scholarly journals Evolution and emergence: higher order information structure in protein interactomes across the tree of life

2021 ◽  
Author(s):  
Brennan Klein ◽  
Erik Hoel ◽  
Anshuman Swain ◽  
Ross Griebenow ◽  
Michael Levin
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Information Structure ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Higher Order ◽  
Protein Protein Interactions ◽  
Black Boxes ◽  
Sensitivity Tests ◽  
Network Simulations

Abstract The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein–protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein–protein interactions, at different scales. We show the emergence of higher order ‘macroscales’ in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared with nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of eukaryota, as compared with prokaryota; these results hold even after sensitivity tests where we recalculate the emergent macroscales under network simulations where we add different edge weights to the interactomes. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being ‘certain’ at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

scholarly journals Evolution leads to emergence: An analysis of protein interactomes across the tree of life

2020 ◽  
Author(s):  
Erik Hoel ◽  
Brennan Klein ◽  
Anshuman Swain ◽  
Ross Grebenow ◽  
Michael Levin
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Protein Protein Interactions ◽  
Black Boxes ◽  
Protein Interactome ◽  
Gene Regulatory ◽  
Lower Uncertainty ◽  
Lower Noise

AbstractThe internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8,782,166 protein-protein interactions, at different scales. We demonstrate the emergence of higher order ‘macroscales’ in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared to nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of Eukaryota, as compared to Prokaryota. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being ‘certain’ at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

Protein Interactions for Functional Genomics

2012 ◽  
Vol 3 (4) ◽  
pp. 15-30
Author(s):  
Pablo Minguez ◽  
Joaquin Dopazo
Keyword(s):  
Graph Theory ◽  
Protein Interactions ◽  
Biological Networks ◽  
State Of The Art ◽  
Protein Protein Interactions ◽  
Pairwise Interactions ◽  
Novel Approach ◽  
Functional Profiling ◽  
Available Resources

Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic experiments. They report the available resources and methodologies used to create a curated compilation of ppis introducing a novel approach to filter interactions. Special attention is paid in the complexity of the topology of the networks formed by proteins (nodes) and pairwise interactions (edges). These networks can be studied using graph theory and a brief introduction to the characterization of biological networks and definitions of the more used network parameters is also given. Also a report on the available resources to perform different modes of functional profiling using ppi data is provided along with a discussion on the approaches that have typically been applied into this context. They also introduce a novel methodology for the evaluation of networks and some examples of its application.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

scholarly journals Signaling Protein Networks as Targets of New Antineoplastic Drugs

2003 ◽  
Vol 18 (1) ◽  
pp. 57-61 ◽  
Author(s):  
S. Alberti ◽  
S. Parodi
Keyword(s):  
Protein Interactions ◽  
High Throughput Screening ◽  
Regulatory Networks ◽  
Signaling Proteins ◽  
Protein Protein Interactions ◽  
Therapeutic Approaches ◽  
Chemical Libraries ◽  
Drug Candidates ◽  
Microbial Extracts ◽  
Depth Analysis

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from “-omic” approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, “intelligent” drug candidates. An additional medicinal chemistry strategy (via 40–50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

MLL Translocation Partners AF4, AF5q31, and ENL Associate in a Higher Order Protein Complex with CDK9 and Cyclin T1.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 99-99
Author(s):  
Min Lin ◽  
Michael L. Cleary
Keyword(s):  
Western Blotting ◽  
Protein Interactions ◽  
Protein Complex ◽  
Fusion Proteins ◽  
Higher Order ◽  
Chromosomal Translocations ◽  
Transcriptional Elongation ◽  
Protein Protein Interactions ◽  
Methyltransferase Activity ◽  
Cyclin T1

Abstract The Mixed Lineage Leukemia (MLL) gene is frequently involved in chromosomal translocations that cause acute leukemia. More than 40 different genes have been identified as MLL translocation partners, with the expression of corresponding MLL fusion proteins. The MLL protein has histone methyltransferase activity and is required for embryonic development and hematopoiesis. Several proteins have been demonstrated to associate with MLL in a macromolecular complex, which is believed to have chromatin remodeling function. However, the C-terminal SET domain of MLL, which carries the histone methyltransferase activity, is lost in all MLL fusion proteins, thus making the biochemical functions of the fusion proteins unclear. Moreover, the promiscuity of MLL translocation partners, most of them with no known functions, further complicates an understanding of MLL leukemogenic mechanisms. In this study, we purified a protein complex containing AF4, the most common MLL translocation partner, using a combination of conventional column chromatography and immunoaffinity techniques. The AF4 protein complex contains AF5q31 and ENL, two other MLL translocation partners, as well as CDK9 and Cyclin T1, a heterodimer that regulates transcriptional elongation. Gel filtration confirmed that these five proteins co-fractionate with an estimated overall size of 0.8 MDa. All protein-protein interactions were further confirmed by immunoprecipitation-western blotting from K562 cell nuclear extract. To investigate whether these protein-protein interactions are retained in corresponding MLL fusion proteins, immunoprecipitation-western blotting assays were carried out in human leukemia cell lines harboring MLL chromosomal translocations. We found that MLL-AF4, MLL-AF5q31, MLL-ENL and MLL-AF9 each associate with wild type AF4 complex components, including CDK9 and Cyclin T1. In contrast, MLL-AF6 does not associate with any of the AF4 complex components. We propose that the four nuclear MLL translocation partner proteins (AF4, AF5q31, ENL/AF9), whose translocations are found in over 75% of MLL leukemias, associate in a higher order protein complex with CDK9 and Cyclin T1 and thus function in part to regulate transcriptional elongation. The association of CDK9 and Cyclin T1 with the four MLL fusion proteins suggests a common leukemogenic mechanism that may involve transcriptional elongation, which we are currently investigating. Conversely, MLL-cytosolic fusions, e.g. MLL-AF6, appear to function independently of association with the AF4 protein complex, possibly through a homo-dimerization pathway.

scholarly journals Robustness and lethality in multilayer biological molecular networks

2019 ◽  
Author(s):  
Xueming Liu ◽  
Enrico Maiorino ◽  
Arda Halu ◽  
Joseph Loscalzo ◽  
Jianxi Gao ◽  
...  
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Metabolic Diseases ◽  
Biological Systems ◽  
Interaction Network ◽  
Network Models ◽  
Global Network ◽  
Molecular Networks ◽  
Cancer Genes ◽  
Gene Regulatory

AbstractRobustness is a prominent feature of most biological systems. In a cell, the structure of the interactions between genes, proteins, and metabolites has a crucial role in maintaining the cell’s functionality and viability in presence of external perturbations and noise. Despite advances in characterizing the robustness of biological systems, most of the current efforts have been focused on studying homogeneous molecular networks in isolation, such as protein-protein or gene regulatory networks, neglecting the interactions among different molecular substrates. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, and protein-protein interaction layer and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system’s robustness, defined as its influence over the global network. We find that highly influential genes are enriched in essential and cancer genes, confirming the central role of these genes in critical cellular processes. Further, we determine that the metabolic layer is more vulnerable to perturbations involving genes associated to metabolic diseases. By comparing the robustness of the network to multiple randomized network models, we find that the real network is comparably or more robust than expected in the random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within or between layers. These results provide new insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

scholarly journals Inferring cell cycle phases from a partially temporal network of protein interactions

2021 ◽  
Author(s):  
Maxime Lucas ◽  
Arthur Morris ◽  
Alex Townsend-Teague ◽  
Laurent Tichit ◽  
Bianca H Habermann ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Protein Interactions ◽  
Time Series Data ◽  
A Priori ◽  
Biological Systems ◽  
Series Data ◽  
Biological Knowledge ◽  
Temporal Network ◽  
Protein Protein Interactions ◽  
Temporal Organisation

The temporal organisation of biological systems into phases and subphases is often crucial to their functioning. Identifying this multiscale organisation can yield insight into the underlying biological mechanisms at play. To date, however, this identification requires a priori biological knowledge of the system under study. Here, we recover the temporal organisation of the cell cycle of budding yeast into phases and subphases, in an automated way. To do so, we model the cell cycle as a partially temporal network of protein-protein interactions (PPIs) by combining a traditional static PPI network with protein concentration or RNA expression time series data. Then, we cluster the snapshots of this temporal network to infer phases, which show good agreement with our biological knowledge of the cell cycle. We systematically test the robustness of the approach and investigate the effect of having only partial temporal information. The generality of the method makes it suitable for application to other, less well-known biological systems for which the temporal organisation of processes plays an important role.

scholarly journals Boolean factor graph model for biological systems: the yeast cell-cycle network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami
Keyword(s):  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Error Propagation ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Yeast Cell Cycle ◽  
Computational Framework ◽  
Gene Regulatory

Abstract Background The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. Results This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. Conclusion Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

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