Prediction of Secondary Structure of Proteins Using Cellular Automata

Tato práce popisuje návrh metody predikce sekundární struktury proteinů založenou na celulárních automatech (CA) - CASSP. Optimální parametry modelu a přechodové funkce jsou získany pomocí evolučního algoritmu. Predikční model využíva pouze statistických vlastností aminokyselin, takže je velice rychlý. Dosažené výsledky byly porovnány s výsledky existujících metod. Byla také otestováná společná predikce navrženého systému CASSP s existujícím nástrojem PSIPRED. Nepodařilo se však dosáhnout výsledků, ktoré by tento existujíci nástroj převyšovali. Částečné zlepšení se dosáhlo při predikci pouze motivů sekndární struktury alpha-helix, co může pomoci v případe, že požadujeme co nejpřesenjší predikcii právě těchto motivů. K navrženému systému bylo také vytvořeno webové rozhraní.This work describes a method of the secondary structure prediction of proteins based on cellular automaton (CA) model - CASSP. Optimal model and CA transition rule parameters are acquired by evolutionary algorithm. Prediction model uses only statistical characteristics of amino acids, so its prediction is fast. Achieved results was compared with results of other tools for this purpose. Prediction cooperation with a existing tool PSIPRED was also tested. It didn't succeed to beat this existing tool, but partial improvement was achieved in prediction of only alpha-helix secondary structure motif, what can be helful if we need the best prediction of alpha-helices. It was developed also a web interface of designed system.

Prediction of the Secondary Structure of Proteins by Cellular Automaton

Tato práce přináší nový přístup k predikci sekundární struktury proteinů. K existujícím přístupům k této problematice zavádí novou metodu, založenou na celulárním automatu a jeho charakteristických vlastnostech. Hlavním cílem práce je zvýšení rychlosti predikce i za cenu mírného snížení její úspěšnosti. Pro nalezení optimálních parametrů běhu celulárního automatu je využito genetického algoritmu s vhodně definovanými genetickými operátory. Dosažené výsledky byly porovnány s výsledky existujících metod. Parametry predikce, se kterými bylo dosaženo nejvyšší úspěšnosti, byly použity ve výpočetním frameworku pro predikci sekundární struktury libovolného analyzovaného proteinu.This thesis presents a new approach to the prediction of the secondary structure of proteins. It employs a new method based on cellular automata and its characteristic properties. The main objective is to increase speed of the prediction even at the cost of slight decrease of overall accuracy. Optimal parameters of cellular automata was found by genetic algorithm using suitable genetic operators. These parameters are incorporated into developed application for prediction. Finally, the results was compared with results of other tools for this purpose.

The role of time in biological systems: a computational analysis ranging from molecular dynamics to biological network simulations.

Time is the main character of this thesis, and it has been used in many ways. Starting from molecular dynamics simulations to biological networks, time has been analysed under different light and roles. The main subjects characterizing this work are Von Hippel-Lindau syndrome and circadian rhythm, although a method for molecular dynamics simulations trajectory analysis is presented at the end. Several works are presented here, analysing different aspects of protein dynamics as well as pathway alterations depending on the time coordinate. The first work analyses the interaction between protein Von Hippel-Lindau (pVHL) and its main interactor, Hipoxya Inducibe Factor 1α by means of molecular dynamics simulations, investigating on a non-conventional proline hydroxylation event. As a result, we obtained that a specific hydrogen bond network rearrangement and improved electrostatic energy for hydroxylated P567 appear to be compatible with an increase in HIF-1α binding affinity. Sequence analysis also confirms P567 to be vastly conserved during evolution, indicating a possible role for this alternative, PHD-3 driven, post translational modification in pVHL–HIF-1α complex formation. The second work dealt with the same main subject, but investigated through biological network simulations, particularly with Petri net models. In this work, we presented a novel manually curated Petri Net (PN) model of the main pVHL functional pathways. The model was built using functional information derived from the literature. It includes all major pVHL functions and is able to credibly reproduce VHL syndrome at the molecular level. The reliability of the PN model also allowed in silico knockout experiments, driven by previous model analysis. Interestingly, PN analysis suggests that the variability of different VHL manifestations is correlated with the concomitant inactivation of different metabolic pathways. In the third work, investigating the structural role of flavin-adenine-dinucleotide (FAD) through molecular dynamics simulations, we analyzed the Drosophila melanogaster Cryptochrome crystal structure, elucidating how this large co-factor within the receptor could be crucial for CRY structural stability. The co-factor appears indeed to improve receptor motility, providing steric hindrance. Moreover, multiple sequence alignments revealed that conserved motifs in the C-terminal tail could be necessary for functional stability. The fourth work focused on the sequence impact on the modern folds. We shuffled the sequences of 10 natural proteins and obtained 40 different and apparently unrelated folds. Our results suggest that shuffled sequences are sufficiently stable and may act as a basis to evolve functional proteins. The common secondary structure of modern proteins is well represented by a small set of permuted sequences, which also show the emergence of intrinsic disorder and aggregation-prone stretches of the polypeptide chain. The last work presented here is a method to quickly analyse molecular dynamics simulations trajectories. The complexity related to their interpretation and analysis is still one of the major challenges for most users. In this work we introduce RING MD, which is able to identify the most important frames (PDB structures) and key residues that cause different conformers transitions, providing a simple interpretation useful for non-expert users. Comparison with the classical analysis of three MD simulations, Ubiquitin, T4 Lysozyme and T4 Glutaredoxin, confirmed RING MD results and effectiveness. At the end, time should not be considered simply as something entraining the environment, it is what indeed modifies systems and environment. Different systems simply change in different ways, because of different mechanisms, but the main driving force should always be considered time

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Models of self-organization in biological development

Bibliography: p. 297-320.In this thesis we thus wish to consider the concept of self-organization as an overall paradigm within which various theoretical approaches to the study of development may be described and evaluated. In the process, an attempt is made to give a fair and reasonably comprehensive overview of leading modelling approaches in developmental biology, with particular reference to self-organization. The work proceeds from a physical or mathematical perspective, but not unduly so - the major mathematical derivations and results are relegated to appendices - and attempts to fill a perceived gap in the extant review literature, in its breadth and attempted impartiality of scope. A characteristic of the present account is its markedly interdisciplinary approach: it seeks to place self-organization models that have been proposed for biological pattern formation and morphogenesis both within the necessary experimentally-derived biological framework, and in the wider physical context of self-organization and the mathematical techniques that may be employed in its study. Hence the thesis begins with appropriate introductory chapters to provide the necessary background, before proceeding to a discussion of the models themselves. It should be noted that the work is structured so as to be read sequentially, from beginning to end; and that the chapters in the main text were designed to be understood essentially independently of the appendices, although frequent references to the latter are given. In view of the vastness of the available information and literature on developmental biology, a working knowledge of embryological principles must be assumed. Consequently, rather than attempting a comprehensive introduction to experimental embryology, chapter 2 presents just a few biological preliminaries, to 'set the scene', outlining some of the major issues that we are dealing with, and sketching an indication of the current status of knowledge and research on development. The chapter is aimed at furnishing the necessary biological, experimental background, in the light of which the rest of the thesis should be read, and which should indeed underpin and motivate any theoretical discussions. We encounter the different hierarchical levels of description in this chapter, as well as some of the model systems whose experimental study has proved most fruitful, some of the concepts of experimental embryology, and a brief reference to some questions that will not be addressed in this work. With chapter 3, we temporarily move away from developmental biology, and consider the wider physical and mathematical concepts related to the study of self-organization. Here we encounter physical and chemical examples of spontaneous structure formation, thermodynamic considerations, and different approaches to the description of complexity. Mathematical approaches to the dynamical study of self-organization are also introduced, with specific reference to reaction-diffusion equations, and we consider some possible chemical and biochemical realizations of self-organizing kinetics. The chapter may be read in conjunction with appendix A, which gives a somewhat more in-depth study of reaction-diffusion equations, their analysis and properties, as an example of the approach to the analysis of self-organizing dynamical systems and mathematically-formulated models. Appendix B contains a more detailed discussion of the Belousov-Zhabotinskii reaction, which provides a vivid chemical paradigm for the concepts of symmetry-breaking and self-organization. Chapter 3 concludes with a brief discussion of a model biological system, the cellular slime mould, which displays rudimentary development and has thus proved amenable to detailed study and modelling. The following two chapters form the core of the thesis, as they contain discussions of the detailed application of theoretical concepts and models, largely based on self-organization, to various developmental situations. We encounter a diversity of models which has arisen largely in the last quarter century, each of which attempts to account for some aspect of biological pattern formation and morphogenesis; an aim of the discussion is to assess the extent of the underlying unity of these models in terms of the self-organization paradigm. In chapter 4 chemical pre-patterns and positional information are considered, without the overt involvement of cells in the patterning. In chapter 5, on the other hand, cellular interactions and activities are explicitly taken into account; this chapter should be read together with appendix C, which contains a brief introduction to the mathematical formulation and analysis of some of the models discussed. The penultimate chapter, 6, considers two other approaches to the study of development; one of these has faded away, while the other is still apparently in the ascendant. The assumptions underlying catastrophe theory, the value of its applications to developmental biology and the reasons for its decline in popularity, are considered. Lastly, discrete approaches, including the recently fashionable cellular automata, are dealt with, and the possible roles of rule-based interactions, such as of the so-called L-systems, and of fractals and chaos are evaluated. Chapter 7 then concludes the thesis with a brief assessment of the value of the self-organization concept to the study of biological development

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

A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA

Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging. This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations

2017 GREAT Day Program

SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

06. 2010 IMSAloquium Student Investigation Showcase

https://digitalcommons.imsa.edu/class_of_2010/1004/thumbnail.jp

2010 IMSAloquium, Student Investigation Showcase

IMSA students engage in investigations in nanotechnology, particle physics, law, neonatal medicine, literature, transplantation biology, water purity, the educational achievement gap, neurobiology and memory, ethics, theatre, discrete mathematics, economics, and more.https://digitalcommons.imsa.edu/archives_sir/1002/thumbnail.jp