Toward the dynamic interactome: it's about time

Przytycka, Teresa M.; Singh, Mona; Slonim, Donna K.

doi:10.1093/bib/bbp057

Cited by 240 publications

(169 citation statements)

References 177 publications

(192 reference statements)

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“…However, much of the biological functionality comes from the fact that the connections are not active all the time. For this reason, Przytycka et al [126] believe that a "shift from static to dynamic network analysis is essential for further understanding" of the interactome. There is already a body of literature investigating the temporal aspects of protein interaction and gene-regulatory networks [52,78,92,151].…”

Section: Cell Biologymentioning

confidence: 99%

Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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Section: Cell Biologymentioning

confidence: 99%

Temporal networks

2012

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“…This is particularly true for quantitative models, which aim to simulate very detailed patterns of expression, increasing the number of parameters to be inferred. However, such models can provide extremely useful insight on the gene expression process, where improvement of reverse engineering techniques is an ongoing aim of Systems Biology [16].…”

Section: Introductionmentioning

confidence: 99%

“…Given the challenges posed by available gene expression data and poor model robustness to date, a new direction is integration of several data types, [16], and these reports have started to appear, mostly for coarse-grained analysis [8]. These integrate expression data with other types of measurements, such as binding affinities or protein interactions, to better discriminate between candidate models, but usually are limited, (i.e.…”

Section: Introductionmentioning

confidence: 99%

EGIA – Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach

2014

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Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement.

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“…This quantity relates to the ability of two proteins to bind. Even if this information were available, the chemical and biological state of a cell will dynamically determine if binding actually takes place (30). The uncertainty increases if we were to probe this interaction via an experimental technique that would affect the chemical and biological state of the cell as well as the proteins' microenvironment.…”

mentioning

confidence: 99%

Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets

Ivanic

Memišević

et al. 2011

Molecular & Cellular Proteomics

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We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over-and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge. Molecular & Cellular Proteomics 10: 10.1074/mcp.M111.012500, 1-17, 2011.The collection of proteins and protein assemblies in a cell constitutes a vital and integral part of the machinery required to sustain all cellular functions and processes (1). Given that most proteins are part of one or more protein complexes, protein-protein interactions are essential in understanding the nature of protein-mediated biological processes. Therefore, because of the large number of potential protein interactions, high-throughput technologies are essential for generating whole-cell maps of these interactions (2, 3). Several largescale protein interaction data sets of the yeast Saccharomyces cerevisiae have been determined using diffe...

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Toward the dynamic interactome: it's about time

Cited by 240 publications

References 177 publications

Temporal networks

Temporal networks

EGIA – Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach

Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets

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