Mutational signatures in GATA3 transcription factor and its DNA binding domain that stimulate breast cancer and HDR syndrome.

Transcription factors (TFs) play important roles in many biochemical processes. Many human genetic disorders have been associated with mutations in the genes encoding these transcription factors, and so those mutations became targets for medications and drug design. In parallel, since many transcription factors act either as tumor suppressors or oncogenes, their mutations are mostly associated with cancer.

A Real-Time ECG Feature Extraction Algorithm for Detecting Meditation Levels within a General Measurement Setup.

This paper presents a setup for the real-time extraction of Electroencephalography (EEG) and Electrocardiogram (ECG) features indicating the level of focus, relaxation, or meditation of a given subject. An algorithm for detecting meditation in real-time using the extracted ECG features is designed and shown to lead to accurate results using an online ECG measurement dataset. Similar methods can be used for EEG data, such that the proposed measurement setup can be used, for example, for investigating the effect of virtual reality based EEG training, with and without neurofeedback, on the capability of subjects to focus, relax, or meditate.

Examining the need & potential for biomedical engineering to strengthen health care delivery for displaced populations & victims of conflict.

Conflict and the subsequent displacement of populations creates unique challenges in the delivery of quality health care to the affected population. Equitable access to quality care demands a multi-pronged strategy with a growing need, and role, for technological innovation to address these challenges. While there have been significant contributions towards alleviating the burden of conflict via data informatics and analytics, communication technology, and geographic information systems, little has been done within biomedical engineering.

Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application.

Degradomics is a novel discipline that involves determination of the proteases/substrate fragmentation profile, called the substrate degradome, and has been recently applied in different disciplines. A major application of degradomics is its utility in the field of biomarkers where the breakdown products (BDPs) of different protease have been investigated. Among the major proteases assessed, calpain and caspase proteases have been associated with the execution phases of the pro-apoptotic and pro-necrotic cell death, generating caspase/calpain-specific cleaved fragments.

Novel Bioinformatics-Based Approach for Proteomic Biomarkers Prediction of Calpain-2 &Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein.

The crucial biological role of proteases has been visible with the development of degradomics discipline involved in the determination of the proteases/substrates resulting in breakdown-products (BDPs) that can be utilized as putative biomarkers associated with different biological-clinical significance. In the field of cancer biology, matrix metalloproteinases (MMPs) have shown to result in MMPs-generated protein BDPs that are indicative of malignant growth in cancer, while in the field of neural injury, calpain-2 and caspase-3 proteases generate BDPs fragments that are indicative of different neural cell death mechanisms in different injury scenarios. Advanced proteomic techniques have shown a remarkable progress in identifying these BDPs experimentally.

Electrostatic study of Alanine mutational effects on transcription: application to GATA-3:DNA interaction complex.

Protein-DNA interaction is of fundamental importance in molecular biology, playing roles in functions as diverse as DNA transcription, DNA structure formation, and DNA repair. Protein-DNA association is also important in medicine; understanding Protein-DNA binding kinetics can assist in identifying disease root causes which can contribute to drug development. In this perspective, this work focuses on the transcription process by the GATA Transcription Factor (TF).

Designing pooling systems for noisy high-throughput protein-protein interaction experiments using boolean compressed sensing.

Group testing, also known as pooling, is a common technique used in high-throughput experiments in molecular biology to significantly reduce the number of tests required to identify rare biological interactions while correcting for experimental noise. Central to the group testing problem are 1) a pooling design that lays out how items are grouped together into pools for testing and 2) a decoder that interprets the results of the tested pools, identifying the active compounds. In this work, we take advantage of decoder guarantees from the field of compressed sensing (CS) to address the problem of efficient and reliable detection of biological interaction in noisy high-throughput experiments.

A multiorganism based method for Bayesian gene network estimation.

The primary goal of this article is to infer genetic interactions based on gene expression data. A new method for multiorganism Bayesian gene network estimation is presented based on multitask learning. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from true correlations that would lead to actual edges when modeling the gene interactions as a Bayesian network.

Modeling promoter search by E. coli RNA polymerase: one-dimensional diffusion in a sequence-dependent energy landscape.

We present a biophysical model of promoter search by Escherichia coli RNA polymerase. We use an unconventional weight matrix derived from promoter strength data to extract the energy landscape common to a large set of known promoters. This exhibits a continuous strengthening of the binding energy when approaching the transcription start site from either side.

Translation initiation modeling and mutational analysis based on the 3(')-end of the Escherichia coli 16S rRNA sequence.

A model for the process of translation in gene expression is proposed. The model is based on the assumption that the ribosome decodes the mRNA sequences using consecutive subsequences of the 3(')-end of its 16S rRNA subunit. The biological consistency of the model is validated by successful detection of the Shine-Dalgarno signal and the start codon.

Fine-scale genetic mapping using independent component analysis.

The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process.

Modeling DNA-binding of Escherichia coli sigma70 exhibits a characteristic energy landscape around strong promoters.

We present a computational model of DNA-binding by sigma70 in Escherichia coli which allows us to extract the functional characteristics of the wider promoter environment. Our model is based on a measure for the binding energy of sigma70 to the DNA, which is derived from promoter strength data and used to build up a non-standard weight matrix. Opposed to conventional approaches, we apply the matrix to the environment of 3765 known promoters and consider the average matrix scores to extract the common features.

Gene mapping and marker clustering using Shannon's mutual information.

Finding the causal genetic regions underlying complex traits is one of the main aims in human genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop general yet sensitive methods for gene mapping. We discuss the use of Shannon's information theory for population-based gene mapping of discrete and quantitative traits and for marker clustering.