# 统计代写|网络分析代写Network Analysis代考|MY561

## 统计代写|网络分析代写Network Analysis代考|Methods for protein interaction generation

The interaction between a pair of protein molecules is measured using a variety of assays, such as immunoprecipitation, tandem affinity purification, and the yeast two-hybrid approach. Recently, these techniques have been scaled up to measure interactions on a genome-wide level [98]. High-throughput techniques have also been developed to systematically identify protein complexes using affinity purification techniques, followed by mass spectrometry (MS/MS) to sequence the proteins $[21,35]$. The process of building a protein interaction network starts in wet lab with many experimental assays. Each experiment could reveal a binary interaction or multiple interactions; therefore a complete investigation requires the planning of a set of assays, under the guidance of some optimization strategy. Experimental techniques may aim to discover binary or multiple interactions, or they may investigate qualitatively or quantitatively the interactions themselves.

A set of techniques are aimed to determine the interaction of two or more proteins without looking at the dynamics of the reactions. Usually, these techniques are based on a bait-prey model. Therefore a bait protein is used as a test to demonstrate its relations with one or more proteins, called preys. These models have been employed in many experiments $[21,35,41,98]$.

## 统计代写|网络分析代写Network Analysis代考|Fitting protein networks into models

The determination of a correct model for PPI networks could be important for effective experimental planning, helping to determine possible interactions. Currently, there exist five common models used for PPI network, as discussed before: (i) random graph model, (ii) scale-free model, (iii) geometric random graph model, (iv) STICKY model and (v) hypergeometric graph model.
In network theory, the individuation of a theoretical model, to which network belongs may provide relevant information, i.e., as a tool for prèdiction of novel interactions, and for automatèd analysis. For instance, Lappe et al. [54], used a theoretical model to suggest correct experiments for the individuation of the human PIN to reduce the total number of experiments.

The development of a general model for interaction networks can capture relevant information and can provide a tool for predictive inference. The introduction of a common shared model is currently a research area. The simple representation of interaction datasets as a graph does not consider the following problems as noted in [77]: (i) it does not take into account a set of supplementary information about the interactions, such as spatial and temporal information, kinetics parameters, number of experiment confirming the interaction (see [101]), and (ii) the large number of false-positive and false negative interactions present in the dataset. For instance, when a physical interaction is detected of mediators.

Conversely, a correct model of PPI network enables researcher to build predictive experiments (i.e., predicting the possible interaction, and hence verifying them with experiment), to analyze structural properties (e.g., individuation of complexes or pathway) and to bring together information about the function of themselves.

As described in [54], for the human PPI network, a model has been used to plan high-throughput experiments. In that work, a theoretical model is used to guide experiments, thus avoiding unuseful experiment in classical strategies. In a typical experiment, a so-called pull-down strategy is adopted. This strategy involves choosing a single protein that plays the role of a bait and identifying all interacting partners of this protein without any optimization strategy.

## 统计代写|网络分析代写网络分析代考|将蛋白质网络拟合到模型中

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