On Computable Protein Functions

Proteins are biological machines that perform the majority of functions necessary for life. Nature has evolved many different proteins, each of which perform a subset of an organism’s functional repertoire. One aim of biology is to solve the sparse high dimensional problem of annotating all proteins with their true functions. Experimental characterisation remains the gold standard for assigning function, but is a major bottleneck due to resource scarcity. In this thesis, we develop a variety of computational methods to predict protein function, reduce the functional search space for proteins, and guide the design of experimental studies. Our methods take two distinct approaches: protein-centric methods that predict the functions of a given protein, and function-centric methods that predict which proteins perform a given function. We applied our methods to help solve a number of open problems in biology. First, we identified new proteins involved in the progression of Alzheimer’s disease using proteomics data of brains from a fly model of the disease. Second, we predicted novel plastic hydrolase enzymes in a large data set of 1.1 billion protein sequences from metagenomes. Finally, we optimised a neural network method that extracts a small number of informative features from protein networks, which we used to predict functions of fission yeast proteins

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Holistic biomimicry: a biologically inspired approach to environmentally benign engineering

Humanity's activities increasingly threaten Earth's richness of life, of which mankind is a part. As part of the response, the environmentally conscious attempt to engineer products, processes and systems that interact harmoniously with the living world. Current environmental design guidance draws upon a wealth of experiences with the products of engineering that damaged humanity's environment. Efforts to create such guidelines inductively attempt to tease right action from examination of past mistakes. Unfortunately, avoidance of past errors cannot guarantee environmentally sustainable designs in the future. One needs to examine and understand an example of an environmentally sustainable, complex, multi-scale system to engineer designs with similar characteristics. This dissertation benchmarks and evaluates the efficacy of guidance from one such environmentally sustainable system resting at humanity's doorstep - the biosphere. Taking a holistic view of biomimicry, emulation of and inspiration by life, this work extracts overarching principles of life from academic life science literature using a sociological technique known as constant comparative method. It translates these principles into bio-inspired sustainable engineering guidelines. During this process, it identifies physically rooted measures and metrics that link guidelines to engineering applications. Qualitative validation for principles and guidelines takes the form of review by biology experts and comparison with existing environmentally benign design and manufacturing guidelines. Three select bio-inspired guidelines at three different organizational scales of engineering interest are quantitatively validated. Physical experiments with self-cleaning surfaces quantify the potential environmental benefits generated by applying the first, sub-product scale guideline. An interpretation of a metabolically rooted guideline applied at the product / organism organizational scale is shown to correlate with existing environmental metrics and predict a sustainability threshold. Finally, design of a carpet recycling network illustrates the quantitative environmental benefits one reaps by applying the third, multi-facility scale bio-inspired sustainability guideline. Taken as a whole, this work contributes (1) a set of biologically inspired sustainability principles for engineering, (2) a translation of these principles into measures applicable to design, (3) examples demonstrating a new, holistic form of biomimicry and (4) a deductive, novel approach to environmentally benign engineering. Life, the collection of processes that tamed and maintained themselves on planet Earth's once hostile surface, long ago confronted and solved the fundamental problems facing all organisms. Through this work, it is hoped that humanity has taken one small step toward self-mastery, thus drawing closer to a solution to the latest problem facing all organisms.Ph.D.Committee Chair: Bert Bras; Committee Member: David Rosen; Committee Member: Dayna Baumeister; Committee Member: Janet Allen; Committee Member: Jeannette Yen; Committee Member: Matthew Realf