3,558 resources related to Systems Biology
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The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore
2019 IEEE 58th Conference on Decision and Control (CDC)
The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).
The IEEE Sensors Conference is a forum for presentation, discussion, and exchange of state-of-the art information including the latest research and development in sensors and their related fields. It brings together researchers, developers, and practitioners from diverse fields including international scientists and engineers from academia, research institutes, and companies to present and discuss the latest results in the general field of sensors.
robotics, intelligent systems, automation, mechatronics, micro/nano technologies, AI,
ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.
2007 Chinese Control Conference, 2007
After "human genome project" has been accomplished, the life science comes to a new era, the post-genome era. In the post-genome era, the "big sciences" such as genomics, proteomics and metabolomics (so-called "omics") gradually become a new popular research methodology to provide global pictures of cells or organisms, although the classical experimental biology (small sciences) such as molecular biology or ...
2016 International Conference on Bioinformatics and Systems Biology (BSB), 2016
Deciphering the underlying mechanisms of all complex interactions involved in different signaling pathways is a pivotal step in the dissection and study of network-based data. Heuristic statistical solutions are conventionally used across the world to derive a meaningful perspective of the network based data by identifying related biological networks. However, classical pathway analysis gives us elusive results by ignoring important ...
2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006
Systems biology aims to describe and to understand the operation of complex biological systems with the goal of developing predictive models of human disease. The most powerful systems biology approaches would ideally integrate information from (1) large datasets of gene, protein, and metabolite measurement ("omics" data), (2) complex cell- and tissue-level in vitro models of the disease process, and (3) ...
2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009
Cancer is a chronic, complex disease. With advanced high-throughput technologies i.e. various -omics approaches, molecular imaging and computer technologies, conventional medicine and its research of cancer is leading to the emergence of systems biology. Traditional Chinese medicine (TCM) focuses on the diseased person as a whole to formulate the therapeutic strategies to create a holistic systemic approach to treat the ...
2010 3rd International Conference on Biomedical Engineering and Informatics, 2010
Life is one of the most complex phenomena in the universe. To understand complex biological systems, it requires the integration of experimental and computational research - in other words a systems biology approach. Many theoretical methods and models exist for exploring systems biology including well-known examples such as statistical inference, graph analysis, network inference, and dynamic modeling. These systems play ...
EMBC 2011-Program-Systems in Synthetic Biology (Part I)-Bruce Tidor
EMBC 2011-Workshop- Biological Micro Electro Mechanical Systems (BioMEMS): Fundamentals and Applications-Utkan Demirci
EMBC 2011-Workshop- Biological Micro Electro Mechanical Systems (BioMEMS): Fundamentals and Applications-Ali Khademhosseini
EMBC 2011-Keynote Lecture-Engineering Drug Dosing in Dynamic Biological Systems - David J. Balaban
EMBC 2011-Workshop- Biological Micro Electro Mechanical Systems (BioMEMS): Fundamentals and Applications-Mehmet R. Dokmeci
EMBC 2011-Workshop-Biological Micro Electro Mechanical Systems (BioMEMS): Fundamentals and Applications-Michelle Khine
IMS 2015 Keynote: The Century of Biology is Great for Engineering
Robotics History: Narratives and Networks Oral Histories: Ruzena Bajcsy
EMBC 2011-Keynote Lectures and Panel Discussion-PT IV-Discussion
EMBC 2011 - Course: Virtual Reality and Robotics in Neurorehabilitation-Sergei Adamovich, PhD
History of Robotics and Automation: Ruzena Bajcsy
EMBC 2011 - Course: Virtual Reality and Robotics in Neurorehabilitation-Judith Deutsch VR and Video Gaming
Yuan-ting Zhang AMA EMBS Individualized Health
From Biology to Robot and Back
ICASSP 2010 - Advances in Neural Engineering
Engineering Our Future - Q and A with Panel
Mayo Clinic Motion Lab
EMBC 2011-Workshop-Nanobiomaterials-Ali Khademhosseini
EMBC 2011-Workshop-Nanobiomaterials-Ehsan Jabbarzadeh
After "human genome project" has been accomplished, the life science comes to a new era, the post-genome era. In the post-genome era, the "big sciences" such as genomics, proteomics and metabolomics (so-called "omics") gradually become a new popular research methodology to provide global pictures of cells or organisms, although the classical experimental biology (small sciences) such as molecular biology or cell biology is still the mainstream in life sciences. The concept and strategy of omics is completely different from the classical experimental biology. The omics is called a "discovery science", of which the goal is to identify all the genes or proteins in the organisms, whereas the classical experimental biology such as molecular biology is called a "hypothesis-driven science", since the researches of these disciplines are initiated based on the scientific hypothesis and focus on studying the structure and functions of individual gene or protein. Systems biology is a newly born discipline in the post-genome era, which integrates the research strategy of classical experimental biology such as molecular biology with the new research strategy of "omics". Systems biology is also a new interdisciplinary frontier based mainly on the integration of the "wet" experiments such as molecular biology or "omics" with the "dry" experiments such as bioinformatics and computational biology. Technology of systems biology includes the "omics" platforms such as proteomics-platform and the theoretical platforms for computing and modeling. From these properties, Systems biology is defined as an integrating methodology for analyzing the components and dynamical behavior of biological systems as a whole. More importantly, these properties have made systems biology as a powerful analytical tool to reveal the complex diseases such as cancer and diabetes. Although the complex diseases have been extensively studied for a long term, it is far beyond understanding the mechanisms of the disease-process and curing these diseases. The difficulties for dealing with the complex diseases arise from the aspects of the complex diseases: 1) the causes of the initiation and development of the complex diseases involve multiple genetic factors, environment factors and the interaction of these two kinds of factors. 2) the different kinds of cells or tissues involve in the diseases. For example, the brain, pancreas, liver, skeletal muscle and adipose tissue mainly involve in the type 2 diabetes. 3) the molecular defects for the complex disease disrupt the normal behaviors of the complex molecular networks of genes and proteins. The classical bio-medicine based on molecular biology, cell biology, genetics and other experimental biology has made significant progress for against disease in general. However, the researchers on the bio- medicine area still face the great challenge for against the complex diseases such as cancer and diabetes since the methodology of the classical experimental biology is based on studying individual gene and protein and treat the organisms as a simple and linear system, which is not good enough to solve such problems of the complex diseases. Therefore, it is clear that the methodology and techniques of system biology must be applied for analyzing the molecular mechanisms of the complex diseases, and provide new solutions for preventing and curing the diseases.
Deciphering the underlying mechanisms of all complex interactions involved in different signaling pathways is a pivotal step in the dissection and study of network-based data. Heuristic statistical solutions are conventionally used across the world to derive a meaningful perspective of the network based data by identifying related biological networks. However, classical pathway analysis gives us elusive results by ignoring important aspects of biology. To overcome the limitations of the classical analysis, we have implemented systems biology approach which includes enrichment analysis of Alzheimer's disease (AD) gene set and topological enrichment analysis. Exploration of pathway ranking and regression analysis on the basis of XD-Score and Fisher-q value is also elucidated. Topology-based enrichment studies gave us insight on important parameters such as shortest path length, node betweenness, degree, clustering coefficient, eigenvector centrality and their association in AD based statistical score which turned out to be significantly high (5.062) at a significant threshold (0.74). A linear fit in regression plot and enrichment in associated gene pathways were observed.
Systems biology aims to describe and to understand the operation of complex biological systems with the goal of developing predictive models of human disease. The most powerful systems biology approaches would ideally integrate information from (1) large datasets of gene, protein, and metabolite measurement ("omics" data), (2) complex cell- and tissue-level in vitro models of the disease process, and (3) in silico computer simulations that integrate information flow from the pathway to organism levels. Systems biology provides a framework for understanding biological processes and more rationally identifying points of intervention for therapeutics. While fully integrated models of human disease are still a distant goal, simplified systems biology efforts can (and are beginning to) have an enormous impact on both our understanding of disease and the success of the drug discovery process.
Cancer is a chronic, complex disease. With advanced high-throughput technologies i.e. various -omics approaches, molecular imaging and computer technologies, conventional medicine and its research of cancer is leading to the emergence of systems biology. Traditional Chinese medicine (TCM) focuses on the diseased person as a whole to formulate the therapeutic strategies to create a holistic systemic approach to treat the imbalanced systems of a person. TCM practices are usually descriptive, and empirical. To facilitate cross discipline dialog, and potentially research collaboration, it is important to move TCM research toward systems biological approaches. Analysis of FY2006 and 2007 NCI funded TCM research grants portfolio and research approaches will be discussed.
Life is one of the most complex phenomena in the universe. To understand complex biological systems, it requires the integration of experimental and computational research - in other words a systems biology approach. Many theoretical methods and models exist for exploring systems biology including well-known examples such as statistical inference, graph analysis, network inference, and dynamic modeling. These systems play a key role in the development of systems biology. The trend in the development of these methods and models gives an integrative framework to acquire a global perspective beyond the traditional reductionistic views of molecular biology. We will here present our review which specifically focuses on network theory to analyze systems biology with two goals in mind: to aid researchers in efficiently understanding the network theory for systems biology analysis; and to illustrate the necessary and realistic goals how complex networks can be integrated into systems biology research.
Genetical systems biology or systems genetics treats the genome as the central reference point for all omics variations and is an emerging new branch of systems biology. Quantitative genetic principles were developed for high- throughput genomic, transcriptomic and metabolomic data observed in large populations. New statistical genetic models were developed for expression quantitative trait loci (eQTL), namely, marker regression eQTL mapping and marker-expression co-factor mapping. Evaluations of power to detect eQTL showed that sample size requirements are higher for detecting trans-acting genes than cis-acting genes. Power is higher for eQTL with high heritability than for eQTL with low heritability. These results will be valuable for systems genetic investigations. Gonadotrophin releasing hormone (GnRH) and its receptor gene (GnRH-R) are crucial for mammalian reproduction. Whole genome scan of eQTLs for GnRH-R gene expression in mouse showed three possible trans- eQTL regions on chr 13 and 19, harbouring regulatory genes. Applications of genetical genomics in systems biology were identified as: (1) detection and validation of causal gene for complex traits; (2) development of genetic interaction networks; (3) prediction of transcription factor binding sites and (4) in data-driven systems biology. These applications were illustrated using data on eQTL, protein network and signalling pathways for GnRH. Gpr54 (G protein-coupled receptor kinase 54), Prl (prolactin), Ins1 (insulin) and Fos (viral oncogenes) were found to be major regulators of GnRH and GnRH-R; thus validating their important role in reproduction, mammary gland development and sexual (im)maturity. These results will be useful for further study of mammalian reproductive biology.
The integrative approach of systems biology promises a better understanding of complex phenotypes such as cardiovascular disease, diabetes or chronic obstructive pulmonary disease (COPD). It aims to interconnect current knowledge with experimental data, in-silico analysis and simulation. To this end the existing knowledge must be semantically integrated in a single environment and dynamically organised into structured networks that are connected with experimental data. From the resulting information local sub- networks with associated experimental data need to be extracted. The EU BioBridge project coordinates clinical, experimental and computational groups generating a systems biology workflow for the analysis and therapeutic intervention of COPD. In order to address the integration, extraction and reporting challenge we deployed the BioXM knowledge management environment as central infrastructure for the BioBridge project. BioXM allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Wizards automatically adapted to the created knowledge model allow to map data from external sources for use as federated resource or for import. The Java based client-server application thus provides a flexible and customisable framework for knowledge management. BioXM implements visual browse and query interfaces used to traverse the knowledge network and construct complex queries and retrievals. Using BioXM a COPD specific instance of the system has been set-up as part of the BioBridge project http://www.biobridge.eu/bio/. It allows users to mine COPD specific molecular networks, clinical and experimental data and provides pre-structured queries and reports to retrieve sub-networks spanning protein-protein interaction, pathway, gene - disease and gene -compound data for subsequent data analysis, model building and simulation. In the current version, the knowledge base integrates more than 20 different databases and ontologies representing a total of 80 793 genes (30 246 human, 27 237 mouse, 23 310 rat), 1 307 pathways, 78 528 compounds, 1 525 474 protein interactions and the entire gene expression omnibus database resulting in a total of 3 666 313 connections within the knowledge network. This public information is used for the integrative analysis of project specific clinical e.g. questionnaires, anthropometric and physiologic data with experimental e.g. gene expression and metabolimcs data and subject specific literature derived molecular knowledge. A command line and a SOAP Web service API allow to integrate BioXM into larger bioinformatic infrastructures. Analytical applications such as R-scripts can be integrated transparently as native BioXM views or analyses. Within BioBridge we created an SBML based integration with modelling and simulation tools such as MathModellica, IsoDyn and ByoDyn which allow to generate and simulate deterministic models. To parameterise and constrain the models, these are combined with data analysis tools such as BANJO and ARACHNE which allow the generation of probabilistic networks from expression, metabolomic and proteomic data. We will present technical information about the BioBrige infrastructure as well as describing the COPD specific resources which become publicly available to researches.
The three papers in this special section focus on machine intelligence approaches to systems biology.
The more-or-less artificial barrier between information visualization and scientific visualization hinders knowledge discovery. Having an integrated view of many aspects of the target data, including a seamlessly interwoven visual display of structural abstract data and 3D spatial information, could lead to new discoveries, insights, and scientific questions. Such a view also could reduce the user's cognitive load--that is, reduce the effort the user expends when comparing views.
Aging, an extremely complex and system-level process, has attracted much attention in medical research, especially since chronic diseases are quite prevalent in the aged population. These may be the result of both gene mutations that lead to intrinsic perturbations and environmental changes that may stimulate signaling in the body. In addition, aging may result in diminished homeostasis and increased organism vulnerability, resulting in a reduced response to environmental stimuli such as carcinogens, oxidative stress and pro-inflammatory molecules. Therefore, measuring robustness to intrinsic perturbations and response ability to external stimuli of aging- related gene regulatory network may provide insight into the systematic mechanisms of aging. We show that an aging-related gene network is more robust to intrinsic perturbations in the elderly than the young, and therefore is less responsive to external stimuli i.e. there is a tradeoff between robustness and response ability in aging-related gene network in the aging process. These observations are consistent with experimental findings in phenotypes common in the aged population, for example tumorigenesis, pro- inflammation and declining resistance to oxidative stress. The methods described in this study may be used for future studies of gene regulatory networks involved in various biological processes and may have potential applications for gene therapy and drug target selection.
No standards are currently tagged "Systems Biology"
Computational Systems Biology Postdoctoral Researcher
Lawrence Livermore National Laboratory
Computational Systems Modeling Postdoctoral Researcher
Lawrence Livermore National Laboratory
Lawrence Livermore National Laboratory
Musculoskeletal Biology - Postdoctoral Researcher
Lawrence Livermore National Laboratory