 (data not shown).
Open-access databases are thus the main repositories of the large-scale pathway reaction and interaction data necessary for cellular-scale systems modelling.
In particular, when jointly analyzing gene expression and interaction data one should consider that: Many cellular processes cannot be monitored by studying gene expression alone.
The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological systems.
In addition, using reliable pathway-based gene interaction data, we constructed a more comprehensive cellular map for OC based on top 100-ranked genes.
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