Biological network

A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical analysis of connections found in ecological, evolutionary, and physiological studies, such as neural networks. The analysis of biological networks with respect to human diseases has led to the field of network medicine.

Network biology and bioinformatics

Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide-array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness centrality. Degree (or connectivity, a distinct usage from that used in graph theory) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network. Nodes with high betweenness essentially serve as bridges between different portions of the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network.

Ecological footprint

An ecological footprint is a measure of human impact on Earth's ecosystems. Its typically measured in area of wilderness or amount of natural capital consumed each year. A common way of estimating footprint is, the area of wilderness of both land and sea needed to supply resources to a human population; This includes the area of wilderness needed to assimilate human waste.

At a global scale, it is used to estimate how rapidly we are depleting natural capital. The Global Footprint Network calculates the global ecological footprint from UN and other data. They estimate that as of 2007 our planet has been using natural capital 1.5 times as fast as nature can renew it.

Footprint measurements

In 2007, the Global Footprint Network estimated the global ecological footprint as 1.6 planet Earths; that is, they judged that ecological services were being used 1.6 times as quickly as they were being renewed.

Ecological footprints can be calculated at any scale: for an activity, a person, a community, a city, a region, a nation or humanity as a whole. Cities, due to population concentration, have large ecological footprints and have become ground zero for footprint reduction.

Footprint

Footprints (or footmarks) are the impressions or images left behind by a person walking or running. Hoofprints and pawprints are those left by animals with hooves or paws rather than feet, while "shoeprints" is the specific term for prints made by shoes. They may either be indentations in the ground or something placed onto the surface that was stuck to the bottom of the foot. A "trackway" is set of footprints in soft earth left by a life-form; animal tracks are the footprints, hoofprints, or pawprints of an animal.

Footprints can be followed when tracking during a hunt or can provide evidence of activities. Some footprints remain unexplained, with several famous stories from mythology and legend. Others have provided evidence of prehistoric life and behaviours.

Footprints in detective work

The print left behind at a crime scene can give vital evidence to the perpetrator of the crime. Shoes have many different prints based on the sole design and the wear that it has received – this can help to identify suspects. Photographs or castings of footprints can be taken to preserve the finding. Analysis of footprints and shoeprints is a specialist part of forensic science.

Network analysis

Network analysis can refer to:

Analysis of general networks: see Network theory

Electrical network analysis see Network analysis (electronics)

Social network analysis

Network (lobby group)

Network (stylized NETWORK), A National Catholic Social Justice Lobby, is headquartered in Washington, D.C. The organization focuses its lobbying efforts in the areas of economic justice, immigration reform, healthcare, peace making and ecology. Sister Simone Campbell is the executive director of NETWORK.

History

Network was founded in December 1971 when 47 Catholic Sisters involved in education, healthcare, and other direct service activities gathered from across the U.S. at Trinity College in Washington, D.C. , with the intent to form a new type of justice ministry. This was a time when the Catholic Church was undergoing dramatic changes in response to Vatican II reforms and calls from the Vatican and U.S. Bishops to seek "Justice in the World". Individual women religious had already become involved in the civil rights movement, and anti-war activism.

The 47 Sisters voted to form a national "network" of Sisters to lobby for federal policies and legislation that promote economic and social justice. This was the founding of Network, A National Catholic Social Justice Lobby. In April 1972 they opened a two-person office in Washington, D.C.

1:07:13

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to
Donna Slonim, Tufts University Algorithmic Challenges in Genomics Boot Camp https://simons.berkeley.edu/talks/donna-slonim-2016-01-22-2
57:31

Introduction to Biological Network Analysis I: Network Basics and Properties

Introduction to Biological Network Analysis I: Network Basics and Properties

Introduction to Biological Network Analysis I: Network Basics and Properties
Donna Slonim, Tufts University Algorithmic Challenges in Genomics Boot Camp https://simons.berkeley.edu/talks/donna-slonim-2016-01-22-1
1:24:31

The Logic of Biological Networks

The Logic of Biological Networks

The Logic of Biological Networks
Dr. Jehoshua Bruck CalTech October 30, 2006 -_-_-_-_-_-_-_-_-_-_-_- Samuel D. Conte Distinguished Lecture Series in Computer Science Sponsored by the Purdue University Department of Computer Science
12:19

NASB Fall 2013 Lecture 8 - Types of Biological Networks

NASB Fall 2013 Lecture 8 - Types of Biological Networks

NASB Fall 2013 Lecture 8 - Types of Biological Networks
This lecture is a part of an online course that was given on Coursera during the fall of 2013 by the Ma'ayan Lab. https://class.coursera.org/netsysbio-001
1:06:20

Introduction to Biological Network Analysis III: Identifying Network Modules

Introduction to Biological Network Analysis III: Identifying Network Modules

Introduction to Biological Network Analysis III: Identifying Network Modules
Roded Sharan, Tel-Aviv University Algorithmic Challenges in Genomics Boot Camp https://simons.berkeley.edu/talks/roded-sharan-2016-01-22-1
55:29

Statistical Learning of Sparse and Structured Biological Networks

Statistical Learning of Sparse and Structured Biological Networks

Statistical Learning of Sparse and Structured Biological Networks
We know that genes, proteins, and other molecules operate not on their own, but as part of complex pathways or regulatory networks. Here we consider the task of uncovering such regulatory networks on the basis of gene expression data. In this context, we draw an edge between a pair of genes that are partially correlated -- that is, correlated conditional on all of the other genes. We present some techniques for estimating such networks on the basis of high-dimensional gene expression data sets. Daniella Witten,PhD , Assistant Professor, Biostatistics 3/27/2013
6:10

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?
What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean? BIOLOGICAL NETWORK INFERENCE meaning - BIOLOGICAL NETWORK INFERENCE definition - BIOLOGICAL NETWORK INFERENCE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Biological network inference is the process of making inferences and predictions about biological networks. In a topological sense, a network is a set of nodes and a set of directed or undirected edges between the nodes. Many types of biological networks exist, including transcriptional, signalling and metabolic. Few such networks are known in anything approaching their complete structure, even in the simplest bacteria. Still less is known on the parameters governing the behavior of such networks over time, how the networks at different levels in a cell interact, and how to predict the complete state description of a eukaryotic cell or bacterial organism at a given point in the future. Systems biology, in this sense, is still in its infancy. There is great interest in network medicine for the modelling biological systems. This article focuses on a necessary prerequisite to dynamic modeling of a network: inference of the topology, that is, prediction of the "wiring diagram" of the network. More specifically, we focus here on inference of biological network structure using the growing sets of high-throughput expression data for genes, proteins, and metabolites. Briefly, methods using high-throughput data for inference of regulatory networks rely on searching for patterns of partial correlation or conditional probabilities that indicate causal influence. Such patterns of partial correlations found in the high-throughput data, possibly combined with other supplemental data on the genes or proteins in the proposed networks, or combined with other information on the organism, form the basis upon which such algorithms work. Such algorithms can be of use in inferring the topology of any network where the change in state of one node can affect the state of other nodes. Genes are the nodes and the edges are directed. A gene serves as the source of a direct regulatory edge to a target gene by producing an RNA or protein molecule that functions as a transcriptional activator or inhibitor of the target gene. If the gene is an activator, then it is the source of a positive regulatory connection; if an inhibitor, then it is the source of a negative regulatory connection. Computational algorithms take as primary input data measurements of mRNA expression levels of the genes under consideration for inclusion in the network, returning an estimate of the network topology. Such algorithms are typically based on linearity, independence or normality assumptions, which must be verified on a case-by-case basis. Clustering or some form of statistical classification is typically employed to perform an initial organization of the high-throughput mRNA expression values derived from microarray experiments, in particular to select sets of genes as candidates for network nodes. The question then arises: how can the clustering or classification results be connected to the underlying biology? Such results can be useful for pattern classification – for example, to classify subtypes of cancer, or to predict differential responses to a drug (pharmacogenomics).
9:32

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning
What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning - BIOLOGICAL NETWORK definition - BIOLOGICAL NETWORK explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks. The analysis of biological networks with respect to human diseases has led to the field of network medicine. Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide-array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness centrality. Degree (or connectivity, a distinct usage from that used in graph theory) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network. Nodes with high betweenness essentially serve as bridges between different portions of the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network. As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states represented as a finite state machine. Recent complex systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter). Bioinformatics has increasingly shifted its focus from individual genes, proteins, and search algorithms to large-scale networks often denoted as -omes such as biome, interactome, genome and proteome. Such theoretical studies have revealed that biological networks share many features with other networks such as the Internet or social networks, e.g. their network topology. Many protein–protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges. PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions. Recent studies have indicated conservation of molecular networks through deep evolutionary time. Moreover, it has been discovered that proteins with high degrees of connectedness are more likely to be essential for survival than proteins with lesser degrees. This suggests that the overall composition of the network (not simply interactions between protein pairs) is important for the overall functioning of an organism.
1:06:43

Statistical Modelling of Biological Networks

Statistical Modelling of Biological Networks

Statistical Modelling of Biological Networks
New experimental techniques in molecular biology make it possible to probe cellular processes in unprecedented detail. In particular, information on the activity profiles of genes is available from large scale microarray or reporter assay experiments. To infer detailed models for cellular processes poses formidable inference problems, if models beyond simple clustering or association graphs are envisaged. I will discuss some ideas and examples for the inference of static as well as dynamic models for gene regulation using a Bayesian model selection framework. The fact that most data sets are high dimensional but comparatively small suggests exploiting the simplicity and flexibility of Gaussian processes for modelling nonlinear relationships in dynamical models.
33:34

Diffusion-Based Metrics for Biological Network Analysis

Diffusion-Based Metrics for Biological Network Analysis

Diffusion-Based Metrics for Biological Network Analysis
Lenore Cowen, Tufts University Network Biology https://simons.berkeley.edu/talks/lenore-cowen-04-11-16
5:00

Bio-Inspire FullDome A/V Performance

Bio-Inspire FullDome A/V Performance

Bio-Inspire FullDome A/V Performance
Bio-inspire A/V Dome Performance IAIA | Institute of American Indian Arts in New Mexico, USA, 2015 Fiske Planetarium - University of Colorado Boulder in Colorado, USA, 2015 BioInspire is an A/V dome performance which has screened in the Institute of American Indian Arts(New Mexico, USA) and Fiske Planetarium - University of Colorado Boulder(Colorado, USA). Artificial neural networks(ANNs), a largely used method in machine learning and cognitive science, are inspired by the biological neural networks, namely the neural system of the animals. The ANNs are composed of several nodes, layers and connections which simulate to some extent the message exchange and processing through a biological neural network. In fact, ANNs tries to find an approximate functions that evaluate all the inputs in order to give a meaningful output concerning the data. Each function is specific to the learned data according to which the form of the ANN is shaped. So, ANN adapt itself to learn from the data, to mine it; it then turns into a generic processing model that evaluates, classify, categorize the inputs, likewise our brain, neurons and synapses. As the quantity of layers, nodes and connections in an artificial neural network increase, the structure gets more complex. Getting the inspiration from the structure of an ANN, our aim is to redefine the complexity of a neural network using abstract objects and sound referring to it's natural form. Direction & Animation: Void bevoid.co Director: Yusuf Emre Kucur, Bahadır Dağdelen Concept Development: Şerife Seda Kucur, Tuğçe Akkoç, Selay Karasu Project Manager: Evren Erbaşol A/V Artists: Yusuf Emre Kucur, Bahadır Dağdelen Documentation: Cüneyt Korkut Keleşoglu Sound Design: Selçuk Can Güven, Erhan Kabakci Special Thanks: Rob Chapman, Barış Serdar, Ali Ata Kavame, Can Bilgin
1:45

TO UNDERSTAND IS TO PERCEIVE PATTERNS

TO UNDERSTAND IS TO PERCEIVE PATTERNS

TO UNDERSTAND IS TO PERCEIVE PATTERNS
Follow me on Twitter: https://twitter.com/JasonSilva @JasonSilva and @notthisbody Special thanks to filmmaker/photographer Rob Whitworth for allowing a clip from his video (https://vimeo.com/32958521) to be featured. Check out his website: www.robwhitworth.co.uk My videos: Beginning of Infinity - http://vimeo.com/29938326 Imagination - http://vimeo.com/34902950 INSPIRATION: The Imaginary Foundation says "To Understand Is To Perceive Patterns"... Albert-László Barabási, think about NETWORKS: “Networks are everywhere. The brain is a network of nerve cells connected by axons, and cells themselves are networks of molecules connected by biochemical reactions. Societies, too, are networks of people linked by friendships, familial relationships and professional ties. On a larger scale, food webs and ecosystems can be represented as networks of species. 'For decades, we assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. Steven Johnson, author of Where Good Ideas Come From, writes about recurring patterns and networks: “Coral reefs are sometimes called “the cities of the sea”, and we need to take the metaphor seriously: the reef ecosystem is so innovative because it shares some defining characteristics with actual cities. These patterns of innovation and creativity are fractal: they reappear in recognizable form as you zoom in and out, from molecule to neuron to pixel to sidewalk. Whether you’re looking at original innovations of carbon-based life, or the explosion of news tools on the web, the same shapes keep turning up... when life gets creative, it has a tendency to gravitate toward certain recurring patterns, whether those patterns are self-organizing, or whether they are deliberately crafted by human agents” “Put simply: cities are like ant colonies are like software is like slime molds are like evolution is like disease is like sewage systems are like poetry is like the neural pathways in our brain. Everything is connected. "...Johnson uses ‘The Long Zoom’ to define the way he looks at the world—if you concentrate on any one level, there are patterns that you miss. When you step back and simultaneously consider, say, the sentience of a slime mold, the cultural life of downtown Manhattan and the behavior of artificially intelligent computer code, new patterns emerge.” Geoffrey West, from The Santa Fe Institute, "...Network systems can sustain life at all scales, whether intracellularly or within you and me or in ecosystems or within a city.... If you have a million citizens in a city or if you have 1014 cells in your body, they have to be networked together in some optimal way for that system to function, to adapt, to grow, to mitigate, and to be long term resilient." Author Paul Stammetts writes about The Mycelial Archetype: He compares the mushroom mycelium with the overlapping information-sharing systems that comprise the Internet, with the networked neurons in the brain, and with a computer model of dark matter in the universe. "Adrian Bejan takes the recurring patterns in nature—trees, tributaries, air passages, neural networks, and lightning bolts—and reveals how a single principle of physics, the Constructal Law, accounts for the evolution of these and all other designs in our world. Everything—from biological life to inanimate systems—generates shape and structure and evolves in a sequence of ever-improving designs in order to facilitate flow. River basins, cardiovascular systems, and bolts of lightning are very efficient flow systems to move a current—of water, blood, or electricity. Geoffrey WEST on The sameness of organisms, cities, and corporations: http://blog.ted.com/2011/07/26/qa-with-geoffrey-west/ Stephen Johnson’s LONG VIEW http://www.nytimes.com/2006/10/08/magazine/08games.html?pagewanted=all http://dumbofeather.com/blog/post/on-slime-molds-and-sewage-steven-johnson-s-origin-of-the-idea/ http://www.guardian.co.uk/science/2010/oct/19/steven-johnson-good-ideas?cat=science&type;=article A collaboration of /Jason Silva and /Notthisbody incorporating: /Aaron Koblin /entpm /Andrea Tseng /Genki Ito /ItoWorld /Dominic /Cheryl Colan /TheNightElfik /Paulskiart /Grant Kayl /blyon /resonance /gtAlumniMag /Katie Armstrong /Page Stephenson /Jesse Kanda /Jared Raab /Angela Palmer /elliottsellers /flight404 /Pedro Miguel Cruz /Takuya Hosogane /kimpimmel /Rob Whitworth **and some original animations from Tiffany Shlain's film CONNECTED: An Autoblogography about Love, Death & Technology // music is Clint Mansell's "We're going home" from Moon Soundtrack. Buy it on iTunes!
1:55:23

Gulf of Mexico Legal Biological Military Operation

Gulf of Mexico Legal Biological Military Operation

Gulf of Mexico Legal Biological Military Operation
What do 50 USC 1512 & 1520a, 111 STATUTE 915, Public Law 105-85 and 483 U.S. 669 have in common? They are the US Codes, US Statute, US Congressional, and US Supreme Court means by which the US Department of Defense was given the legal Federal authority to conduct the synthetically bioengineered experiment in the Gulf of Mexico to eliminate the BP oil and gas. This has resulted in the legal deaths and diseases of human, animal and plant life; the deterioration of food and water; and the damaging alteration of the environment. While this was a legal Federal Government act, such insanity is a far cry from being moral or lawful. Once again, the civilian population in America has been the object of a biological experiment. Listen in every Friday for two hours of Gulf of Mexico Blue Plague discussions, interviews with special guests, and updates with Gulf coast resident and Living Light Network host Michael Edward. Media Video and Audio © 2011 Living Light Network
5:03

Distributed Information Processing in Biological and Computational Systems

Distributed Information Processing in Biological and Computational Systems

Distributed Information Processing in Biological and Computational Systems
Co-author Saket Navlakha discusses "Distributed Information Processing in Biological and Computational Systems," (http://cacm.acm.org/magazines/2015/1/181614) his Review Article in the January 2015 Communications of the ACM. --- TRANSCRIPT 00:00-00:10 When you hear the words "distributed computing", do you think of this? Or this? 00:10-00:19 Both are collections of independent information processors, passing data to each other through a network. 00:19-00:28 What can these natural systems teach us about computers? And what can computer science tell us about the natural world? 00:28-00:40 Join me in San Diego as I talk with Savet Navlakha about Distributed information processing in biological and computational systems. 00:40-00:50 [Intro graphics/music] 00:50-00:58 The Salk Institute is situated by the sea, in an area famous for its natural beauty. 00:58-01:13 It's a fitting place for Dr. Navlakha. Here, he examines how the natural world solves problems, reaches decisions, and coordinates responses -- behaviors also central to distributed computing systems. 01:13-01:20 His work started, as with so much in biology, with Drosophila melanogaster -- the common fruit fly. 01:20-01:50 DR. NAVLAKHA: It's long been known that cells, during this developmental process, some of them become these sensory cells where they have little bristles coming out of their head... And this pattern that forms is what's called a maximal independent set... It serves the basis of a lot routing algorithms, a lot of clustering algorithms... And so developing algorithms based on how the fruit fly was solving this problem led to improved algorithms for this very fundamental distributed computing problem. 01:50-01:57 The fruit fly's developing cells communicate with each other using what Dr. Navlakha and co-author Ziv Bar-Joseph call the "Beeping" model. 01:57-02:19 DR. NAVLAKHA: O.K., so this is an extremely simple and restricted setting, where I have no idea necessarily how many neighbors I have, I don't even know who is actually beeping, all I know is if somebody around me has beeped... And what's been shown is that this maximal independent set problem can actually be solved even within this very restricted environment. 02:19-02:30 But the beeping model is only one that's employed both in nature and computer science. In their paper, Drs. Navlakha and Bar-Joseph identify three others. 02:30-02:42 First is the "stone-age model", where a node initiates events only after it receives signals beyond a certain threshhold. It's how both neurons and network traffic balancers work. 02:42-03:00 Then there's "population protocol". Like a group of friends calling each other to make a dinner date, each node talks to only one partner per step -- but partners change frequently. This communication method is used by flocking birds and TCP-based networks alike. 03:00-03:08 Finally, some biological systems communicate using "shared memory", much like writing bits to a hard drive. 03:08-03:29 DR. NAVLAKHA: If you think of DNA as shared memory, then you have specific proteins that are leaving marks on the DNA... And one of the classic results in distributed computing is that any algorithm that you can solve using a message-passing algorithm... that problem can also be solved via shared-memory algorithm. 03:29-03:59 But biological and computer systems are similar in ways beyond how they pass messages. They also both vary in their topology, from densely connected cliques... to weakly connected modules... to collections of nodes only barely connected. Each topology is a tradeoff between speed and robustness. These structures have a lot to teach us -- and in fact, nature has taught computer science quite a lot already. 03:59-04:14 DR. NAVLAKHA: Genetic algorithms came from biology; deep learning and neural networks originally came from how we thought the brain was working; non-negative matrix factorization; ant colony optimization; these are all ideas that have come from biology. 04:14-04:20 At the same time, computer science gives us special insight into the natural world. 04:20-04:37 DR. NAVLAKHA: And if you think of biology as also trying to solve computational problems, then we have really good frameworks and models to understand what different constraints are in place, and what you can do given those constraints to solve a problem. 04:37-04:47 Find out more in this month's Communications of the ACM, in the review article, "Distributed information processing in biological and computational systems". 04:47-05:02 [Outro and credits]

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Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Donna Slonim, Tufts University Algorithmic Challenges in Genomics Boot Camp https://simons...

published: 25 Jan 2016

Play in Full Screen

Introduction to Biological Network Analysis I: Network Basics and Properties

Donna Slonim, Tufts University Algorithmic Challenges in Genomics Boot Camp https://simons...

published: 25 Jan 2016

Play in Full Screen

The Logic of Biological Networks

Dr. Jehoshua Bruck CalTech October 30, 2006 -_-_-_-_-_-_-_-_-_-_-_- Samuel D. Conte Distin...

published: 02 Mar 2012

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Introduction to Biological Network Analysis III: Identifying Network Modules

Roded Sharan, Tel-Aviv University Algorithmic Challenges in Genomics Boot Camp https://sim...

published: 25 Jan 2016

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Statistical Learning of Sparse and Structured Biological Networks

We know that genes, proteins, and other molecules operate not on their own, but as part of...

published: 18 Nov 2013

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Diffusion-Based Metrics for Biological Network Analysis

Lenore Cowen, Tufts University Network Biology https://simons.berkeley.edu/talks/lenore-co...

published: 15 Apr 2016

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Statistical Modelling of Biological Networks

New experimental techniques in molecular biology make it possible to probe cellular proces...

published: 07 Sep 2016

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Network Medicine: From Cellular Interactions to Human Diseases

Network Medicine: From Cellular Interactions to Human Diseases Air date: Wednesday, Febru...

published: 21 Feb 2013

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The Great Debate - What is Life?

http://www.thesciencenetwork.org Richard Dawkins, J. Craig Venter, Nobel laureates Sidney...

published: 07 Mar 2013

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Biological Networks -By Prof. Shekhar C Mande

Talk titled "Context-dependent regulatory networks" delivered on Workshop on system biolo...

published: 04 Apr 2017

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Statistical Learning of Sparse and Structured Biol...

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Statistical Modelling of Biological Networks...

Network Medicine: From Cellular Interactions to Hu...

The Great Debate - What is Life?...

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Asia-Pacific Biologic Response Modifiers Industry Environment Development Trend & Forecast Report 2017

Edit Community news 12 Jul 2017

Biologic Response Modifiers Market Research. Report Monitor Present's Asia-Pacific Biologic Response Modifiers Market 2017 Industry Trend Enhances The Decision Making Capabilities And Helps To Create An Effective Counter Strategies To Gain Competitive Advantage ... The Asia-Pacific Biologic Response Modifiers Report Offers A Comprehensive Evaluation Of The Asia-Pacific Biologic Response Modifiers Industry....

Biological Dynamics Announces Publication of Study Demonstrating Use of ACE Technology for Direct Isolation of Exosomes

Edit Market Watch 12 Jul 2017

SAN DIEGO, July 12, 2017 /PRNewswire/ -- Biological Dynamics announced the publication of data in ACS Nano demonstrating that the company's proprietary lab-on-a-chip ExoVerita™ system can simplify and streamline the process for isolation and recovery of exosomes ... The ExoVerita system is built upon the same novel proprietary Alternating Current Electrokinetic (ACE) platform that powers Biological Dynamics' cell-free DNA program....

Cabinet nod to manage the use of polythene

Edit Sri Lanka News 12 Jul 2017

In addition, provision of tax concessions in importation of machinery used in biologically degradable plastic productions and imposing a cess tax of 15% on the importation of plastic row material and goods will be implemented as mid-term measures while completely prohibition of recycled plastic products will be implemented as a long term measure....

Police unit investigating case of abandoned baby

Edit Independent online (SA) 12 Jul 2017

The FCS Unit is busy with an investigation to try and trace the biological mother or family of the baby.” ... Children are placed there by court orders ... Should the biological mother or family not be found, alternative permanent placement, like adoption, will be considered for the child in question. Should the biological mother be found, Abba will provide her with the necessary counselling and help,” she said ... ....

Plant roots grow and spread like neurons in the human brain, shocked scientists discover

Edit Natural News 12 Jul 2017

(Natural News) A research project that was conducted by a team of researchers at the Salk Institute for Biological Studies in San Diego, California that was titled A Statistical Description of Plant Shoot Architecture and was published in the journal Current Biology, concluded that plants grow very similarly like neurons in the human brain. Saket ... ....

Meet the woman who claims to be 'secret daughter' of Sanjay Gandhi

Edit Mid Day 12 Jul 2017

A woman, named Priya Singh Paul, who claims to be Sanjay Gandhi's biological daughter alleged that the trailer of the movie 'Indu Sarkar' is grossly misleading in its portrayal of the late Congress leader ... Paul said she had been adopted as a baby and claimed she was told about Sanjay Gandhi being her biological father after she grew up....

MIT's Andrew Lo On Adaptive Markets, Passive Indexing, And Systemic Shocks

Edit Seeking Alpha 12 Jul 2017

It's called the adaptive market hypothesis (AMH), which looks at the market less as a high-performance supercomputer and more as a biological organism or ecosystem - an ecosystem that, unfortunately, has become less diverse and more fragile in recent years ... Ultimately, this led Lo to formulate an understanding of the market not in mechanistic terms, as we see under the EMH, but in terms of evolutionary biology and neuroscience....

Abattoir Adopts Triark Pumps for Biological Solution

Edit Process and Control Today 12 Jul 2017

Essex based Triark was recently approached by a leading abattoir to supply pumps that could assist in the creation of a biological approach to the discarding of waste ... Water is then added and the pit is constantly aerated, so that Mother Nature can take her course, organically and biologically breaking down the material ... For more information, please contact.. Ablah Williams. Triark Pumps Ltd. The Sail Loft. Burnham Business Park ... Essex ... Tel....

Europe Market Report for Bone Graft Substitutes 2017 - Research and Markets

Edit Market Watch 12 Jul 2017

Both the smallest and fastest declining segment was the DBM market ... and Spain ... Key Topics Covered. ... 1 ... Companies Mentioned Fidia Medtronic TRB Chemedica Meda Pharma Sanofi Genzyme Depuy Synthes Bioventus Laboratoire Genevrier Recordati Stryker RTI Biologics Croma-Pharma Zimmer Biomet Anika Therapeutics Bioiberica Documedica Ferring Geistlich Integra Biologics Kuros LCA Pharma Orotech Plasmaconcept AG Stella PharmaceuticalsAlphatec Spine....

‘Sixth extinction’ of wildlife faster than feared: scientists

Edit The News International 12 Jul 2017

“This is the case of a biological annihilation occurring globally,” said Stanford professor Rodolfo Dirzo, co-author of a study published on Monday in the peer-reviewed US journal, Proceedings of the National Academy of Sciences (PNAS) ... as “a massive erosion of the greatest biological diversity in the history of Earth.”....

House Republicans Attack Clean Air, Climate in Budget Bill

Edit Common Dreams 12 Jul 2017

Center for Biological Diversity Legislation funding the Environmental Protection Agency contains multiple policy provisions that would delay Clean Air Act safeguards and cripple the Environmental Protection Agency’s ability to address climate change. Compounding the impact of these policy riders, the bill cuts the overall EPA budget by 6 percent ... ....

'Sixth extinction' of wildlife faster than feared: Scientists

Edit The Economic Times 12 Jul 2017

More than 30% of animals with a backbone are declining in both range and population, according to the first comprehensive analysis of these trends. "This is the case of a biological annihilation occurring globally," said Stanford professor Rodolfo Dirzo, co-author of a study published in the US journal, Proceedings of the National Academy of Sciences ... ....

Sixth mass extinction is underway

Edit The Star 12 Jul 2017

Sydney. The world is experiencing a “biological annihilation” of its animal species due to “human-caused global environmental problems” in recent decades, a new scientific study has found ... ....

Tata Group's innovation push

Edit CNBC 12 Jul 2017

Gopichand Katragadda, CTO of Tata Sons, says the intersection of biology, materials and computing will be an importance space to watch ... ....

New algorithms extract biological structure from limited data

Edit PhysOrg 12 Jul 2017

Understanding the 3D molecular structure of important nanoobjects like proteins and viruses is crucial in biology and medicine. With recent advances in X-ray technology, scientists can now collect diffraction images from individual particles, ultimately allowing researchers to visualize molecules at room temperature ... ....

Biological network

Network biology and bioinformatics

Ecological footprint

Footprint measurements

Footprint

Footprints in detective work

Network analysis

See also

Network (lobby group)

History

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Introduction to Biological Network Analysis I: Network Basics and Properties

The Logic of Biological Networks

NASB Fall 2013 Lecture 8 - Types of Biological Networks

Introduction to Biological Network Analysis III: Identifying Network Modules

Statistical Learning of Sparse and Structured Biological Networks

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning

Statistical Modelling of Biological Networks

Diffusion-Based Metrics for Biological Network Analysis

Bio-Inspire FullDome A/V Performance

TO UNDERSTAND IS TO PERCEIVE PATTERNS

Gulf of Mexico Legal Biological Military Operation

Distributed Information Processing in Biological and Computational Systems

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Introduction to Biological Network Analysis I: Network Basics and Properties

The Logic of Biological Networks

NASB Fall 2013 Lecture 8 - Types of Biological Networks

Introduction to Biological Network Analysis III: Identifying Network Modules

Statistical Learning of Sparse and Structured Biological Networks

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning

Statistical Modelling of Biological Networks

Diffusion-Based Metrics for Biological Network Analysis

Bio-Inspire FullDome A/V Performance

TO UNDERSTAND IS TO PERCEIVE PATTERNS

Gulf of Mexico Legal Biological Military Operation

Distributed Information Processing in Biological and Computational Systems

Network Dance

The neuronal network organization of the biological clock

04 - Supply (Biocapacity) - GLOBAL FOOTPRINT NETWORK

The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013

Noise in Biological Systems - Gordon Pipa, Max Planck Institute for Brain Research

02 - What the Footprint Measures (GLOBAL FOOTPRINT NETWORK)

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Introduction to Biological Network Analysis I: Network Basics and Properties

The Logic of Biological Networks

NASB Fall 2013 Lecture 8 - Types of Biological Networks

Introduction to Biological Network Analysis III: Identifying Network Modules

Statistical Learning of Sparse and Structured Biological Networks

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning

Statistical Modelling of Biological Networks

Diffusion-Based Metrics for Biological Network Analysis

Bio-Inspire FullDome A/V Performance

TO UNDERSTAND IS TO PERCEIVE PATTERNS

Gulf of Mexico Legal Biological Military Operation

Distributed Information Processing in Biological and Computational Systems

Network Dance

The neuronal network organization of the biological clock

04 - Supply (Biocapacity) - GLOBAL FOOTPRINT NETWORK

The Power of Graphs to Analyze Biological Data - Davy Suvee @ GraphConnect London 2013

Noise in Biological Systems - Gordon Pipa, Max Planck Institute for Brain Research

02 - What the Footprint Measures (GLOBAL FOOTPRINT NETWORK)

Introduction to Biological Network Analysis II: Protein-Protein Interaction Networks: From Graphs to

Introduction to Biological Network Analysis I: Network Basics and Properties

The Logic of Biological Networks

NASB Fall 2013 Lecture 8 - Types of Biological Networks

Introduction to Biological Network Analysis III: Identifying Network Modules

Statistical Learning of Sparse and Structured Biological Networks

What is BIOLOGICAL NETWORK INFERENCE? What does BIOLOGICAL NETWORK INFERENCE mean?

What is BIOLOGICAL NETWORK? What does BIOLOGICAL NETWORK mean? BIOLOGICAL NETWORK meaning

Statistical Modelling of Biological Networks

Diffusion-Based Metrics for Biological Network Analysis

Bio-Inspire FullDome A/V Performance

TO UNDERSTAND IS TO PERCEIVE PATTERNS

Gulf of Mexico Legal Biological Military Operation

Distributed Information Processing in Biological and Computational Systems

Network Dance

The neuronal network organization of the biological clock