Natalie Stanley

Clustering Network Layers with the Strata Multilayer Stochastic Block Model

Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the “strata multilayer stochastic block model” (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called “strata”, which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.

Identifying Security Critical Properties for the Dynamic Verification of a Processor

We present a methodology for identifying security critical properties for use in the dynamic verification of a processor. Such verification has been shown to be an effective way to prevent exploits of vulnerabilities in the processor, given a meaningful set of security properties. We use known processor errata to establish an initial set of security-critical invariants of the processor. We then use machine learning to infer an additional set of invariants that are not tied to any particular, known vulnerability, yet are critical to security. We build a tool chain implementing the approach and evaluate it for the open-source OR1200 RISC processor. We find that our tool can identify 19 (86.4%) of the 22 manually crafted security-critical properties from prior work and generates 3 new security properties not covered in prior work.

Fenofibrate unexpectedly induces cardiac hypertrophy in mice lacking MuRF1.

The muscle-specific ubiquitin ligase muscle ring finger-1 (MuRF1) is critical in regulating both pathological and physiological cardiac hypertrophy in vivo. Previous work from our group has identified MuRF1s ability to inhibit serum response factor and insulin-like growth factor-1 signaling pathways (via targeted inhibition of cJun as underlying mechanisms). More recently, we have identified that MuRF1 inhibits fatty acid metabolism by targeting peroxisome proliferator-activated receptor alpha (PPARα) for nuclear export via mono-ubiquitination. Since MuRF1-/- mice have an estimated fivefold increase in PPARα activity, we sought to determine how challenge with the PPARα agonist fenofibrate, a PPARα ligand, would affect the heart physiologically. In as little as 3 weeks, feeding with fenofibrate/chow (0.05% wt/wt) induced unexpected pathological cardiac hypertrophy not present in age-matched sibling wild-type (MuRF1+/+) mice, identified by echocardiography, cardiomyocyte cross-sectional area, and increased beta-myosin heavy chain, brain natriuretic peptide, and skeletal muscle α-actin mRNA. In addition to pathological hypertrophy, MuRF1-/- mice had an unexpected differential expression in genes associated with the pleiotropic effects of fenofibrate involved in the extracellular matrix, protease inhibition, hemostasis, and the sarcomere. At both 3 and 8 weeks of fenofibrate treatment, the differentially expressed MuRF1-/- genes most commonly had SREBP-1 and E2F1/E2F promoter regions by TRANSFAC analysis (54 and 50 genes, respectively, of the 111 of the genes >4 and <-4 log fold change; P ≤ .0004). These studies identify MuRF1s unexpected regulation of fenofibrates pleiotropic effects and bridges, for the first time, MuRF1s regulation of PPARα, cardiac hypertrophy, and hemostasis.

Compressing Networks with Super Nodes

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of ‘super nodes’, where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the ‘CoreHD’ ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network.

Multi-layer Large-Scale Functional Connectome Reveals Infant Brain Developmental Patterns

Understanding human brain functional development in the very early ages is of great importance for charting normative development and detecting early neurodevelopmental disorders, but it is very challenging. We propose a group-constrained, robust community detection method for better understanding of developing brain functional connectome from neonate to two-year-old. For such a multi-subject, multi-age-group network topology study, we build a multi-layer functional network by adding inter-subject edges, and detect modular structure (communities) to explore topological changes of multiple functional systems at different ages and across subjects. This “Multi-Layer Inter-Subject-Constrained Modularity Analysis (MLISMA)” can detect group consistent modules without losing individual information, thus allowing assessment of individual variability in the brain modular topology, a key metric for developmental individualized fingerprinting. We propose a heuristic parameter optimization strategy to wisely determine the necessary parameters that define the modular configuration. Our method is validated to be feasible using longitudinal 0–1–2 year’s old infant brain functional MRI data, and reveals novel developmental trajectories of brain functional connectome. This work was supported by the NIH grants, EB022880, 1U01MH110274, and MH100217.

Cezanne/OTUD7B is a cell cycle‐regulated deubiquitinase that antagonizes the degradation of APC/C substrates

The anaphase‐promoting complex/cyclosome (APC/C) is an E3 ubiquitin ligase and key regulator of cell cycle progression. Since APC/C promotes the degradation of mitotic cyclins, it controls cell cycle‐dependent oscillations in cyclin‐dependent kinase (CDK) activity. Both CDKs and APC/C control a large number of substrates and are regulated by analogous mechanisms, including cofactor‐dependent activation. However, whereas substrate dephosphorylation is known to counteract CDK, it remains largely unknown whether deubiquitinating enzymes (DUBs) antagonize APC/C substrate ubiquitination during mitosis. Here, we demonstrate that Cezanne/OTUD7B is a cell cycle‐regulated DUB that opposes the ubiquitination of APC/C targets. Cezanne is remarkably specific for K11‐linked ubiquitin chains, which are formed by APC/C in mitosis. Accordingly, Cezanne binds established APC/C substrates and reverses their APC/C‐mediated ubiquitination. Cezanne depletion accelerates APC/C substrate degradation and causes errors in mitotic progression and formation of micronuclei. These data highlight the importance of tempered APC/C substrate destruction in maintaining chromosome stability. Furthermore, Cezanne is recurrently amplified and overexpressed in numerous malignancies, suggesting a potential role in genome maintenance and cancer cell proliferation.