Definition
          The authors’ definition of the autopoietic system has
          evolved through the years. One of them states that
          an autopoietic system is organized (defined as a unity)
          as a network of processes of production (transformation
          and destruction) of components that produces the
          components which: (1) through their interactions and
          transformations regenerate and realize the network of
          processes (relations) that produced them; and (2) constitute
          it (the machine) as a concrete unity in the space
          in which they exist by specifying the topological
          domain of its realization as such a network (Varela
          1979, p. 13). Nearly the same formula was earlier used
          to define an autopoietic machine (Maturana and Varela
          1973/1980, 1984/1987, p. 135

Bich, Leonardo

Etxeberria, Arantza

English

PhilPapers

pathways, of the role of speciﬁc viral and host genes inall steps of the viral life cycle, and of the host responseto viral infection. The aim of systems virology is theidentiﬁcation and characterization of key networkcomponents or connections, and their interplay in thevirus–host interaction network as a whole. Usingmodel analysis, systems virology aims to identifyload- and choke points of viral infection and replica-tion processes, which can be used as potential newtargets for antiviral drug design. Ultimately, the prom-ise of systems virology is to provide profound knowl-edge about the complex virus–host system, and totranslate this knowledge into predictive, preventive,and personalized medicine to combat viral infection.To achieve these objectives at a systems level,large-scale experimental data sets are required. Sys-tems virology, therefore, beneﬁts greatly from majoradvances in molecular virology and from the develop-ment of high-throughput experimental techniques andassociated data processing and analysis methods in therecent years. These include microarray-based func-tional genomics, high-throughput and high-contentsiRNA screening, live cell imaging, high-throughputprotein interaction measurements using Yeast-2-Hybrid screens, automated mass spectrometry and pro-tein arrays, and next generation sequencing (Peng et al.2009). These technological developments areparalleled by novel developments in data processing,data integration, and data analysis techniques in theﬁelds of statistical data analysis, bioinformatics, datamining, and machine learning, which are employed toreconstruct virus–host interaction networks anddevelop a basis for more detailed, quantitative, anddynamic models of virus–host interactions.Systems virology typically proceeds in an iterativecycle, consisting of systematic and large-scale pertur-bation of individual entities in the virus–host system,measuring the outcome using high-throughput tech-nologies, and then trying to relate the change at themolecular level to global properties of the systemduring the infection, using modeling and simulation,followed by the design of further experiments to ﬁll theknowledge gap highlighted by the difference betweenthe model simulation and the real system (Kitano et al.2002). As an example strategy, large-scale siRNAscreens to identify new host factors involved in viralreplication are followed by live cell imaging and moredetailed biochemical characterization of identiﬁed hostprocesses to develop quantitative, dynamic models ofHIV and HCV infection at Heidelberg University, lay-ing the basis for computational modeling and modelanalysis of virus–host systems. Modeling and data anal-ysis are then carried out using a combination ofmachinelearning approaches for the data-driven reconstructionof virus–host networks, bioinformatics annotation anddatabase queries, and forward modeling using knowl-edge-based approaches and based on differential equa-tions. Ultimately, all these approaches are mapped ontoone, virus and host cell type–speciﬁc, integrated modelof virus–host interactions. Suchmodels integrating viraland host processes can then be used to identify criticalpoints in the infection cycle, to design new drugs withoptimal efﬁciency and minimizing side effects, and togain a better understanding of host immune responseand thus vaccines development.Cross-References▶ Systems ImmunologyReferencesClermont G, Auffray C, Moreau Y, Rocke DM, Dalevi D,Dubhashi D, Marshall DR, Raasch P, Dehne F, Provero P,Tegner J, Aronow BJ, Langston MA, Benson M (2009)Bridging the gap between systems biology and medicine.Genome Med 1:88.1–88.3Kitano H (2002) Systems biology: a brief overview. Science295:1662–1664Peng X, Chan EY, Li Y, Diamond DL, Korth MJ, Katze MG(2009) Virus-host interactions: from systems biology totranslational research. Curr Opin Microbiol 12:432–438Tan S, Ganji G, Paeper B, Proll S, Katze MG (2007) Systemsbiology and the host response to viral infection. NatBiotechnol 25:1383–1389Systems, AutopoieticLeonardo Bich and Arantza EtxeberriaDepartment of Logic and Philosophy of Science,IAS-Research Center for Life, Mind, and Society,University of the Basque Country, UPV/EHU,Donostia-San Sebastia´n, SpainDefinitionThe authors’ deﬁnition of the autopoietic system hasevolved through the years. One of them states thatS 2110 Systems, Autopoietican autopoietic system is organized (deﬁned as a unity)as a network of processes of production (transforma-tion and destruction) of components that produces thecomponents which: (1) through their interactions andtransformations regenerate and realize the network ofprocesses (relations) that produced them; and (2) con-stitute it (the machine) as a concrete unity in the spacein which they exist by specifying the topologicaldomain of its realization as such a network (Varela1979, p. 13). Nearly the same formula was earlier usedto deﬁne an autopoietic machine (Maturana and Varela1973/1980, 1984/1987, p. 135).CharacteristicsThe Chilean biologists H. Maturana and F. Varelaproposed the term autopoiesis in the early 1970s toaccount for the organization of individual livingbeings, characterized as a process by which theyproduce their own identity in a mechanistic way.The autopoietic approach to life is very differentfrom that of the Theory of Evolution and MolecularBiology: On the one hand, instead of reproduction orevolution, the theory focuses on autonomy and identityto naturalize them as marks of life; on the other hand, itconsiders that all system components have the samestatus to explain the self-referent dynamics by whichthey produce a unity; that is to say, living phenome-nology is not explained in terms of some componentsbeing information carriers.Autopoietic systems, also called initiallyautopoietic machines, explore the general relationalscheme common to all living systems as the conﬁgu-ration of transformative processes whose result is theconﬁguration itself, so that identity and activity,producer and product coincide. Unlike Turingmachines, set by external programmers (thus beingheteropoietic) to compute problems referring to issuesother than the system itself (thus being allopoietic),autopoietic machines realize a self-deﬁned identity ina space of interactions. Already in 1974 (Varela et al.1974), the authors presented their account of livingorganization with a computational model in cellularautomata which was later rehearsed by BarryMcMullin (in Di Paolo 2004).Some of these distinctions, for example, betweenautopoietic and heteropoietic, already appear inCanguilhem’s La connaisance de la vie. In fact, theautopoietic approach belongs to a systemic traditionfocused on the problem of the relational unity of theliving, associated to Kant’s understanding of organ-isms in the Critique of Judgment, Claude Bernard’sconcept ofmilieu inte´rieur, and the organicist traditionthat considers life as organization (G. Canguilhem,H. Jonas, J. Piaget among others, seeWeber and Varela2002), and opposed to the mainstream of the time, suchas some of the views of Jacob´s La logique du vivant.Other clear associations are with the cyberneticmovement, especially with second-order cybernetics.The inﬂuence of the autopoietic approach has beensigniﬁcant in theoretical Biology (especially on workon the deﬁnition of life and origins and organization ofminimal living systems), Artiﬁcial Life, and CognitiveScience. In contrast, it has had no comparable effect onmainstream biology (e.g., Molecular and EvolutionaryBiology), although it appears to be more present inSystems Biology, whose approach is less centered onmaster molecules and information.The Main Conceptual DevelopmentAutopoietic systems aim to grasp what makes anorganism be a unity of a speciﬁc kind, that is to say,how a system appears out of a continuous ﬂux oftransformations at the level of its components.The system is characterized by its organizationalclosure, a notion that provides a reinterpretation of thecybernetic notion of circular self-stabilization, whichinstead of considering single regulatory processes inisolation and then coupling them together (as homeo-static machines, acting on internal variables, behave)refers to the whole living system: The autopoieticsystem is organized in such a way that it does notonly maintain the interval of stability of somevariables, but also the global organization is keptinvariant.Some of the main concepts of the theory refer todistinctions, such as the following:• Organization and structure: This emphasizes that anorganism is not characterized by its material orphysicochemical processes, but by how the interac-tions are related to produce and maintain theintegrated biological unity they belong to.The structure refers to the variant aspect of a livingsystem: to its physical realization, whereas the notionof organization aims to grasp the invariant one: thetopology of the relations that constitute it. Thus, theauthors embrace a particular form of multipleSystems, Autopoietic 2111 SSrealizability between organization and structure, asthe autopoietic organization is proposed asa main invariant underlying the diverse biologicalphenomenology, that is conserved through the onto-genetic and phylogenetic changes.• Openness and closure: Whereas living systems areopen to the exchange of matter and energy at thelevel of structure, the network of processes thatconstitutes their organization is closed in the formof a global cyclical process that determines andregenerates itself. Rosen developed a similar viewindependently and expressed it mathematically inthe notions of the system being open to materialcausation and closed to efﬁcient causation (Letelieret al. 2006). The distinction between open structureand closed organization can be also found inPiaget’s Biologie et connaissance, complementedby an internal mechanism of adaptation to pertur-bations in terms of Waddington’s assimilation andaccommodation.Another characteristic feature of the theory is itsinternalism, present through the notion of structuraldeterminism. In each time step, the system interactsand changes in a way totally determined by its struc-ture, which speciﬁes the set of all possible changes toeffective perturbations. The latter do not deﬁne, butonly trigger structural changes. Thus, environmentalperturbations do not have intrinsic meaning, theireffect depends on the structure of the receiver: Unlikein input-output relations, the same stimulus can causedifferent alterations. F. Varela (in Varela et al. 1991)showed this peculiarity through a cellular automatamodel called Bitorio. Similar to this is the ideathat in the communication between two systems,there is no transmission of information but a structuralcoupling.In this framework, evolution is reinterpreted inneutralist terms as a natural drift. The idea of adapta-tion as optimization of the organism’s traits by naturalselection is replaced by one of conservation of adapta-tion, as the maintenance of a speciﬁc form of couplingbetween the living system and its environment(Maturana and Varela 1984).Further DevelopmentsDevelopments of the autopoietic theory have particu-larly been connected with the deﬁnition of lifeand autonomy and with agency and cognition.Finally, some have tried, without success so far, toextend the notion of autopoiesis from the cellular levelto that of multicellular organisms and social systems.• Deﬁnition of life as autonomy. The main inﬂuenceof autopoietic systems has been in ﬁelds related tothe deﬁnition of life and its organization, such asArtiﬁcial Life, Synthetic Biology, Astrobiology or,in general, Systems Biology. The main impact of theautopoietic theory in these areas has been throughthe notion of autonomy as an ingredient of thedeﬁnition of life.The goals of the initial approach to Artiﬁcial Lifewere congenial to the theory of autopoietic systemsin the signiﬁcance of form above matter, but verydifferent in what concerns the nature of life, whichwas there thought to be connected to reproductionand evolution by the mainstream, not to autonomyor organization as the autopoietic theory maintains.Nevertheless, for some authors, it is problematic toconsider the operations of the living only at a formalabstract level, without considering the complexitiesof material and historical realizations of life as weknow it. For example, the formal account of auton-omy fails to meet the thermodynamic criteriarequired to realistically maintain the state of activ-ity of any candidate system in its environment, andthis has been one of the main developments of theoriginal theory by researchers who, accepting therelevance of autonomy, would not want to explore itonly in formal models but related to materialconstraints.Similarly, in the Origins of Life ﬁeld, the theoryof autopoiesis has been particularly inﬂuentialamong those pursuing the cellular origins of life(as opposed to molecular origins) in the generationof self-maintaining and self-reproducing systems(Luisi 2006).In Systems Biology, autopoietic theory hasrevealed itself promising as a theoretical guidelinein developing a notion of system as a integratedunity, in modeling the cellular metabolism asa closed and intertwined network of processes, inreinterpreting the role of the genomes in the cell ina more ecological fashion, and in pointing out therelevance of self-regulation at different hierarchicallevels (Boogerd et al. 2007).• Agency and cognition. From the autopoieticperspective, cognition is the system’s capability toS 2112 Systems, Autopoieticprovide meaning to the world, a property connected,if not coincident, with life. An increasingly relevantissue raised in the investigation of cognition is the oneconcerning how to characterize the speciﬁc mecha-nism of self-maintenance instantiated by biologicalmetabolism in its basic form, being the notion of self-production insufﬁcient to account for agency as theability to act in the environment. There have beenproposals to expand the deﬁnition of self-productionthrough the introduction of active mechanisms ofself-regulation.With respect to the impact on the study ofcognition in the conventional sense, autopoietictheory has provided an analysis of the biologicalroots of knowledge by considering humanobservers as structurally determined systems.In doing so it has pointed out the limits of thenotions of representation and objectivity and con-tributed to the development of an epistemologicalperspective known as “radical constructivism”according to which the natural world emerges ascoherences in the coupling between the observerand its medium. In cognitive sciences, theautopoietic theory has pointed to the need todevelop embodied and situated accounts to charac-terize autonomous agents, by inaugurating theso-called enactive approach (Varela et al. 1991).• Other levels of organization. As autopoietic sys-tems deﬁne life at the cellular level, multicellularliving systems and social ones – respectivelydeﬁned as autopoietic systems of the second andthird order – are considered as derivative, evenif not trivially, with respect to the properties ofcellular ones. But satisfactory criteria for thisoperation of expansion of the theory have not beenprovided in the original formulations.In spite of these acknowledged difﬁculties,the notion of autopoiesis brings forth a relevantscenario for inquiry about the nature of life,providing an intuitive idea of what it means tobe alive, autonomy, which is lacking in otherapproaches.Acknowledgments LB has a Juan de la Cierva ResearchContract ((MICINN/UPV-EHU). AE’s research was fundedby the MICINN (FFI2008-06348-C02-01/FISO & FFI2008-06348-C02). AE’s and LB’s research was funded by the BasqueGovernment grant (IT 505-10).Cross-References▶Autonomy▶Closure, Causal▶Organization▶ Self-Organization▶ Synthetic Biology, Predictability and ReliabilityReferencesBoogerd FC, Bruggerman FJ, Hofmeyr J-HS, Westerhoff HV(eds) (2007) Systems biology. Philosophical foundations.Elsevier, AmsterdamDi Paolo EA (2004) Special issue on: unbinding biologicalautonomy: Francisco Varela’s contributions to artiﬁcial life.Artiﬁcial Life 10:231–360Letelier J-C, Soto-Andrade J, Guinez-Abarzua F, Cardenas M-L,Cornish-Bowden A (2006) Organizational invariance andmetabolic closure: analysis in terms of (M, R) systems.Journal of Theoretical Biology 238:949–961Luisi PL (2006) The emergence of life. From chemicalorigins to synthetic biology. Cambridge University Press,New YorkMaturana M, Varela F (1973/1980) De ma´quinas y seres vivos.Autopoiesis: La organizacio´n de lo vivo. EditorialUniversitaria, Santiago. (English translation: Autopoiesisand cognition. The realization of the living. Reidel, Boston)Maturana H, Varela F (1984/1987) El a´rbol del conocimiento.EditorialUniversitaria, Santiago deChile. (English translation:The tree of knowledge. Shambhala, Boston)Varela F (1979) Principles of biological autonomy. ElsevierNorth Holland, New YorkVarela F, Maturana H, Uribe R (1974) Autopoiesis: the organi-zation of living systems, its characterization and a model.Biosystems 5:187–196Varela F, Thompson E, Rosch E (1991) The embodied mind.Cognitive science and human experience. MIT Press,Cambridge, MAWeber A, Varela FJ (2002) Life after kant: naturalpurposes and the autopoietic foundations of biological indi-viduality. Phenomenology and the Cognitive Sciences1(2):97–125Systems, Autopoietic 2113 SS

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