network biology
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Spices form the basis of food pairing in Indian cuisine

Culinary practices are influenced by climate, culture, history and geography. Molecular composition of recipes in a cuisine reveals patterns in food preferences. Indian cuisine encompasses a number of diverse sub-cuisines separated by geographies, climates and cultures. Its culinary system has a long history of health-centric dietary practices focused on disease prevention and promotion of health. We study food pairing in recipes of Indian cuisine to show that, in contrast to positive food pairing reported in some Western cuisines, Indian cuisine has a strong signature of negative food pairing; more the extent of flavor sharing between any two ingredients, lesser their co-occurrence. This feature is independent of recipe size and is not explained by ingredient category-based recipe constitution alone. Ingredient frequency emerged as the dominant factor specifying the characteristic flavor sharing pattern of the cuisine. Spices, individually and as a category, form the basis of ingredient composition in Indian cuisine. We also present a culinary evolution model which reproduces ingredient use distribution as well as negative food pairing of the cuisine. Our study provides a basis for designing novel signature recipes, healthy recipe alterations and recipe recommender systems.

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PLOS ONE: An Approach for the Identification of Targets Specific to Bone Metastasis Using Cancer Genes Interactome and Gene Ontology Analysis

PLOS ONE: An Approach for the Identification of Targets Specific to Bone Metastasis Using Cancer Genes Interactome and Gene Ontology Analysis | network biology | Scoop.it
PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.
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Exploration of generic and specific cancer mechanisms using Cancer Genes Network

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Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty

Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty | network biology | Scoop.it

Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management. Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic, their effects on the evolution of the pathogen and durability of resistance has not received attention. We formulated a stochastic epidemiological model, based on the Kramer-Moyal expansion of the Master Equation, to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance. We focused on two hypotheses: firstly, a previous deterministic model has suggested that the effect of cropping ratio (the proportion of land area occupied by the resistant crop) on the durability of crop resistance is negligible. Increasing the cropping ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity, but found that the durability does depend on the cropping ratio. Secondly, we tested whether a superimposed external source of stochasticity (for example due to environmental variation or to intermittent fungicide application) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance. We show that in the pathosystem considered here, in general large stochastic fluctuations in epidemics enhance extinction of the pathogen. This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation (stochastic resonance). The results suggest possible disease control practises by exploiting the natural sources of stochasticity.


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The Problem of Thresholding in Small-World Network Analysis

The Problem of Thresholding in Small-World Network Analysis | network biology | Scoop.it

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues.


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Rescooped by Ganesh Bagler from Papers
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Opportunities and challenges for a sustainable energy future

Access to clean, affordable and reliable energy has been a cornerstone of the world's increasing prosperity and economic growth since the beginning of the industrial revolution. Our use of energy in the twenty–first century must also be sustainable. Solar and water–based energy generation, and engineering of microbes to produce biofuels are a few examples of the alternatives. This Perspective puts these opportunities into a larger context by relating them to a number of aspects in the transportation and electricity generation sectors. It also provides a snapshot of the current energy landscape and discusses several research and development opportunities and pathways that could lead to a prosperous, sustainable and secure energy future for the world.

 

Opportunities and challenges for a sustainable energy future

Steven Chu & Arun Majumdar

Nature 488, 294–303 (16 August 2012) http://dx.doi.org/10.1038/nature11475


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Op-Ed Contributor - Put a Little Science in Your Life - Op-Ed - NYTimes.com

Op-Ed Contributor - Put a Little Science in Your Life - Op-Ed - NYTimes.com | network biology | Scoop.it
Science is a language of hope and inspiration, providing discoveries that fire the imagination and instill a sense of connection to our lives and our world.
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The first chemical circuit developed

The first chemical circuit developed | network biology | Scoop.it

Klas Tybrandt, doctoral student in Organic Electronics at Linköping University, Sweden, has developed an integrated chemical chip. The results have just been published in the prestigious journal Nature Communications.

 

The Organic Electronics research group at Linköping University previously developed ion transistors for transport of both positive and negative ions, as well as biomolecules. Tybrandt has now succeeded in combining both transistor types into complementary circuits, in a similar way to traditional silicon-based electronics.

An advantage of chemical circuits is that the charge carrier consists of chemical substances with various functions. This means that we now have new opportunities to control and regulate the signal paths of cells in the human body.


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Rescooped by Ganesh Bagler from Complex Systems
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Stochastic Physics, Complex Systems and Biology

In complex systems, the interplay between nonlinear and stochastic dynamics gives rise to an evolution process in Darwinian sense with punctuated equilibrium, random "mutations" and "adaptations". The emergent discrete states in such a system, i.e., attractors, have natural robustness against both internal and external perturbations. Epigenetic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a framework.

 


Via David Rodrigues
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Rescooped by Ganesh Bagler from Center for Collective Dynamics of Complex Systems (CoCo)
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PyCX 0.2 now available

PyCX 0.2 now available | network biology | Scoop.it
The PyCX Project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems simulations, including iterative maps, cellular automata, dynamical networks and agent-based models.

Via Hiroki Sayama
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Determining the Size of the Network | Knowledge Matters

Determining the Size of the Network | Knowledge Matters | network biology | Scoop.it
Knowledge Matters™ - understanding business knowledge relationships by using applied social and organisational network analysis.

Via Pradeep Banerjee
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Allan Jones: A map of the brain | Video on TED.com

TED Talks How can we begin to understand the way the brain works? The same way we begin to understand a city: by making a map.

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Albert-László Barabási, TEDMED2012

Albert-László Barabási, TEDMED2012 | network biology | Scoop.it

Networks guru and author Albert-László Barabási says diseases are the results of system breakdowns within the body, and mapping intracellular protein networks will help us discover cures.


Via Complexity Digest, Pradeep Banerjee
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Rescooped by Ganesh Bagler from D.I.Y. electronics
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Arduino Beginner Parts KIT

Arduino Beginner Parts KIT | network biology | Scoop.it
Arduino Beginner Parts KIT including components Box!
This is a kit pack that gives you all of the basic thru-hole components you will need to get started playing with embedded projects.

Via embeddedcomputer
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embeddedcomputer's curator insight, September 12, 2013 7:44 AM

Arduino Beginner Parts KIT including components Box!

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How Did Topological Data Analysis (TDA) Lead to the Birth of Ayasdi?

How Did Topological Data Analysis (TDA) Lead to the Birth of Ayasdi? | network biology | Scoop.it

http://www.ayasdi.com/

 

Topological Data Analysis (TDA) brings together mathematics with computer science, and uses algorithms and concepts from algebraic topology to extract insights from complex multi-dimensional data structures. In more layman’s terms: Topological Data Analysis studies the underlying shape of data with the principle that “Data has Shape, Shape has Meaning.”

 

While Topological Data Analysis (TDA) may seem like something only for math people, Ayasdi’s founder and Stanford Mathematics Professor Gunnar Carlsson had a goal to make TDA into something that anyone could use without having a Ph.D. in mathematics.


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Visual Data Mining of Biological Networks: One Size Does Not Fit All

Visual Data Mining of Biological Networks: One Size Does Not Fit All | network biology | Scoop.it

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.


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Surprising finding: Tree's leaves genetically different from its roots

Surprising finding: Tree's leaves genetically different from its roots | network biology | Scoop.it

Black cottonwood trees (Populus trichocarpa) can clone themselves to produce offspring that are connected to their parents by the same root system. Now, after the first genome-wide analysis of a tree, it turns out that the connected clones have many genetic differences, even between tissues from the top and bottom of a single tree. The variation within a tree is as great as the variation across unrelated trees. Such somatic mutations — those that occur in cells other than sperm or eggs — are familiar to horticulturalists, who have long bred new plant varieties by grafting mutant branches onto ‘normal’ stocks. But until now, no one has catalogued the total number of somatic mutations in an individual plant.

 

In one tree, the top buds of the parent and offspring were genetically closer to each other than to their respective roots or lower branches. In another tree, the top bud was closer to the reference cottonwood genome than to any of the other tissues from the same individual.The tissue-specific mutations affected mainly genes involved in cell death, immune responses, metabolism, DNA binding and cell communication. Olds think that this may be because many of the mutations are harmful, and the tree reacts by destroying the mutated tissues or altering its metabolic pathways and the way it controls its genes, which leads to further mutations.

 

The findings have parallels to cancer studies, which have recently shown that separate parts of the same tumor can evolve independently and build up distinct genetic mutations, meaning that single biopsies give only a narrow view of the tumor’s diversity.


Via Dr. Stefan Gruenwald, Complexity Digest
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A Whole-Cell Computational Model Predicts Phenotype from Genotype

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

 

A Whole-Cell Computational Model Predicts Phenotype from Genotype
Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival Jr., Nacyra Assad-Garcia, John I. Glass, Markus W. Covert

Cell Volume 150, Issue 2, 20 July 2012, Pages 389–401

http://dx.doi.org/10.1016/j.cell.2012.05.044


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Research Shows That the Smarter People Are, the More Susceptible They Are to Cognitive Bias

Research Shows That the Smarter People Are, the More Susceptible They Are to Cognitive Bias | network biology | Scoop.it
Here’s a simple arithmetic question: A bat and ball cost a dollar and ten cents. The bat costs a dollar more than the ball. How much does the ball cost? The vast majority of people respond quickly and confidently,...
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YouTube - Nassim Taleb's Anti-Fragility

Nassim Taleb describes Antifragility for the Economist


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Plants may be able to 'hear' others

Plants may be able to 'hear' others | network biology | Scoop.it

THEY can "smell" chemicals and respond to light, but can plants hear sounds? It seems chilli seeds can sense neighbouring plants even if those neighbours are sealed in a box, suggesting plants have a hitherto-unrecognised sense.

 

Plants are known to have many of the senses we do: they can sense changes in light level, "smell" chemicals in the air and "taste" them in the soil (New Scientist, 26 September 1998, p 24). They even have a sense of touch that detects buffeting from strong winds.

 

The most controversial claim is that plants can hear, an idea that dates back to the 19th century. Since then a few studies have suggested that plants respond to sound, prompting somewhat spurious suggestions that talking to plants can help them grow.


Via Ashish Umre
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Rescooped by Ganesh Bagler from Center for Collective Dynamics of Complex Systems (CoCo)
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Personality Traits As Emergent Causal States: A Dynamic Model of Behavior Consistency and Change

Collective Dynamics of Complex Systems Research Group Seminar Series March 21, 2012 Seth Spain (School of Management, Binghamton University) "Personality Traits As Emergent Causal States: A Dynamic Model of Behavior Consistency and Change"...

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Social Media Network Analysis Report

Social Media Network Analysis Report | network biology | Scoop.it
Social Media Network Analysis Report http://t.co/BxrlHVGK...

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Computational Complexity and Information ... - Hedgehogs.net

This information is anonymous and for use solely by Hedgehogs.net. Take survey. Not now. point ... Computational Complexity and Information Asymmetry in Financial Products - http://t.co/1ilxJ65q #tcm #complexity #quant ...

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NEUROTHEOLOGY: Is The Human Brain Hardwired for God?

NEUROTHEOLOGY: Is The Human Brain Hardwired for God? | network biology | Scoop.it

Why do we care whether or not God exists? And why do so many people believe? A new generation of neuroscientists is addressing those questions directly, with the ambitious goal of measuring what happens to the human brain during spiritual experiences.


Via 11th Dimension Team, Sakis Koukouvis, Pradeep Banerjee
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