Abstract
What happens when some of the traditional questions and concerns of the philosophy of science are brought to the non-traditional field of synthetic biology? Given that synthetic biology is a very diverse field, this might serve to highlight the many ways in which it is business as usual. However, prominent concepts and research practices of synthetic biology can be seen to confound established ideas of how knowledge is produced and validated in the sciences. By highlighting and readying for discussion the tension between alternative images of knowledge production in synthetic biology, this paper seeks to open up debate among philosophers of science, and within the diverse community of synthetic biologists. With the advance of emerging technosciences like synthetic biology what is at stake is not primarily how they might or might not change the world. At stake, first of all, are epistemic values, the ethos and authority of science, and the relation of knowledge and power. Building on ongoing discussions, the paper begins by exhibiting contested notions of understanding, rational engineering, and design. In a second step, it turns to different conceptions of biological “systems” by presenting divergent accounts of the origin of synthetic biology and of how systems biology gave rise to synthetic biology. Finally, it seeks to focus the debate on a definition of synthetic biology, according to which it builds, for constructive purposes, on achievements of technical control of biological complexity, that is, that it uses these achievements to generate, rather than reduce, complexity.
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Notes
- 1.
Here and throughout, the default meaning of “complexity” is simply that a structure or system is “not simple” or “difficult to conceive as a sum of simple processes” or “complicated.” Science and classical theories of knowledge conceive the task of the human intellect as making sense of a bewildering multitude of sensory impressions by isolating from them simple patterns or lawful causal relations. This “reduction of complexity” is considered a major achievement of the mind. Accordingly, the first “limit of complexity” arises when things get to be too complicated to be tractable by the human mind (though a computer might still be able to achieve predictive control or to isolate strict causal dependencies). In contrast, the challenge of synthetic biology is seen as building up or generating complexity (the first sessions at the 2013 SynBio 6.0 conference were dedicated to the question of “realizing biological complexity,” see the program under http://sb6.biobricks.org/, accessed January 5, 2014; also (Mast et al. 2013). Only at two points in the following discussion (see Footnotes 10 and 16 below), does “complexity” assume a more exalted systems-theoretic status, thereby explicitly becoming a theoretical term. In the theoretical context of systems thinking, there is as second limit of complexity, namely irreducibility in principle. And only systems thinking thus conceived calls for an alternative, non-reductionist approach and thus a different kind of “reduction of complexity”—reduction not to aggregates of simple processes but to dynamic systems as integrated wholes.
- 2.
Many readers will not be familiar with the juxtaposition of science and technoscience as distinct modes of knowledge production. This is not necessary. The distinction will take shape over the course of this discussion as different ways of conceiving synthetic biology become aligned with the different epistemic values and ideals of science and of technoscience (Bensaude-Vincent 2009a; Forman 2007; Nordmann 2010b).
- 3.
A review article speaks of the “repetitively, almost dogmatically, cited Feynman quote” (Rollié et al. 2012). The source of the repetitively cited maxim is a photograph of “Feynman’s last blackboard” which can be found, for example, at http://archives.caltech.edu/pictures/1.10-29.jpg (accessed January 3, 2014), compare e.g. (Schmidt 2009).
- 4.
Aquinas argues that only God truly knows the world because he created it and one can only know what one creates (Aquinas 1986); Bacon declared that the power to control or to make things is a criterion of knowledge (which is why the statement “knowledge is power” is often attributed to him, see e.g. Smith (2004, 238–241); Giambattista Vico distinguished mathematics and the sciences of human culture from the natural.
- 5.
It is not clear why Feynman formulated this strong requirement on his “last blackboard.” This may have been his objection to string theory in physics, or informed perhaps by his recent experience of discovering and demonstrating the cause of the Challenger space shuttle accident, see O’Malley (2009, 385–386).
- 6.
The paraphrase suggests, for example, that understanding is more than the ability to explain and predict something. This prompts the empiricist suspicion that it is in this case too much to ask for scientific understanding. Also, if the material (re)production of a process or phenomenon is not the only way of creating what one seeks to understand, does the creation in one’s mind require intellectual tractability as in a thought-experiment, or would a highly complex computer simulation also fit the bill?
- 7.
Famously, Craig Venter and his collaborators encoded in 2010 the Feynman quote as an identifying watermark in the genetic code of the first chemically synthetized genome of a working bacterial cell. Notoriously, in so doing they misquoted Feynman ever so slightly.
- 8.
Schmidt (2009) warns that the inversion of Feynman’s dictum does not follow logically from the original formulation. For examples of authors who adopt the second reading, see Sect. 3 below. But see also the example of Alfonso Jaramillo who proved quite committed to the second reading during his oral presentation at the CAS Conference Synthetic Biology (July 23–25, 2012, at the Biocenter of the LMU, Munich). Arguing for automatic design, computational evolution, high throughput characterization he claimed for these methods that one does not need that much (theoretical) knowledge about structure to succeed, and that they allow quantitative testing with and in spite of limited knowledge. In published work this is expressed in a more muted fashion, more careful, in particular, to advertise this as a virtue of his approach: “As our automated methodology uses few specifications as inputs, it could also be used to test new mechanisms and hypotheses despite the lack of a complete molecular understanding of the living cell” (Rodrigo et al. 2012).
- 9.
The term “black box” refers to a technical unit of reliable functioning that is not and need not be scrutinized for the specific causal processes that would account for its functioning (Royal Academy of Engineering 2009, 19–20). Not all modules in a modularized architecture are black boxes, but black boxes can serve as modules (see, paradigmatically, Canton et al. 2008). Black-boxing is the decision or strategy to create black boxes. Tal (2013) offers a critique of the notion of black box and seeks to identify instead rational strategies that provide “ignorance affordances.”
- 10.
Note that for the analytic enterprise of science, the issue of calculability is central and, by the same token, the nature of complexity or of emergent properties. Science seeks to know whether biological structures and processes are irreducible in principle or subject, sooner or later, to an analytic reduction of complexity. In contrast, the technoscientific interest in generating complexity is quite indifferent to this question. Perhaps, new and irreducible systems qualities emerge over the course of iterating the design cycle, perhaps not. No matter how one conceives the “limits of complexity,” the design process aims to overcome them (compare Footnote 1).
- 11.
This important qualification owes to comments by Maureen O’Malley. “Reduction of complexity” usually and in this text refers to an intellectual achievement: Complex phenomena can be reduced to simple processes and their aggregate effects. But some speak of a different kind of reduction of complexity: “synthetic biologists simplify and build” (Ferber 2004; Calvert 2010). Whereas systems biology seeks total information and thus incorporates into its representational models all the findings of Omics-research, synthetic biology wants to find out how far we can get with what little we know—it does not try to incorporate as much information as possible into the process of generating biological complexity. Synthetic biology attempts to find technical means which afford ignorance (Tal 2013), allowing it to succeed with less information rather than more. This might be considered synthetic biology’s technical “reduction” of complexity.
- 12.
In the terms of Daniel Dennett, after rejecting that natural organisms are the product of design, one can adopt a design stance towards them and studying them as if they had been engineered.
- 13.
The place where evolutionary considerations are most likely to appear is in “what if” scenarios that begin by valorizing synthetic biology and portraying its success at creating artificial organisms. Only then the question is asked what will happen once these are subject to evolution—either by way of “mutating” from benign to dangerous organisms, or by way of their ability to outcompete natural organisms, changing the make-up of biological diversity, and the like. The engagement with evolutionary concepts thus tends to begin only when synthetic biologists look at the potential impact of their work through the perspective of technology assessment. Arguably, though, it should enter in right at the beginning of their work, in reflections on the rhyme and reason of naturally evolved biological complexity.
- 14.
For a sketch of a third story, see Footnote 20 below.—Like all myths of origin, these three are idealized to the point of caricature, and they are told for reasons not of descriptive accuracy but of the moral they contain. Each in its own way has normative implications, suggesting what synthetic biology ought to be and what opportunities and risks it poses, what obligations and expectations come with it.
- 15.
Following upon and adding to the section on “familiar concepts, divergent meanings” the two stories might be said to expose the divergent meanings within synthetic biology of the notion of “system.”
- 16.
Here, then, “complexity” becomes a theoretical term that differentiates complex systems from merely very complicated aggregates of simple processes (see Footnote 1 above).
- 17.
- 18.
Computation for systems biology enabled better ways to “acquire, store, analyze, graphically display, model, and distribute” information. Without yet going there, the discussion of computer models in systems biology prepares the ground for the exploitation of what they afford in terms of performance, behavior, intervention and construction (Ideker et al. 2001). This holds also for that brand of systems biology that takes complexity seriously. Here the proposals by Kitano (2002, 2004), for example, mark the point of transition. He advocates engineering concepts and computing tools for the purposes of modeling, representation, and theoretical understanding of biological complexity. He thereby paves the way for modes of constructing and handling such systems without reference to complexity theory: his concepts and tools afford their employment towards constructive ends by synthetic biology (O’Malley et al. 2008, 62).—This point of transition is also discussed by Schmidt (2015, this volume). He sees bioengineers who adopt systems thinking. The story of technical opportunism sees systems thinking appropriated and vulgarized by the ordinary idiom of engineering (Nordmann 2010a).
- 19.
Gabriele Gramelsberger identifies the simulation approach as a common denominator of systems and synthetic biology and suggests that it provides rational design methods that support tinkering in the lab (Gramelsberger 2013). She thereby downplays that modelling in systems biology is said to be “for basic research (i.e. generating knowledge) whereas synthetic biology’s modelling is for the design of constructs” (O’Malley et al. 2008, 62): “Ultimately, mathematical models developed for research purposes (e.g. in systems biology) will be employed as design models in synthetic biology” (Heinemann and Panke 2006, 2796).
- 20.
There are other stories that could be told. One does not have to assume that synthetic biology is somehow derived from, or intimately related to, systems biology. Instead, one might foreground the relation between chemistry and biology as exemplified, for example, by the work of Steven Benner (Benner et al. 2011). Just as physicists were told, many years ago, that there wasn’t much work to be done in physics anymore but that they might find interesting problems in biology, so chemists have been told a similar story in recent years (I owe this suggestion to H. Ulrich Göringer). On this account, it is the chemical approach that distinguishes synthetic biology and genetic engineering. The possibility that the “synthetic” in synthetic biology derives from synthetic chemistry was discussed by Bensaude-Vincent (2009b, c, 2013b).
- 21.
Arguably, the initial promise and attractiveness of synthetic biology is much like that of nanotechnology. However, the clash between epistemic communities is far less pronounced in nanotechnology than in the case of synthetic biology. Nanotechnological research is “pure technoscience” because it is geared to the development of basic capabilities of control that generally expand the toolset of technology—it isn’t dedicated to any one engineering agenda but seeks to recruit scientific theories, scientific expertise, scientific labor for the purpose of putting technological change on a new footing. Nanotechnology is thus an effort to retool the scientific enterprise by dedicating the accumulated knowledge, methods, and personnel for knowledge production to a different, perhaps complementary end. Synthetic biology is “pure technoscience” in a different way. It does not seek to retool or rededicate laboratories and academically trained researchers. Instead it seeks to produce new kinds of researchers even before it produces new kinds of biological entities. The creation of epistemic communities with non-traditional values is part of what synthetic biology is and, for some of its protagonists, what it ought to be. The promise and attractiveness of synthetic biology thus lies also in its appeal to a new generation of researchers. This is somewhat problematic, however, since the staging of a generational conflict over epistemic ideals does not go so well with the idea of drawing together a diverse group of researchers.
- 22.
The editors’ text continues here in agreement with the story of technical opportunism which was told above and which contradicts the idea that synthetic biology advances basic science: “In that aspect [of using mathematical formalism to manage data beyond human intuition], synthetic and systems biology now seem indissociable.”
- 23.
See Sect. 3 in Schmidt (2015, this volume) as to why “evolutionary design” is an oxymoronic misnomer. Though they both work with variation and selection, Darwinian evolution by natural selection is different from breeding by artificial selection: what is selected for and against in natural selection does not depend on the specifications of a designer, but on adaptedness to the complex and changing conditions of life. What Bujara and Panke are referring to is more appropriately called “design by breeding.”
- 24.
The graph suggests continuity and thus makes the implicit, albeit highly problematic, assumption that the knowledge required for better ways of running the design cycle is the kind of knowledge that could provide the basis for rational design. Indeed, in their paper Bujara and Panke question that “reducing the complexity of biological systems will facilitate its engineering,” commenting that this is only “a hypothesis that still needs to be confirmed in the laboratory” (2010, 589). This cautionary remark applies to the reduction of complexity by an increase of knowledge of causal relations and also to its reduction by insulating engineered biological systems from natural ones. If it were possible to run such an experiment, the laboratory test proposed by Bujara and Panke would measure the scientific assignment of priority always to the improvement of causal knowledge against technoscientific requirements of what it takes to achieve effective control. And if the hypothesis would fail to be confirmed, discontinuity would be reestablished.
- 25.
Tabor offers an epistemologically telling description of the design-cycle approach: “Here, the first design is based on the ligand-inhibited repressors LacI and TetR. Each is initially placed upstream of an associated fluorescent reporter on a polycistronic mRNA. The operons show poor reporter expression, which is then improved by ‘plugging in’ additional copies of the appropriate promoter upstream of each reporter. This increases reporter expression, but reveals that the circuit cannot reach the TetR-dominated state. The tetR promoter is then swapped for a stronger version, but this overcompensates for the problem making only the TetR state stable. A library of random tetR ribosome binding sites (RBSs) is then screened, and a variant that hits the bistable sweet spot is found” (Tabor 2012, 1063; compare Litcofsky et al. 2012).
- 26.
Compare the Wikipedia entry “Hello world program.”
- 27.
“[A]s opposed to simulation models transformed into a computational algorithm and run on a digital computer, here the theoretical model rendered as a synthetic model is of the same ‘natural kind’ as the native networks as well as being embedded in a simulation environment of the ‘same materiality,’ i.e., the host organism” (Knuuttila and Loettgers 2013, 168). Knuutila and Loettgers argue that this supports a “basic-science approach to synthetic biology.” However, whether it actually does this or not depends on the question whether one can pick out “theoretical models” as traditionally conceived.
- 28.
Also, this perspective affords a way of distinguishing the simulation approach in synthetic biology from that in systems biology, and thus a way of re-interpreting the examples discussed in Gramelsberger (2013), compare Footnotes 18 and 19 above.
- 29.
To be sure, “de facto achievements of technical control of biological complexity” does not require an understanding of biological complexity, it refers only to the local and partial success stories where some biological process can be manipulated or replicated (in a biological system or in a simulation model).
- 30.
For critical comments and suggestions I would like to thank Marta Bertolaso, Annamaria Carusi, Bernd Giese, Kay Hamacher, Reinhard Heil, Thorsten Kohl, Maureen O’Malley, and Jan C. Schmidt.
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Nordmann, A. (2015). Synthetic Biology at the Limits of Science. In: Giese, B., Pade, C., Wigger, H., von Gleich, A. (eds) Synthetic Biology. Risk Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-02783-8_2
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