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Canalization and control in automata networks: body segmentation in Drosophila melanogaster

We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). This way, our approach provides a method to link micro- to macro-level dynamics -- a crux of complexity. Several new results ensue from this methodology: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. We exemplify our methodology with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster.

 

Canalization and control in automata networks: body segmentation in Drosophila melanogaster

Manuel Marques-Pita, Luis M. Rocha

http://arxiv.org/abs/1301.5831


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Rescooped by Walter Stickle from Papers
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Canalization and control in automata networks: body segmentation in Drosophila melanogaster

We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). This way, our approach provides a method to link micro- to macro-level dynamics -- a crux of complexity. Several new results ensue from this methodology: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. We exemplify our methodology with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster.

 

Canalization and control in automata networks: body segmentation in Drosophila melanogaster

Manuel Marques-Pita, Luis M. Rocha

http://arxiv.org/abs/1301.5831


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BIOCOMPUTATION: some history and prospects

At first glance, Biology and Computer Science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing.


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AITopics / Future

AITopics / Future | reality, complexity, and how stuff works | Scoop.it
Introducing the Future of AI. James Hendler's Letter from the Editor. IEEE Intelligent Systems (May/June 2006) 21(3) 2-4. "To explore our field's future, we invited a number of well-known AI scientists to contribute articles speculating about ...
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Phase Resetting Reveals Network Dynamics Underlying a Bacterial Cell Cycle

Phase Resetting Reveals Network Dynamics Underlying a Bacterial Cell Cycle | reality, complexity, and how stuff works | Scoop.it

Genomic and proteomic methods yield networks of biological regulatory interactions but do not provide direct insight into how those interactions are organized into functional modules, or how information flows from one module to another. In this work we introduce an approach that provides this complementary information and apply it to the bacterium Caulobacter crescentus, a paradigm for cell-cycle control. Operationally, we use an inducible promoter to express the essential transcriptional regulatory gene ctrA in a periodic, pulsed fashion. This chemical perturbation causes the population of cells to divide synchronously, and we use the resulting advance or delay of the division times of single cells to construct a phase resetting curve. We find that delay is strongly favored over advance. This finding is surprising since it does not follow from the temporal expression profile of CtrA and, in turn, simulations of existing network models. We propose a phenomenological model that suggests that the cell-cycle network comprises two distinct functional modules that oscillate autonomously and couple in a highly asymmetric fashion. These features collectively provide a new mechanism for tight temporal control of the cell cycle in C. crescentus. We discuss how the procedure can serve as the basis for a general approach for probing network dynamics, which we term chemical perturbation spectroscopy (CPS).


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Knowledge – Inside Search – Google

Knowledge – Inside Search – Google | reality, complexity, and how stuff works | Scoop.it

Walter Stickle's insight:

I love this idea....   knowledge is not information, but is the CONNECTIONS between bits of information.   

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The Attention Economy I: Thinking about yourself in a complex system.

The Attention Economy I: Thinking about yourself in a complex system. | reality, complexity, and how stuff works | Scoop.it
The idea has also found some use in the cognitive sciences. ... But for now, I'll be talking about the Attention Economy as a way of modeling attention behavior in a complex, organized system of attenders.
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Rescooped by Walter Stickle from Information, Complexity, Computation
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Complex Adaptive Systems Modeling - a SpringerOpen journal

Complex Adaptive Systems Modeling  - a SpringerOpen journal | reality, complexity, and how stuff works | Scoop.it

Complex Adaptive Systems Modeling (CASM) is a highly multidisciplinary modeling and simulation journal that serves as a unique forum for original, high-quality peer-reviewed papers with a specific interest and scope limited to agent-based and complex network-based modeling paradigms for Complex Adaptive Systems (CAS).


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Journal on Self Computing (JSC)

Journal on Self Computing (JSC) | reality, complexity, and how stuff works | Scoop.it

Journal on Self Computing (JSC), a quarterly peer-reviewed journal, aims to provide a prestigious forum for researchers and practitioners world-wide to exchange new results in design and development of computers, networks, and control systems with self-properties, where examples of self-properties include self-stabilizing, self-organizing, self-repairing, self-healing, self-adaptive, self-aware, self-coordinating, self-protecting, etc. Its scope includes:

Autonomic and adaptive systems
Self-optimizing and self-protecting systems
Self-organizing computing and networking techniques
Impossibility results and lower bounds on self-computing
Self-properties and their relation with classical fault-tolerance and security
Self-techniques for sensor networks, ad-hoc networks, vehicular networks
Self-control and actuation systems
Cyber-physical systems with self-properties
Bio-inspired techniques on self-systems
Stochastic, physical, and biological models with self-properties


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Rescooped by Walter Stickle from Tracking the Future
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Proving quantum computers feasible

Proving quantum computers feasible | reality, complexity, and how stuff works | Scoop.it

With a new contribution to probability theory, researchers show that relatively simple physical systems could yield powerful quantum computers.


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Rescooped by Walter Stickle from Tracking the Future
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Building Medical Robots, Bacteria sized

We learned of the existence of bacteria over 300 years ago and we have far more of them in our bodies than human cells, but it was less than 40 years ago when we first realized how they swim. With the discovery of the rotary motor of E. coli in 1973, a motor just 45 nanometers in diameter, some claimed this incredible mechanism as evidence of God, though it is really just a step along the path of evolution. Now we can actually build nanorobots that swim similar to bacteria like E. coli. We're working to use these to deliver drugs to specific locations in the body. E. coli itself is a kind of robot: it has sensors (chemoreceptors), motors, communication along protein guided pathways, and software (DNA). When we look at a bacterium from this perspective it seems like a machine, even one that we will be hopefully able to duplicate someday. So if bacteria are really just machines then what are we?

Bradley Nelson talks at TEDxZurich


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Network Cosmology : how complex systems including the univers are similar

Network Cosmology : how complex systems including the univers are similar | reality, complexity, and how stuff works | Scoop.it
Prediction and control of the dynamics of complex networks is a central problem in network science.
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The evolutionary origins of modularity

A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.


Via Ashish Umre, Complexity Digest
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The Single Theory That Could Explain Emergence, Organisation And The Origin of Life | MIT Technology Review

The Single Theory That Could Explain Emergence, Organisation And The Origin of Life | MIT Technology Review | reality, complexity, and how stuff works | Scoop.it
Biochemists have long imagined that autocatalytic sets can explain the origin of life. Now a new mathematical approach to these sets has even broader implications
Walter Stickle's insight:

Stu Kaufmann is the MAN!   General, domain independant, analysis of autocatalysis has VAST implications

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Rescooped by Walter Stickle from CxBooks
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Antifragile: Things That Gain from Disorder: Nassim Nicholas Taleb

Antifragile: Things That Gain from Disorder

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Nassim Nicholas Taleb, the bestselling author of The Black Swan and one of the foremost thinkers of our time, reveals how to thrive in an uncertain world.

Just as human bones get stronger when subjected to stress and tension, and rumors or riots intensify when someone tries to repress them, many things in life benefit from stress, disorder, volatility, and turmoil. What Taleb has identified and calls “antifragile” is that category of things that not only gain from chaos but need it in order to survive and flourish.

In The Black Swan, Taleb showed us that highly improbable and unpredictable events underlie almost everything about our world. In Antifragile, Taleb stands uncertainty on its head, making it desirable, even necessary, and proposes that things be built in an antifragile manner. The antifragile is beyond the resilient or robust. The resilient resists shocks and stays the same; the antifragile gets better and better.


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Walter Stickle's insight:

Despite the fact the the writing is deliberately provocative, carelessly dogmatic and typically lacking in rigour... the books central thesis is both highly important and almost completely lacking in our culture's worldview.  Read it!

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Against 'Objective' Algorithms: The Case of Google News

Against 'Objective' Algorithms: The Case of Google News | reality, complexity, and how stuff works | Scoop.it
Whole new categories of weird noise are being introduced into the news world as a result of Google’s algorithm, whatever its virtues.

If something comes out of a computer on the basis of statistics, it must be objective, right? No bias is even possible, unlike the judgment of us flawed Homo sapiens!

But… that’s not actually true. Over at Nieman Journalism Lab, Nick Diakopoulos has a great story about the ways that various algorithms introduce biases that are different from the human ones, but no less real.

His story is well worth reading for the ways in which it shows how many algorithms are now at play in the news ecosystem and the potential they have for bending the information people receive in one way or another.

What I want to discuss, though, is how the rather simple application of a series of rigid rules can introduce new and bad behaviors on the part of human actors who realize that they can exploit the system. Whole new categories of weird noise are being introduced into the news world as a result of Google’s algorithm, whatever its virtues.

Because the rules are quite rigid, e.g. newer is *always* better, different organizations try to have the newest stories about a given popular event. So, in the lead up to the early December snowstorm here in California, the Weather Channel’s website published a great preview of the storm on November 29th or 30th. I read it on or about when it came out. *After* the storm on December 3rd, I went looking to see which of the predictions from the story had come true. I popped a few search terms into Google News and lo and behold, there was a December 3rd story from the Weather Channel. Excitedly, I clicked through the link and found … the exact same preview with a timestamp that now read December 3, 2012, 9:08 AM.

Keep in mind that this now makes the story completely nonsensical. It is a preview of an event dated after that event has already passed. It’s like a story dated November 7th story about who might win the presidential election. A Christmas preview on December 29th.

In short, this is lunacy! At least to a human.
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Reality check for DNA nanotechnology: Lowering barriers to DNA-based nanomanufacturing

Reality check for DNA nanotechnology: Lowering barriers to DNA-based nanomanufacturing | reality, complexity, and how stuff works | Scoop.it
Two major barriers to the advancement of DNA nanotechnology beyond the research lab have been knocked down. This emerging technology employs DNA as a programmable building material for self-assembled, nanometer-scale structures.
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Rescooped by Walter Stickle from CxAnnouncements
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SourceForge.net: PyCX Project: PyCX 0.2 now available

SourceForge.net: PyCX Project: PyCX 0.2 now available | reality, complexity, and how stuff works | 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.

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Rescooped by Walter Stickle from Information, Complexity, Computation
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Announcing the Santa Fe Institute’s Massive Open On-Line Courses | Santa Fe Institute

Announcing the Santa Fe Institute’s Massive Open On-Line Courses | Santa Fe Institute | reality, complexity, and how stuff works | Scoop.it

Santa Fe Institute will be launching a series of MOOCs (Massive Open On-line Courses), covering the field of complex systems science. Our first course, Introduction to Complexity, will be an accessible introduction to the field, with no pre-requisites. You don't need a science or math background to take this introductory course; it simply requires an interest in the field and the willingness to participate in a hands-on approach to the subject.
In this ten-week course, you'll learn about the tools used by complex systems scientists to understand, and sometimes to control, complex systems. The topics you'll learn about include dynamics, chaos, fractals, information theory, computation theory, evolution and adaptation, agent-based modeling, and networks. You’ll also get a sense of how these topics fit together to help explain how complexity arises and evolves in nature, society, and technology.
Introduction to Complexity will be free and open to anyone. The instructor is Melanie Mitchell, External Professor at SFI, Professor of Computer Science at Portland State University, and author of the award-winning book, Complexity: A Guided Tour. The course will begin in early 2013.
To receive e-mail updates about how to register for this course, please visit http://www.santafe.edu/mooc/subscribe


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Philosophy of Complex Systems

Philosophy of Complex Systems | reality, complexity, and how stuff works | Scoop.it

The domain of nonlinear dynamical systems and its mathematical underpinnings has been developing exponentially for a century, the last 35 years seeing an outpouring of new ideas and applications and a concomitant confluence with ideas of complex systems and their applications from irreversible thermodynamics. A few examples are in meteorology, ecological dynamics, and social and economic dynamics. These new ideas have profound implications for our understanding and practice in domains involving complexity, predictability and determinism, equilibrium, control, planning, individuality, responsibility and so on.

Our intention is to draw together in this volume, we believe for the first time, a comprehensive picture of the manifold philosophically interesting impacts of recent developments in understanding nonlinear systems and the unique aspects of their complexity. The book will focus specifically on the philosophical concepts, principles, judgments and problems distinctly raised by work in the domain of complex nonlinear dynamical systems, especially in recent years.

 

Philosophy of Complex Systems

Edited by Cliff Hooker


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The Algorithmic Origins of Life - Sara Walker (SETI Talks)

The origin of life is arguably one of the greatest unanswered questions in science. A primary challenge is that without a proper definition for life -- a notoriously challenging problem in its own right -- the problem of how life began is not well posed. Here we propose that the transition from non-life to life may correspond to a fundamental shift in causal structure, where information gains direct, and context-dependent, causal efficacy over matter, a transition that may be mapped to a nontrivial distinction in how living systems process information.

 

Dr. Walker will discuss potential measures of such a transition, which may be amenable to laboratory study, and how the proposed mechanism corresponds to the onset of the unique mode of (algorithmic) information processing characteristic of living systems.


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Glass Half Empty

Glass Half Empty | reality, complexity, and how stuff works | Scoop.it

What if a glass of water was, all of a sudden, literally half empty?

—Vittorio Iacovella

The pessimist is probably more right about how it turns out than the optimist.

When people say “glass half empty”, they usually mean something like a glass containing equal parts water and air:

Traditionally, the optimist sees the glass as half full while the pessimist sees it as half empty. This has spawned a zillion joke variants—e.g., the engineer sees a glass that’s twice as big as it needs to be, the surrealist sees a giraffe eating a necktie, etc.

But what if the empty half of the glass were actually empty—a vacuum? (Even a vacuum arguably isn’t truly empty, but that’s a question for quantum semantics.)

The vacuum would definitely not last long. But exactly what happens depends on a key question that nobody usually bothers to ask: Which half is empty?

For our scenario, we’ll imagine three different half-empty glasses, and follow what happens to them microsecond by microsecond.

In the middle is the traditional air/water glass. On the right is a glass like the traditional one, except the air is replaced by a vacuum. The glass on the left is half full of water and half empty—but it’s the bottom half that’s empty.

We’ll imagine the vacuums appear at time t=0.

For the first handful of microseconds, nothing happens. On this timescale, even the air molecules are nearly stationary.

For the most part, air molecules jiggle around at speeds of a few hundred meters per second. But at any given time, some happen to be moving faster than others. The fastest few are moving at over 1000 meters per second. These are the first to drift into the vacuum in the glass on the right.

The vacuum on the left is surrounded by barriers, so air molecules can’t easily get in. The water, being a liquid, doesn’t expand to fill the vacuum in the same way air does. However, in the vacuum of the glasses, it does start to boil, slowly shedding water vapor into the empty space.

While the water on the surface in both glasses starts to boil away, in the glass on the right, the air rushing in stops it before it really gets going. The glass on the left continues to fill with a very faint mist of water vapor.

After a few hundred microseconds, the air rushing into the glass on the right fills the vacuum completely and rams into the surface of the water, sending a pressure wave through the liquid. The sides of the glass bulge slightly, but they contain the pressure and do not break. A shockwave reverberates through the water and back into the air, joining the turbulence already there.

The shockwave from the vacuum collapse takes about a millisecond to spread out through the other two glasses. The glass and water both flex slightly as the wave passes through them. In a few more milliseconds, it reaches the humans’ ears as a loud bang.

Around this time, the glass on the left starts to visibly lift into the air.

The air pressure is trying to squeeze the glass and water together. This is the force we think of as suction. The vacuum on the right didn’t last long enough for the suction to lift the glass, but since air can’t get into the vacuum on the left, the glass and the water begin to slide toward each other.

The boiling water has filled the vacuum with a very small amount of water vapor. As the space gets smaller, the buildup of water vapor slowly increases the pressure on the water’s surface. Eventually, this will slow the boiling, just like higher air pressure would.

However, the glass and water are now moving too fast for the vapor buildup to matter. Less than ten milliseconds after the clock started, they’re flying toward each other at several meters per second. Without a cushion of air between them—only a few wisps of vapor—the water smacks into the bottom of the glass like a hammer.

Water is very nearly incompressible, so the impact isn’t spread out—it comes as a single sharp shock. The momentary force on the glass is immense, and it breaks.

This “water hammer” effect (which is also responsible for the “clunk” you sometimes hear in old plumbing when you turn off the faucet) can be seen in the well-known party trick (recorded on Mythbusters, analyzed in physics classes, and demonstrated in countless student dorms) of smacking the top of a glass bottle to blow out the bottom.

When the bottle is struck, it’s pushed suddenly downward. The liquid inside doesn’t respond to the suction (air pressure) right away—much like in our scenario—and a gap briefly opens up. It’s a small vacuum—a few fractions of an inch thick—but when it closes, the shock breaks the bottom of the bottle.

In our situation, the forces would be more than enough to destroy even the heaviest drinking glasses.

The bottom is carried downward by the water and thunks against the table. The water splashes around it, spraying droplets and glass shards in all directions.

Meanwhile, the detached upper portion of the glass continues to rise.

After half a second, the observers, hearing a pop, have begun to flinch. Their heads lift involuntarily to follow the rising movement of the glass.

The glass has just enough speed to bang against the ceiling, breaking into fragments…

… which, their momentum now spent, return to the table.

The lesson: If the optimist says the glass is half full, and the pessimist says the glass is half empty, the physicist ducks.

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