The genetics and kinetics of BCL2 driven lymphoid malignancies

Introduction: Non-Hodgkin Lymphoma (NHL) is rising in incidence. Treatment of this genetically heterogeneous disease has toxic side effects and significant numbers of relapsers / non-responders. BCL2, an anti-apoptotic protein, is commonly overexpressed in NHL as a result of the t(14;18) translocation. A number of BCL2 inhibitors have shown success in clinical trials but variable efficacy has meant that none have been licenced for use. Methods: Retroviral insertional mutagenesis (RIM), using Moloney Murine Leukaemia Virus (MoMuLV) in transgenic mice overexpressing BCL2, was used to identify putative target genes deregulated alongside BCL2 in lymphomagenesis. This project aimed to update MoMuLV integration site identification and sequencing, allowing quantification of integration site clonal abundance. Cohorts of mice were sacrificed at time points prior to disease onset in order to interrogate integration site kinetics. To test the oncogenic potential of candidate genes, C57BL/6 Vav-BCL2 p53+/- mouse B cells were retrovirally transduced with genes of interest and transplanted into mice to study the speed of lymphoma onset. Results & Conclusions: A novel, high throughput, quantitative library preparation and sequencing protocol compatible with an Illumina platform was validated. RIM screening in BCL2 transgenic and wild-type mice identified different insertion sites profiles, detecting known oncogenes and tumour suppressor genes as well as novel candidate genes involved in pathways of lymphoid organ development, B-cell activation and differentiation. Study of insertion kinetics over time showed three patterns of clonal abundance and also allowed the study of specific gene deregulation prior to disease onset. Overexpression of Cd86 slowed disease onset whilst Ildr1 expedited disease onset suggesting the former is a tumour suppressor gene and the latter an oncogene. Discovering genes mutated with BCL2 in lymphoma may help to explain the lack of efficacy of BCL2 inhibitors and also identify novel therapeutic targets.Open Acces

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Digital control networks for virtual creatures

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Computational Methods for Mass Spectrometry-based Study of Protein-RNA or Protein-DNA Complexes and Quantitative Metaproteomics

In the last decade, the use of high-throughput methods has become increasingly popular in various fields of life sciences. Today, a wide range of technologies exist that allow gathering detailed quantitative insights into biological systems. With improved instrumentation and technological advances, a massive growth in data volume from these techniques has been observed. Bioinformatics copes with these heaps of data by providing computational methods that process raw data to extract biological knowledge. Computational mass spectrometry is a research field in bioinformatics that collects and analyzes data from mass-spectrometric high-throughput experiments. In this thesis, we present two new methods as well as a new data format for computational mass spectrometry. The first method applies to a scientific problem from the field of structural biology: to determine spatial interactions between protein and nucleic acids. For this purpose, we develop experimental protocols, programs, and analysis workflows that allow identifying UV-induced cross-links in (ribo-)nucleoprotein complexes from mass spectrometry data. An outstanding feature of our method is the ability to exactly localize amino acids and (ribo-)nucleotides in contact with each other. Applied to data from yeast and human we identify new interaction partners with, to date, unmatched resolution. The second method applies to metaproteomic studies of complex communities of microorganisms. In an unmanageable number, bacteria, simple fungi, or plants populate the most varied habitats. They are found in a high number of symbiotic or parasitic relationships which serve predominantly for the uptake of nutrients. Organisms differ in their biochemical repertoire allowing them to decompose a wide range of substrates. Remarkably, this enables functional groups of soil bacteria to even nourish themselves from environmental toxins. We present a method from the field of metaproteomics, which allows for identification of organisms involved in substrate degradation as well as methods to group them according to their function in the degradation process. To this end, we use substrates labeled with stable isotopes, which are metabolized by the organisms. The isotope abundance in proteins serves as an indicator for the conversion of the substrate. This abundance is automatically determined by our novel computational method and assigned to the individual organisms. The automation of this process reduces the manual work from several months to a few minutes and, thus, enables large study sizes. The third part of this work contributes to the better communication and processing of results from metabolomics and proteomics studies. We present a tabular, standardized, human-readable and machine-processable data format mzTab as a complement to existing data formats. We provide software components that allow processing of the format and demonstrate how the format can be integrated into complex proteomic and metabolomic workflows. The recent acceptance of mzTab by the largest proteomic data repositories represents a significant success. Also, we see an already widespread adoption by academic software developers and the first support by a commercial software vendor. Our novel format facilitates meta-analyses and makes research results from the field of proteomics and metabolomics available to scientists from other research areas

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Coarse-grained modelling of protein structure and internal dynamics: comparative methods and applications

The first chapter is devoted to a brief summary of the basic techniques commonly used to characterise protein's internal dynamics, and to perform those primary analyses which are the basis for our further developments. To this purpose we recall the basics of Principal Component Analysis of the covariance matrix of molecular dynamics (MD) trajectories. The overview is aimed at motivating and justifying a posteriori the introduction of coarse-grained models of proteins. In the second chapter we shall discuss dynamical features shared by different conformers of a protein. We'll review previously obtained results, concerning the universality of the vibrational spectrum of globular proteins and the self-similar free energy landscape of specific molecules, namely the G-protein and Adk. Finally, a novel technique will be discussed, based on the theory of Random Matrices, to extract the robust collective coordinates in a set of protein conformers by comparison with a stochastic reference model. The third chapter reports on an extensive investigation of protein internal dynamics modelled in terms of the relative displacement of quasi-rigid groups of amino acids. Making use of the results obtained in the previous chapters, we shall discuss the development of a strategy to optimally partition a protein in units, or domains, whose internal strain is negligible compared to their relative uctuation. These partitions will be used in turn to characterise the dynamical properties of proteins in the framework of a simplified, coarse-grained, description of their motion. In the fourth chapter we shall report on the possibility to use the collective uctuations of proteins as a guide to recognise relationships between them that may not be captured as significant when sequence or structural alignment methods are used. We shall review a method to perform the superposition of two proteins optimising the similarity of the structures as well as the dynamical consistency of the aligned regions; then, we shall next discuss a generalisation of this scheme to accelerate the dynamics-based alignment, in the perspective of dataset-wide applications. Finally, the fifth chapter focuses on a different topic, namely the occurrence of topologically-entangled states (knots) in proteins. Specifically, we shall investigate the sequence and structural properties of knotted proteins, reporting on an exhaustive dataset-wide comparison with unknotted ones. The correspondence, or the lack thereof, between knotted and unknotted proteins allowed us to identify, in knotted chains, small segments of the backbone whose `virtual' excision results in an unknotted structure. These `knot-promoting' loops are thus hypothesised to be involved in the formation of the protein knot, which in turn is likely to cover some role in the biological function of the knotted proteins