Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

Innovative Algorithms and Evaluation Methods for Biological Motif Finding

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins

Mining real-world networks in systems biology and economics

Recent advances in biotechnology have yielded an explosion of data describing biological systems, creating rich opportunities for new insights into cellular inner-workings and therapeutic discoveries. To keep up with this rapid growth and increase in data complexity, we need novel static, integrative, and dynamic methodologies to continue mining these networked systems. In this thesis we introduce new static, integrative, and dynamic computational frameworks for network analysis, and combine existing ones in new ways, to elucidate the biotechnological biases and functional principles governing molecular interactions and their implications in disease. We focus on mining new knowledge from the yeast and human interactomes, since these are currently the most complete data in biology. We perform three lines of experimental work: 1) the macro-scale study, where we model the yeast and human interactomes and show that their interactome data are growing in structurally and functionally principled ways, characterised by a non-random dual topological nature; 2) the micro-scale study, where we zoom into the specifics of wiring patterns around individual genes and uncover a unique core sub-structure within the human interactome, which contains driver genes dubbed to be the main triggers for disease onset; and 3) the data integration study, where we introduce a new computational framework for fusing multiple types of molecular interaction data and use it to construct the first unified model of the cell’s functional organisation and cross-communication lines. Similarly, a new field of systems economics has gained recent attention, with more financial and economic network data emerging at an increasing pace. Hence, we introduce a new computational methodology for tracking network dynamics and use it to quantify the micro- and macro-scale topological changes in the world trade network over the past 50 years, and to demonstrate the fundamental relationship between topological perturbations and indicators of countries’ political and economic stabilities.Open Acces

Modular architecture in biological networks

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.Includes bibliographical references (p. 201-207).In the past decade, biology has been revolutionized by an explosion in the availability of data. Translating this new wealth of information into meaningful biological insights and clinical breakthroughs will require a complete overhaul both in the questions being asked, and the methodologies used to answer them. One of the largest challenges in organizing and understanding the data coming from genome sequencing, microarray experiments, and other high-throughput measurements, will be the ability to find large-scale structure in biological systems. Ideally, this would lead to a simplified representation, wherein the thousands of genes in an organism can be viewed as a much smaller number of dynamic modules working in concert to accomplish cellular functions. Toward demonstrating the importance of higher-level, modular structure in biological systems, we have performed the following analyses: 1. Using computational techniques and pre-existing protein-protein interaction (PPI) data, we have developed general tools to find and validate modular structure. We have applied these approaches to the PPI networks of yeast, fly, worm, and human.(cont.) 2. Utilizing a modular scaffold, we have generated predictions that attempt to explain existing system-wide experiments as well as predict the function of otherwise uncharacterized proteins. 3. Following the example of comparative genomics, we have aligned biological networks at the modular level to elucidate principles of how modules evolve. We show that conserved modular structure can further aid in functional annotation across the proteome. In addition to the detection and use of modular structure for computational analyses, experimental techniques must be adapted to support top-down strategies, and the targeting of entire modules with combinations of small-molecules. With this in mind, we have designed experimental strategies to find sets of small-molecules capable of perturbing fimctional modules through a variety of distinct, but related, mechanisms. As a first test, we have looked for classes of small-molecules targeting growth signaling through the phosphatidyl-inositol-3-kinase (PI3K) pathway. This provides a platform for developing new screening techniques in the setting of biology relevant to diabetes and cancer. In combination, these investigations provide an extensible computational approach to finding and utilizing modular structure in biological networks, and experimental approaches to bring them toward clinical endpoints.by Gopal Ramachandran.Ph.D

Comparative genomics and systems biology of environmental stress responses relevant to fungal virulence

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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