Multi-Component Dark Matter: the vector and fermion case

Multi-component dark matter scenarios constitute natural extensions of standard single-component setups and offer attractive new dynamics that could be adopted to solve various puzzles of dark matter. In this work we present and illustrate properties of a minimal UV-complete vector-fermion dark matter model where two or three dark sector particles are stable. The model we consider is an extension of the Standard Model (SM) by spontaneously broken extra $U(1)_X$ gauge symmetry and a Dirac fermion. All terms in the Lagrangian which are consistent with the assumed symmetry are present, so the model is renormalizable and consistent. To generate mass for the dark-vector $X_\mu$ the Higgs mechanism with a complex singlet $S$ is employed in the dark sector. Dark matter candidates are the massive vector boson $X_\mu$ and two Majorana fermions $\psi_\pm$. All the dark sector fields are singlets under the SM gauge group. The set of three coupled Boltzmann equations has been solved numerically and discussed. We have performed scans over the parameter space of the model implementing the total relic abundance and direct detection constraints. The dynamics of the vector-fermion dark matter model is very rich and various interesting phenomena appear, in particular, when the standard annihilations of a given dark matter are suppressed then the semi-annihilations, conversions and decays within the dark sector are crucial for the evolution of relic abundance and its present value. Possibility of enhanced self-interaction has been also discussed.Comment: v2: 25 pages + appendices, 12 captioned figures, a section on multi-component self-interacting dark matter is added, matches the journal accepted versio

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study

Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors

dryads-research

Supplementary information for: Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer gene

Inferring the properties of transcription factor regulation

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 93-95).The regulatory targets of transcription factors are costly to directly detect using existing technologies. Many computational models have thus been developed to infer the genes targeted by TFs using gene expression profiles, position weight matrices modeling TF protein binding, histone modifications, and other secondary datasets. We develop a framework for scoring the potential targets of various TFs using models that take the profile of motif hits on the proximity of transcription start sites as input, and describe methods to validate this framework using expression datasets. These models are then extended to include cis-regulatory regions inferred from epigenetic data.by Michal R. Grzadkowski.S.M