Modeling And Identification Of Differentially Regulated Genes Using Transcriptomics And Proteomics Data

Photosynthetic organisms are complex dynamical systems, showing a remarkable ability to adapt to different environmental conditions for their survival. Mechanisms underlying the coordination between different cellular processes in these organisms are still poorly understood. In this dissertation we utilize various computational and modeling techniques to analyze transcriptomics and proteomics data sets from several photosynthetic organisms. We try to use changes in expression levels of genes to study responses of these organisms to various environmental conditions such as availability of nutrients, concentrations of chemicals in growth media, and temperature. Three specific problems studied here are transcriptomics modifications in photosynthetic organisms under reduction-oxidation: redox) stress conditions, circadian and diurnal rhythms of cyanobacteria and the effect of incident light patterns on these rhythms, and the coordination between biological processes in cyanobacteria under various growth conditions. Under redox stresses caused by high light treatments, a strong transcriptomic level response, spread across many biological processes, is discovered in the cyanobacterium Synechocystis sp. PCC 6803. Based on statistical tests, expression levels of about 20% of genes in Synechocystis 6803 are identified as significantly affected due to influence of high light. Gene clustering methods reveal that these responses can mainly be classified as transient and consistent responses, depending on the duration of modified behaviors. Many genes related to energy production as well as energy utilization are shown to be strongly affected. Analysis of microarray data under two stress conditions, high light and DCMU treatment, combined with data mining and motif finding algorithms led to a discovery of novel transcription factor, RRTF1 that responds to redox stresses in Arabidopsis thaliana. Time course transcriptomics data from Cyanothece sp. ATCC 51142 have shown strong diurnal rhythms. By combining multiple experimental conditions and using gene classification algorithms based on Fourier scores and angular distances, it is shown that majority of the diurnal genes are in fact light responding. Only about 10% of genes in the genome are categorized as being circadian controlled. A transcription control model based on dynamical systems is employed to identify the interactions between diurnal genes. A phase oscillator network is proposed to model the behavior of different biological processes. Both these models are shown to carry biologically meaningful features. To study the coordination between different biological processes to various environment and genetic modifications, an interaction model is derived using Bayesian network approach, combining all publicly available microarray data sets for Synechocystis sp. PCC 6803. Several novel relationships between biological processes are discovered from the model. Model is used to simulate several experimental conditions, and the response of the model is shown to agree with the experimentally observed behaviors

Path planning for active tensegrity structures

This paper presents a path planning method for actuated tensegrity structures with quasi-static motion. The valid configurations for such structures lay on an equilibrium manifold, which is implicitly defined by a set of kinematic and static constraints. The exploration of this manifold is difficult with standard methods due to the lack of a global parameterization. Thus, this paper proposes the use of techniques with roots in differential geometry to define an atlas, i.e., a set of coordinated local parameterizations of the equilibrium manifold. This atlas is exploited to define a rapidly-exploring random tree, which efficiently finds valid paths between configurations. However, these paths are typically long and jerky and, therefore, this paper also introduces a procedure to reduce their control effort. A variety of test cases are presented to empirically evaluate the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.Peer ReviewedPostprint (author's final draft

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

Synthetic multistability in mammalian cells

In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require interactions among cells in distinct states. Here we introduce MultiFate, a naturally-inspired, synthetic circuit that supports long-term, controllable, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design, we engineered MultiFate circuits that generate up to seven states, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs, and can be easily expanded with additional transcription factors. Together, these results provide a foundation for engineering multicellular behaviors in mammalian cells

Introducing deep learning -based methods into the variant calling analysis pipeline

Biological interpretation of the genetic variation enhances our understanding of normal and pathological phenotypes, and may lead to the development of new therapeutics. However, it is heavily dependent on the genomic data analysis, which might be inaccurate due to the various sequencing errors and inconsistencies caused by these errors. Modern analysis pipelines already utilize heuristic and statistical techniques, but the rate of falsely identified mutations remains high and variable, particular sequencing technology, settings and variant type. Recently, several tools based on deep neural networks have been published. The neural networks are supposed to find motifs in the data that were not previously seen. The performance of these novel tools is assessed in terms of precision and recall, as well as computational efficiency. Following the established best practices in both variant detection and benchmarking, the discussed tools demonstrate accuracy metrics and computational efficiency that spur further discussion

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Model checking is a well-established technique for automaticallyverifying complex systems. Recently, model checkers have appearedin computer tools for the analysis of biochemical (and generegulatory) networks. We survey several such tools to assess thepotential of model checking in computational biology. Next, our overviewfocuses on direct applications of existing model checkers, as well ason algorithms for biochemical network analysis influenced by modelchecking, such as those using binary decision diagrams or Booleansatisfiability solvers. We conclude with advantages and drawbacks ofmodel checking for the analysis of biochemical networks

Stochastic models of intracellular transport

The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an over-damped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of ATP hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review we present a wide range of analytical methods and models of intracellular transport. In the case of diffusive transport, we consider narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion. In the case of active transport, we consider Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasi-steady-state reduction methods, and mean field approximations. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self–organization of subcellular structures

QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles

Background: Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth ("deep sequencing"), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset. Results: For default application of QQ-SNV, variants were called using a SNV probability cutoff of 0.5 (QQ-SNVD). To improve the sensitivity we used a SNV probability cutoff of 0.0001 (QQ-SNVHS). To also increase specificity, SNVs called were overruled when their frequency was below the 80th percentile calculated on the distribution of error frequencies (QQ-SNVHS-P80). When comparing QQ-SNV versus the other methods on the plasmid mixture test sets, QQ-SNVD performed similarly to the existing approaches. QQ-SNVHS was more sensitive on all test sets but with more false positives. QQ-SNVHS-P80 was found to be the most accurate method over all test sets by balancing sensitivity and specificity. When applied to a paired-end HCV sequencing study, with lowest spiked-in true frequency of 0.5 %, QQ-SNVHS-P80 revealed a sensitivity of 100 % (vs. 40-60 % for the existing methods) and a specificity of 100 % (vs. 98.0-99.7 % for the existing methods). In addition, QQ-SNV required the least overall computation time to process the test sets. Finally, when testing on a clinical sample, four putative true variants with frequency below 0.5 % were consistently detected by QQ-SNVHS-P80 from different generations of Illumina sequencers. Conclusions: We developed and successfully evaluated a novel method, called QQ-SNV, for highly efficient single nucleotide variant calling on Illumina deep sequencing virology data

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency