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For some seconds, just imagine if bacteria had Twitter… As new research suggests microbial life can – in fact – be even richer: highly social, intricately networked, and teeming with interactions. So it’s probably time for you to say hello to… several trillion of your inner body friends. So much so, that the metabolic activity performed by these bacteria is equal to that of a virtual organ, leading to gut bacteria being termed a “forgotten” organ [O’Hara and Shanahan, “The gut flora as a forgotten organ“. EMBO reports 7, 688 – 693 (01 Jul 2006)]. My question however is, are they doing all these going beyond regular communication?

Flocks of migrating birds and schools of fish are familiar examples of spatial self-organized patterns formed by living organisms through social foraging. Such aggregation patterns are observed not only in colonies of organisms as simple as single-cell bacteria, as interesting as social insects like ants and termites as well as in colonies of multi-cellular vertebrates as complex as birds and fish but also in human societies [14]. Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective swarm intelligence. For example, termite colonies build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defence without any central decision-making ability [8,53].(*)

Slime mould is another perfect example. These are very simple cellular organisms with limited motile and sensory capabilities, but in times of food shortage they aggregate to form a mobile slug capable of transporting the assembled individuals to a few feeding area. Should food shortage persist, they then form into a fruiting body that disperses their spores using the wind, thus ensuring the survival of the colony [30,44,53]. New research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions [47]. Bassler [3] and other researchers have determined that bacteria communicate using molecules comparable to pheromones. By tapping into this cell-to-cell network, microbes are able to collectively track changes in their environment, conspire with their own species, build mutually beneficial alliances with other types of bacteria, gain advantages over competitors, and communicate with their hosts – the sort of collective strategizing typically ascribed to bees, ants, and people, not to bacteria. Eshel Ben-Jacob [6] indicate that bacteria have developed intricate communication capabilities (e.g. quorum-sensing, chemotactic signalling and plasmid exchange) to cooperatively self-organize into highly structured colonies with elevated environmental adaptability, proposing that they maintain linguistic communication. Meaning-based communication permits colonial identity, intentional behavior (e.g. pheromone-based courtship for mating), purposeful alteration of colony structure (e.g. formation of fruiting bodies), decision-making (e.g. to sporulate) and the recognition and identification of other colonies – features we might begin to associate with a bacterial social intelligence. Such a social intelligence, should it exist, would require going beyond communication to encompass unknown additional intracellular processes to generate inheritable colonial memory and commonly shared genomic context. Moreover, Eshel [5,4] argues that colonies of bacteria are able to communicate and even alter their genetic makeup in response to environmental challenges, asserting that the lowly bacteria colony is capable of computing better than the best computers of our time, and attributes to them properties of creativity, intelligence, and even self-awareness.(*)

These self-organizing distributed capabilities were also found in plants. Peak and co-workers [37,2] point out that plants may regulate their uptake and loss of gases by distributed computation – using information processing that involves communication between many interacting units (their stomata). As described by Ball [2], leaves have openings called stomata that open wide to let CO2 in, but close up to prevent precious water vapour from escaping. Plants attempt to regulate their stomata to take in as much CO2 as possible while losing the least amount of water. But they are limited in how well they can do this: leaves are often divided into patches where the stomata are either open or closed, which reduces the efficiency of CO2 uptake. By studying the distributions of these patches of open and closed stomata in leaves of the cocklebur plant, Peak et al. [37] found specific patterns reminiscent of distributed computing. Patches of open or closed stomata sometimes move around a leaf at constant speed, for example. What’s striking is that it is the same form of mechanism that is widely thought to regulate how ants forage. The signals that each ant sends out to other ants, by laying down chemical trails of pheromone, enable the ant community as a whole to find the most abundant food sources. Wilson [54] showed that ants emit specific pheromones and identified the chemicals, the glands that emitted them and even the fixed action responses to each of the various pheromones. He found that pheromones comprise a medium for communication among the ants, allowing fixed action collaboration, the result of which is a group behaviour that is adaptive where the individual’s behaviours are not.(*)

In the offing… we should really look and go beyond regular communication to encompass unknown additional intracellular processes.

(*) excerpts from V. Ramos et al.: [a] Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes. (pdf) / [b] Computational Chemotaxis in Ants and Bacteria over Dynamic Environments. (pdf) / [c] (pdf) Societal Implicit Memory and his Speed on Tracking Dynamic Extrema. (pdf)

Time-lapse imaging in live zebrafish embryos reveals that cerebellar granule cells migrate in chain-like structures as discovered by a recent article [1] [Köster et al., PLoS, Nov. 2009]. Figure above – Granule cells taken from the cerebellum of a pigeon (above, B) are shown in this 1899 drawing by legendary neuroscientist Santiago Ramón y Cajal.

Did talk about sticky objects and self-organization in the past,  how positive and negative feedback’s  stigmergic-like agents integrated could promote changes and learning over a complex system.  Same happens to bacteria as also ants. On the other hand, we do know memes are also sticky (e.g. Chip Heath, Dan Heath, “Made to Stick: Why Some Ideas Survive and Others Die“, Random House, ISBN 978-1-4000-6428-1, January 2007). What’s new however, is that there are increasing proofs that our own brains my follow similar mechanisms (as Douglas Hofstadter in the past did made some analogies with how brains could work and how ant colonies raid different environments). In this recent new study, Köster and colleagues [1] [PLoS, Nov. 2009] reveal crucial pieces of this puzzle, showing how (neuronal) cells orient themselves to migrate together (like bacteria, above). The team studied zebrafish, one of the workhorses of developmental neurobiology, because its transparent body allows researchers to track movements of cells inside of it. As explained by Mason Inman [2]:

[…] Neurons in the developing brain complete their own self-organized waltz, coordinating with their neighbors to migrate to the right spots to form the cerebellum, visual cortex, or other parts of the brain. In this issue of PLoS Biology, Reinhard Köster and colleagues show that some of these brain cells behave much like slime molds, coordinating with other cells of the same type to migrate in a herd. They found that one particular protein called Cadherin-2 is crucial in allowing the cells to adhere to their neighbors so they can coordinate their movements and all wind up in the right spot. […] Slime molds provide a textbook example of self-organization. They live as single cells until food becomes scarce. Then, they broadcast chemical signals that trigger their mass assembly into a fruiting body, with some cells forming a stalk and others turning into spores that cast about in the winds to spread far and wide. […] Neurons in the developing brain complete their own self-organized waltz, coordinating with their neighbors to migrate to the right spots to form the cerebellum, visual cortex, or other parts of the brain. In this issue of PLoS Biology, Reinhard Köster and colleagues show that some of these brain cells behave much like slime molds, coordinating with other cells of the same type to migrate in a herd. They found that one particular protein called Cadherin-2 is crucial in allowing the cells to adhere to their neighbors so they can coordinate their movements and all wind up in the right spot.[…]

[…] But the mechanisms behind this coordinated movement – in particular, how each cell adjusts its inner workings to move to the right place at the right time – are only now starting to be revealed, using imaging that tracks these cells in live animals as they develop. […] To figure out what triggers the cells to line up and move together, the authors looked at what other kinds of cells were in the neighborhood. Many studies have shown that support cells, known as glial cells, often help guide neurons during these kinds of migrations. But during the first few days of the zebrafish embryo’s development, Köster and colleagues found, there were no glial cells along the granular cells’ migration route. That means these cells must go it alone, the team reasoned, with their own mechanism for signaling between each other to line up into chains and make their move. […] Although the study focused on just one type of brain cell, the findings could explain how many types of neurons find their way to their proper spots as the brain develops. There are still some pieces of the puzzle missing, however. While the findings explain how the granule cells are able to coordinate and follow their neighbors, it’s still not clear how the first few cells to head out on the journey – those at the front of the “conga line” – get oriented in the right direction. This suggests there must be some kind of signal from surrounding cells to get them headed in the right direction, the authors argue – yet another level of organization. […] , in Mason Inman (Nov., 2009) Migrating Brain Cells Stick Together, PloS. [2]

[1] Rieger S, Senghaas N, Walch A, Köster RW (Nov., 2009) Cadherin-2 Controls Directional Chain Migration of Cerebellar Granule Neurons. PLoS Biology.
[2] Mason Inman (Nov., 2009) Migrating Brain Cells Stick Together, PloS Biology.

 

Dynamic Optimization Problems (DOP) solved by Swarm Intelligence (dynamic environment) - Vitorino Ramos

a) Dynamic Optimization Problems (DOP) tackled by Swarm Intelligence (in here a quick snapshot of the dynamic environment)

Swarm adaptive response over time, under sever dynamics

b) Swarm adaptive response over time, under severe dynamics, over the dynamic environment on the left (a).

Figs. – Check animated pictures in here. (a) A 3D toroidal fast changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (b) A self-organized swarm emerging a characteristic flocking migration behaviour surpassing in intermediate steps some local optima over the 3D toroidal landscape (left), describing a Dynamic Optimization (DO) Control Problem. Over each foraging step, the swarm self-regulates his population and keeps tracking the extrema (44 frames in total).

 [] Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, On Self-Regulated Swarms, Societal Memory, Speed and Dynamics, in Artificial Life X – Proc. of the Tenth Int. Conf. on the Simulation and Synthesis of Living Systems, L.M. Rocha, L.S. Yaeger, M.A. Bedau, D. Floreano, R.L. Goldstone and A. Vespignani (Eds.), MIT Press, ISBN 0-262-68162-5, pp. 393-399, Bloomington, Indiana, USA, June 3-7, 2006.

PDF paper.

Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective “swarm” intelligence. Termite colonies – for instance – build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS’s were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different sub-populations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work. 

 

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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