Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Beckman, Kenneth B., and Bruce N. Ames. The Free Radical Theory of Aging Matures. Physiol. Rev. 78: 547–581, 1998. — The free radical theory of aging, conceived in 1956, has turned 40 and is rapidl...

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The Free Radical Theory of Aging Matures

Mitochondrial fission and fusion play critical roles in maintaining functional mitochondria when cells experience metabolic or environmental stresses. Fusion helps mitigate stress by mixing the contents of partially damaged mitochondria as a form of complementation. Fission is needed to create new mitochondria, but it also contributes to quality control by enabling the removal of damaged mitochondria and can facilitate apoptosis during high levels of cellular stress. Disruptions in these processes affect normal development, and they have been implicated in neurodegenerative diseases, such as Parkinson’s.

Mitochondrial Fission, Fusion, and Stress

The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine. 

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Hallmarks of Cancer: New Dimensions

Understanding the odd science of aging

Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress Among these processes, metabolic alterations seem to play an important role We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions

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Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress

Preface. Foreword. Part I: General Introduction. 1. Basic Principles. 2. Biology in a Nutshell. 3. Mathematics in a Nutshell. 4. Experimental Techniques in a Nutshell. Part II: Standard Models and Approaches in Systems Biology. 5. Metabolism. 6. Signal Transduction. 7. Selected Biological Processes. 8. Modeling of Gene Expression. 9. Analysis of Gene Expression Data. 10. Evolution and Self-organization. 11. Data Integration. 12. What's Next? Part III: Computer-based Information Retrieval and Examination. 13. Databases and Tools on the Internet. 14. Modeling Tools. Subject Index.

Systems Biology in Practice: Concepts, Implementation and Application

INTRODUCTION TO SYSTEMS BIOLOGY Introduction Modeling of Biochemical Systems Specific Biochemical Systems Model Fitting Analysis of High-throughput Data Gene Expression Models Stochastic Systems and Variability Network Structures, Dynamics, and Function Optimality and Evolution BIOLOGICAL AND MATHEMATICAL FUNDAMENTALS AND METHODS Cell Biology Experimental Techniques in Molecular Biology Mathematics Statistics Stochastic Processes Linear Control Systems Databases Modeling Tools

Systems Biology: A Textbook

Evolution theory indicates that ageing is caused by progressive accumulation of defects, since the evolutionary optimal level of maintenance is always below the minimum required for indefinite survival. Evolutionary theories also suggest that multiple processes are operating in parallel, but unfortunately they make no predictions about specific mechanisms. To understand and evaluate the many different mechanistic theories of ageing which have been proposed, it is therefore important to understand and study the network of maintenance processes which control cellular homeostasis. In this paper we describe a Network Theory of Ageing which integrates the contributions of defective mitochondria, aberrant proteins, and free radicals to the ageing process, and which includes the protective effects of antioxidant enzymes and proteolytic scavengers. The model simulations not only confirm and explain many experimental, age related findings like an increase in the fraction of inactive proteins, a significant rise in protein half-life, an increase in the amount of damaged mitochondria, and a drop in the energy generation per mitochondrion, but they also show interactions between the different theories which could not have been observed without the network approach. In some simulations, for example, the mechanism of the final breakdown seems to be a consequence of the cooperation of mitochondrial and cytoplasmic reactions, the mitochondria being responsible for a long term, gradual change which eventually triggers a short lived cytoplasmic error loop.

Axel Kowald

Papers

Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress

Systems Biology in Practice: Concepts, Implementation and Application

Systems Biology: A Textbook

A network theory of ageing: the interactions of defective mitochondria, aberrant proteins, free radicals and scavengers in the ageing process

Morpho-dynamic changes of mitochondria during ageing of human endothelial cells.