Publications

Lymphocytic infiltration is associated with better prognosis in several epithelial malignancies including breast cancer. The tumor suppressor TP53 is mutated in approximately 30% of breast adenocarcinomas, with varying frequency across molecular subtypes. In this study of 1,420 breast tumors, we tested for interaction between TP53 mutation status and tumor subtype determined by PAM50 and integrative cluster analysis. In integrative cluster 10 (IC10)/basal-like breast cancer, we identify an association between lymphocytic infiltration, determined by an expression score, and retention of wild-type TP53. The expression-derived score agreed with the degree of lymphocytic infiltration assessed by pathologic review, and application of the Nanodissect algorithm was suggestive of this infiltration being primarily of cytotoxic T lymphocytes (CTL). Elevated expression of this CTL signature was associated with longer survival in IC10/Basal-like tumors. These findings identify a new link between the TP53 pathway and the adaptive immune response in estrogen receptor (ER)-negative breast tumors, suggesting a connection between TP53 inactivation and failure of tumor immunosurveillance.

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli.

We consider the problem of reconstructing the shape of an impenetrable sound-soft obstacle from scattering measurements. The input data is assumed to be the far-field pattern generated when a plane wave impinges on an unknown obstacle from one or more directions and at one or more frequencies. It is well known that this inverse scattering problem is both ill posed and nonlinear. It is common practice to overcome the ill posedness through the use of a penalty method or Tikhonov regularization. Here, we present a more physical regularization, based simply on restricting the unknown boundary to be band-limited in a suitable sense. To overcome the nonlinearity of the problem, we use a variant of Newton's method. When multiple frequency data is available, we supplement Newton's method with the recursive linearization approach due to Chen. During the course of solving the inverse problem, we need to compute the solution to a large number of forward scattering problems. For this, we use high-order accurate integral equation discretizations, coupled with fast direct solvers when the problem is sufficiently large.

We present SEEK (search-based exploration of expression compendia; http://seek.princeton.edu/), a query-based search engine for very large transcriptomic data collections, including thousands of human data sets from many different microarray and high-throughput sequencing platforms. SEEK uses a query-level cross-validation–based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify genes, pathways and processes co-regulated with the query. SEEK provides multigene query searching with iterative metadata-based search refinement and extensive visualization-based analysis options.

Scholars and commentators have debated whether lower-threshold forms of political engagement on social media should be treated as being conducive to higher-threshold modes of political participation or a diversion from them. Drawing on an original survey of a representative sample of Italians who discussed the 2013 election on Twitter, we demonstrate that the more respondents acquire political information via social media and express themselves politically on these platforms, the more they are likely to contact politicians via e-mail, campaign for parties and candidates using social media, and attend offline events to which they were invited online. These results suggest that lower-threshold forms of political engagement on social media do not distract from higher-threshold activities, but are strongly associated with them.

...These events — and the corresponding responses on social media — illustrate what has become increasingly evident: it is almost impossible to think of a major political protest or upheaval occurring without social media being part of both the incident and the ensuing narrative. The Euromaidan protests, which culminated in the flight of President Yanukovych from Ukraine in late February 2014, are a case in point. Indeed, the Ukrainian Euromaidan protest movement may go down in history as the first truly successful social media uprising. Earlier movements labeled social media revolutions subsequently have been criticized for not having had much important activity on social media (Moldova, Arab Spring) or for having had a large social media presence but ultimately failing to make much of a long-term impact as a protest movement (Spain’s Los Indignados, Occupy Wall Street, Gezi Park in Turkey). In Ukraine, a government fell, a region was annexed, a civilian plane was shot down, and what some are calling a civil war continues to this day in the eastern part of the country. Clearly, the movement was consequential and, as we will show, social media usage was widespread and significant.

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force–posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function, these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state, which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step toward an algorithmic theory of neural computation.

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force-posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans (PDF Download Available). Available from: https://www.researchgate.net/publication/270342595_A_consistent_muscle_activation_strategy_underlies_crawling_and_swimming_in_Caenorhabditis_elegans [accessed Jul 26, 2017].

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).