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ANAT 2.0: reconstructing functional protein subnetworks

2017
*
BMC Bioinformatics
*

ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. Results: Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological

doi:10.1186/s12859-017-1932-1
pmid:29145805
pmcid:PMC5689176
fatcat:qr3esye4sfc6xikza6dam6obga
## more »

... a sets. Conclusions: ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species.##
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Optimally Orienting Physical Networks
[chapter]

2011
*
Lecture Notes in Computer Science
*

In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This problem is NP-complete and no approximation algorithms are known for it. It arises in the context of analyzing physical networks of protein-protein and protein-DNA interactions. While the latter are naturally directed

doi:10.1007/978-3-642-20036-6_39
fatcat:occm7yjcenczrps2ddka7p452y
## more »

... from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ILP formulation for this problem. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified versions that do not use information on edge directions. The obtained orientations can lead to better understanding of the structure and function of the network. These authors contributed equally to this work. High-throughoutput technologies are routinely used nowadays to detect physical interactions in the cell, including chromatin immuno-precipitation experiments for measuring protein-DNA interactions (PDIs) [11] , and yeast two-hybrid assays [7] and co-immunoprecipitation screens [9] for measuring protein-protein interactions (PPIs). These networks serve as the scaffold for signal processing in the cell and are, thus, key to understanding cellular response to different genetic or environmental cues. While PDIs are naturally directed (from a transcription factor to its regulated genes), PPIs are not. Nevertheless, many PPIs transmit signals in a directional fashion, with kinase-substrate interactions (KPIs) being one of the prime examples. These directions are vital to understanding signal flow in the cell, yet they are not measured by most current techniques. Instead, one tries to infer these directions from perturbation experiments. In these experiments, a gene (cause) is perturbed and as a result other genes change their expression levels (effects). Assuming that each cause-effect pair should be connected by a directed pathway in the physical network, one can predict an orientation (direction assignments) to the undirected part of the network that will best agree with the cause-effect information. The resulting combinatorial problem can be formalized by representing the network as a mixed graph, where undirected edges model interactions with unknown causal direction, and directed edges represent interactions with known directionality. The cause-effect pairs are modeled by a collection of source-target vertex pairs. The goal is to orient (assign single directions to) the undirected edges so that a maximum number of source-target pairs admit a directed path from the source to the target. Previous work on this and related problems can be classified into theoretical and applied work. On the theoretical side, Arkin and Hassin [1] studied the decision problem of orienting a mixed graph and showed that this problem is NP-complete. Decision and optimization versions of the problems of finding reachability preserving orientations are well studied for the case where the set of vertex pairs contains all vertex pairs from the graph [16, 3, 6, 5, 10] . For a comprehensive discussion of the various kinds of graph orientations (not necessarily reachability preserving), we refer to the textbook of Bang-Jensen and Gutin [2] . For the special case of an undirected network (with no pre-directed edges), the orientation problem was shown to be NP-complete and hard to approximate to within a constant factor of 11/12 [13]. On the positive side, Medvedovsky et al. [13] provided an ILP-based algorithm, and showed that the problem is approximable to within a ratio of O(log n), where n is the number of vertices in the network. The approximation ratio was recently improved to O(log n/ log log n) [8] . The authors considered also the more general problem on mixed graphs, but the polylogarithmic approximation ratio attained was not satisfying as its power depends on some properties of the actual paths. On the practical side, several authors studied the orientation problem and related annotation problems using statistical approaches [18, 14] . However, these approaches rely on enumerating all paths up to a certain length between a pair of nodes, making them infeasible on large networks. Our main contribution in this paper is a first efficient ILP formulation of the orientation problem on mixed graphs, leading to an optimal solution of the problem on current networks. We implemented our approach and applied it to a large data set of physical interactions and knockout pairs in yeast. We collected interaction and cause-effect pair information from different publications and integrated them into a physical network with 3,660 proteins, 4,000 PPIs, 4,095 PDIs, along with 53,809 knockout pairs among the molecular components of the network. We carried out a number of experiments to measure the accuracy of the orientations produced by our method for different input scenarios. In particular, we study how the portion of undirected interactions and the number of cause-effect pairs affect the orientations. We further compare our performance to that of two layman approaches that are based on orienting undirected networks, ignoring##
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Optimally Orienting Physical Networks

2011
*
Journal of Computational Biology
*

In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This problem is NP-complete and no approximation algorithms are known for it. It arises in the context of analyzing physical networks of protein-protein and protein-DNA interactions. While the latter are naturally directed

doi:10.1089/cmb.2011.0163
pmid:21999286
fatcat:ykdvlozy75ekrjfrenajryq764
## more »

... from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ILP formulation for this problem. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified versions that do not use information on edge directions. The obtained orientations can lead to better understanding of the structure and function of the network. These authors contributed equally to this work. High-throughoutput technologies are routinely used nowadays to detect physical interactions in the cell, including chromatin immuno-precipitation experiments for measuring protein-DNA interactions (PDIs) [11] , and yeast two-hybrid assays [7] and co-immunoprecipitation screens [9] for measuring protein-protein interactions (PPIs). These networks serve as the scaffold for signal processing in the cell and are, thus, key to understanding cellular response to different genetic or environmental cues. While PDIs are naturally directed (from a transcription factor to its regulated genes), PPIs are not. Nevertheless, many PPIs transmit signals in a directional fashion, with kinase-substrate interactions (KPIs) being one of the prime examples. These directions are vital to understanding signal flow in the cell, yet they are not measured by most current techniques. Instead, one tries to infer these directions from perturbation experiments. In these experiments, a gene (cause) is perturbed and as a result other genes change their expression levels (effects). Assuming that each cause-effect pair should be connected by a directed pathway in the physical network, one can predict an orientation (direction assignments) to the undirected part of the network that will best agree with the cause-effect information. The resulting combinatorial problem can be formalized by representing the network as a mixed graph, where undirected edges model interactions with unknown causal direction, and directed edges represent interactions with known directionality. The cause-effect pairs are modeled by a collection of source-target vertex pairs. The goal is to orient (assign single directions to) the undirected edges so that a maximum number of source-target pairs admit a directed path from the source to the target. Previous work on this and related problems can be classified into theoretical and applied work. On the theoretical side, Arkin and Hassin [1] studied the decision problem of orienting a mixed graph and showed that this problem is NP-complete. Decision and optimization versions of the problems of finding reachability preserving orientations are well studied for the case where the set of vertex pairs contains all vertex pairs from the graph [16, 3, 6, 5, 10] . For a comprehensive discussion of the various kinds of graph orientations (not necessarily reachability preserving), we refer to the textbook of Bang-Jensen and Gutin [2] . For the special case of an undirected network (with no pre-directed edges), the orientation problem was shown to be NP-complete and hard to approximate to within a constant factor of 11/12 [13]. On the positive side, Medvedovsky et al. [13] provided an ILP-based algorithm, and showed that the problem is approximable to within a ratio of O(log n), where n is the number of vertices in the network. The approximation ratio was recently improved to O(log n/ log log n) [8] . The authors considered also the more general problem on mixed graphs, but the polylogarithmic approximation ratio attained was not satisfying as its power depends on some properties of the actual paths. On the practical side, several authors studied the orientation problem and related annotation problems using statistical approaches [18, 14] . However, these approaches rely on enumerating all paths up to a certain length between a pair of nodes, making them infeasible on large networks. Our main contribution in this paper is a first efficient ILP formulation of the orientation problem on mixed graphs, leading to an optimal solution of the problem on current networks. We implemented our approach and applied it to a large data set of physical interactions and knockout pairs in yeast. We collected interaction and cause-effect pair information from different publications and integrated them into a physical network with 3,660 proteins, 4,000 PPIs, 4,095 PDIs, along with 53,809 knockout pairs among the molecular components of the network. We carried out a number of experiments to measure the accuracy of the orientations produced by our method for different input scenarios. In particular, we study how the portion of undirected interactions and the number of cause-effect pairs affect the orientations. We further compare our performance to that of two layman approaches that are based on orienting undirected networks, ignoring##
###
Approximation algorithms for orienting mixed graphs

2013
*
Theoretical Computer Science
*

A formal correctness proof of this reduction is given by

doi:10.1016/j.tcs.2012.03.044
fatcat:xnlp26mjlnaermoav5xzh3wl4i
*Silverbush*et al. [21] . ...*Silverbush*et al. [21] developed an ilp-based algorithm to optimally orient mixed networks, but the approximability of the problem (for non-constant l) was left open. ...##
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Approximation Algorithms for Orienting Mixed Graphs
[chapter]

2011
*
Lecture Notes in Computer Science
*

A formal correctness proof of this reduction is given by

doi:10.1007/978-3-642-21458-5_35
fatcat:oefqitkwnrfcfc5cuzvzbbwivu
*Silverbush*et al. [21] . ...*Silverbush*et al. [21] developed an ilp-based algorithm to optimally orient mixed networks, but the approximability of the problem (for non-constant l) was left open. ...##
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ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules
[article]

2018
*
bioRxiv
*
pre-print

The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating

doi:10.1101/288399
fatcat:ly3fd3emjzczfpiishuit2fy4i
## more »

... ltiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.##
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On the Approximability of Reachability-Preserving Network Orientations

2011
*
Internet Mathematics
*

We introduce a graph orientation problem arising in the study of biological networks. Given an undirected graph and a list of ordered source-target vertex pairs, the goal is to orient the graph such that a maximum number of pairs admit a directed source-to-target path. We study the complexity and approximability of this problem. We show that the problem is NP-hard even on star graphs and hard to approximate to within some constant factor. On the positive side, we provide an Ω(log log n/ log

doi:10.1080/15427951.2011.604554
fatcat:msvkismcd5er7kxxlgrq4div7y
## more »

... actor approximation algorithm for the problem on n-vertex graphs. We further show that for any instance of the problem there exists an orientation of the input graph that satisfies a logarithmic fraction of all pairs and that this bound is tight up to a constant factor. Our techniques also lead to constant factor approximation algorithms for some restricted variants of the problem.##
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Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

2016
*
Cancer Research
*

*Silverbush*. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. ...

##
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INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION

2016
*
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
*

We present a computational strategy to simulate drug treatment in a personalized setting. The method is based on integrating patient mutation and differential expression data with a protein-protein interaction network. We test the impact of in-silico deletions of different proteins on the flow of information in the network and use the results to infer potential drug targets. We apply our method to AML data from TCGA and validate the predicted drug targets using known targets. To benchmark our

pmid:26776182
fatcat:6akvfkfsx5aovpgbrrj65nusiq
## more »

... tient-specific approach, we compare the personalized setting predictions to those of the conventional setting. Our predicted drug targets are highly enriched with known targets from DrugBank and COSMIC (p < 10(-5) outperforming the non-personalized predictions. Finally, we focus on the largest AML patient subgroup (~30%) which is characterized by an FLT3 mutation, and utilize our prediction score to rank patient sensitivity to inhibition of each predicted target, reproducing previous findings of in-vitro experiments.##
###
Inferring Cancer Progression from Single-Cell Sequencing while Allowing Mutation Losses

2020
*
Bioinformatics
*

Motivation In recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging Single-Cell Sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there

doi:10.1093/bioinformatics/btaa722
pmid:32805010
pmcid:PMC8058767
fatcat:pbxz77hbengcrlfzimskwvdyfu
## more »

... emain some advancements to be made. Results We present SASC (Simulated Annealing Single-Cell inference): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS data sets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real data sets and in comparison with some other available methods. Availability The Simulated Annealing Single-Cell inference (SASC) tool is open source and available at https://github.com/sciccolella/sasc. Supplementary information Supplementary data are available at Bioinformatics online.##
###
Evolution of metastases-associated fibroblasts in the lung microenvironment is driven by stage-specific transcriptional plasticity
[article]

2019
*
bioRxiv
*
pre-print

Mortality from breast cancer is almost exclusively a result of tumor metastasis, and lungs are one of the main metastatic sites. Cancer-associated fibroblasts (CAFs) are prominent players in the microenvironment of breast cancer. However, their role in the metastatic niche is largely unknown. In this study, we profiled the transcriptional co-evolution of lung fibroblasts isolated from transgenic mice at defined stage-specific time points of metastases formation. Employing multiple

doi:10.1101/778936
fatcat:7nnyt464lrdu7l6bfvoaeuc6ji
## more »

... d platforms of data analysis provided powerful insights on functional and temporal regulation of the transcriptome of fibroblasts. We demonstrate that fibroblasts in lung metastases are transcriptionally dynamic and plastic, and reveal stage-specific gene signatures that imply functional tasks, including extracellular matrix remodeling, stress response and shaping the inflammatory microenvironment. Furthermore, we identified Myc as a central regulator of fibroblast rewiring and found that stromal upregulation of Myc transcriptional networks is associated with worse survival in human breast cancer.##
###
Evolution of fibroblasts in the lung metastatic microenvironment is driven by stage-specific transcriptional plasticity

2021
*
eLife
*

Mortality from breast cancer is almost exclusively a result of tumor metastasis, and lungs are one of the main metastatic sites. Cancer-associated fibroblasts (CAFs) are prominent players in the microenvironment of breast cancer. However, their role in the metastatic niche is largely unknown. In this study, we profiled the transcriptional co-evolution of lung fibroblasts isolated from transgenic mice at defined stage-specific time points of metastases formation. Employing multiple

doi:10.7554/elife.60745
pmid:34169837
pmcid:PMC8257251
fatcat:ohkhgkxwxzepxct5ton52i4uhq
## more »

... d platforms of data analysis provided powerful insights on functional and temporal regulation of the transcriptome of fibroblasts. We demonstrate that fibroblasts in lung metastases are transcriptionally dynamic and plastic, and reveal stage-specific gene signatures that imply functional tasks, including extracellular matrix remodeling, stress response and shaping the inflammatory microenvironment. Furthermore, we identified Myc as a central regulator of fibroblast rewiring and found that stromal upregulation of Myc transcriptional networks is associated with disease progression in human breast cancer.##
###
Electrical and synaptic integration of glioma into neural circuits

2019
*
Nature
*

High-grade gliomas are lethal brain cancers whose progression is robustly regulated by neuronal activity. Activity-regulated release of growth factors promotes glioma growth, but this alone is insufficient to explain the effect that neuronal activity exerts on glioma progression. Here we show that neuron and glioma interactions include electrochemical communication through bona fide AMPA receptor-dependent neuron-glioma synapses. Neuronal activity also evokes non-synaptic activity-dependent

doi:10.1038/s41586-019-1563-y
pmid:31534222
pmcid:PMC7038898
fatcat:nq2q2p3lkjeh7glcixwx3lxhne
## more »

... ssium currents that are amplified by gap junction-mediated tumour interconnections, forming an electrically coupled network. Depolarization of glioma membranes assessed by in vivo optogenetics promotes proliferation, whereas pharmacologically or genetically blocking electrochemical signalling inhibits the growth of glioma xenografts and extends mouse survival. Emphasizing the positive feedback mechanisms by which gliomas increase neuronal excitability and thus activity-regulated glioma growth, human intraoperative electrocorticography demonstrates increased cortical excitability in the glioma-infiltrated brain. Together, these findings indicate that synaptic and electrical integration into neural circuits promotes glioma progression.##
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An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

2019
*
Cell
*

All animal studies were performed according to

doi:10.1016/j.cell.2019.06.024
pmid:31327527
pmcid:PMC6703186
fatcat:uxxy5uznzzdknct6lcmf4w775e
*Dana*-Farber/Harvard Cancer Center Institutional and the Salk Institute Animal Care and Use Committee (IACUC)-approved protocols. ...##
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An optimization framework for network annotation

2018
*
Bioinformatics
*

Acknowledgements We thank Eytan Ruppin, Sridhar Hannenhalli and

doi:10.1093/bioinformatics/bty236
pmid:29949973
pmcid:PMC6022690
fatcat:uzhdyewo2ze2dagrdlpmgnztvm
*Dana**Silverbush*for helpful discussions about this work. Conflict of Interest: none declared. ... ., 2011;*Silverbush*et al., 2011;*Silverbush*and Sharan, 2014) , potentially restricting the length of the path connecting each cause-effect pair. ... This is done by applying a breadth-first-search starting from each source and target (*Silverbush*and Sharan, 2014) . ...
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