Past Discussions

10.29.14

This entry was posted in Abstracts discussion and tagged arabidopsis coregulation Matlab Systems Biology time series wigwams on by

Wigwams: identifying gene modules co-regulated across multiple biological conditions

  1. Krzysztof Polanski1,†,
  2. Johanna Rhodes1,†‡,
  3. Claire Hill2,
  4. Peijun Zhang2,
  5. Dafyd J. Jenkins1,
  6. Steven J. Kiddle1,§,
  7. Aleksey Jironkin1,
  8. Jim Beynon1,2,
  9. Vicky Buchanan-Wollaston1,2,
  10. Sascha Ott1 and
  11. Katherine J. Denby1,2,*

+ Author Affiliations


  1. 1Warwick Systems Biology Centre and 2School of Life Sciences, University of Warwick, CV4 7AL, UK
  1. ↵*To whom correspondence should be addressed.
  • Received September 17, 2013.
  • Revision received December 12, 2013.
  • Accepted December 13, 2013.

Abstract

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.

Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions.

Availability and implementation: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams.

Contact: k.j.denby@warwick.ac.uk

Supplementary Data: Supplementary data are available at Bioinformatics online.

10.15.14

This entry was posted in Abstracts discussion and tagged Algorithms Graemlin 2.0 network aligner scoring function Systems Biology on by

Automatic Parameter Learning for Multiple Network Alignment

Jason Flannick1, Antal Novak1, Chuong B. Do1, Balaji S. Srinivasan2, and Serafim Batzoglou1
1Department of Computer Science, Stanford University, Stanford, CA 94305, USA
2Department of Statistics, Stanford University, Stanford, CA 94305, USA

Abstract

We developed Græmlin 2.0, a new multiple network aligner with (1) a novel scoring func-
tion; (2) an algorithm that automatically learns the scoring function’s parameters; and (3) an
algorithm that uses the scoring function to globally align multiple networks. Existing alignment
tools use heuristic scoring functions, which must be hand-tuned to a given set of networks and
do not apply to multiple network alignment.
Our scoring function can use arbitrary features of a multiple network alignment, such as
protein deletions, protein duplications, protein mutations, and interaction losses. Our parameter
learning algorithm uses a training set of known network alignments to learn parameters for
our scoring function and thereby automatically adapts it to any set of networks. Our global
alignment algorithm finds approximate multiple network alignments in linear time.
We tested Græmlin 2.0’s accuracy on protein interaction networks from IntAct, DIP, and
the Stanford Network Database. We show that, on each of these datasets, Græmlin 2.0 has
higher sensitivity and specificity than existing network aligners. Græmlin 2.0 is available under
the GNU public license at http://graemlin.stanford.edu.

10.01.14

This entry was posted in Abstracts and tagged Algorithms biological networks breadth depth Eigenvector Centrality social networks Systems Biology on by

A Family of Algorithms for Computing Consensus about Node State from Network Data

Eleanor R. Brush, David C. Krakauer, Jessica C. Flack

Abstract

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node’s state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node’s direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms’ cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.

09.17.14

This entry was posted in Abstracts discussion and tagged DNA methlation epigenetics gene expression gioblastoma PARADIGM Systems Biology on by

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

 

Charles J. Vaske1,†, Stephen C. Benz2,†, J. Zachary Sanborn2, Dent Earl2, Christopher Szeto2, Jingchun Zhu2, David Haussler1,2 and Joshua M. Stuart2,*

+ Author Affiliations

1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA

* To whom correspondence should be addressed.

Abstract

Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines.

Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.

Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway’s activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference.

Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.

Availability:Source code available at http://sbenz.github.com/Paradigm

Contact: jstuart@soe.ucsc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

09.03.14

This entry was posted in Abstracts and tagged Systems Biology TP53 TP63 within-a-tissue on by

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Katherine A. Hoadley1, 20, Christina Yau2, 20, Denise M. Wolf3, 20, Andrew D. Cherniack4, 20, David Tamborero5, Sam Ng6, Max D.M. Leiserson7, Beifang Niu8, Michael D. McLellan8, Vladislav Uzunangelov6, Jiashan Zhang9, Cyriac Kandoth8, Rehan Akbani10, Hui Shen11, 22, Larsson Omberg12, Andy Chu13, Adam A. Margolin12, 21, Laura J. van’t Veer3, Nuria Lopez-Bigas5, 14, Peter W. Laird11, 22, Benjamin J. Raphael7, Li Ding8, A. Gordon Robertson13, Lauren A. Byers10, Gordon B. Mills10, John N. Weinstein10, Carter Van Waes18, Zhong Chen19, Eric A. Collisson15,The Cancer Genome Atlas Research Network, Christopher C. Benz2, , , Charles M. Perou1, 16, 17, , , Joshua M. Stuart6, ,

1 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
2 Buck Institute for Research on Aging, Novato, CA 94945, USA
3 Department of Laboratory Medicine, University of California San Francisco, 2340 Sutter St, San Francisco, CA, 94115, USA
4 The Eli and Edythe Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
5 Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona 08003, Spain
6 Department of Biomolecular Engineering, Center for Biomolecular Sciences and Engineering, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA
7 Department of Computer Science and Center for Computational Molecular Biology, Brown University, 115 Waterman St, Providence RI 02912, USA
8 The Genome Institute, Washington University, St Louis, MO 63108, USA
9 National Cancer Institute, NIH, Bethesda, MD 20892, USA
10 UT MD Anderson Cancer Center, Bioinformatics and Computational Biology, 1400 Pressler Street, Unit 1410, Houston, TX 77030, USA
11 USC Epigenome Center, University of Southern California Keck School of Medicine, 1450 Biggy Street, Los Angeles, CA 90033, USA
12 Sage Bionetworks 1100 Fairview Avenue North, M1-C108, Seattle, WA 98109-1024, USA
13 Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
14 Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys, 23, Barcelona 08010, Spain
15 Department of Medicine, University of California San Francisco, 450 35d St, San Francisco, CA, 94148, USA
16 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
17 Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
18 Building 10, Room 4-2732, NIDCD/NIH, 10 Center Drive, Bethesda, MD 20892
19 Head and Neck Surgery Branch, NIDCD/NIH, 10 Center Drive, Room 5D55, Bethesda, MD 20892

Received 23 December 2013, Revised 4 April 2014, Accepted 23 June 2014, Available online 7 August 2014

Summary

Recent genomic analyses of pathologically defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

08.06.2014

This entry was posted in Abstracts discussion and tagged Cdx2 ChIP-seq MultiGPS regulatory proteins Systems Biology on by

An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding

 

Shaun Mahony*, Matthew D. Edwards*, Esteban O. Mazzoni, Richard I. Sherwood, Akshay Kakumanu, Carolyn A. Morrison, Hynek Wichterle, David K. Gifford

*equal contributor

Published: March 27, 2014    DOI: 10.1371/journal.pcbi.1003501

Abstract

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS’s multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.

07.09.2014 and 07.23.14

This entry was posted in Abstracts discussion and tagged DREAM 8 signaling networks signaling proteins Systems Biology time course trajectories on by

Predicting Dynamic Signaling Network Response under Unseen Perturbations

Fan Zhu 1 and Yuanfang Guan 1,2,3,*

1 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

2 Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA

3 Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA

* To whom correspondence should be addressed.

ABSTRACT

Motivation: Predicting trajectories of signaling networks under complex perturbations is one of the most valuable but challenging tasks in systems biology. Signaling networks are involved in most of the biological pathways and modeling their dynamics has wide applications including drug design and treatment outcome prediction.

Results: In this paper, we report a novel model for predicting the cell type-specific time course response of signaling proteins under unseen perturbations. This algorithm achieved the top performance in the 2013 8th Dialogue for Reverse Engineering Assessments and Methods (DREAM 8) sub challenge: time course prediction in breast cancer cell lines. We formulate the trajectory prediction problem into a standard regularization problem; the solution becomes solving this discrete ill-posed problem. This algorithm includes three steps: denoising, estimating regression coefficients and modeling trajectories under unseen perturbations. We further validated the accuracy of this method against simulation and experimental data. Furthermore, this method reduces computational time by magnitudes compared to state-of-the-art methods, allowing genome-wide modeling of signaling pathways and time course trajectories to be carried out in a practical time.

Availability and Implementation: Source code is available at http://guanlab.ccmb.med.umich.edu/DREAM/code.html and as supplementary file online. Contact: gyuanfan@umich.edu

 

06.25.2014

This entry was posted in Abstracts discussion and tagged DNA methylation gene expression Giovanni Montana regression Systems Biology on by

Network-guided regression for detecting associations between DNA methylation and gene expression

 

Zi Wang1, Edward Curry2 and Giovanni Montana1,3,*

1Department of Mathematics, Imperial College London, London SW7 2AZ.

2 Division of Cancer, Imperial College London, Hammersmith Hospital, London, W12 0NN

3 Department of Biomedical Engineering, King’s College London, St Thomas’ Hospital, London SE1 7EH

*To whom correspondence should be addressed. Giovanni Montana, E-mail: giovanni.montana@kcl.ac.uk

 

Abstract 

Motivation: High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. This data could yield discoveries that lead to breakthroughs in the diagnosis and treatment of human disease, but requires statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.

Results: We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to TCGA datasets on primary ovarian tumours.

Availability: R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/~gmontana/

06.11.2014

This entry was posted in Abstracts discussion and tagged BATF cellular plasticity Fosl2 IRF4 RORγt STAT3 TH17 transcription factor on by

A Validated Regulatory Network for Th17 Cell Specification

Maria Ciofani1, 10, Aviv Madar3, 4, 10, Carolina Galan1, MacLean Sellars1, Kieran Mace3, Florencia Pauli5, Ashish Agarwal3, Wendy Huang1, Christopher N. Parkurst1, Michael Muratet5, Kim M. Newberry5, Sarah Meadows5, Alex Greenfield2, Yi Yang1, Preti Jain5, Francis K. Kirigin2, Carmen Birchmeier6, Erwin F. Wagner7, Kenneth M. Murphy8, 9, Richard M. Myers5, Richard Bonneau3, 4, Corresponding author contact information, E-mail the corresponding author, Dan R. Littman1, 9, Corresponding author contact information, E-mail the corresponding author

1 Molecular Pathogenesis Program, The Kimmel Center for Biology and Medicine of the Skirball Institute, New York University School of Medicine, New York, NY 10016, USA

2 Computational Biology Program, The Sackler Institute, New York University School of Medicine, New York, NY 10016, USA

3 Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, 10003 USA

4 Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY, 10003 USA

5 HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA

6 Developmental Biology, Max Delbruck for Molecular Medicine, 13125 Berlin, Germany

7 Cancer Cell Biology Programme, Spanish National Cancer Research Centre (CNIO), E-28029 Madrid, Spain

8 Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63108, USA

9 The Howard Hughes Medical Institute

 

Summary

Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively bound BATF and IRF4 contribute to initial chromatin accessibility and, with STAT3, initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple data sets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease.

04.16.14

This entry was posted in Abstracts and tagged Algorithms gene expression knockout effects Signaling-regulatory Pathway INferencE SPINE Systems Biology on by

SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments

Oved Ourfali 1, Tomer Shlomi 1, Trey Ideker 3, Eytan Ruppin 1,2 and Roded Sharan 1

1 School of Computer Science, 2 School of Medicine, Tel-Aviv University, Tel-Aviv, Israel and 3 Department of Bioengineering, University of California, San Diego, CA 92093, USA

Abstract:
Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.

Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein–protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver.

We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.

Availability: The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html