Interactive Models In Synthetic Biology: Exploring Biological And .

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CONCEPTUAL ANALYSISpublished: 15 April 2020doi: 10.3389/fpsyg.2020.00682Interactive Models in SyntheticBiology: Exploring Biological andCognitive Inter-IdentitiesLeonardo Bich*IAS-Research Centre for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the BasqueCountry (UPV/EHU), San Sebastián, SpainEdited by:Luisa Damiano,University of Messina, ItalyReviewed by:Takashi Ikegami,The University of Tokyo, JapanJavier Suárez,University of Barcelona, Spain*Correspondence:Leonardo ialty section:This article was submitted toTheoretical and PhilosophicalPsychology,a section of the journalFrontiers in PsychologyReceived: 06 November 2019Accepted: 20 March 2020Published: 15 April 2020Citation:Bich L (2020) Interactive Modelsin Synthetic Biology: ExploringBiological and CognitiveInter-Identities.Front. Psychol. 11:682.doi: 10.3389/fpsyg.2020.00682The aim of this article is to investigate the relevance and implications of synthetic modelsfor the study of the interactive dimension of minimal life and cognition, by taking intoconsideration how the use of artificial systems may contribute to an understanding ofthe way in which interactions may affect or even contribute to shape biological identities.To do so, this article analyzes experimental work in synthetic biology on different types ofinteractions between artificial and natural systems, more specifically: between protocellsand between biological living cells and protocells. It discusses how concepts such ascontrol, cognition, communication can be used to characterize these interactions froma theoretical point of view, which criteria of relevance and evaluation of synthetic modelscan be applied to these cases, and what are their limits.Keywords: regulation, synthetic models, communication, minimal cognition, criteria of relevance, criteria ofevaluation, Turing testINTRODUCTIONThe last decade has been characterized by an increased interest in synthetic models of interactivebiological phenomena, from the study of properties of collective prebiotic systems in origins oflife scenarios1 and biological communication,2 to the exploration of the possible contributions ofSynthetic Biology to research in Artificial Intelligence.3The aims, scope and conceptual foundations of this enterprise are still in course of definition,and this article addresses some of the main conceptual issues raised by it. It focuses on how syntheticbiology can contribute to the study of those biological and cognitive phenomena, such as forexample communication, that arise in nature from the interaction between biological systems. Indoing so, it takes into considerations different types of inter-systems interactions studied throughsynthetic models: structural and (minimally) cognitive. Structural interactions are defined as those1For example, colonies of giant vesicles (Carrara et al., 2012) and predator–prey interactions in protocells (Qiao et al., 2017).This research line has been focusing on interactions between biological cells and protocells (Gardner et al., 2009;Lentini et al., 2017; Rampioni et al., 2014).3This research line has been pursued by focusing either on information technologies that realize computation through biochemical systems (Amos et al., 2011), or on biochemically-grounded embodied artificial intelligence (Stano et al., 2018).2Frontiers in Psychology www.frontiersin.org1April 2020 Volume 11 Article 682

BichInteractive Models in Synthetic Biologyof Interactive Synthetic Models,” are employed in section“The Realization of Interactive Synthetic Models” to discussexperimental examples of two classes of interactive syntheticmodels, which cover different types of interacting entities atdistinct levels of organization:interactions that directly affect the constitution of the system;cognitive ones as those interactions that are mediated by sensory–effector regulatory mechanisms.This article discusses what kind of impact these interactionshave on the systems involved, and whether and how theyaffect the identities of such systems. The “identity” of thesystem, in this context, is defined by the specific organizationthat characterizes it, and which is kept invariant despite thestructural variations that may affect the components (Maturanaand Varela, 1980).4 As part of the research topic “Inter-identities’in Life, Mind, and Society,” this article analyzes how identitiesin interaction can be studied by means of synthetic biology. Itis important to emphasize that synthetic models raise closelyinterconnected theoretical and epistemological questions inrelation to interactive identities. The theoretical question istwofold. On the one hand it concerns the relationship betweenthe identity of a system and its interactive capabilities, i.e., howthe organization of a system specifies the types of interactionsthe system can participate in. On the other hand, it concernswhether and how interactions between systems may change theirintrinsic properties. Yet analyzing models cannot be separatedfrom the problem of assessing their relevance for studying casesof interacting identities in nature, and from the complex questionof how to evaluate whether the models are successful or not incontributing to an understanding of these phenomena.Accordingly, this article analyzes and discusses four differentissues regarding interactive synthetic models: theoreticalgrounding, criteria of relevance, realization, and strategies ofevaluation. To address the issue of the theoretical groundingof interactive synthetic models, in section “TheoreticalGrounding: Structural and Cognitive Interactions” it providesa characterization of interactions at the specific level whichis relevant for synthetic biology. Of particular interest in thiscontext are those interactive properties that can be realized insynthetic biological systems through biochemical mechanisms.The paper adopts a specific theoretical account of minimalcognition based on the notion of biological autonomy todistinguish between structural and cognitive interactions andto provide theoretical tools for their synthetic investigation.It applies this framework to the analysis of those syntheticmodels that explore interactions – e.g., communication –between systems (i.e., between artificial systems, and betweenartificial and living systems), rather than between one systemand its generic environment, and puts into evidence the mainchallenges they face.On the basis of the theoretical framework proposed, section“Criteria of Relevance of Interactive Synthetic Models” providesan epistemological analysis of the criteria of relevance of syntheticmodels, and discusses how they apply to this specific scenario inwhich the focus is on structural and cognitive interactions.The third issue addressed in this article is the realization ofinteractive synthetic models. The theoretical and epistemologicaltools developed in sections “Theoretical Grounding: Structuraland Cognitive Interactions” and “Criteria of Relevance(1) Interactions between protocells.(2) Communication between living cells and protocells.Finally, section “Conclusions: Evaluation Strategies” discusseslimits and merits of three different strategies of evaluationof interactive synthetic models: Turing tests, demarcatingdefinitions, and operational approaches. It argues thatoperational evaluation strategies are the most suitable withregards to the types of phenomena described and theoreticalquestions addressed by interactive models.THEORETICAL GROUNDING:STRUCTURAL AND COGNITIVEINTERACTIONSIn order to discuss the contributions of synthetic models tothe study of biological and cognitive interactive phenomena, atheoretical framework is required. The framework adopted inthis article is the organizational one, based on the notion ofautonomy (Varela, 1979; Maturana and Varela, 1980; Kauffman,2000; Moreno and Mossio, 2015). The notion of autonomy hasbeen often applied in Synthetic Biology to study origins of life,minimal life (Luisi and Varela, 1989; Ruiz-Mirazo and Moreno,2004; Luisi, 2006), and minimal cognition (Bourgine and Stewart,2004; Stano et al., 2012; Bich and Moreno, 2016). This frameworkhas also been used to develop epistemological tools to analyzesynthetic models (Damiano et al., 2011).According to this framework, a biological organization – suchas a bacterium – is autonomous because it is capable of producingits own functional components and maintaining itself in far fromequilibrium conditions. A living system cannot exist unless itmaintains a continuous coupling with its environment, madepossible by an internal dynamical variability, which enables thesystem to exert a fine-tuned control upon the exchanges of matterand energy with the surroundings and bring forth different viableresponses to a variety of environmental perturbations.In this scenario, the identity of the system is identified withits self-maintaining autonomous organization: the dimension ofthe system that is maintained invariant despite the continuousstructural variations that occur as its components are synthesized,transformed and degraded and its dynamics are perturbed byinteractions with the environment and with other autonomoussystems. To analyze interacting identities from this perspective,it is necessary to consider (1) what types of interactions betweenbiological autonomous systems are enabled by their distinctiveorganizations and (2) how such interactions may affect theidentities of the interacting systems.Let us start by considering how interactions are characterizedwithin this framework. Traditional work on biologicalautonomy – in particular Piaget’s (1967) and Maturana and4For the more general philosophical debate on the notion of identity, seeWilliamson (1990); Lowe (2002), Miller (2010), and Noonan and Curtis (2018).Frontiers in Psychology www.frontiersin.org2April 2020 Volume 11 Article 682

BichInteractive Models in Synthetic Biologyin response to interactions with the environment or to internalvariations, due to the action of dedicated (second-order)subsystems that are specifically sensitive to these variations.When regulation is at work, the internal dynamics of thebasic first-order regime of self-production of the living systemare modulated by specialized second-order sensory–effectormechanisms. The activation of these mechanism is triggeredby external or internal variations, and as a result of theirregulatory activity, the system is able to maintain its viability.Minimal examples of regulatory mechanisms are the lac-operon,the tryptophan operon, or the chemotactic signal transductionpathway, to cite just a few well known cases of modulationof the basic (first-order) metabolic and agential dynamics ofa system. The distinctive feature of regulatory mechanisms isthat as second-order control subsystems they are operationallydecoupled from the first-order regime they regulate.6After distinguishing these two general types of adaptive7interactions, based on dynamic stability and regulationrespectively, it is possible to discuss whether or not theycan ground cognitive properties, as claimed by the L C thesis.Let us consider a distinctive feature of cognition, which canbe realized by minimal living systems. It is the capability toidentify or distinguish between some features of their interactionwith the environment (for example, the sensing of variations inboundary conditions, concentrations of nutrients, and presenceof predators) and to act accordingly, in such a way as tomaintain viability. As argued in Bich and Moreno (2016), thesecognitive capabilities, at a minimal level, necessarily requireregulatory mechanisms in the context of a self-maintainingbiological system.Structural interactions, sustained by distributed responses in aregime of dynamic stability, cannot account for this distinctivelycognitive capacity to make meaningful distinctions and to actaccordingly. In this type of interactions perturbations just triggerinternal changes that are percolated through the system bymeans of reciprocal adjustments between the activities of thecomponents of the first-order network: the environment is only asource of generic noise.The requirement for cognition can be met, instead, in presenceof dedicated regulatory mechanisms, endowed with sensory–effector capabilities, whose response is the result of the evaluationof perturbations. By means of second-order, operationallydecoupled regulatory mechanisms, the system establishes somecategories in the environment (sensory capability), and employsthem to modulate its own internal dynamics in a viable way(effector capability) in such a way that the system maintainsits identity. The organism does things according to whatit distinguishes in its interactions with the environment. ItVarela’s (1980) – and more recent contributions (Stewart, 1992;Bourgine and Stewart, 2004) have defended the thesis, alsoknown as the “Life Cognition Thesis” (Heschl, 1990),according to which the interactive dimension of life is relatedto, or coincides with cognition. In this perspective cognition isdefined as the interactions with the environment and the relativeinternal modifications that an organism can undergo withoutlosing its identity (see also Bitbol and Luisi, 2004; Damiano andLuisi, 2010; Bich and Damiano, 2012). The thesis is based onthe implicit assumption that living systems are adaptive, in thesense that they are capable of interacting viably with a changingenvironment by modifying their internal structures. Whereasa perturbation is just external influence for physical systems,living systems, instead, adaptively integrate, and transform it intoa “meaningful interpretation” (Heschl, 1990, p. 13). However,the identification of minimal cognition with all the interactionsan organism can undergo without losing its identity5 can becriticized as too broad, on the grounds that it would include:(1) cases of mere covariance between system and environment;(2) the metabolizing of environmental substrates; (3) purelymechanical interactions that cause changes in the systemsinvolved (see also Bich and Moreno, 2016).To provide a more precise account of the types of interactionsbiological autonomous systems can experience, and whetheror not they may be considered as minimally cognitive, let usfirst consider which internal changes a minimal living systemcan undergo without losing its identity while interacting withthe environment. According to recent developments of theautonomy framework, the internal changes an autonomousbiological system can undergo fall into two general categories:dynamic stability and regulation (Bich et al., 2016). Dynamicstability is an internal response to interactions with theenvironment instantiated, for example, by the basic (first-order)metabolic network of processes of production of the components(e.g., enzymes catalyzing metabolic reactions) that realize theliving system. It is a general network property: variations ina given process or subsystem can propagate throughout theliving system, producing the change of one or several otherprocesses which, in turn, compensate for the initial one. As aresult, the system can be regarded as stable. At the level of thebasic first-order metabolic regime of self-production and selfmaintenance of the system, the compensation for perturbationsoccurs through reciprocal adjustments between the activityof components, such as enzymes, involved in processes ofproduction, usually stoichiometrically, depending on changes inconcentrations of metabolites. These internal changes supporta type of interaction with the environment that relies on thestructural plasticity of the system. These structural interactionsare governed by first-order mechanisms and are the mostbasic responses a system can bring forth while interacting withits environment.The second type of response falls under the category ofregulation, and requires, instead, a more complex, hierarchicalarchitecture. It consists in the capability to selectively switchbetween different basic (first-order) regimes of self-maintenance56Regulatory mechanisms are specialized subsystems dedicated to the modulationof the first-order regime they control. The relation between regulator and regulatedsubsystems, therefore, is asymmetrical. Regulatory subsystems do not operate asnodes of the same basic network of mechanisms of production of components, butexhibit degrees of freedom that are not specified by the dynamics of the regulatedsubsystems. Such a local independence allows regulatory subsystems to modulatefirst-order ones in a relatively independent way. See Bich et al. (2016) for moredetails on the features and requirements for decoupled regulatory mechanisms.7“Adaptive” is used in this context as an interaction that triggers a viable responseby means of internal changes in the perturbed system.A strong “L C Thesis”.Frontiers in Psychology www.frontiersin.org3April 2020 Volume 11 Article 682

BichInteractive Models in Synthetic Biologyto the existence of the same system that produces them.10The synthetic modeling of these interactions can be pursuedin two different ways. The first consists in the realization offull-fledged interactive systems. It requires integrating regulatorymechanisms into a whole regime of self-production and selfmaintenance. However, this approach is especially problematicto pursue in protocells, due to difficulties in realizing a fullself-maintaining metabolism (Rampioni et al., 2014). Therefore,at the current state of the art, it is pursued by using metabolicallyactive biological cells as starting points. The second approachaims to realize life-like adaptive systems in order to investigateby means of artificial systems specific aspects that are of specialinterest for a better understanding of structural or minimallycognitive interactions.Before analyzing how these approaches can be pursued insynthetic biology to investigate interactive identities and howto evaluate the results obtained, let us take an epistemologicalstep and discuss the criteria to assess the criteria of relevance ofinteractive synthetic models to the study these interactions. Asargued by Damiano et al. (2011) and Damiano and Cañamero(2010, 2012), one of the goals of a theoretically inspired syntheticapproach is to create trans-disciplinary exchanges with naturalsciences that inspire naturally based technologies, and providenew insights into natural phenomena by means of artificialsystems.11 In this context, the synthetic approach can allow toexperimentally explore aspects of life and cognition that are not(easily) accessible by directly investigating natural systems. It cando so by actually constructing the object of study, an alternativebiological or proto-biological system, and study the propertiesand behaviors it exhibits.What is the relevance of synthetic models for the scientificinvestigation of the target interactive biological or cognitivephenomena? Damiano et al. (2011) distinguish two main typesof relevance: phenomenological and organizational. A syntheticmodel is phenomenologically relevant if it produces, accordingto explicit parameters, the same phenomenology as a livingor cognitive system, regardless of the underlying mechanisms,which can be very different. In the case of minimal cognition, forexample, a model is relevant at the phenomenological level if itproduces the same behavior as a cognitive system, or it engagesin similar interactive dynamics.A paradigmatic case of phenomenological relevance ofinteractive synthetic models is constituted by relatively simpleartificial (chemical) systems such as self-propelled oil dropletscapable of chemotaxis (Hanczyc and Ikegami, 2010) (Figure 1B).Chemotaxis is a behavior also exhibited by biological systemssuch as bacteria (Figure 1A), and it is often considereda hallmark of minimal cognition (van Duijn et al., 2006;Bich and Moreno, 2016).Both systems, bacteria and droplets, show a similarphenomenology: they are capable of moving and followinga chemical gradient. Yet, despite the similarity of behavior,modulates its own constitutive dynamics coherently with thevariations that activated the regulatory mechanisms, and as aresult it maintains its viability in the changing environment: forexample, it changes direction of movement or synthesizes a newset of enzymes that allows it to metabolize different substances.In this way, perturbations achieve an endogenous, operationalmeaningful, significance for the system, which can be consideredcognitive in a minimal sense. According to this perspective,therefore, the adaptive behavior of minimal organisms such asbacteria is already cognitive, but only insofar as it is supportedby regulatory mechanisms.8From this theoretical standpoint it is possible to discriminatebetween cognitive and non-cognitive (structural) adaptiveinteractions. The advantage of this framework is that itprovides conceptual tools that can be operationally applied inthe biochemical domain to study different minimal biologicalinteractions – structural and cognitive – by means of syntheticmodels. An example of synthetic realization of cognitive adaptiveproperties is the implementation of biochemical sensory–effectorregulatory mechanisms in protocells or semisynthetic cells (e.g.,compartmentalized riboswitches) (Martini and Mansy, 2011).The synthetic investigation of structural and cognitiveinteractions and of their relationship with the identity of thesystems involved can be pursued in two ways. One is to focuson one artificial system and to analyze how it interacts adaptivelywith its environment by means of either distributed or selfregulatory mechanisms (see Bich and Moreno, 2016). The otherway, which is discussed in the rest of this article, is to explore thepossibilities opened by the adaptive interactions between systems(artificial systems or artificial systems with biological ones). It isinspired by a long research tradition in cybernetics and systemstheory opened by the pioneering work carried out by Ashby(1954, 1956), Beer (1972), and Pask (1975), among others –,who had been focusing on the interactive dynamics of systemsof different nature (e.g., computational, biological, social, etc.)endowed with self-regulatory mechanisms.9CRITERIA OF RELEVANCE OFINTERACTIVE SYNTHETIC MODELSIn biological systems, constitutive and regulatory adaptivemechanisms – which underlie structural and cognitiveinteractions, respectively – are endogenously produced andmaintained. With their activity, they functionally contribute8Very different views have been defended with regards to the nature and lowerboundaries of minimal cognition. Some cognitive capabilities have been attributedto chemical systems below the threshold of life, like oil droplets, justified on thebasis of their chemotactic behavior which mimics that of bacteria (Hanczyc andIkegami, 2010). Approaches focused on the specific features of the organizations –i.e., minimal biochemical mechanisms – underlying cognitive capabilities, suchas chemotaxis and communication, identify cognition at the level of prokaryotes(van Duijn et al., 2006; Bich and Moreno, 2016). Others identify cognition only inorganisms with nervous system (Christensen and Hooker, 2000; Barandiaran andMoreno, 2006). See Godfrey-Smith (2016) for a discussion of different accounts ofminimal cognition and of the main transitions in the evolution of cognition andsubjectivity.9See Damiano (2009) and Pickering (2010) for an analysis of this researchtradition.Frontiers in Psychology www.frontiersin.org10This is an important difference with hardware-based artificial systems, in whichparts are put together from without, and interact to produce a certain effect withouttheir operations affecting their conditions of existence.11See also Pfeifer and Scheier (1999); Ruiz-Mirazo and Moreno (2013), andGreen (2017).4April 2020 Volume 11 Article 682

BichInteractive Models in Synthetic BiologyFIGURE 1 Systems capable of chemotactic behavior by means of radically different mechanisms: (A) The sensory motor pathway of a chemotactic bacterium andits relative independence from metabolism (from Egbert et al., 2010, reproduced under the terms of the Creative Commons Attribution License); and (B) aself-propelled droplet (reproduced with permission from Hanczyc et al., 2007). Copyright (2007) American Chemical Society.of relations between components) can be realized by differentstructures (Maturana and Varela, 1980). Synthetic biologists,therefore, can use whichever minimal biochemical tools theyhave available to achieve their modeling goal and produceorganizationally relevant systems, without the need to reproducethe exact composition of current biological systems, which is theresult of a long, complex, and not yet well understood historicalprocess of prebiotic and biological evolution.The ultimate target for organizationally relevant syntheticmodels in the framework of biological autonomy is to realize selfproducing and self-maintaining protocells capable of interactingadaptively with their environment. Another way to developorganizationally relevant models consists in narrowing downthe scope of the model and investigating a specific propertyor capability of a living of cognitive system, instead of thewhole, integrated, entity. In such cases a model achieves whatcan be called a mechanism-related organizational relevance byrealizing the same underlying mechanism responsible for aspecific behavior or phenomenon.An example of this approach in relation to the studyminimal cognition is the case of sensory–effector mechanismsimplemented in protocells, which allow protocells to sense theenvironment and change their internal activity accordingly.Such mechanisms have been realized by endowing protocellswith riboswitches. As shown experimentally by Martini andMansy (2011), protocells enclosing riboswitches can indeed sensespecific molecules and respond to them by activating DNAtranscription mechanisms (Figure 2). This approach allowsinvestigating specific mechanisms by means of synthetic models,without incurring into the overwhelming difficulties of realizingfully fledged artificial autonomous systems. While at the momentthis model does not provide direct insight upon the contributionof such adaptive mechanisms to the internal dynamics andmaintenance of the system, it can be particularly interesting froma point of view focused on interactions, as a starting point to studythe roots of minimal interactive capabilities of biological systemsby modeling their underlying adaptive mechanisms.Organizational relevance refers to the capability of models toaccount for the identity of the natural systems under scrutiny. Tostudy interactive identities synthetic models should include thosetypes of internal mechanisms responsible for adaptive structuralor cognitive adaptive interactions. Yet the target of these modelsthe way behavior itself is generated is very different in the twocases. Self-propelled droplets do not self-maintain like livingcells do. The movement of droplets does not rely on nutrientsencountered while exploring the environment, but they moveby consuming the internal propeller (oleic anhydride) that isalready available. In turn, the movement does not contribute tothe existence and maintenance of the droplet as it does, instead,in bacteria. There is no internal organizational differentiation(no modular subsystems) within the droplets. Bacteria, instead,exhibit a complex regulatory mechanism (the signal transductionpathway) that modulates a motor system, plus a decoupledmetabolism which provides movement and energy to the system.Finally, the direction of the movement of the droplet is directlycontrolled by the gradient rather than, like in bacteria, by thespecific organization of a sensory–effector regulatory subsystem.While giving precious information on the interactivedynamics of the entities involved, synthetic models that exhibitphenomenological relevance alone – insofar as they provide apoint of view that is external to the system and focused on abehavioral description – fail to account for the distinctive featuresof minimally interactive systems and to discriminate betweendifferent types of interactions. For example, if the definingfeatures of minimal cognition are identified in self-regulatorybiochemical mechanisms subject to a regime of self-maintenance,then they need to be investigated at a different level of analysis,internal to the system. The same holds for structural interactions,which rely on distributed compensatory mechanisms. Modelingthese interactions, therefore, requires different types of syntheticmodels, whose relevance lies in the organizational, instead ofphenomenological, dimension.A synthetic model is organizationally relevant if it realizesthe same organization as the living or cognitive system whichis the object of investigation (Damiano et al., 2011); in otherwords, if it realizes the same or a very similar identity.This criterion of relevance focuses the attention on the waycomponents and processes are wired together, according to aspecific theory of life and/or cognition. The primary target isnot the features of a phenomenon or behavior, but how it isgenerated. However, achieving organizational relevance does notimply that there should be a strict correspondence betweenthe specific components of the model and the target system.The same type of organization (understood as the topologyFrontiers in Psychology www.frontiersin.org5April 2020 Volume 11 Article 682

BichInteractive Models in Synthetic BiologyFIGURE 2 Compartmentalized, cell-like systems that sense and respond to their environments through riboswitch activity. (A) The presence of an extravesicularligand converts the cell-like system from the OFF-state to the ON-state. (B) RNA (squiggly line) is transcribed from DNA (double line). RNA is only translated intoprotein (star) in the pres

challenges they face. On the basis of the theoretical framework proposed, section "Criteria of Relevance of Interactive Synthetic Models" provides an epistemological analysis of the criteria of relevance of synthetic models, and discusses how they apply to this specific scenario in which the focus is on structural and cognitive interactions.

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