Efficient and Robust Algorithms for Statistical Inference in Gene Regulatory Networks

Inferring gene regulatory networks (GRNs) is of profound importance in the field of computational biology and bioinformatics. Understanding the gene-gene and gene- transcription factor (TF) interactions has the potential of providing an insight into the complex biological processes taking place in cells. High-throughput genomic and proteomic technologies have enabled the collection of large amounts of data in order to quantify the gene expressions and mapping DNA-protein interactions. This dissertation investigates the problem of network component analysis (NCA) which estimates the transcription factor activities (TFAs) and gene-TF interactions by making use of gene expression and Chip-chip data. Closed-form solutions are provided for estimation of TF-gene connectivity matrix which yields advantage over the existing state-of-the-art methods in terms of lower computational complexity and higher consistency. We present an iterative reweighted ℓ2 norm based algorithm to infer the network connectivity when the prior knowledge about the connections is incomplete. We present an NCA algorithm which has the ability to counteract the presence of outliers in the gene expression data and is therefore more robust. Closed-form solutions are derived for the estimation of TFAs and TF-gene interactions and the resulting algorithm is comparable to the fastest algorithms proposed so far with the additional advantages of robustness to outliers and higher reliability in the TFA estimation. Finally, we look at the inference of gene regulatory networks which which essentially resumes to the estimation of only the gene-gene interactions. Gene networks are known to be sparse and therefore an inference algorithm is proposed which imposes a sparsity constraint while estimating the connectivity matrix.The online estimation lowers the computational complexity and provides superior performance in terms of accuracy and scalability. This dissertation presents gene regulatory network inference algorithms which provide computationally efficient solutions in some very crucial scenarios and give advantage over the existing algorithms and therefore provide means to give better understanding of underlying cellular network. Hence, it serves as a building block in the accurate estimation of gene regulatory networks which will pave the way for finding cures to genetic diseases

Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering

This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then fed to a LASSO based least squares regression operation, which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with Extended Kalman filtering (EKF), employing Mean Square Error as fidelity criterion using synthetic data and real biological data. Extensive computer simulations illustrate that the particle filter based gene network inference algorithm outperforms EKF and therefore, it can serve as a natural framework for modeling gene regulatory networks

Twisted ovarian cyst in pregnancy: a case report

Ovarian cysts are frequently encountered during pregnancy due to the use of routine prenatal ultrasound. Most of them are benign but in some cases, complications can occur such as torsion, rupture and malignant change. In pregnancy risk of torsion increases 5-fold. It carries significant risk to a pregnant woman and her intrauterine foetus. Here we are reporting a 30-year-old female G3 P1+1L2 with 15 weeks of gestation who presented to antenatal OPD with complain of dull aching abdominal pain for 1 month and nausea and vomiting for 5 days. On ultrasound bilateral ovarian cysts were found, with one of the cyst with multiple septations. She underwent laparotomy, a right sided twisted ovarian cyst was found for which salpingoophrectomy was done. Left sided cyst was simple where cystectomy was done. Her histopathology report showed a bilateral benign serous cystadenoma. Her pregnancy was followed up. She delivered a healthy male baby at term. Ovarian cyst diagnosed in pregnancy can be followed up with serial ultrasound but if associated with complication such as torsion then urgent surgical intervention has to be done

The effects of common structural variants on 3D chromatin structure

Background Three-dimensional spatial organization of chromosomes is defined by highly self-interacting regions 0.1-1 Mb in size termed Topological Associating Domains (TADs). Genetic factors that explain dynamic variation in TAD structure are not understood. We hypothesize that common structural variation (SV) in the human population can disrupt regulatory sequences and thereby influence TAD formation. To determine the effects of SVs on 3D chromatin organization, we performed chromosome conformation capture sequencing (Hi-C) of lymphoblastoid cell lines from 19 subjects for which SVs had been previously characterized in the 1000 genomes project. We tested the effects of common deletion polymorphisms on TAD structure by linear regression analysis of nearby quantitative chromatin interactions (contacts) within 240 kb of the deletion, and we specifically tested the hypothesis that deletions at TAD boundaries (TBs) could result in large-scale alterations in chromatin conformation. Results Large (&gt; 10 kb) deletions had significant effects on long-range chromatin interactions. Deletions were associated with increased contacts that span the deleted region and this effect was driven by large deletions that were not located within a TAD boundary (nonTB). Some deletions at TBs, including a 80 kb deletion of the genes CFHR1 and CFHR3, had detectable effects on chromatin contacts. However for TB deletions overall, we did not detect a pattern of effects that was consistent in magnitude or direction. Large inversions in the population had a distinguishable signature characterized by a rearrangement of contacts that span its breakpoints. Conclusions Our study demonstrates that common SVs in the population impact long-range chromatin structure, and deletions and inversions have distinct signatures. However, the effects that we observe are subtle and variable between loci. Genome-wide analysis of chromatin conformation in large cohorts will be needed to quantify the influence of common SVs on chromatin structure.</p

Efficient and Robust Algorithms for Statistical Inference in Gene Regulatory Networks

Inferring gene regulatory networks (GRNs) is of profound importance in the field of computational biology and bioinformatics. Understanding the gene-gene and gene- transcription factor (TF) interactions has the potential of providing an insight into the complex biological processes taking place in cells. High-throughput genomic and proteomic technologies have enabled the collection of large amounts of data in order to quantify the gene expressions and mapping DNA-protein interactions. This dissertation investigates the problem of network component analysis (NCA) which estimates the transcription factor activities (TFAs) and gene-TF interactions by making use of gene expression and Chip-chip data. Closed-form solutions are provided for estimation of TF-gene connectivity matrix which yields advantage over the existing state-of-the-art methods in terms of lower computational complexity and higher consistency. We present an iterative reweighted ℓ2 norm based algorithm to infer the network connectivity when the prior knowledge about the connections is incomplete. We present an NCA algorithm which has the ability to counteract the presence of outliers in the gene expression data and is therefore more robust. Closed-form solutions are derived for the estimation of TFAs and TF-gene interactions and the resulting algorithm is comparable to the fastest algorithms proposed so far with the additional advantages of robustness to outliers and higher reliability in the TFA estimation. Finally, we look at the inference of gene regulatory networks which which essentially resumes to the estimation of only the gene-gene interactions. Gene networks are known to be sparse and therefore an inference algorithm is proposed which imposes a sparsity constraint while estimating the connectivity matrix.The online estimation lowers the computational complexity and provides superior performance in terms of accuracy and scalability. This dissertation presents gene regulatory network inference algorithms which provide computationally efficient solutions in some very crucial scenarios and give advantage over the existing algorithms and therefore provide means to give better understanding of underlying cellular network. Hence, it serves as a building block in the accurate estimation of gene regulatory networks which will pave the way for finding cures to genetic diseases

Recurrent spontaneous multiple pregnancy: a case report

Multiple gestations are usually iatrogenic like use of assisted reproductive techniques (ART), infertility treatment but it is rare in spontaneous conception. High order multiple pregnancies (HOMPs) are major cause of maternal, fetal and neonatal morbidity. Multiple gestations carries 2 complications either abortion in early gestation or a preterm delivery in late pregnancy (more common). Preterm delivery is common (50%) and patient usually delivers by 30-32 weeks. Discordance of fetal growth is very common and even more than in twins. Perinatal loss is inversely related to birth weight. The mothers should be counseled about regular ante natal care (ANC) check-ups for early identification of multiple pregnancies so that proper care can be given to prolong the gestational age and reduce the complications associated with multiple pregnancies

Leiomyosarcoma: a rare complication of uterine fibroid

Uterine sarcomas are rare tumours of mesodermal origin. Malignant change occurring in uterine fibroid is termed as leiomyosarcoma. They constitute around 2-6 % uterine malignancies and 25-36% of uterine sarcomas. The tumour is common in women between the age group 40-50 years. It has an aggressive course & usually metastasis goes to the lungs. The prognosis for women with uterine sarcoma primarily depends on the extent of disease at the time of diagnosis and mitotic index. Women with tumor size >5 cm in maximum diameter have poor prognosis. These tumours should be diagnosed and managed with no delay and must be followed vigilantly as the rate of recurrence & metastasis is very high

Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering

This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then fed to a LASSO based least squares regression operation, which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with Extended Kalman filtering (EKF), employing Mean Square Error as fidelity criterion using synthetic data and real biological data. Extensive computer simulations illustrate that the particle filter based gene network inference algorithm outperforms EKF and therefore, it can serve as a natural framework for modeling gene regulatory networks

NILAI-NILAI PENDIDIKAN ISLAM : (Analisis Buku Misteri Banjir Nabi Nuh Karya Yosep Rafiqi)

Yosep Rafiki's work on the mysterious story of Nuh flood is not just a reading, it contains educational values ??that are useful to human life, both individually and as a community. In the mysterious story of the flood Noah described the state of society and the soul of a living being in a time and place of trial and trial. In this study using library research, the results found are: Faith values, Commandments affirming Allah SWT, Commandments to believe in Allah and His Messenger, preaching to Allah and His Messenger, believing in the day of retribution. Worship Value: Performing the order amar ma'ruf nahi munkar to others including his people to invoke a straightforward course of faith in Allah Almighty. Moral Values: Be gentle in preaching, be polite, compassionate and advise each other, be patient, Prohibition to be arrogant, and prohibit no respect for others

Common DNA sequence variation influences 3-dimensional conformation of the human genome.

BACKGROUND:The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. RESULTS:To address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. CONCLUSION:Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized