What is Cybernetics?


Cybernetics is the interdisciplinary study of the Structure of Regulatory system. Cybernetics is closely related to control theory and systems theory. Both in its origins and in its evolution in the second-half of the 20th century, cybernetics is equally applicable to physical and social (that is, language-based) systems.

Contemporary cybernetics began as an interdisciplinary study connecting the fields of control systems, electrical network theory, mechanical engineering, logic modeling, evolutionary biology, neuroscience, anthropology, and psychology in the 1940s, often attributed to the Macy Conferences.

Other fields of study which have influenced or been influenced by cybernetics include game theory, system theory (a mathematical counterpart to cybernetics), psychology(especially neuropsychology, behavioral psychology, cognitive psychology, philosophy, and architecture.

Monday, February 2, 2009

Complex system

A complex system is a system composed of interconnected parts that as a whole exhibit one or more properties (behavior among the possible properties) not obvious from the properties of the individual parts. A system’s complexity may be of one of two forms: disorganized complexity and organized complexity. In essence, disorganized complexity is a matter of a very large number of parts, and organized complexity is a matter of the subject system (quite possibly with only a limited number of parts) exhibiting emergent properties. Examples of complex systems include ant colonies, human economies, climate, nervous systems, cells and living things, including human beings, as well as modern energy or telecommunication infrastructures. Indeed, many systems of interest to humans are complex systems.

Complex systems are studied by many areas of natural science, mathematics, and social science. Fields that specialize in the interdisciplinary study of complex systems include systems theory, complexity theory, systems ecology, and cybernetics.



Overview

A complex system is any system featuring a large number of interacting components, whose aggregate activity is nonlinear and typically exhibits self-organization under selective pressures. Now the term complex systems has multiple meaning:

  • A specific kind of systems, that are complex
  • A field of science studying these systems, see further complex systems
  • A paradigm, that complex systems have to be studied with non-linear dynamics, see further complexity

Various informal descriptions of complex systems have been put forward, and these may give some insight into their properties. A special edition of Science about complex systems highlighted several of these:

  • A complex system is a highly structured system, which shows structure with variations (Goldenfeld and Kadanoff)
  • A complex system is one whose evolution is very sensitive to initial conditions or to small perturbations, one in which the number of independent interacting components is large, or one in which there are multiple pathways by which the system can evolve (Whitesides and Ismagilov)
  • A complex system is one that by design or function or both is difficult to understand and verify (Weng, Bhalla and Iyengar)
  • A complex system is one in which there are multiple interactions between many different components (D. Rind)
  • Complex systems are systems in process that constantly evolve and unfold over time (W. Brian Arthur).

History

Although one can argue that humans have been studying complex systems for thousands of years, the modern scientific study of complex systems is relatively young when compared to areas of science such as physics and chemistry. The history of the scientific study of these systems follows several different strands.

In the area of mathematics, arguably the largest contribution to the study of complex systems was the discovery of chaos in deterministic systems, a feature of certain dynamical systems that is strongly related to nonlinearity. The study of neural networks was also integral in advancing the mathematics needed to study complex systems.

The notion of self-organizing systems is tied up to work in nonequilibrium thermodynamics, including that pioneered by chemist and Nobel laureate Ilya Prigogine in his study of dissipative structures.


Types of complex systems

A commonly accepted taxonomy of complex systems does not exist yet, but most characteristic are the following.

Chaotic systems

For a dynamical system to be classified as chaotic, most scientists will agree that it must have the following properties:

  1. it must be sensitive to initial conditions,
  2. it must be topologically mixing, and
  3. its periodic orbits must be dense.

Sensitivity to initial conditions means that each point in such a system is arbitrarily closely approximated by other points with significantly different future trajectories. Thus, an arbitrarily small perturbation of the current trajectory may lead to significantly different future behaviour.

Complex adaptive systems

Complex adaptive systems (CAS) are special cases of complex systems. They are complex in that they are diverse and made up of multiple interconnected elements and adaptive in that they have the capacity to change and learn from experience. Examples of complex adaptive systems include the stock market, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and any human social group-based endeavour in a cultural and social system such as political parties or communities.

Nonlinear system

A nonlinear system is one whose behavior can't be expressed as a sum of the behaviors of its parts (or of their multiples.) In technical terms, the behavior of nonlinear systems is not subject to the principle of superposition. Linear systems are subject to superposition.


Topics on complex systems

Features of complex systems

Complex systems may have the following features.

Boundaries are difficult to determine
It can be difficult to determine the boundaries of a complex system. The decision is ultimately made by the observer.
Complex systems may be open
Complex systems are usually open systems — that is, they exist in a thermodynamic gradient and dissipate energy. In other words, complex systems are frequently far from energetic equilibrium: but despite this flux, there may be pattern stability.
Complex systems may have a memory
The history of a complex system may be important. Because complex systems are dynamical systems they change over time, and prior states may have an influence on present states. More formally, complex systems often exhibit hysteresis.
Complex systems may be nested
The components of a complex system may themselves be complex systems. For example, an economy is made up of organisations, which are made up of people, which are made up of cells - all of which are complex systems.
Dynamic network of multiplicity
As well as coupling rules, the dynamic network of a complex system is important. Small-world or scale-free networks which have many local interactions and a smaller number of inter-area connections are often employed. Natural complex systems often exhibit such topologies. In the human cortex for example, we see dense local connectivity and a few very long axon projections between regions inside the cortex and to other brain regions.
May produce emergent phenomena
Complex systems may exhibit behaviors that are emergent, which is to say that while the results may be deterministic, they may have properties that can only be studied at a higher level. For example, the termites in a mound have physiology, biochemistry and biological development that are at one level of analysis, but their social behavior and mound building is a property that emerges from the collection of termites and needs to be analysed at a different level.
Relationships are non-linear
In practical terms, this means a small perturbation may cause a large effect, a proportional effect, or even no effect at all. In linear systems, effect is always directly proportional to cause. See nonlinearity.
Relationships contain feedback loops
Both negative (damping) and positive (amplifying) feedback are often found in complex systems. The effects of an element's behaviour are fed back to in such a way that the element itself is altered.

Complex systems

Complex systems is a scientific field which studies the common properties of systems considered complex in nature, society and science. It is also called complex systems theory, complexity science, study of complex systems, sciences of complexity, non-equilibrium physics, and historical physics. The key problems of such systems are difficulties with their formal modeling and simulation. From such perspective, in different research contexts complex systems are defined on the base of their different attributes. At present, the consensus related to one universal definition of complex system does not exist yet.


Overview

A Braitenberg simulation, programmed in breve, an artificial life simulator.

The study of complex systems is bringing new vitality to many areas of science where a more typical reductionist strategy has fallen short. Complex systems is therefore often used as a broad term encompassing a research approach to problems in many diverse disciplines including neurosciences, social sciences, meteorology, chemistry, physics, computer science, psychology, artificial life, evolutionary computation, economics, earthquake prediction, molecular biology and enquiries into the nature of living cells themselves.

In these endeavors, scientists often seek simple non-linear coupling rules which lead to complex phenomena (rather than describe - see above), but this need not be the case. Human societies (and probably human brains) are complex systems in which neither the components nor the couplings are simple. Nevertheless, they exhibit many of the hallmarks of complex systems. It is worth remarking that non-linearity is not a necessary feature of complex systems modeling: macro-analyses that concern unstable equilibrium and evolution processes of certain biological/social/economic systems can usefully be carried out also by sets of linear equations, which do nevertheless entail reciprocal dependence between variable parameters.

Traditionally, engineering has striven to keep its systems linear, because that makes them simpler to build and to predict. However, many physical systems (for example lasers) are inherently "complex systems" in terms of the definition above, and engineering practice must now include elements of complex systems research.

Information theory applies well to the complex adaptive systems, CAS, through the concepts of object oriented design, as well as through formalized concepts of organization and disorder that can be associated with any systems evolution process.


History

Complex Systems is a new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment.

The earliest precursor to modern complex systems theory can be found in the classical political economy of the Scottish Enlightenment, later developed by the Austrian school of economics, which says that order in market systems is spontaneous (or emergent) in that it is the result of human action, but not the execution of any human design.

Upon this the Austrian school developed from the 19th to the early 20th century the economic calculation problem, along with the concept of dispersed knowledge, which were to fuel debates against the then-dominant Keynesian economics. This debate would notably lead economists, politicians and other parties to explore the question of computational complexity.

A pioneer in the field, and inspired by Karl Popper's and Warren Weaver's works, Nobel prize economist and philosopher Friedrich Hayek dedicated much of his work, from early to the late 20th century, to the study of complex phenomena, not constraining his work to human economies but to other fields such as psychology, biology and cybernetics.

Further Steven Strogatz from Sync stated that "every decade or so, a grandiose theory comes along, bearing similar aspirations and often brandishing an ominous-sounding C-name. In the 1960s it was cybernetics. In the '70s it was catastrophe theory. Then came chaos theory in the '80s and complexity theory in the '90s."


Topics in the complex systems study

Complexity and modeling

A way of modelling Complex Adaptive System

One of Hayek's main contributions to early complexity theory is his distinction between the human capacity to predict the behaviour of simple systems and its capacity to predict the behaviour of complex systems through modeling. He believed that economics and the sciences of complex phenomena in general, which in his view included biology, psychology, and so on, could not be modeled after the sciences that deal with essentially simple phenomena like physics. Hayek would notably explain that complex phenomena, through modeling, can only allow pattern predictions, compared with the precise predictions that can be made out of non-complex phenomena.

Complexity and chaos theory

Complexity theory is rooted in Chaos theory, which in turn has its origins more than a century ago in the work of the French mathematician Henri Poincaré. Chaos is sometimes viewed as extremely complicated information, rather than as an absence of order. The point is that chaos remains deterministic. With perfect knowledge of the initial conditions and of the context of an action, the course of this action can be predicted in chaos theory. As argued by Prigogine, Complexity is non-deterministic, and gives no way whatsoever to predict the future. The emergence of complexity theory shows a domain between deterministic order and randomness which is complex. This is referred as the 'edge of chaos'.

A plot of the Lorenz attractor

When one analyses complex systems, sensitivity to initial conditions, for example, is not an issue as important as within the chaos theory in which it prevails. As stated by Colander, the study of complexity is the opposite of the study of chaos. Complexity is about how a huge number of extremely complicated and dynamic set of relationships can generate some simple behavioural patterns, whereas chaotic behaviour, in the sense of deterministic chaos, is the result of a relatively small number of non-linear interactions.

Therefore, the main difference between Chaotic systems and complex systems is their history. Chaotic systems don’t rely on their history as complex ones do. Chaotic behaviour pushes a system in equilibrium into chaotic order, which means, in other words, out of what we traditionally define as 'order'. On the other hand, complex systems evolve far from equilibrium at the edge of chaos. They evolve at a critical state built up by a history of irreversible and unexpected events. In a sense chaotic systems can be regarded as a subset of complex systems distinguished precisely by this absence of historical dependence. Many real complex systems are, in practice and over long but finite time periods, robust. However, they do possess the potential for radical qualitative change of kind whilst retaining systemic integrity. Metamorphosis serves as perhaps more than a metaphor for such transformations.


Research centers, conferences, and journals

Institutes and research centers


  • New England Complex Systems Institute

  • Santa Fe Institute

  • Center for Social Dynamics & Complexity (CSDC) at Arizona State University

Journals

  • Interdisciplinary Description of Complex Systems journal


See Also

Complex adaptive system

Complex adaptive systems are special cases of complex systems. They are complex in that they are diverse and made up of multiple interconnected elements and adaptive in that they have the capacity to change and learn from experience. The term complex adaptive systems (CAS) was coined at the interdisciplinary Santa Fe Institute (SFI), by John H. Holland, Murray Gell-Mann and others.


Overview

The term complex adaptive systems, or complexity science, is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory— it encompasses more than one theoretical framework and is highly interdisciplinary, seeking the answers to some fundamental questions about living, adaptable, changeable systems.

Examples of complex adaptive systems include the stock market, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and any human social group-based endeavour in a cultural and social system such as political parties or communities. There are close relationships between the field of CAS and artificial life. In both areas the principles emergence and self-organization are very important.

CAS ideas and models are essentially evolutionary, grounded in modern biological views on adaptation and evolution. The theory of complex adaptive systems bridges developments of systems theory with the ideas of generalized Darwinism, which suggests that Darwinian principles of evolution can explain a range of complex material phenomena, from cosmic to social objects.

Definitions

A CAS is a complex, self-similar collection of interacting adaptive agents. The study of CAS focuses on complex, emergent and macroscopic properties of the system. Various definitions have been offered by different researchers:

  • John H. Holland
A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents.
  • Kevin Dooley
A CAS behaves/evolves according to three key principles: order is emergent as opposed to predetermined (c.f. Neural Networks), the system's history is irreversible, and the system's future is often unpredictable. The basic building blocks of the CAS are agents. Agents scan their environment and develop schema representing interpretive and action rules. These schema are subject to change and evolution.
  • Other definitions
Macroscopic collections of simple (and typically nonlinearly) interacting units that are endowed with the ability to evolve and adapt to a changing environment.

General properties

Complex Adaptive System

What distinguishes a CAS from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. A MAS is simply defined as a system composed of multiple, interacting agents. In CASs, the agents as well as the system are adaptive: the system is self-similar. A CAS is a complex, self-similar collectivity of interacting adaptive agents. Complex Adaptive Systems are characterised by a high degree of adaptive capacity, giving them resilience in the face of perturbation.

Other important properties are adaptation (or homeostasis), communication, cooperation, specialization, spatial and temporal organization, and of course reproduction. They can be found on all levels: cells specialize, adapt and reproduce themselves just like larger organisms do. Communication and cooperation take place on all levels, from the agent to the system level. The forces driving co-operation between agents in such a system can be analysed with game theory.


Evolution of complexity

Passive versus active trends in the evolution of complexity. CAS at the beginning of the processes are colored red. Changes in the number of systems are shown by the height of the bars, with each set of graphs moving up in a time series.

Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms. This observation has led to the common idea of evolution being progressive and leading towards what are viewed as "higher organisms".

If this were generally true, evolution would possess an active trend towards complexity. As shown below, in this type of process the value of the most common amount of complexity would increase over time. Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.

However, the idea of a general trend towards complexity in evolution can also be explained through a passive process. This involves an increase in variance but the most common value, the mode, does not change. Thus, the maximum level of complexity increases over time, but only as an indirect product of there being more organisms in total. This type of random process is also called a bounded random walk.

In this hypothesis, the apparent trend towards more complex organisms is an illusion resulting from concentrating on the small number of large, very complex organisms that inhabit the right-hand tail of the complexity distribution and ignoring simpler and much more common organisms. This passive model emphasizes that the overwhelming majority of species are microscopic prokaryotes, which comprise about half the world's biomass, constitute the vast majority of Earth's biodiversity. Therefore, simple life remains dominant on Earth, and complex life appears more diverse only because of sampling bias.

This lack of an overall trend towards complexity in biology does not preclude the existence of forces driving systems towards complexity in a subset of cases. These minor trends are balanced by other evolutionary pressures that drive systems towards less complex states.

Sunday, February 1, 2009

Systems biology

Example of systems biology research.

Systems biology is a biology-based inter-disciplinary study field that focuses on the systematic study of complex interactions in biological systems, thus using a new perspective (integration instead of reduction) to study them. Particularly from year 2000 onwards, the term is used widely in the biosciences, and in a variety of contexts. Because the scientific method has been used primarily toward reductionism, one of the goals of systems biology is to discover new emergent properties that may arise from the systemic view used by this discipline in order to understand better the entirety of processes that happen in a biological system.


Overview

Systems biology can be considered from a number of different aspects:

  • Some sources discuss systems biology as a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway).
  • Other sources consider systems biology as a paradigm, usually defined in antithesis to the so-called reductionist paradigm, although fully consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations:
"The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models" Science
"Systems biology...is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different....It means changing our philosophy, in the full sense of the term" Denis Noble
  • Still other sources view systems biology in terms of the operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.
  • Finally, some sources see it as a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide (Such as the Institute for Systems Biology in Seattle, Washington, USA).


History

Systems biology finds its roots in:

  • the quantitative modelling of enzyme kinetics, a discipline that flourished between 1900 and 1970,
  • the simulations developed to study neurophysiology, and
  • control theory and cybernetics.

One of the theorists who can be seen as a precursor of systems biology is Ludwig von Bertalanffy with his general systems theory. One of the first numerical simulations in biology was published in 1952 by the British neurophysiologists and Nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley, who constructed a mathematical model that explained the action potential propagating along the axon of a neuronal cell. Their model described a cellular function emerging from the interaction between two different molecular components, a potassium and a sodium channels, and can therefore be seen as the beginning of computational systems biology. In 1960, Denis Noble developed the first computer model of the heart pacemaker.

The formal study of systems biology, as a distinct discipline, was launched by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland, Ohio entitled "Systems Theory and Biology."

The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the Metabolic Control Analysis and the biochemical systems theory. The successes of molecular biology throughout the 1980s, coupled with a skepticism toward theoretical biology, that then promised more than it achieved, caused the quantitative modelling of biological processes to become a somewhat minor field.

However the birth of functional genomics in the 1990s meant that large quantities of high quality data became available, while the computing power exploded, making more realistic models possible. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell.

Around the year 2000, when Institutes of Systems Biology were established in Seattle and Tokyo, systems biology emerged as a movement in its own right, spurred on by the completion of various genome projects, the large increase in data from the omics (e.g. genomics and proteomics) and the accompanying advances in high-throughput experiments and bioinformatics. Since then, various research institutes dedicated to systems biology have been developed. As of summer 2006, due to a shortage of people in systems biology several doctoral training centres in systems biology have been established in many parts of the world.


Techniques associated with systems biology

Overview of signal transduction pathways

According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools, some typical technology platforms are:

  • Transcriptomics: whole cell or tissue gene expression measurements by DNA microarrays or serial analysis of gene expression
  • Proteomics: complete identification of proteins and protein expression patterns of a cell or tissue through two-dimensional gel electrophoresis and mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins.
  • Metabolomics: identification and measurement of all small-molecules metabolites within a cell or tissue
  • Glycomics: identification of the entirety of all carbohydrates in a cell or tissue.

In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes:

  • Interactomics which is used mostly in the context of protein-protein interaction but in theory encompasses interactions between all molecules within a cell
  • Fluxomics, which deals with the dynamic changes of molecules within a cell over time
  • Biomics: systems analysis of the biome.

The investigations are frequently combined with large scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries. Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.

The investigations of a single level of biological organization (such as those listed above) are usually referred to as Systematic Systems Biology. Other areas of Systems Biology includes Integrative Systems Biology, which seeks to integrate different types of information to advance the understanding the biological whole, and Dynamic Systems Biology, which aims to uncover how the biological whole changes over time (during evolution, for example, the onset of disease or in response to a perturbation). Functional Genomics may also be considered a sub-field of Systems Biology.

The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used. Other aspects of computer science and informatics are also used in systems biology. These include new forms of computational model, such as the use of process calculi to model biological processes, the integration of information from the literature, using techniques of information extraction and text mining, the development of online databases and repositories for sharing data and models (such as BioModels Database), approaches to database integration and software interoperability via loose coupling of software, websites and databases and the development of syntactically and semantically sound ways of representing biological models, such as the Systems Biology Markup Language.


Synthetic biology

Synthetic biology is a new area of biological research that combines science and engineering in order to design and build ("synthesize") novel biological functions and systems.
A light programmable biofilm made by the UT Austin / UCSF team during the 2004 Synthetic Biology competition, displaying "Hello World"


History of the term

In 1974, the Polish geneticist Waclaw Szybalski introduced the term "synthetic biology", writing: Let me now comment on the question "what next". Up to now we are working on the descriptive phase of molecular biology. ... But the real challenge will start when we enter the synthetic biology phase of research in our field. We will then devise new control elements and add these new modules to the existing genomes or build up wholly new genomes. This would be a field with the unlimited expansion potential and hardly any limitations to building "new better control circuits" and ..... finally other "synthetic" organisms, like a "new better mouse". ... I am not concerned that we will run out exciting and novel ideas, ... in the synthetic biology, in general. When in 1978 the Nobel Prize in Physiology or Medicine was awarded to Arber, Nathans and Smith for the discovery of restriction enzymes, Waclaw Szybalski wrote in an editorial comment in the journal Gene: The work on restriction nucleases not only permits us easily to construct recombinant DNA molecules and to analyze individual genes, but also has led us into the new era of synthetic biology where not only existing genes are described and analyzed but also new gene arrangements can be constructed and evaluated. Nevertheless, the term was largely unused or abandoned until the early 21st century (e.g., SB1.0, the First International Meeting on Synthetic Biology, was held in 2004).

Biology

Biologists are interested in learning more about how natural living systems work. One simple, direct way to test our current understanding of a natural living system is to build an instance (or version) of the system in accordance with our current understanding of the system. Michael Elowitz's early work on the Repressilator is one good example of such work. Elowitz had a model for how gene expression should work inside living cells. To test his model, he built a piece of DNA in accordance with his model, placed the DNA inside living cells, and watched what happened. Slight differences between observation and expectation highlight new science that may be well worth doing. Work of this sort often makes good use of mathematics to predict and study the dynamics of the biological system before experimentally constructing it. A wide variety of mathematical descriptions have been used with varying accuracy, including graph theory, Boolean networks, ordinary differential equations, stochastic differential equations, and Master equations (in order of increasing accuracy). Good examples include the work of Adam Arkin, Jim Collins and Alexander van Oudenaarden. See also the PBS Nova special on artificial life.


Chemistry

Biological systems are physical systems that are made up of chemicals. Around 100 years ago, the science of chemistry went through a transition from studying natural chemicals to trying to design and build new chemicals. This transition led to the field of synthetic chemistry. In the same tradition, some aspects of synthetic biology can be viewed as an extension and application of synthetic chemistry to biology, and include work ranging from the creation of useful new biochemicals to studying the origins of life. Eric Kool's group at Stanford, Steven Benner's group at Florida, Carlos Bustamante's group at Berkeley, and Jack Szostak's group at Harvard are good examples of this tradition.


Engineering

Engineers view biology as a technology. Synthetic Biology includes the broad redefinition and expansion of biotechnology, with the ultimate goals of being able to design and build engineered biological systems that process information, manipulate chemicals, fabricate materials and structures, produce energy, provide food, and maintain and enhance human health and our environment . A good example of these technologies include the work of Chris Voigt, who redesigned the Type III secretion system used by Salmonella typhimurium to secrete spider silk proteins, a strong elastic biomaterial, instead of its own natural infectious proteins. One aspect of Synthetic Biology which distinguishes it from conventional genetic engineering is a heavy emphasis on developing foundational technologies that make the engineering of biology easier and more reliable. Good examples of engineering in Synthetic Biology include the pioneering work of Tim Gardner and Jim Collins on an engineered genetic toggle switch, the Registry of Standard Biological Parts, and the International Genetically Engineered Machine competition (iGEM).


Re-writing

Re-writers are Synthetic Biologists who are interested in testing the idea that since natural biological systems are so complicated, we would be better off re-building the natural systems that we care about, from the ground up, in order to provide engineered surrogates that are easier to understand and interact with. Re-writers draw inspiration from refactoring, a process sometimes used to improve computer software. Drew Endy and his group have done some preliminary work on re-writing (e.g., Refactoring Bacteriophage T7). Oligonucleotides harvested from a photolithographic or inkjet manufactured DNA chip combined with DNA mismatch error-correction allows inexpensive large-scale changes of codons in genetic systems to improve gene expression or incorporate novel amino-acids (see George Church's and Anthony Forster's lab synthetic cell projects. As in the T7 example above, this favors a synthesis-from-scratch approach.


Human practices: Emerging social, ethical, legal challenges

In addition to numerous bioscientific challenges, the vast potential of synthetic biology to play a formative role in contemporary human life raises new questions for bioethics, biosecurity, biosafety, health, energy and intellectual property. To date considerable focus has been given to the so-called dual-use challenge. For example, while the study of synthetic biology can lead to more efficient ways to produce cures (e.g. against malaria), it may also lead to synthesis or redesign of harmful pathogens (e.g., smallpox). In addition, scientists, funders, policymakers, ethicists and others have recognized challenges presented by a post 9/11 political milieu. A new range of potentially malicious actors and actions (i.e., terrorists/terrorism) must now be taken into account by those seeking to govern scientific domains; and the internet and other new media provide global access to technological know-how and scientific knowledge. Such global access cannot be addressed using existing models of nation-specific regulation.(New Scientist, November 12, 2005). Some detailed suggestions for licensing and monitoring the various phases of gene and genome synthesis are beginning to appear. Other initiatives, such as OpenWetWare, diybio, biopunk, biohack, and possibly others, take a more proactive approach towards the proliferation of open source synbio projects.

There is also an ongoing, comprehensive, and open online discussion of so-called “societal issues” online at OpenWetWare, at the SYNBIOSAFE forum on issues regarding ethics, safety, security, IPR, governance, and public perception (background document).

Recently, efforts have been made to think beyond the “societal issues” model of ethics, politics, and science in relation to synthetic biology. This effort refuses the established convention of imagining society outside of and downstream of scientific practices, such that bioethics is assigned the task of limiting the negative impact of science on society. By contrast recent approaches focus on the integral and mutually formative relations among scientific and other human practices. These human practices approaches attempt to invent ongoing and regular forms of collaboration among synthetic biologists, ethicists, political analysts, funders, human scientists and civil society activists. To date collaborative work on “governance” or “society” or “ethics” in relation to synthetic biology has largely consisted either of intensive, short term meetings, aimed at producing guidelines or regulations, or standing committees whose purpose is limited to protocol review or rule enforcement. Such work has proven valuable in identifying the ways in which synthetic biology intensifies already-known challenges in rDNA technologies. However, these forms are not suited to identifying new challenges as they emerge. An example of efforts to develop ongoing collaboration is the Human Practices component of the Synthetic Biology Engineering Research Center (SynBERC), an NSF funded collaboration among a number of leading research universities. In Europe, the multi-partner project SYNBIOSAFE, coordinated by IDC, is investigating the biosafety, biosecurity and ethical aspects of synthetic biology. The International Consortium for Polynucleotide Synthesis was formed in 2006 to encourage sharing of ideas and resources for pro-actively monitoring synthetic gene orders and enforcing safe practices, (ICPS). The recently formed Industry Association Synthetic Biology (IASB) has also started to tackle open biosecurity problems for biotech companies doing gene synthesis.


Key enabling technologies

There are several key enabling technologies that are critical to the growth of synthetic biology. The key concepts include standardization of biological parts and hierarchical abstraction to permit using those parts in increasingly complex synthetic systems. Achieving this is greatly aided by basic technologies of reading and writing of DNA (sequencing and fabrication), which are improving in price/performance exponentially (Kurzweil 2001). Measurements under a variety of conditions are needed for accurate modeling and computer-aided-design (CAD).

Sequencing

Synthetic biologists make use of DNA sequencing in their work in several ways. First, large-scale genome sequencing efforts continue to provide a wealth of information on naturally occurring organisms. This information provides a rich substrate from which synthetic biologists can construct parts and devices. Second, synthetic biologists use sequencing to verify that they fabricated their engineered system as intended. Third, fast, cheap and reliable sequencing can also facilitate rapid detection and identification of synthetic systems and organisms.

Fabrication

A critical limitation in synthetic biology today is the time and effort expended during fabrication of engineered genetic sequences. To speed up the cycle of design, fabrication, testing and redesign, synthetic biology requires more rapid and reliable de novo DNA synthesis and assembly of fragments of DNA.

In 2002 researchers at SUNY Stony Brook succeeded in synthesizing the 7741 base poliovirus genome from its published sequence, producing the first synthetic organism. This took about two years of painstaking work. In 2003 the 5386 bp genome of the bacteriophage Phi X 174 was assembled in about two weeks. In 2006, the same team, at the J. Craig Venter Institute, has constructed and patented a synthetic genome of a novel minimal bacterium, Mycoplasma laboratorium and is working on getting it functioning in a living cell.

In 2007 it was reported that several companies were offering the synthesis of genetic sequences up to 2000 bp long, for a price of about $1 per base pair and a turnaround time of less than two weeks.

Modeling

Models inform the design of engineered biological systems by allowing synthetic biologists to better predict system behavior prior to fabrication. Synthetic biology will benefit from better models of how biological molecules bind substrates and catalyze reactions, how DNA encodes the information needed to specify the cell and how multi-component integrated systems behave. Recently, multiscale models of gene regulatory networks have been developed that focus on synthetic biology applications. Simulations have been used that model all biomolecular interactions in transcription, translation, regulation, and induction of gene regulatory networks, guiding the design of synthetic systems.

Measurement

Precise and accurate quantitative measurements of biological systems are crucial to improving understanding of biology. Such measurements often help to elucidate how biological systems work and provide the basis for model construction and validation. Differences between predicted and measured system behavior can identify gaps in understanding and explain why synthetic systems don't always behave as intended. Technologies which allow many parallel and time-dependent measurements will be especially useful in synthetic biology. Microscopy and flow cytometry are examples of useful measurement technologies.



Medical cybernetics

Medical Cybernetics is a field of applied cybernetics which utilizes the concepts of cybernetics to medical research and practice. It covers an emerging working program for the application of systems- and communications-theory, connectionism and decision theory on biomedical research and health related questions.



Overview

Medical Cybernetics searches for quantitative descriptions of biological dynamics primarily in the intact, but beyond also in the diseased organism in order to gain new insights into the principles of life and its perturbations and to gather evidence based foundations for clinical decision making. It investigates intercausal networks in human biology, medical decision making and information processing structures in the living organism.

Topics in Medical cybernetics:

  • Systems Theory in medical sciences: The scope of systems theory in the medical sciences is searching for and modelling of physiological dynamics in the intact and diseased organism to gain deeper insights into the organizational principles of life and its perturbations.
  • Medical information and Communication Theory: Motivated by the awareness of information as an essential principle of life the application of communication theory to biomedicine aims to mathematically describe signalling processes and information storage in different physiological layers.
  • Connectionism: Connectionistic models describe information processing in neural networks - thus forming a bridge between biological and technological research.
  • Medical Decision Theory (MDT): The Goal of MDT is to gather evidence based foundations for decision making in the clinical setting.

Homeostasis

Homeostasis (from Greek: ὅμος, hómos, "equal"; and ιστημι, histēmi, "to stand"; coined by Walter Bradford Cannon) is the property of a system, either open or closed, that regulates its internal environment so as to maintain a stable, constant condition. Typically used to refer to a living organism, the concept came from that of milieu interieur that was created by Claude Bernard and published in 1865. Multiple dynamic equilibrium adjustment and regulation mechanisms make homeostasis possible.



Biological

With regard to any given life system parameter, an organism may be a conformer or a regulator. Regulators try to maintain the parameter at a constant level over possibly wide ambient environmental variations. On the other hand, conformers allow the environment to determine the parameter. For instance, endothermic animals maintain a constant body temperature, while exothermic animals exhibit wide body temperature variation. Examples of endothermic animals include mammals and birds, examples of exothermic animals include reptiles and some sea animals.

Conformers may still have behavioral adaptations allowing them to exert some control over a given parameter. For instance, reptiles often rest on sun-heated rocks in the morning to raise their body temperature. Vice versa, regulators are usually responsive to external circumstances: if the same sun-baked boulder happens to host a ground squirrel, its metabolism will adjust to the lesser need for internal heat production.

Thermal image of a cold-blooded tarantula on a warm-blooded human hand

An advantage of homeostatic regulation is that it allows an organism to function effectively in a broad range of environmental conditions. For example, ectotherms tend to become sluggish at low temperatures, whereas a co-located endotherm may be fully active. That thermal stability comes at a price since an automatic regulation system requires additional energy. One reason snakes may eat only once a week is that they use much less energy to maintain homeostasis.

Most homeostatic regulation is controlled by the release of hormones into the bloodstream. However other regulatory processes rely on simple diffusion to maintain a balance.

Homeostatic regulation extends far beyond the control of temperature. All animals also regulate their blood glucose, as well as the concentration of their blood. Mammals regulate their blood glucose with insulin and glucagon. These hormones are released by the pancreas, the inadequate production of the two for any reason, would result in diabetes. The kidneys are used to remove excess water and ions from the blood. These are then expelled as urine. The kidneys perform a vital role in homeostatic regulation in mammals, removing excess water, salt, and urea from the blood. These are the body's main waste products.

Another homeostatic regulation occurs in the gut. Homeostasis of the gut is not fully understood but it is believed that Toll-like receptor (TLR) expression profiles contribute to it. Intestinal epithelial cells exhibit important factors that contribute to homeostasis: 1)They have different cellular distribution of TLR’s compared to the normal gut mucosa. An example of this is how TLR5 (activated by flagellin) can redistribute to the basolateral membrane which is the perfect place where flagellin can be detected. 2)The enterocytes express high levels of TLR inhibitor Toll-interacting protein (Tollip). Tollip is a human gene that is a part of innate immune system and is highest in a healthy gut, it correlates to luminal bacterial load. 3)Surface enterocytes also express high levels of Interleukin-1 receptor (IL-1R) -containing inhibitory molecule. IL-1R are also referred to as single immunoglobulin IL-1R (SIGIRR). Animals deficient of this are more susceptible to induced colitis, implying that SIGIRR might possibly play a role in tuning mucosal tolerance towards commensal flora. Nucleotide-binding Oligomerization Domain containing 2 (NOD2) is suggested to have an affect on suppressing inflammatory cascades based on recent evidence. It is believed to modulate signals transmitted through TLRs, TLR3, 4, and 9 specifically. Mutation of it has resulted in Crohn's disease. Excessive T-helper 1 responses to resident flora in the gut are controlled by inhibiting the controlling influence of regulatory T cells and tolerance-inducing dendritic cells.

Sleep timing depends upon a balance between homeostatic sleep propensity, the need for sleep as a function of the amount of time elapsed since the last adequate sleep episode, and circadian rhythms which determine the ideal timing of a correctly structured and restorative sleep episode.

Control Mechanisms

All homeostatic control mechanisms have at least three interdependent components for the variable being regulated: The receptor is the sensing component that monitors and responds to changes in the environment. When the receptor senses a stimulus, it sends information to a control center, the component that sets the range at which a variable is maintained. The control center determines an appropriate response to the stimulus. In most homeostatic mechanisms the control center is the brain. The control center then sends signals to an effector which can be muscles, organs or other structures that receive signals from the control center. After receiving the signal, a change occurs to correct the deviation by either enhancing it with positive feedback or depressing it with negative feedback.

Positive Feedback

Positive feedback is a mechanism by which an output is enhanced, such as protein levels. However, in order to avoid any fluctuation in the protein level, the mechanism is inhibited stochastically (I), therefore when the concentration of the activated protein (A) is past the threshold ([I]), the loop mechanism is activated and the concetration of A increases exponentially if d[A]=k [A]

Positive feedback mechanisms are designed to accelerate or enhance the output created by a stimulus that has already been activated.

Unlike negative feedback mechanisms that initiate to maintain or regulate physiological functions within a set and narrow range, the positive feedback mechanisms are designed to push levels out of normal ranges. To achieve this purpose, a series of events initiates a cascading process that builds to increase the effect of the stimulus. This process can be beneficial but is rarely used by the body due to risks of the acceleration becoming uncontrollable.

One positive feedback example event in the body is blood platelet accumulation, which, in turn, causes blood clotting in response to a break or tear in the lining of blood vessels. Another example is the release of oxytocin to intensify the contractions that take place during childbirth.

Positive feedback can also be harmful. One particular example is when a fever causes a positive feedback within homeostasis that pushes the temperature continually higher. Body temperature can reach extremes of 45°C (113°F), at which cellular proteins denature, causing the active site in proteins to change, thus causing metabolism to stop, resulting in death.

Negative Feedback

Negative feedback mechanism consists of reducing the output or activity of any organ or system back to its normal range of functioning. A good example of this is regulating blood pressure. Blood vessels can sense resistance of blood flow against the walls when blood pressure increases. The blood vessels act as the receptors and they relay this message to the brain. The brain then sends a message to the heart and blood vessels, both are the effectors. The heart rate would decrease as the blood vessels increase in diameter (or vasodilation). This change would result in the blood pressure to fall back to its normal range. The opposite would happen when blood pressure decreases, and would cause vasoconstriction.

Another important example is when the body is deprived of food. The body would then reset the metabolic set point to a lower than normal value. This would allow the body to continue to function, at a slower rate, even though the body is starving. Therefore people who deprive themselves of food while trying to lose weight, would find it easy to shed weight initially and much harder to lose more after. This is due to the body readjusting itself to a lower metabolic set point to allow the body to survive with its low supply of energy. Exercise can change this effect by increasing the metabolic demand.

Another good example of negative feedback mechanism is temperature control. The hypothalamus which monitors the body temperature, is capable of determining even the slightest of variation of normal body temperature (37 degrees Celsius). Response to such variation could be stimulation of glands that produces sweat to reduce the temperature or signaling various muscles to shiver to increase body temperature.

Both feedbacks are equally important for the healthy functioning of ones body. Complications can arise if any of the two feedbacks are affected or altered in any way.

Homeostatic Imbalance

Much disease results from disturbance of homeostasis, a condition known as homeostatic imbalance. As it ages, every organism will lose efficiency in its control systems. The inefficiencies gradually result in an unstable internal environment that increases the risk for illness. In addition, homeostatic imbalance is also responsible for the physical changes associated with aging. Even more serious than illness and other characteristics of aging, is death. Heart failure has been seen where nominal negative feedback mechanisms become overwhelmed, and destructive positive feedback mechanisms then take over.

Diseases that result from a homeostatic imbalance include diabetes, dehydration, hypoglycemia, hyperglycemia, gout, and any disease caused by a toxin present in the bloodstream. All of these conditions result from the presence of an increased amount of a particular substance. In ideal circumstances, homeostatic control mechanisms should prevent this imbalance from occurring, but, in some people, the mechanisms do not work efficiently enough or the quantity of the substance exceeds the levels at which it can be managed. In these cases, medical intervention is necessary to restore the balance, or permanent damage to the organs may result.

Varieties

The Dynamic Energy Budget theory for metabolic organisation delineates structure and (one or more) reserves in an organism. Its formulation is based on three forms of homeostasis:

  • Strong homeostasis, wherein structure and reserve do not change in composition. Since the amount of reserve and structure can vary, this allows a particular change in the composition of the whole body (as explained by the Dynamic Energy Budget theory).
  • Weak homeostasis, wherein the ratio of the amounts of reserve and structure becomes constant as long as food availability is constant, even when the organism grows. This means that the whole body composition is constant during growth in constant environments.
  • Structural homeostasis, wherein the sub-individual structures grow in harmony with the whole individual; the relative proportions of the individuals remain constant.

Ecological

Ecological homeostasis is found in a climax community of maximum permitted biodiversity, given the prevailing ecological conditions.

An example of a disturbed ecosystems or sub-climax biological communities was the island of Krakatoa after its major eruption in 1883: the established stable homeostasis of the previous forest climax ecosystem was destroyed and all life eliminated from the island. In the years after the eruption, Krakatoa went through a sequence of ecological changes in which successive groups of new plant or animal species followed one another, leading to increasing biodiversity and eventually culminating in a re-established climax community. This ecological succession on Krakatoa occurred in a number of stages; a sere is defined as "a stage in a sequence of events by which succession occurs". The complete chain of seres leading to a climax is called a prisere. In the case of Krakatoa, the island reached its climax community, with eight hundred different recorded species, in 1983, one hundred years after the eruption that cleared all life off the island. Evidence confirms that this number has been homeostatic for some time, with the introduction of new species rapidly leading to elimination of old ones.

The evidence of Krakatoa, and other disturbed or virgin ecosystems, shows that the initial colonization by pioneer or R strategy species occurs through positive feedback reproduction strategies, wherein species are weeds, producing huge numbers of possible offspring, but investing little in the success of any one. Rapid boom and bust plague or pest cycles are observed with such species. As an ecosystem starts to approach climax, these species get replaced by more sophisticated climax species, which, through negative feedback, adapt themselves to specific environmental conditions. These species, closely controlled by carrying capacity, follow K strategies, wherein species produce fewer numbers of potential offspring, but invest more heavily in securing the reproductive success of each one to the micro-environmental conditions of its specific ecological niche.

It begins with a pioneer community and ends with a climax community. This climax community occurs when the ultimate vegetation has achieved equilibrium with the local environment.

Such ecosystems form nested communities or heterarchies, in which homeostasis at one level contributes to homeostatic processes at another holonic level. For example, the loss of leaves on a mature rainforest tree creates space for new growth, and contributes to the plant litter and soil humus build-up upon which such growth depends. Of equal importance, a mature rainforest tree reduces the sunlight falling on the forest floor and helps prevent invasion by other species. But trees too fall to the forest floor, and a healthy forest glade is dependent upon a constant rate of forest regrowth, produced by the fall of logs, and the recycling of forest nutrients through the respiration of termites and other insect, fungal, and bacterial decomposers. In a similar manner, such forest glades contribute ecological services, such as the regulation of microclimates or of the hydrological cycle for an ecosystem, and a number of different ecosystems act together to maintain homeostasis, perhaps of a number of river catchments within a bioregion. A diversity of bioregions, in like manner, makes up a stable homeostatic biological region or biome.

In the Gaia hypothesis, James Lovelock stated that the entire mass of living matter on Earth (or any planet with life) functions as a vast homeostatic superorganism that actively modifies its planetary environment to produce the environmental conditions necessary for its own survival. In this view, the entire planet maintains homeostasis. Whether this sort of system is present on Earth is still open to debate. However, some relatively simple homeostatic mechanisms are generally accepted. For example, when atmospheric carbon dioxide levels rise, certain plants are able to grow better and thus act to remove more carbon dioxide from the atmosphere. When sunlight is plentiful and atmospheric temperature climbs, the phytoplankton of the ocean surface waters thrive and produce more dimethyl sulfide, DMS. The DMS molecules act as cloud condensation nuclei, which produce more clouds, and thus increase the atmospheric albedo and this feeds back to lower the temperature of the atmosphere. As scientists discover more about Gaia, vast numbers of positive and negative feedback loops are being discovered, that, together, maintain a metastable condition, sometimes within very broad range of environmental conditions.


Reactive

Example of use: "Reactive homeostasis is an immediate response to a homeostatic challenge such as predation."

However, any homeostasis is impossible without reaction - because homeostasis is and must be a "feedback" phenomenon.

The phrase "reactive homeostasis" is simply short for: "reactive compensation reestablishing homeostasis", that is to say, "reestablishing a point of homeostasis." - it should not be confused with a separate kind of homeostasis or a distinct phenomenon from homeostasis; it is simply the compensation (or compensatory) phase of homeostasis.


Other fields

The term has come to be used in other fields, as well.

Risk

An actuary may refer to risk homeostasis, where (for example) people that have anti-lock brakes have no better safety record than those without anti-lock brakes, because the former unconsciously compensate for the safer vehicle via less-safe driving habits. Previous to the innovation of anti-lock brakes, certain maneuvers involved minor skids, evoking fear and avoidance: now the anti-lock system moves the boundary for such feedback, and behavior patterns expand into the no-longer punitive area. It has also been suggested that ecological crises are an instance of risk homeostasis in which behavior known to be dangerous continues until dramatic consequences actually occur.

Stress

Sociologists and psychologists may refer to stress homeostasis, the tendency of a population or an individual to stay at a certain level of stress, often generating artificial stresses if the "natural" level of stress is not enough.

Jean Francois Lyotard, a postmodern theorist, has applied this term to societal 'power centers' that he describes as being 'governed by a principle of homeostasis,' for example, the scientific hierarchy, which will sometimes ignore a radical new discovery for years because it destabilizes previously-accepted norms.

Waste

Andrew Potter has used the term waste homeostasis in reference to the lack of net gain from energy-saving technologies.

Conversational

A 2007 study purported to find (and show clinically) conversational homeostasis in which overly-familiar people (such as spouses) condense their speech so much that they are actually worse at communicating novel information than strangers are, while not being conscious of this problem.

Metabolic

Some herbal medicines, known as adaptogens, have been defined to function as non-toxic metabolic regulators that can enhance metabolic homeostasis during stress.