Emergence (biology)

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Scientists and philosophers of science commonly have used the term emergence to refer to the manifestation of novel collective phenomena in some large systems stemming from a complex organization of their many constituent parts.[1]

The concept of emergence refers to the coming out of new properties linked to the complexity of an organization. In scientific context, self-organization models have an important place in the formalization of emergence.[1]   —[Indeed, Gianfranco Minati and Eliano Pessa note that since 1998, with the work of John Holland,[2] "the terms of emergence and self-organization have been considered as synonyms".][3]

Emergence relates to phenomena that arise from and depend on some more basic phenomena yet are simultaneously autonomous from that base.[4]

Emergent phenomena are said to arise out of and be sustained by more basic phenomena, while at the same time exerting a ‘top-down’ control, constraint or some other sort of influence upon those very sustaining processes.[5]

The concept of emergence has continued to evolve since the philosopher John Stuart Mill introduced it, under a different name, in the nineteenth-century (1843),[6] and it continues as an active, if not free of controversial aspects,[4] subject of discussion in the second decade of the twenty-first century.[5]

In systems biology and theoretical biology, discussion focuses emergence in living systems, including candidate examples of termite and bee nests constructed by the cooperative action of multitudes of individuals.

In biology candidates for emergence include such phenomena as locomotion, sexual display, flocking, and conscious experiencing, as well as the phenomena observed in biological subsystems, such as mitochondria and other organelles of living cells.

Emergent properties are viewed by some as novel properties, functions and behaviors, ones not observed in the system's subsystems and their components, and neither explicable nor predictable from even a complete understanding of the components' properties/functions/behaviors considered in isolation. Others take the view that these novel properties are the outcome of interactions between the constituents understandable from microscopic behavior, but more readily envisioned by introduction of novel organizational concepts.

This article will explore emergence as a concept with a long history, differing interpretations, and much controversy.

Is water an example?

Do the properties we associate with liquid water, its transparency, its wetness, its ability to flow, etc., 'emerge' from the properties of oxygen and hydrogen and their organizational dynamics? Consider these questions:

  • Would we expect that oxygen and hydrogen interacting in accord with their known properties, enabling them to self-assemble into a self-organized dynamic, to result in a fluid with the characteristics of a bowlful of water as we observe them with our native senses?
  • Would we need to know thoroughly the characteristics/dynamics of the environment — to see how they might co-determine the behavior of hydrogen and oxygen, known to behave differently under different environmental conditions — in order to approach the task of visualizing water from its particulate/energetic components, hydrogen and oxygen?
  • Would constructing a computer requiring the resources of the entire universe provide sufficient information processing capability to explain the look and feel, and the chemistry, of water as medium for living systems and their systems?
  • Wouldn't the computer need the ability to compute water molecule interactions with every other compound/ion in the system?

These questions suggest that classification of certain properties of water as "'emergent' is to a degree a question of our capacity to predict these behaviors. However, our state of knowledge and computer technology are not really part of the identification of a property as emergent. A different perspective is that the descriptions of water at a microscopic level and at a macroscopic level are couched in a different vocabulary, and the terms used in one description may not appear in another, although (perhaps only in principle) they might be related to one another by some intermediary theory. An example might be the dielectric properties of water, such as its transparency in visible light, which is related by condensed matter physics to the underlying microscopic quantum mechanical description of water's constituent electrons and nucleii.

For some emergent properties of some systems, the theory intermediating between the system properties and the properties of its subsystems might not yet exist, and some scholars believe such an intermediary theory in some cases might be impossible to obtain, even in principle. A view that approximates the present state of science is model-dependent realism, which takes the view that explanations are a patchwork of overlapping descriptions that use different terminologies, that agree with one anther in areas where they both apply, and with vocabularies that can be related to each other where they overlap, but which cover different areas of experience and are not reducible one to another outside their region of overlap. It may be noted that the regions of overlap may change as knowledge and calculation advance, and new theories may evolve that encompass and extend older theories, as special relativity extends Newtonian mechanics.

Nominal, strong, and weak emergence

See also Causality

In the above paragraphs the distinctive property of emergence has been taken to be a collective behavior of subsystems imbedded in a complex system. There are several identifiable forms of emergence, whether or not all of them exist in nature:[7]

  1. Emergence is nominal when it depends upon micro-level phenomena in the sense that wholes are dependent upon their constituents and their autonomous interactions, and the emergent properties do not apply to the underlying entities themselves.
  2. Emergence is strong when the emergent properties have power over the underlying entities that is not reducible to the properties of these entities. These "macro causal powers" have effects on both the macro and micro-levels, and macro-to-micro effects are termed downward causation.[8]
  3. Emergence is weak when it is explained by nominal emergence, but only by a very nontrivial inference that involves extensive computation.

The strong emergence school of thought invokes an influence of the whole upon its parts, not simply by connections between the parts, but by communication of the whole to its parts, possibly by exerting 'configurational forces'. A basic issue is to decide what constitutes the "whole": for example, enactivists place emphasis upon the interaction between an organism and its environment, each shaping the other. A treatment that separates the whole into interactions between internally autonomous subsystems prejudices understanding of both.[9]

Another name for this approach based upon communication of the whole to its parts is organicism:

"...complex wholes are inherently greater than the sum of their parts in the sense that the properties of each part are dependent upon the context of the part within the whole in which they operate. Thus, when we try to explain how the whole system behaves, we have to talk about the context of the whole and cannot get away talking only about the parts. This philosophical stance is variously called wholism, holism, or organicism. ...top-down and bottom-up approaches must both be used to explain phenomena." Gilbert & Sarkar, Embracing Complexity [10]

What explains the behavior of an organism fleeing from a predator if studying all the subsystems does not visualize the predator-prey phenomenon? To see this emergent phenomenon one must study the organized behavior of those subsystems and the dynamics of that organized behavior. One may view this perspective as a macro-level description of collective behavior that organized interacting subsystems engender, and that natural selection operates on its variations. A more extreme position is that the subsystem behavior is not responsible for this collective behavior, and the macro-level description is not deducible from autonomous subsystem behavior. [10]

Assuming organisism applies, some might be tempted to see a type of 'vitalism', or 'life force', in living systems. However, because biologists and their co-scientists can explain emergent properties/phenomena, if only sometimes in principle, by mechanisms that do not transcend interactions of matter and energy, any ‘vitalism’ position properly can credit only a new sort of 'vitalism': ‘materialistic vitalism’ that incorporates the realization that organisms organize themselves, having developed their pattern of behavior by trial and error and natural selection. Scientists of philosophic bent and philosophers of science like to consider those kinds of interesting meta-science questions.

The concept of strong emergence has some difficulties. One may observe:[11]

" Although strong emergence is logically possible, it is uncomfortably like magic. How does an irreducible but supervenient downward causal power arise, since by definition it cannot be due to the aggregation of the micro-level potentialities....Their mysteriousness will only heighten the traditional worry that emergence entails illegitimately getting something from nothing." Mark Bedau, Weak emergence [11]

It can be noted that strong emergence, by suggesting that a collective can introduce "downward causation", also provides an opening for a belief in free will, the idea that we can control our own destinies by exercising a hypothesized causative agency of mind, a supposed aspect of an emergent consciousness.

A moderate view is that strong emergence is a classification that may have to be removed if a more complete theory emerges rendering the emergent phenomena in the weak or nominal category, but even if such a reductionist approach is possible, the language and models based upon strong emergence might continue to prove useful and efficient in describing the collective behavior. As discussed next, a macroscopic (large scale or global) description of behavior sometimes has advantages over a microscopic (small scale) description, both in conceptual simplicity and in ease of comparison with measurements and observations.

Scales: macroscopic and microscopic

Both in time and in space, phenomena occur on different scales. Referring to a gas of atoms, on a microscopic scale individual atomic motions can be examined, and on a macroscopic scale group descriptions in terms like temperature and pressure are more understandable.

These different scales are interrelated. The manipulation of the feedback between microscopic and macroscopic scales is a common occurrence in the control and optimization of complex systems,[12] for example, in traffic control:[13]

"At the micro-level, vehicles adapt to evolving traffic conditions, affecting the state of the system. At the macro-level, traffic control devices monitor the state of the system and adapt the system conditions to maintain the overall system stability." Zalila-Wenkerstern et al., A self-organizing architecture for traffic management [13]

In meteorology the interplay between phenomena at different scales is well recognized: microscale (< 1 km), mesoscale (5-1000km), synoptic (> 1000 km).[14]

As another example, in numerical calculation of partial differential equations, multilevel techniques are used such as multigrid methods.[15] These methods also find application in molecular dynamics. The idea is to separate the potential deciding the forces between atoms into a short-range (rapidly varying) and a long-range (smoothly varying) part, and recursively solving first one part and then the other.[16] In effect, emergent properties on the large scale are fed back to the atomic movements on a small scale and vice versa, resulting in a much more efficient simulation than one attempting to use only a microscopic calculation. One might conjecture that this interdependence of scales used in these numerical algorithms is a reflection or a representation of how nature itself operates.

Different time scales also are significant, for example, in meteorology, in connecting weather (short term) with climate (long term) changes.[17] The progress of a hurricane occurs over days but depends upon sea temperatures established over months or even years.[18]


A concept often used in discussing emergence is supervenience, often mistakenly taken to indicate what controls what, and confused with downward causation. State A is supervenient of state B if and only if every change in A requires a change in B.[19]

Supervenience is a deceptive descriptor. For example, consider the relations between a microscopic theory like kinetic theory based upon specific interactions, a mesoscopic theory like statistical mechanics largely independent of the specifics of interactions, and a macroscopic theory like thermodynamics where microscopic considerations about interactions are not considered. According to the definition above, microscopic theory is not supervenient of thermodynamics, because many different systems have the same thermodynamic description. On the other hand, a change in temperature is a key element in thermodynamics, related to microscopic theory by statistical mechanics, and changes in temperature always imply correlated changes in the microscopic description. So it appears that a macroscopic theory is supervenient of a more microscopic theory that explains the macroscopic theory on a more fundamental level. Although strictly within a thermodynamic description it may be correct to say that a change in temperature causes a phase change, say from ice to water, the microscopic theory provides a deeper understanding of phase changes entirely in terms of the fundamental atomic interactions.[20]

In a similar vein, the notions of chemical bonding, valence, and so forth are used directly in molecular physics and chemistry to explain how atoms engage with one another, and one might say a particular molecule is the result of such factors, which supervene atomic behavior. But the explanation of these factors lies within quantum mechanics, which does not use the term "bond" at all, and describes all chemistry in terms of electron behavior and electromagnetic interactions.[21]

One may well assert the "poverty of the supervenience relation".[19]

Examples of emergence

One example of emergence: When the components of signaling pathway, one that enable between-cell communication, interact to form a functional network of signaling systems, novel properties/behaviors can arise — such as a self-sustaining feedback loop and generation of the signals themselves; signal integration across multiple time scales; and, generation of distinct outputs depending on input strength and duration.[22] Such a network is an emergent phenomenon.

For another example, in studying a protein separated from the cellular system that embeds it in a cell, one can observe many of its chemical and physical properties. But the operation of catalyzing a biochemical reaction, or of binding to other proteins to form a protein complex that generates novel behavior, emerge in the context of the protein’s environment — how it interacts in the context of the system as a whole, they are emergent properties. Moreover, those emergent properties may result in effects within the system that, in a feedback way, further alters the emergent behavior of the protein in the system, as when a reaction product alters the catalytic ability of the protein.

Emergent processes have been recognized as, for example, contributing to understanding:

Emergent phenomena appear even in non-biological physical systems.[28] Emergent phenomena attract the attention of cellular neuroscientists;[29]  and cognitive scientists[30]. Emergent properties manifest in the behaviour of ant colonies and in swarm intelligence.[31] Systems scientists have simulated emergent phenomena.[32]  Emergent phenomena in human societies has also received attention. [33]. Biologists even explain the biosphere itself as emergent.[34]

Why emergence?

(PD) Image: John R. Brews
Marangoni-Bénard temperature cells, an ordered arrangement of temperature patterns produced in a thin film subject to a large temperature difference between two planes parallel to the plane of the image.[35]

Do some of the properties/behaviors of a complex system result from something other than the properties of its components? After all, the reductionist paradigm that dominated the scientific method in the 20th century operated on the assumption that they could.

It is obvious from any standpoint that context is important to system behavior, whether that is as simple as a violin player's finger on a string or as complicated as psychotherapy.[36] A real system always operates in a context (its external environment, or surroundings), and those surroundings, in turn, always affect the properties of the system-as-a-whole.

As an example, in the figure a liquid film heated from below forms Marangoni-Bénard temperature cells, a regular pattern of temperature variations that forms spontaneously when a film originally with a completely uniform temperature pattern reaches a steady state in response to sufficient heat input from below.[35] For sufficient temperature differences, the system switches from a uniform steady state to the patterned steady state that differs by exhibiting reduced symmetry and long-range correlation. In this case, the pattern depends essentially upon the non-linearity of the system and upon both the film thickness and the lateral dimensions of the film. This dependence upon the dimensions of the system is not as simple as the dependence of the modes of a vibrating drum upon its geometry, both due to the effects of boundary layers: regions of rapid variation in behavior that can occur near the boundaries, and due to the nonlinear dynamics.[37] The approach to explaining this behavior is based upon (for example) the equations of hydrodynamics (in principle deducible from microscopic atomic or molecular behavior) and boundary conditions, often empirical or idealized; in short, a reductionist approach is assumed.

The impact of environmental context affects the dynamic organization of the components within the system. Environmental signals can activate or suppress a metabolic pathway, reorganizing cellular activity. For example, the behavior of a human kidney cell depends not only on its cellular physiology, but also on all the properties of the kidney organ that constitutes the cell's environment. The kidney's overall structure and function influence the cell’s structure and behavior (e.g., by physical confinement and by cell-to-cell signaling), which in turn influence the organization of its intracellular components. The kidney in turn responds to its environment, namely the individual body that it lives in, and that body responds to its environment, which includes such factors as the availability of particular food items, fresh water, and ambient temperature and humidity.

The effects of environment can be profound. For example, the environment’s effect can sometimes reach down to the genetic database with molecular signals, altering its expression and consequently the characteristics of the cells without altering the database itself — so-called 'epigenetic' effects. (Epigenetics seeks to explain features, characters, and developmental mechanisms in terms of interactions above the level of the gene.) When epigenetic alterations of gene expression occur in the reproductive organs, the system changes can be transmitted to the next generation.[38] One aspect of this field is the The ENCODE Project (ENCyclopedia Of DNA Elements) which has discovered that much of what was previously thought ot be "junk" DNA is composed of "switches" that determine how cells develop and operate.[39] Combining twin studies with the ENCODE map of the genome will show how some of these switches are set.[40] " In the case of identical twins, small changes in environmental exposure can slightly alter gene switches, with the result that one twin gets a disease and the other does not."[41]

According to organicism, it may be that the intrinsic properties of a system’s components cannot themselves determine those of the whole system; rather, their 'organizational dynamics' does — how the components interact coordinately in time and space. Those organizational dynamics might include not only the interrelations among the components themselves, but also interactions originating in the many different organizational units in the system. Philosopher of science D.M. Walsh puts it this way: "The constituent parts and processes of a living thing are related to the organism as a whole by a kind of 'reciprocal causation'."[42] As Gilbert and Sarkar[10] puts it: “Thus, when we try to explain how the whole system behaves, we have to talk about its parts in the context of the whole and cannot get away talking only about the parts.” This stance is close to the dualism of Kant and Schopenhauer: the "thing-in-itself" that "we shall never be able to penetrate into [its] inner nature" by means of objective knowledge.[43]

However, 'reciprocal causation', also called 'circular causation',[44] at least in many cases, is traceable to complex feedback mechanisms between a system and its environment or between different scales of interaction, for example, between short-range and longer range interactions. Systems biologists sometimes refer to emergent properties as arising from a combination of bottom-up and top-down feedback effects. The "top" consists in the effects of the system's interactions with its environments. The "bottom", the effects of the interactions of the system's components. Whatever may be the more accurate description, novel properties of the system 'emerge' that characterize neither the environment nor that set of internal components when they do not interact.

A term prevalent today is swarm intelligence, applied to computer and robotic systems as well as biology.[45] Using the example of termites out of whose combined individual behaviors without outside management emerge complex colony mounds, a recent National Research Council report on the role of theory in advancing 21st century biology commented on emergent behavior as follows:[46]

A reasonable way of thinking about emergent behavior might be to focus on the level or scale at which the rules reside. If the rules are specified at a low level, for example, the individual termites, and the patterns and structures, like termite mounds, emerge at a scale where there are no rules specified, we may call this emergent behavior.[46]

Other examples of rule-free emergent behavior for which the 'rules' appear specified at a lower level than the emergent behavior itself include the flocking behavior of birds, and the folding of amino acid polymers into catalytic proteins.

Emergence and complexity

Emergent systems always display what we recognize as ‘complexity’, a feature we have a difficult time precisely defining. The operation of the system itself supplies its own most economical model.

According the paleontologist and origin of life researcher, Robert Hazen, four basic complexity elements underpin emergence in a system: [47]

  • a sufficiently large ‘density’ of components, with increasing complexity as the concentration increases, up to a point;
  • sufficient interconnectivity of the components, with increasing complexity with greater and more varied types of interconnectivity, up to a point;
  • a sufficient energy flow through the system to enable the system’s components to perform the work of interacting in the self-organized way characteristic of the energized system;
  • flow of energy through the system in a cyclic manner, presumably facilitating the spatiotemporal patterning characteristic of organized systems.

PW Anderson has pointed out the role of complexity, stressing that a 'reductionist' approach (the idea that emergent phenomena can be reduced in principle to the laws governing basic entities) is not equivalent to a 'constructionist' approach (the idea that one can start from fundamental laws and reconstruct the universe). He points out that the Standard Model, for instance, appears to have little relevance to real problems of the rest of science. "The behavior of large and complex aggregates of elementary particles, it turns out, is not to be understood in terms of simple extrapolation... Instead, at each level of complexity entirely new properties appear..."[48]

Coherent entities and coherence detectors

We can think of emergence as the appearance of a novel 'coherent entity', coherent in the sense of the entity behaving in a functionally consistent manner, and an entity in virtue of that functionally consistent behavior. Perhaps the human circulatory system exemplifies functionally consistent behavior, and qualifies as a coherent entity. But to see it, we need a way to detect the existence, properties, and behaviors of the circulatory system — a coherence detector. Physiology and systems biology predominately study emergent phenomena, coherent entities exhibiting unexpected behavior produced by dynamic physico-chemical processes of great complexity. The emergent behaviors they study depend on their resources of coherence detectors, making observer-dependence a property of emergence. In biology, emergent systems abound, suggesting greater abundance as coherence detection advances in sensitivity and power.


Citations and notes

  1. 1.0 1.1 Bernard Feltz, Marc Crommelinck, Philippe Goujon (2006). “Introduction”, Bernard Feltz, Marc Crommelinck, Philippe Goujon, eds: Self-organization and Emergence in Life Sciences. Springer. ISBN 1402039166. 
  2. Holland JH. (1998) Emergence from Chaos to Order. Perseus Books, Cambridge, MA. ISBN 0-7382-0142-1
  3. Minati G, Pessa. E. (2007) "Emergence". In:Collective Beings. Chapter 3. Springer.
  4. 4.0 4.1 Bedau MA, Humphreys P. (editors) (2008) Emergence: contemporary readings in philosophy and science. A Bradford book." ISBN 978-0-262-02621-5 (hc), ISBN 978-0-262-52475-9 (pbk)
    • From publisher´s description:  This reader collects...classic writings on emergence from contemporary philosophy and science...three sections ("Philosophical Perspectives," "Scientific Perspectives," and "Background and Polemics")...A bibliography lists more specialized material, and an associated website (http://mitpress.mit.edu/emergence) links to downloadable software and to other sites and publications about emergence.
    • Contributors:  P. W. Anderson, Andrew Assad, Nils A. Baas, Mark A. Bedau, Mathieu S. Capcarrère, David Chalmers, James P. Crutchfield, Daniel C. Dennett, J. Doyne Farmer, Jerry Fodor, Carl Hempel, Paul Humphreys, Jaegwon Kim, Robert B. Laughlin, Bernd Mayer, Brian P. McLaughlin, Ernest Nagel, Martin Nillson, Paul Oppenheim, Norman H. Packard, David Pines, Steen Rasmussen, Edmund M. A. Ronald, Thomas Schelling, John Searle, Robert S. Shaw, Herbert Simon, Moshe Sipper, Stephen Weinberg, William Wimsatt, and Stephen Wolfram
    • About the Editors:  Mark A. Bedau is Professor of Philosophy and Humanities at Reed College in Portland, Oregon. He is the coeditor of Emergence: Contemporary Readings in Science and Philosophy and Protocells: Bridging Nonliving and Living Matter, both published by the MIT Press in 2008….Paul Humphreys is Professor of Philosophy at the University of Virginia.
    • Table of Contents and Downloadable Sample Chapters.quote
  5. 5.0 5.1 Corradini A, O'Connor T. (editors) (2010) Emergence in Science and Philosophy. Routledge. ISBN 0415802164.
  6. Garson J. (2005) Emergence: History of the Concept of Emergence. Page 231. In: The Philosophy of Science: An Encyclopedia, Volume 1. Editors: Sahotra Sarkar, Jessica Pfeifer. Psychology Press. ISBN 0415939275.
  7. This division is a paraphrase of a list in F Varenne (2009). “§5 Types of simulation and types of emergence”, Moulay Aziz-Alaoui, Cyrille Bertelle, eds: From System Complexity to Emergent Properties. Springer, pp. 16-17. ISBN 3642021980. 
  8. Mark Bedau (2002). "Downward causation and the autonomy of weak emergence". Principia: an international journal of epistemology 6 (1): pp. 5-50. A pdf version is found here
  9. Ad J. W. van de Gevel, Charles N. Noussair (2013). “§3.2.2 Enactive artificial intelligence”, The Nexus between Artificial Intelligence and Economics. Springer Science & Business Media, pp. 21 ff. ISBN 9783642336485. “Enactivism emphasizes the idea that subject and object co-arise.” 
  10. 10.0 10.1 10.2 Gilbert SF, Sarkar S. (September 2000). "Embracing complexity: organicism for the 21st century.". Developmental Dynamics 219 (1): pp. 1-9. PMID 10974666
    • Abstract: Organicism (materialistic holism) has provided the philosophical underpinnings for embryology since the time of Kant. It had influenced the founders of developmental mechanics, and the importance of organicism to embryology was explicitly recognized by such figures as O. Hertwig, H. Spemann, R. Harrison, A. M. Dalq, J. Needham, and C. H. Waddington. Many of the principles of organicism remain in contemporary developmental biology, but they are rarely defined as such. A combination of genetic reductionism and the adoption of holism by unscientific communities has led to the devaluation of organicism as a fruitful heuristic for research. This essay attempts to define organicism, provide a brief history of its importance to experimental embryology, outline some sociologically based reasons for its decline, and document its value in contemporary developmental biology. Based on principles or organicism, developmental biology should become a science of emerging complexity. However, this does mean that some of us will have to learn calculus.
  11. 11.0 11.1 Mark A Bedau (1997). "Weak emergence". Philosophical Perspectives: Mind, Causation and World 11: pp. 375-399.
  12. Panagiotis D. Christofides, Antonios Armaou, Yiming Lou, Amit Varshney (2008). Control and Optimization of Multiscale Process Systems. Springer, p. 7. ISBN 0817647929. 
  13. 13.0 13.1 R Zalila-Wenkerstern, T Steel and G Leask (2010). “A self-organizing architecture for traffic management”, Danny Weyns, Sam Malek, Rogério de Lemos, Jesper Andersson, eds: Self-Organizing Architectures: First International Workshop, SOAR 2009. Springer, pp. 230 ff. ISBN 364214411X. 
  14. Paul Markowski, Yvette Richardson (2011). “§1.1 Space and time scales”, Mesoscale Meteorology in Midlatitudes, 2nd. J Wiley & Sons, p. 1. ISBN 0470742135.  E-book isbn= 978-1-1199-6667-8.
  15. For example, see James H Bramble (1993). Multigrid methods. Longman Scientific and Technical. ISBN 0582234352. 
  16. E Cho, AG Bourgeois, JA Fernández-Zepeda (2008). “Examining the feasibility of reconfigurable models of molecular dynamics simulation: §2.2 Multigrid method for molecular dynamics simulation”, Anu G. Bourgeois, Si Quing Zheng, eds: Algorithms and Architectures for Parallel Processing, 8th International Conference, ICA3PP 2008, Agia Napa, Cyprus, June 9-11, 2008, Proceedings. Springer, pp. 112 ff. ISBN 3540695001. 
  17. TT Warner (2010). Numerical weather and climate prediction. Cambridge University Press. ISBN 0521513898. 
  18. There are many books discussing this relationship, see for example: C. Donald Ahrens (2011). “Hurricanes in a warmer world”, Essentials of Meteorology: An Invitation to the Atmosphere, 6th ed. Cengage Learning, p. 338. ISBN 0840049331. 
  19. 19.0 19.1 Cliff A. Hooker (2011). “Conceptualizing reduction, emergence, and self-organization in complex dynamical systems”, Cliff A. Hooker, ed: Philosophy of Complex Systems. Elsevier, p. 212. ISBN 0444520767. 
  20. This view must be tempered by the observation that application of statistical methods to complex systems often imposes statistical assumptions that are not obvious from the microscopic viewpoint.
  21. This view must be tempered by the observation that although quantum mechanics can be used to frame the calculation of molecular properties, the result is too complex to calculate without introduction of approximations that often are not justified using quantum mechanics alone. In fact, the intuitions of chemists about the formation of complex molecules are based upon bonding concepts that quite probably have a quantum-mechanical explanation, but that deeper explanation is not the basis of their intuition.
  22. Bhalla US, Iyengar R (1999) Emergent properties of networks of biological signaling pathways. Science 283:381-387 PMID 9888852
    • Abstract: Many distinct signaling pathways allow the cell to receive, process, and respond to information. Often, components of different pathways interact, resulting in signaling networks. Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Feedback can result in bistable behavior with discrete steady-state activities, well-defined input thresholds for transition between states and prolonged signal output, and signal modulation in response to transient stimuli. These properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.
  23. Tabony J (2006) Microtubules viewed as molecular ant colonies. Biol Cell 98:603-17 PMID 16968217
  24. Theise ND, d'Inverno M (2004) Understanding cell lineages as complex adaptive systems. Blood Cells Mol Dis 32:17-20 PMID 14757407 and Ruiz i Altaba A et al. (2003) The emergent design of the neural tube: prepattern, SHH morphogen and GLI code. Curr Opin Genet Dev 13:513-21 PMID 14550418
  25. Jeong H et al.(2000) The large scale organisation of metabolic networks. Nature 407:651-4
  26. e.g. Grindrod P, Kibble M (2004) Review of uses of network and graph theory concepts within proteomics. Expert Rev Proteomics 1:229-38 PMID 15966817
  27. Ye X et al.(2005) Multi-scale methodology: a key to deciphering systems biology. Front Biosci 10:961-5 PMID 15569634
  28. Cho YS et al. (2005) Self-organization of bidisperse colloids in water droplets. J Am Chem Soc 127:15968-75 PMID 16277541
  29. see e.g. Burak Y, Fiete I (2006) Do we understand the emergent dynamics of grid cell activity? J Neurosci 26:9352-4 PMID 16977716
  30. e.g. Courtney SM (2004) Attention and cognitive control as emergent properties of information representation in working memory. Cogn Affect Behav Neurosci 4:501-16 PMID 15849893
  31. Theraulaz G et al (2002) Spatial patterns in ant colonies. Proc Natl Acad Sci USA 99:9645-9 PMID 12114538
  32. Theraulaz G, Bonabeau E (1999)A brief history of stigmergy. Artif Life 5:97-116 PMID 10633572
  33. Bonabeau E, Meyer C (2001) Swarm intelligence. A whole new way to think about business. Harv Bus Rev 79:106-14 PMID 11345907
  34. Field CB, Behrenfeld MJ, Randerson JT, Falkowski P (1998) Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281:237-40.
  35. 35.0 35.1 An artist's rendition of a photograph in: Klaus Lucas, Peter Roosen (2009). “Fig. 2.3: Infrared camera view of the free surface temperature field of a Marangoni-Bénard instability in a 5mm layer of silicone oil heated from below.”, Emergence, Analysis and Evolution of Structures: Concepts and Strategies Across Disciplines. Springer, p.12. ISBN 3642008690. 
  36. Note: For the example of water, the properties of its environment (e.g., temperature, pressure) affect the way the H2O molecules organize themselves, as ice, or liquid, or steam
  37. Alexander V. Getling (1998). Rayleigh-Bénard Convection: Structures and Dynamics. World Scientific. ISBN 9810226578. 
  38. See, for example:
    • Jablonka E, Lamb MJ (2005) Evolution in Four Dimension: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. Cambridge: MIT Press
    • PMID 14962644 Root Gorelick (2004). "Neo-Lamarckian medicine". Med Hypotheses 62: pp. 299-303.
  39. The ENCODE Project: ENCyclopedia Of DNA Elements. National Human Genome Research Institute. National Institute of Health (July 23, 2012). Guidance to the publications from this project is found in Nature's ENCODE Explorer. Nature ENCODE Explorer. Nature Publishing Group (September 5, 2012). A thread of particular interest here describes how the imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations. Impact of evolutionary selection on functional regions. Nature Publishing Group (September 6, 2012).
  40. ENCODE: Twins and epigenetics. An interview with Tim Spector, Professor of Epidemiology at Kings College London and Director of TwinsUK, who has studied and written about the genetics of twins. Science Museum. Retrieved on 2012-09-07.
  41. Gina Kolata (September 5, 2012). Bits of DNA, far from "junk", play crucial role. New York Times. Retrieved on 2012-09-07.
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  45. M Beekman, GA Sword and SJ Simpson (2008). “Biological foundations of swarm intelligence”, Christian Blum, Daniel Merkle, eds: Swarm Intelligence: Introduction and Applications. Springer, pp. 3 ff. ISBN 3540740880. 
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