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ANAT 2.0: reconstructing functional protein subnetworks

Yomtov Almozlino, Nir Atias, Dana Silverbush, Roded Sharan
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
more » ... a sets. Conclusions: ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species.
doi:10.1186/s12859-017-1932-1 pmid:29145805 pmcid:PMC5689176 fatcat:qr3esye4sfc6xikza6dam6obga

Optimally Orienting Physical Networks [chapter]

Dana Silverbush, Michael Elberfeld, Roded Sharan
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
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
doi:10.1007/978-3-642-20036-6_39 fatcat:occm7yjcenczrps2ddka7p452y

Optimally Orienting Physical Networks

Dana Silverbush, Michael Elberfeld, Roded Sharan
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
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
doi:10.1089/cmb.2011.0163 pmid:21999286 fatcat:ykdvlozy75ekrjfrenajryq764

Approximation algorithms for orienting mixed graphs

Michael Elberfeld, Danny Segev, Colin R. Davidson, Dana Silverbush, Roded Sharan
2013 Theoretical Computer Science  
A formal correctness proof of this reduction is given by 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.  ... 
doi:10.1016/j.tcs.2012.03.044 fatcat:xnlp26mjlnaermoav5xzh3wl4i

Approximation Algorithms for Orienting Mixed Graphs [chapter]

Michael Elberfeld, Danny Segev, Colin R. Davidson, Dana Silverbush, Roded Sharan
2011 Lecture Notes in Computer Science  
A formal correctness proof of this reduction is given by 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.  ... 
doi:10.1007/978-3-642-21458-5_35 fatcat:oefqitkwnrfcfc5cuzvzbbwivu

ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules [article]

Dana Silverbush, Simona Cristea, Gali Yanovich, Tamar Geiger, Niko Beerenwinkel, Roded Sharan
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
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.
doi:10.1101/288399 fatcat:ly3fd3emjzczfpiishuit2fy4i

On the Approximability of Reachability-Preserving Network Orientations

Michael Elberfeld, Vineet Bafna, Iftah Gamzu, Alexander Medvedovsky, Danny Segev, Dana Silverbush, Uri Zwick, Roded Sharan
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
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.
doi:10.1080/15427951.2011.604554 fatcat:msvkismcd5er7kxxlgrq4div7y

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

Dana Silverbush, Shaun Grosskurth, Dennis Wang, Francoise Powell, Berthold Gottgens, Jonathan Dry, Jasmin Fisher
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.  ... 
doi:10.1158/0008-5472.can-16-1578 pmid:27965317 fatcat:j7da354itzceflj4fiebzdpeyi

INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION

Ortal Shnaps, Eyal Perry, Dana Silverbush, Roded Sharan
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
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.
pmid:26776182 fatcat:6akvfkfsx5aovpgbrrj65nusiq

Inferring Cancer Progression from Single-Cell Sequencing while Allowing Mutation Losses

Simone Ciccolella, Camir Ricketts, Mauricio Soto Gomez, Murray Patterson, Dana Silverbush, Paola Bonizzoni, Iman Hajirasouliha, Gianluca Della Vedova, Pier Luigi Martelli
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
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.
doi:10.1093/bioinformatics/btaa722 pmid:32805010 pmcid:PMC8058767 fatcat:pbxz77hbengcrlfzimskwvdyfu

Evolution of metastases-associated fibroblasts in the lung microenvironment is driven by stage-specific transcriptional plasticity [article]

Ophir Shani, Yael Raz, Or Megides, Hila Shacham, Noam Cohen, Dana Silverbush, Lea Monteran, Roded Sharan, Ilan Tsarfaty, Neta Erez
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
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.
doi:10.1101/778936 fatcat:7nnyt464lrdu7l6bfvoaeuc6ji

Evolution of fibroblasts in the lung metastatic microenvironment is driven by stage-specific transcriptional plasticity

Ophir Shani, Yael Raz, Lea Monteran, Ye'ela Scharff, Oshrat Levi-Galibov, Or Megides, Hila Shacham, Noam Cohen, Dana Silverbush, Camilla Avivi, Roded Sharan, Asaf Madi (+4 others)
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
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.
doi:10.7554/elife.60745 pmid:34169837 pmcid:PMC8257251 fatcat:ohkhgkxwxzepxct5ton52i4uhq

Electrical and synaptic integration of glioma into neural circuits

Humsa S. Venkatesh, Wade Morishita, Anna C. Geraghty, Dana Silverbush, Shawn M. Gillespie, Marlene Arzt, Lydia T. Tam, Cedric Espenel, Anitha Ponnuswami, Lijun Ni, Pamelyn J. Woo, Kathryn R. Taylor (+9 others)
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
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.
doi:10.1038/s41586-019-1563-y pmid:31534222 pmcid:PMC7038898 fatcat:nq2q2p3lkjeh7glcixwx3lxhne

An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

Cyril Neftel, Julie Laffy, Mariella G. Filbin, Toshiro Hara, Marni E. Shore, Gilbert J. Rahme, Alyssa R. Richman, Dana Silverbush, McKenzie L. Shaw, Christine M. Hebert, John Dewitt, Simon Gritsch (+46 others)
2019 Cell  
All animal studies were performed according to Dana-Farber/Harvard Cancer Center Institutional and the Salk Institute Animal Care and Use Committee (IACUC)-approved protocols.  ... 
doi:10.1016/j.cell.2019.06.024 pmid:31327527 pmcid:PMC6703186 fatcat:uxxy5uznzzdknct6lcmf4w775e

An optimization framework for network annotation

Sushant Patkar, Roded Sharan
2018 Bioinformatics  
Acknowledgements We thank Eytan Ruppin, Sridhar Hannenhalli and 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) .  ... 
doi:10.1093/bioinformatics/bty236 pmid:29949973 pmcid:PMC6022690 fatcat:uzhdyewo2ze2dagrdlpmgnztvm
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