Attribute Exploration of Gene Regulatory Processes

This thesis aims at the logical analysis of discrete processes, in particular of such generated by gene regulatory networks. States, transitions and operators from temporal logics are expressed in the language of Formal Concept Analysis. By the attribute exploration algorithm, an expert or a computer program is enabled to validate a minimal and complete set of implications, e.g. by comparison of predictions derived from literature with observed data. Here, these rules represent temporal dependencies within gene regulatory networks including coexpression of genes, reachability of states, invariants or possible causal relationships. This new approach is embedded into the theory of universal coalgebras, particularly automata, Kripke structures and Labelled Transition Systems. A comparison with the temporal expressivity of Description Logics is made. The main theoretical results concern the integration of background knowledge into the successive exploration of the defined data structures (formal contexts). Applying the method a Boolean network from literature modelling sporulation of Bacillus subtilis is examined. Finally, we developed an asynchronous Boolean network for extracellular matrix formation and destruction in the context of rheumatoid arthritis.Comment: 111 pages, 9 figures, file size 2.1 MB, PhD thesis University of Jena, Germany, Faculty of Mathematics and Computer Science, 2011. Online available at http://www.db-thueringen.de/servlets/DocumentServlet?id=1960

High Lundquist Number Simulations of Parker\u27s Model of Coronal Heating: Scaling and Current Sheet Statistics Using Heterogeneous Computing Architectures

Parker\u27s model [Parker, Astrophys. J., 174, 499 (1972)] is one of the most discussed mechanisms for coronal heating and has generated much debate. We have recently obtained new scaling results for a 2D version of this problem suggesting that the heating rate becomes independent of resistivity in a statistical steady state [Ng and Bhattacharjee, Astrophys. J., 675, 899 (2008)]. Our numerical work has now been extended to 3D using high resolution MHD numerical simulations. Random photospheric footpoint motion is applied for a time much longer than the correlation time of the motion to obtain converged average coronal heating rates. Simulations are done for different values of the Lundquist number to determine scaling. In the high-Lundquist number limit (S \u3e 1000), the coronal heating rate obtained is consistent with a trend that is independent of the Lundquist number, as predicted by previous analysis and 2D simulations. We will present scaling analysis showing that when the dissipation time is comparable or larger than the correlation time of the random footpoint motion, the heating rate tends to become independent of Lundquist number, and that the magnetic energy production is also reduced significantly. We also present a comprehensive reprogramming of our simulation code to run on NVidia graphics processing units using the Compute Unified Device Architecture (CUDA) and report code performance on several large scale heterogenous machines

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Current issues of security management during martial law

The authors of the book have come to the conclusion that toensuring the country’s security in the conditions of military aggression, it is necessary to use the mechanisms of protection of territories and population, support of economic entities, international legal levers of influence on the aggressor country. Basic research focuses on assessment the resource potential of enterprises during martial law, the analysis of migration flows in the middle of the country and abroad, the volume of food exports, marketing and logistics system. The research results have been implemented in the different decision-making models during martial law, information and economic security management, formation of personnel potential and assets of enterprises, food, energy and environmental security management, use of budgetary levers and financial instruments. The results of the study can be used in the developing of directions, programs and strategies for the post-war recovery of Ukraine’s economy and the attraction of foreign investments in the regions, decision-making at the level of ministries and agencies that regulate security management processes. The results can also be used by students and young scientists in the educational process and conducting scientific research on the problems of ensuring the country’s security

Infobiotics : computer-aided synthetic systems biology

Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible. This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”

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

Infobiotics : computer-aided synthetic systems biology

Until very recently Systems Biology has, despite its stated goals, been too reductive in terms of the models being constructed and the methods used have been, on the one hand, unsuited for large scale adoption or integration of knowledge across scales, and on the other hand, too fragmented. The thesis of this dissertation is that better computational languages and seamlessly integrated tools are required by systems and synthetic biologists to enable them to meet the significant challenges involved in understanding life as it is, and by designing, modelling and manufacturing novel organisms, to understand life as it could be. We call this goal, where everything necessary to conduct model-driven investigations of cellular circuitry and emergent effects in populations of cells is available without significant context-switching, “one-pot” in silico synthetic systems biology in analogy to “one-pot” chemistry and “one-pot” biology. Our strategy is to increase the understandability and reusability of models and experiments, thereby avoiding unnecessary duplication of effort, with practical gains in the efficiency of delivering usable prototype models and systems. Key to this endeavour are graphical interfaces that assists novice users by hiding complexity of the underlying tools and limiting choices to only what is appropriate and useful, thus ensuring that the results of in silico experiments are consistent, comparable and reproducible. This dissertation describes the conception, software engineering and use of two novel software platforms for systems and synthetic biology: the Infobiotics Workbench for modelling, in silico experimentation and analysis of multi-cellular biological systems; and DNA Library Designer with the DNALD language for the compact programmatic specification of combinatorial DNA libraries, as the first stage of a DNA synthesis pipeline, enabling methodical exploration biological problem spaces. Infobiotics models are formalised as Lattice Population P systems, a novel framework for the specification of spatially-discrete and multi-compartmental rule-based models, imbued with a stochastic execution semantics. This framework was developed to meet the needs of real systems biology problems: hormone transport and signalling in the root of Arabidopsis thaliana, and quorum sensing in the pathogenic bacterium Pseudomonas aeruginosa. Our tools have also been used to prototype a novel synthetic biological system for pattern formation, that has been successfully implemented in vitro. Taken together these novel software platforms provide a complete toolchain, from design to wet-lab implementation, of synthetic biological circuits, enabling a step change in the scale of biological investigations that is orders of magnitude greater than could previously be performed in one in silico “pot”

Bio-inspired cellular machines:towards a new electronic paper architecture

Information technology has only been around for about fifty years. Although the beginnings of automatic calculation date from as early as the 17th century (W. Schickard built the first mechanical calculator in 1623), it took the invention of the transistor by W. Shockley, J. Bardeen and W. Brattain in 1947 to catapult calculators out of the laboratory and produce the omnipresence of information and communication systems in today's world. Computers not only boast very high performance, capable of carrying out billions of operations per second, they are taking over our world, working their way into every last corner of our environment. Microprocessors are in everything, from the quartz watch to the PC via the mobile phone, the television and the credit card. Their continuing spread is very probable, and they will even be able to get into our clothes and newspapers. The incessant search for increasingly powerful, robust and intelligent systems is not only based on the improvement of technologies for the manufacture of electronic chips, but also on finding new computer architectures. One important source of inspiration for the research of new architectures is the biological world. Nature is fascinating for an engineer: what could be more robust, intelligent and able to adapt and evolve than a living organism? Out of a simple cell, equipped with its own blueprint in the form of DNA, develops a complete multi-cellular organism. The characteristics of the natural world have often been studied and imitated in the design of adaptive, robust and fault-tolerant artificial systems. The POE model resumes the three major sources of bio-inspiration: the evolution of species (P: phylogeny), the development of a multi-cellular organism by division and differentiation (O: ontogeny) and learning by interaction with the environment (E: epigenesis). This thesis aims to contribute to the ontogenetic branch of the POE model, through the study of three completely original cellular machines for which the basic element respects the six following characteristics: it is (1) reconfigurable, (2) of minimal complexity, (3) present in large numbers, (4) interconnected locally with its neighboring elements, (5) equipped with a display capacity and (6) with sensor allowing minimal interaction. Our first realization, the BioWall, is made up of a surface of 4,000 basic elements or molecules, capable of creating all cellular systems with a maximum of 160 × 25 elements. The second realization, the BioCube, transposes the two-dimensional architecture of the BioWall into a two-dimensional space, limited to 4 × 4 × 4 = 64 basic elements or spheres. It prefigures a three-dimensional computer built using nanotechnologies. The third machine, named BioTissue, uses the same hypothesis as the BioWall while pushing its performance to the limits of current technical possibilities and offering the benefits of an autonomous system. The convergence of these three realizations, studied in the context of emerging technologies, has allowed us to propose and define the computer architecture of the future: the electronic paper