Biological Control - 麻省理工学院人工智能研究专业

Biological Control
Variously Materialized:
Modeling,
Experimentation
and Exploration in
Multiple Media

Tarja Knuuttila
维也纳大学

Andrea Loettgers
维也纳大学

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This paper examines two parallel discussions of scientiﬁc modeling which have in-
voked experimentation in addressing the role of models in scientiﬁc inquiry. One side
discusses the experimental character of models, whereas the other focuses on their ex-
ploratory uses. Although both relate modeling to experimentation, they do so differ-
ently. The former has considered the similarities and differences between models and
实验, addressing, 尤其, the epistemic value of materiality. By con-
特拉斯特, the focus on exploratory modeling has highlighted the various kinds of explor-
atory functions of models in the early stages of inquiry. These two perspectives on
modeling are discussed through a case study in the ﬁeld of synthetic biology. 这
research practice in question explores biological control by making use of an ensemble
of different epistemic means: mathematical models and simulations, synthetic genetic
circuits and intracellular measuring devices, and ﬁnally electronic circuits. We argue
that the study of exploratory modeling should trace the ways different epistemic
方法, in different materialities, are being combined over time. 最后, the epistemic
status of such novel investigative objects as synthetic genetic circuits is evaluated, 和
the conclusion that they can function as both experiments and models.

The authors wish to thank Michael Elowitz, Jordi García-Ojalvo, Axel Gelfert and an anon-
ymous referee for this special issue. This project received funding from the European Re-
search Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (grant agreement No 818772).

科学观点 2021, 卷. 29, 不. 4
© 2021 由麻省理工学院.
根据知识共享署名发布 4.0 国际的 (抄送 4.0) 执照

https://doi.org/10.1162/posc_a_00379

468

科学观点

469

介绍

1.
The philosophical discussion of scientiﬁc models is undergoing a distin-
guishable turn towards the practices of modeling, and away from issues rep-
resentation. Two recent discussions, 尤其, have attempted to ﬂesh
out the idea that models are objects with which we do something in our
epistemic activities. While the one discussion has considered the experimen-
tal character of models (例如, Cartwright 1999; Guala 2002; Mäki 2005;
摩根 2003, 2005), the other one has addressed their exploratory functions
(例如, Gelfert 2016, 2018; Fisher 2017; Massimi 2018). Both discussions
relate modeling to experimentation, but they do so differently. The discus-
sion of the experimental character of models likens them more directly to
实验, highlighting those aspects of modeling and simulation
that come close to experimentation. 因此, a new set of questions has
出现了, concerning the ways modeling and experimentation resemble, 或者
differ from, 彼此, and whether those differences are due to the infer-
ences they license, or the different role materiality plays in experimentation
vis-à-vis modeling.

In contrast to viewing models as a kind of experiment, the interest in explor-
atory modeling does not directly build on the analogy between modeling and
实验. It rather takes the discussion of exploratory experimentation as
its starting point in focusing on the exploratory modes of reasoning character-
istic of early stages of inquiry in which “an established prior body of knowledge”
“cannot be assumed, or is itself at issue” (Gelfert 2018). Experimentation in such
situations takes up the role of conceptual and theoretical exploration (Steinle
1997), as well that of stabilization of phenomena (Burian 1997). 然而,
when applied to modeling, the idea of exploration gets new dimensions due
to the fact that much of modeling is of a theoretical nature at the outset, 和
in many ﬁelds the theoretical activity consists predominantly of modeling.

Given these two parallel discussions of modeling, both inspired by the
philosophical literature on experimentation, it seems interesting to discuss
them side-by-side, in order to better understand the experimentable and
exploratory nature of modeling. The two discussions seem to place emphasis
on different dimensions of modeling. The discussion of the experimental
character of models concentrates on models themselves as workable, exper-
imental objects, addressing in particular the epistemic value of materiality.
The focus on exploratory modeling, 反过来, highlights the different uses of
models in explorative activities. In order to better understand such exploratory
流程, we analyze (parts of) a research trajectory of one leading research
group within the ﬁeld of synthetic biology. Such a processual approach
allows us to more fully understand exploratory modeling. Another impor-
tant aspect of exploratory modeling that we wish to highlight is the use of
various kinds of epistemic means—different kinds of models, 实验

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Biological Control Materialized

and measurements—that any genuinely exploratory process typically
involves. We submit that is crucial not just to study single models but also
to focus on multiple models, in multiple materialities.

The exploratory process we will examine focuses on biological control,
and on how scientists combined different models of various materialities with
彼此, and with other epistemic means. The ensemble of different epistemic
means used to explore the role of noise in biological organization consisted of
mathematical models and simulations, synthetic genetic circuits and measuring
设备, and ﬁnally electronic circuits. The status of synthetic and electronic cir-
cuits in this fabric is especially interesting. While they can be considered as ex-
periments due to their material make-up, they can also be conceptualized as
theoretical explorations. Especially synthetic genetic circuits lie squarely be-
tween modeling and experimentation as they are constructed of the “same stuff”
as the studied biological systems, yet are carefully designed making use of math-
ematical models as blueprints. 或者, the role of electronic circuits in this
exploratory process seems equally intriguing. Why did scientists, already armed
with mathematical models, simulations, and synthetic genetic circuits built
from genes and proteins, start to construct electronic circuits to study biological
控制?

Our case on biological control is based on a laboratory study of the Elowitz Lab
at the California Institute of Technology, including its collaborations, 尤其
with physicist Jordi García-Ojalvo at Universitat Pompeu Fabra and his co-
工人. Michael Elowitz is the co-author of the Repressilator, which is one
of the ﬁrst synthetic genetic circuits to have been published, as well as probably
the most famous one, due to its pioneering nature (Elowitz and Leibler 2000).
There is already ample philosophical literature on the Repressilator (例如,
Knuuttila and Loettgers 2011, 2013A, 2013乙), but the exploratory process it
gave impetus to, has neither been reported, nor studied. We will show how
the process of exploratory modeling has established more conclusive evidence,
now at the molecular level, for the role of stochastic ﬂuctuations in biology. 什么
is more, this evidence amounted to a redeﬁnition of the target system: 尽管
human-made control systems are built to minimize noise, the scientists were able
to make a convincing case that noise is an essential part of biological control.

We will begin with an overview on the philosophical discussion of the
experimental character of models, turning then to exploratory modeling.
After that, we will consider the exploratory research program on biological
control and noise in which the Repressilator model has played a crucial
role in its many incarnations.

2. Models as Experiments?
Once models are couched as experimentable objects, it has proven difﬁcult,
at least on a general philosophical level, to clearly distinguish between modeling

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科学观点

471

and experimentation. Although it seems intuitive to think that there are cru-
cial differences between modeling and experimentation in terms of their re-
spective targets, epistemic results, or materiality, several philosophers have
presented counterarguments that largely bring modeling/simulation and ex-
perimentation together. The recent philosophical discussion has pointed out
two ways in which they resemble each other. 第一的, modeling and experimen-
tation have been viewed as largely analogous operations aiming to isolate some
core causal factors and their effects. The argument is that both in modeling
and in experimentation one aims to seal off the inﬂuence of other causal factors
in order to study how a single causal factor operates on its own. Whereas in
experimentation this sealing off happens through experimental controls, 模组-
elers use various techniques, such as abstraction, idealization, and omission as
vehicles of isolation (例如, Cartwright 1999; Mäki 2005).

One central problem of the isolationist view is due to the fact that idealizing
and simplifying assumptions made in modeling are often driven by the require-
ments of tractability and mathematical convenience rather than those of isola-
的 (例如, Cartwright 1999). This feature of mathematical models is further
enhanced by their use of general, cross-disciplinary computational templates
那是, in the modeling process, adjusted to ﬁt the ﬁeld of application (例如,
Humphreys 2004; Knuuttila and Loettgers 2011, 2016). Such templates are
often transferred from other disciplines, as in the case of synthetic biology, 在哪里
many models, formal methods, and related concepts originate from physics and
工程 (例如, the concepts of oscillator, feedback mechanism, and noise—
见下文).

第二, when it comes to simulation, numerous philosophers and scientists
have pointed out their experimental nature as kinds of “numerical experiments”
(Winsberg 2003).1 Instead of isolation, the stress here is on intervention: both in
modeling and experimentation one seeks to intervene on a system in the light of
the results of this intervention. 最后, simulations can be thought of as
experiments performed on mathematical models, but the question is how deep
this resemblance cuts. Two issues, 尤其, have sparked discussion: 这
supposed target systems of simulations versus experiments, and the role of
materiality they incorporate.

A common intuition seems to be that, whereas in experimentation one in-
tervenes on the real target system of interest, in modeling one merely interacts
with a model system (例如, Barberousse et al. 2009). 然而, a closer examination has
assured several philosophers that these intuitions may be deceptive. Winsberg
argues that both “experiments and simulations have objects on the one hand

1. Another similarity between modeling and experimentation is related to data. 两个都
activities produce data, and deal with data analysis and error management (see Winsberg
2003, Barberousse et al. 2009). We will not discuss this third commonality.

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Biological Control Materialized

and targets on the other, 然后, in each case, one has to argue that the object is
suitable for studying the target” (2009, p. 579; see also Guala 2002). 因此,
both experimentation and modeling/simulation seem to display features
of surrogate reasoning (Swoyer 1991), which is visible, 例如, 在里面
experimentation on model organisms instead of the actual organisms of
兴趣. 最后, the closeness of a relationship of a model or experiment
to its respective target need not distinguish the two activities from each other.
Even though modeling and experimentation appear to come close to each
其他, at the level of scientiﬁc practice we often do not have any difﬁculties in
distinguishing model systems from experimental systems, although borderline
cases do exist. Models and simulations are considered kinds of representations
and are typically are expressed in other media than what their targets are made
的, whereas experimental objects are supposed to share at least partly the same
material make-up as the systems of interest. 的确, the right kind of mate-
riality has been claimed to be the distinguishing mark of experiments and even
the reason for their epistemic superiority to simulations. Guala (2002) 和
摩根 (2003) have argued that the relationship between a simulation and its
target is nevertheless abstract, while the relationship between an experimental
system and its target is grounded in the same material being governed by the
same kinds of causes. 最后, while in simulation one experiments with a
(formal) representation of the target system, in experimentation the experimental
and target systems are made of the “same stuff.” This difference also explains,
according to Morgan and Guala, why experiments have more epistemic leverage
than simulations. 例如, anomalous experimental ﬁndings are more likely
to incur change in our theoretical commitments than unexpected results from
simulations (摩根 2005).

Despite the intuitive appeal of the importance of the “same” materiality, 它
has been contested on different grounds. Morrison (2009) points out that even in
experimental contexts the causal connection with the physical systems of inter-
est is often established via models. 最后, materiality is not able to de-
liver an unequivocal epistemic standard that distinguishes simulation outputs
from experimental results. 派克 (2009) questions the alleged signiﬁcance of
the “same stuff.” She interprets the “same stuff” to mean for instance the same
ﬂuid, and points out that in traditional laboratory experiments on ﬂuid phe-
nomena, and many other things such as the depth of the ﬂuid and the size,
shape, roughness and the movement of any container holding it may matter.
This leads her to suggest that it is the “relevant similarities” that matter for
the justiﬁed inferences about the phenomena. Our case study on synthetic
modeling shows that the “same stuff” was crucial in the study of genetic circuits.
然而, due to the complexity of intracellular mechanisms, and our scant
knowledge of them, other kinds of models were needed that were triangulated
with synthetic models in an exploratory fashion.

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科学观点

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Exploratory Modeling

3.
The discussion of exploratory modeling builds on the already established lit-
erature on experimental exploration (例如, Steinle 1997, 1998; Burian 1997).
Friedrich Steinle introduced the notion of exploratory experimentation in his
study of Ampère’s experiments on Oerstedt’s discovery concerning the inﬂu-
ence of a nearby electric current on a compass needle. This discovery suggested
that there is a connection between electricity and magnetism. Steinle’s studies
of Ampère’s lab books lay out the methodological care with which Ampère
proceeded. His endeavor was not based on trial and error, although no theo-
retical framework that would have explained the connection between electric-
ity and magnetism was available. 例如, Ampère realized right in the
beginning that if he wanted to investigate whether there is a connection be-
tween electricity and magnetism, he had to isolate terrestrial magnetism from
magnetism caused by electricity.2 In order to separate the two effects, he de-
veloped a speciﬁc experimental setup. The case study shows that experiments
do not have to be guided by an encompassing theory, since there are experi-
mental guidelines such as systematic variation of parameters, formulation of
stable empirical rules, and the exploration of which of the experimental con-
ditions were necessary for the effect, and which were not. 而且, such ex-
ploratory experimentation can lead to conceptual developments especially in
the early stages of inquiry.

Contemporaneously to Steinle, and in line with his ﬁndings, Burian (1997,
p. 15) used the notion of exploratory experimentation to characterize the “elab-
orate series of interconnected experiments” that were used to (关于)identify and
localize nucleic acids in the early phases of the study of these still ephemeral
entities that are nowadays called mRNA and tRNA. The notion of exploratory
experimentation provided Burian an answer to the puzzle of how the work of the
experimentalists in different groups and even in different disciplines could con-
verge and stabilize around some shared ‘theoretical’ entities. The various proce-
dures for experimental localization of entities provided exploratory means for
scientists that did not depend “wholly on the speciﬁc disciplinary or theoretical
background of the experimenters, who initiated the work on those objects”
(Burian 1997, p. 17).

Instead of trying to highlight the experimental features of modeling, 这
philosophers interested in exploratory modeling have rather focused on some
explorative features that are speciﬁc to modeling (although they may be found
to apply to other epistemic practices as well). What the two discussions of

Such separation between the two kinds of magnetism has a parallel in the case
discussed in this article, where the scientists investigating noise proceeded to construct
an experimental set-up that would allow them to distinguish between internal and external
noise (见下文).

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Biological Control Materialized

exploration share, 然后, is the focus on the early stages of inquiry. 然而,
Gelfert (2018) is careful to point out that the question is not just about the
chronicle of events. Exploration seeks to get “a grasp of a phenomenon or sci-
entiﬁc problem in the absence of a well-understood and workable theory of the
domain in question” (Gelfert 2018, p. 4). He argues that this “early” character
of exploration distinguishes it from heuristics or abduction. While heuristics is
utilized in all stages of research, abduction, in Gelfert’s construal, applies to
later stages of inquiry, in which there already is a stable target phenomenon
requiring explanation. But such a well-delineated target phenomenon cannot
be taken for granted in the early stages of research. 的确, one of the four
exploratory uses of models that Gelfert puts forth is precisely that of “reasses-
sing the suitability of the target.”

The three other exploratory functions of models that Gelfert introduces are
the uses of models as starting points of inquiry, as proofs of principle, 并作为
sources of potential explanation. These uses are overlapping. Often proofs of
principle provide sources of potential explanation, and as such, starting points
of inquiry—the last one of these uses being so generally characterized that it
does not carry too much analytical potential of its own. The functioning of
models as proofs of principle and potential explanations has been commonly
recognized in the literature of modeling—covering e.g., how-possibly, ﬁctional
and toy models—though the existing discussion has not addressed them
collectively as various modes of exploration. Apart from providing the unify-
ing notion of exploration for many so far separately examined activities of
造型, Gelfert’s discussion of reassessing the suitability of a target system
appears to us to break new ground. He studies such a process through Turing
图案, which present a case of a general mathematical model looking for
suitable targets. Turing’s model (1952) of the chemical basis of morphogenesis
seeks to explain pattern formation in an otherwise homogeneous system
through oscillations in concentrations resulting from the interaction between
two chemical substances. The system consists of an “activator” that can make
more of itself, and a quickly diffusing “inhibitor” that slows down the activa-
托尔. This simple reaction-diffusion system is able to produce very complex pat-
terns and might explain, 除其他事项外, spots and stripes in animals.

With respect to model-based exploration, especially three observations by
Gelfert concerning the history of the Turing patterns seem particularly relevant.
第一的, the biographies of scientiﬁc models are anything but linear, “models can
go out of fashion and subsequently rebound” (Gelfert 2018, p. 266). It was the
better simulation techniques that sparked a new interest in Turing patterns, 喜欢
in the case of many other simple mathematical models examining complex phe-
nomena. 第二, Turing patterns offered only a potential explanation and con-
关注的, in Turing’s own understanding, “imaginary biological systems.” Such
potential explanations are modal in nature; they target principles that might

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科学观点

475

apply to a very wide range of different kinds of systems. 第三, Gelfert men-
tions that apart from the availability of advanced simulation models, also their
interplay with experimental manipulation was crucial for the revival of the
Turing patterns.

We suggest that these two latter points are substantially intertwined.
即, insofar as mathematical models study abstractly the dynamics of
some general phenomena, and in doing so only provide potential explanations
for particular real-world regularities or processes, the experimental grounding
of these models in the real phenomena becomes crucial. Such grounding does
not amount to testing of the very general theory, as pointed out by the liter-
ature on exploratory experimentation. 相当, it situates the general model
into the context of some real-world problem, and the existing knowledge con-
cerning it, through an exploratory interplay of modeling and experimentation.
In the following we will study exploratory modeling of genetic circuitry that
made use of modeling and experimentation in various modes and materiali-
领带: mathematical models and their simulations, synthetic genetic circuits
and measuring devices, and electronic circuits. Different models were con-
structed in different stages of the exploratory process that led, much to the
scientists’ surprise, to the redeﬁnition of the original target, biological control.

Exploration in Biological Control

4.
Control in biological systems has been a central topic of biological sciences
for a long time. Biological control is related to the high degree of organi-
zation in biological systems, from the molecular and cellular levels up to
the organismic level, including properties that are considered to be biology
speciﬁc such as their ability to maintain themselves, 发展, 并重现-
duce. Biological control became a subject of systematic study in the begin-
ning of the twentieth century. Françoise Jacob and Jacque Monod’s (1961)
discovery that gene regulation takes place via transcription factors was a
milestone in experimental molecular biology. These ﬁndings were accom-
panied by theoretical studies making use of engineering principles, math-
ematical modeling, and general systems theory (Von Bertalanffy 1969).
Early work in the 1960s on cybernetics (Wiener 1948) and information
理论 (Shannon 1948) proceeded along the same lines. These different
developments inﬂuenced the modeling of genetic and metabolic regulation
in terms of feedback loops, 在哪里, for example genes are controlled at the
level of transcription by the products of other genes. With the introduction
of synthetic biology at the turn of the twentieth century, it ﬁnally became
possible to study such feedback systems within living cells. 中的一个
pioneers of this approach is the Elowitz lab (Caltech), whose study of gene
regulation extended into the examination of the role of noise in biological
组织.

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Biological Control Materialized

4.1. Mathematical and Synthetic Modeling in the Exploration of Noise
The research on noise in synthetic biology emerged from the question of how
biological control in biological systems, such as in the circadian clock, 可以
have been implemented (Loettgers 2009). Experiments on the circadian clock
in molecular biology, as well as mathematical models by mathematical biolo-
gists like Brian Goodwin (Goodwin 1963) and Arthur Winfree (Winfree 1990)
suggested that this kind of control is based on oscillations in protein levels.
Michael Elowitz together with Stanislas Leibler constructed a synthetic ge-
netic circuit, the Repressilator (Elowitz and Leibler 2000), that aimed to
furnish a proof of principle that such oscillations could be produced by var-
ious kinds of molecular feedback systems.

The Repressilator consists of three genes that repress the protein production
of each of its neighbor gene in the fashion of the rock/paper/scissors game. 这
mathematical model underlying the Repressilator is a system of non-linear
coupled differential equations of the following form:

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dmi
dt

dpi
dt

¼ −mi þ

(西德:1)

1 þ pn
j

(西德:3) þ α0

¼ −β pi − mi
ð

(西德:4)

i ¼ lacI; tetR; cl
j ¼ cl; lacl; tetR

(西德:5)

和:

In this set of equations pi stands for the concentrations of the proteins sup-
pressing the function of the neighbor genes and mi (where i stands for lacI, tetR,
or λcl) are the corresponding concentrations of mRNA. There are six molecule
物种 (3 proteins functioning as repressors and 3 基因), each of them taking
part in transcription, 翻译, and degradation reactions. 一般来说, 有
no analytical solutions for such non-linear coupled differential equations, 所以
Elowitz and Leibler performed computer simulations on the basis of this math-
ematical model. The main purpose of these simulations was the identiﬁcation of
relevant experimental parameters as well as the different possible states that
could be exhibited by the system. There are two such states: a steady state, 和
a state in which the system performs limit-cycle-oscillations. Being interested in
biological control, Elowitz and Leibler aimed for limit-cycle oscillations. 为了

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科学观点

477

attaining these oscillations, the experimental parameters were of critical impor-
坦斯, and the simulations showed that such oscillations require, 例如,
strong promoters and tight transcriptional repression. This information was put
to use in the construction of the actual genetic network, the Repressilator, 为了
which the mathematical model functioned as a blueprint.

Although the computer simulations provided some important experi-
mental parameters, most of the biochemical parameters remained unknown.
The Repressilator did not seek to represent any naturally existing genetic
circuit, it was a very simple construct made of well-characterized molecular
components adopted from different contexts of research in view of obtaining
robust oscillations. The same applies to the mathematical model that under-
lies it. Both models, the mathematical and the synthetic, can better be un-
derstood from the exploratory than the traditional representational
看法; they were purposefully constructed epistemic artifacts for prob-
ing possible architectures and dynamics of genetic circuits (参见部分 5 是-
low for further discussion).

Another important exploratory dimension of the Repressilator was its
implementation within a living bacterial cell that allowed the model to
be explored in its natural biochemical environment. The actual genetic network
was constructed by making use of a plasmid that was introduced into a cell.
Plasmids are extra-chromosomal DNA molecules that are self-replicating.
数字 1 shows the architecture of the Repressilator and the interaction be-
tween the three genes.

The genes of the Repressilator are connected via a negative feedback loop
through which they repress each other’s protein production. Green ﬂuorescent
蛋白质 (GFP) is used as a reporter and it is fused to the tetR gene. This construct
makes the oscillations in the protein level of the gene visible through ﬂuores-
cence microscopy. The entire construct, consisting of the Repressilator and the
记者, is integrated into E. coli bacteria. Its being constructed from biological
成分, and integrated into the bacteria, made the system more biology
喜欢. Although the biochemical interactions in the cell are largely unknown,
this embedment, as Waters (2012) has lucidly spelled out, “avoids having to

数字 1. The diagrams show sketches of the Repressilator and the reporter
(Elowitz and Leibler 2000).

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478

Biological Control Materialized

understand the details of the complexity, not by assuming that complexity is
irrelevant but by incorporating the complexity in the models.” The Repressila-
tor was able to produce oscillations, but they turned out noisy (相比之下
to what the underlying mathematical model predicted). 数字 2 节目
the oscillations of the Repressilator, both in the growing bacteria colony,
and in the diagrams depicting the measurements of oscillations in indi-
vidual Repressilators.

The closer look at the pictures of the blinking bacteria colonies reveals that
the blinking of the bacteria, IE。, the oscillations made visible by the reporter, 是
not synchronized. This non-synchronization is manifested even more obviously
in the lower diagrams (a–c), showing the ﬂuorescence of different sibling cells.
Here the red line is a reference line representing the oscillations of the whole
bacteria colony and the blue and green lines belong to oscillations of single
sibling cells. The diagrams show that the amplitudes of the oscillations of

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数字 2. The upper picture shows the growing population of E. coli bacteria
carrying the Repressilator. The lower picture shows the oscillations of single E. 大肠杆菌
bacteria over time (Elowitz and Leibler 2000, p. 336).

科学观点

479

the sibling cells change over time, meaning that there is a difference in the
amount of proteins produced over time by the reporter gene. 第二, 这
phase of the oscillations in the two bacteria shift over time. 换句话说,
the sibling cells show some individual behavior (phase shift) but there is also
some variability in this individual behavior (changes in amplitude). (这
图形 (d) presents oscillations obtained in different experiments, 和 (e–f )
are the result of negative control experiments.)

The single cell measurements the Elowitz lab performed were laborious.
One source of difﬁculties was due to choosing the “right” parameter values.
Elowitz explained this in the following way:

[氧]ne thing we’ve seen is that, the ﬁrst generation of synthetic
circuits were often overexpressing proteins a lot, and consumed a lot
of resources and [那] often makes the cells grow a little bit slower
than other cells that don’t have the synthetic circuit [……] it just
means that maybe they’re not optimized yet to be sufﬁciently
独立的 [……] the other side of it is that the environment inside
the cell is not the environment of our model where there [是], 你
知道, continuous variables and continuous trajectories. (Elowitz in
interview conducted by Andrea Loettgers)

Another source has been the large apparatus consisting of microscopes,
image processing utilities and computer programs that were brought
into play and adjusted for the analysis of the dynamics of the synthetic
系统. The complexity of this experimental set up contrasts with the
apparent simplicity of the synthetic genetic circuit. Only when one
focuses on the noisy character of the observed fluctuations, is one able to
get a glimpse of the complexity of even such simple systems as the
Repressilator.

The observed individual behavior of cells, shown in the phase shifts and ﬂuc-
tuations in Figure 1, provided a ﬁrst clue that the ﬂuctuations could be of a
stochastic nature. Most probably, the researchers assumed, they were caused
by the limited number of molecules in cells. In order to explore the noisy be-
havior exhibited by the Repressilator, the researchers performed computer sim-
ulations of a stochastic version of the initial mathematical model that seemed to
conﬁrm the stochastic nature of the observed ﬂuctuations. Two related questions
appeared: 第一的, how are regular oscillations possible at all in the stochastic en-
vironment of a cell, 和, 第二, how are stochastic ﬂuctuations related to other
sources of noise that occur independently from the observed stochastic ﬂuctua-
系统蒸发散. Both questions were explored by further models and synthetic constructs.
Of a particular interest is the synthetic intracellular measuring device that the
Elowitz group developed to explore stochastic ﬂuctuations by making them
“visible” on a single cell level.

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480

Biological Control Materialized

4.2. Measuring Stochastic Fluctuations in Biological Systems
A question, which was unable to be explored by the Repressilator, 是
total amount of noise in a biological system. The total amount of noise is
given by the sum of extrinsic and intrinsic noise. The Repressilator only pro-
vides insight into stochastic noise, which is part of intrinsic noise that has at
least two sources:

(西德:129) Individual messenger RNA does not get translated only one time during
the transcription process, but can be translated many times, 导致
the production of many proteins instead of one.

(西德:129) Promoters that regulate the transcription process by turning the genes
‘off’ and ‘on,’ can switch between long-lived ‘off’ and ‘on’ states.

As further experiments on stochastic ﬂuctuations showed, both processes re-
sult in bursts of mRNA and, related to it, bursts of proteins reﬂected by the
observed ﬂuctuations in the protein level (Ingram et al. 2008, 卷. 4). Intrinsic
noise is closely linked to extrinsic noise. While intrinsic noise are ﬂuctuations
generating bursts of proteins, extrinsic noise are the propagated bursts of pro-
teins affecting the expression and protein production of other genes in the sys-
TEM. There are various other extrinsic sources of noise that arise independently of
the gene, yet act on it, IE。, the stage of the cell cycle, the mRNA degradation
machinery and the cell environment ﬂuctuations. Although extrinsic ﬂuctua-
tions are part of the dynamic and behavior of the Repressilator, they cannot
be identiﬁed in its oscillations.

The basic motivation for constructing the synthetic intracellular measur-
ing device was to distinguish between extrinsic and intrinsic ﬂuctuations by
separating them from each other (Elowitz et al. 2002; Swain et al. 2002). 在
constructing the measuring device Elowitz and colleagues made again use
of E. coli bacteria. This time they integrated into the chromosome of the
bacteria cyan cfp and yellow yfp alleles of green ﬂuorescent proteins, 哪个
were then put under the control of identical promoters. In this set-up the
intrinsic noise affects each of the promoters of the two reporter genes sepa-
率地. The two genes are uncorrelated, meaning that the proteins produced
by the genes ﬂuctuate in an uncorrelated fashion (see the lower part B of
数字 3). This gives rise to a population of cells, in which some cells express
more of one ﬂuorescent protein than the others. 作为结果, the cells in
the populations appear in different colors such as yellow, orange, 红色的, 和
绿色的.

In the absence of intrinsic noise at the two reporter genes, the two genes,
which are located in the same cell, are only exposed to extrinsic noise that is
the same for each of the genes. 最后, the cells with the same amount
of each protein appear yellow, as shown by the upper part A of Figure 3.

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科学观点

481

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数字 3. The ﬂuctuations due to the extrinsic (A) and intrinsic (乙) noise and
the corresponding variations in ﬂuorescence (Elowitz et al. 2002, p. 1185).

In an interview Michael Elowitz reﬂected on his expectations concerning

the experiment:

[瓦]hen I ﬁrst was doing that experiment I really didn’t know [……].
There had been a lot of work theoretically, simulations, on how noise
should be signiﬁcant. But depending on the effective biochemical
parameters inside the cell, which really were not known, 和
depending on how big the extrinsic noise is, it was hard to say
whether the extrinsic or intrinsic effects would be stronger. 我认为
what was interesting for me was, going into it, I really had no idea
whether noise would just be a small perturbation on top of these big
extrinsic ﬂuctuations, or whether actually everything would be
dominated by intrinsic noise. 所以, I think that was what was kind of
fun about it [……] (interview conducted by Andrea Loettgers)

In exploring the relation of intrinsic and extrinsic noise under different con-
版本, experiments were performed by introducing the reporter genes into
different strains of E. 大肠杆菌. The differences between these strains depended on
how strongly the genes of the regulator sequences, to which the reporter genes
were fused, were transcribed. By performing these experiments, Elowitz and
co-workers were able to explore how the transcription of a gene is related to
intrinsic and extrinsic noise. The results were differently colored colonies of
cells depending on the level of intrinsic and extrinsic noise (见图 4).

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482

Biological Control Materialized

而且, to study the interplay between the regulatory dynamics and noise,
researchers introduced the Repressilator into one of the strains. From the re-
苏丹, they concluded that changes in the regulatory dynamics may cause sub-
stantial changes in noise levels.

These measurements were constrained by how much the parameters of
the synthetic measuring device could be changed in a controlled fashion.
Even this comparatively simple system turned out to be very complex and
experimentally difﬁcult to access. In addition to the Repressilator, 和
measuring device for distinguishing between extrinsic and intrinsic noise,
a third model was designed and used to explore noise in biological systems.
The third system was not synthetic, instead it was an electronic version of
the Repressilator. What could be learned from an electronic model about
noise in biological systems?

The Electronic Repressilator

4.3.
Even though Elowitz and co-workers had been able to distinguish between
extrinsic and intrinsic noise, and to explore them under different conditions,

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数字 4. The pictures show the outcome of the experiments in which noise was
explored under different experimental conditions by, 例如, changing the
reporter genes. (Elowitz et al. 2002, p. 1186). The last picture (F) shows the
inﬂuence of the periodic dynamic of the Repressilator on the level of intrinsic and
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科学观点

483

the question of how regular oscillations are possible at all under these cir-
cumstances still needed to be answered. To answer this question an elec-
tronic Repressilator was constructed (Buldú et al. (2007). The electronic
Repressilator was inspired partly by the work of Mason et al. (2004), WHO
constructed an electronic circuit based on ordinary differential equations
modeling a genetic network. Such an electronic circuit provides a good
model for the study of robust oscillations, 自从, as the researchers put it
“this system is subject to electronic noise and time delays associated with
its operation, and since its parameters depend on the actual values of
capacitances and resistors [……].” (Mason et al. 2004, p. 709).

While Mason et al. (2004) constructed a hybrid digital-analog circuit,
the electronic Repressilator of Buldú et al. (2007) is purely analog. Among
the advantages of this approach is the experimental ﬂexibility of electronic
circuits as well as the possibility of constructing integrated circuits with a
big number of elements. 数字 5 shows the layout of this electronic cir-
崔特. It is based on the same network design as the biological Repressilator
consisting of “three dynamical elements coupled in chain with an inhibi-
tory interaction” (Buldú et al. 2007, p. 3508).

The electronic version of the Repressilator is, as is the corresponding syn-
thetic model, based on a mathematical model. It consists of three elements,
each of them modeling a gene, which becomes repressed by the proteins pro-
duced by its neighbor gene. The three elements themselves consist of opera-
tional ampliﬁers and RC (R=Resistance C=Capacitor) circuits. 这
operational ampliﬁer, designated as a triangle in the diagram, functions as a
comparator and the RC circuit as ﬁlter. The voltages V1, V2, and V3 are the
analogues to the protein concentrations in the synthetic model. The dynamic
of the electronic circuit can be described in the following way: in case the volt-
age V2 increases, it induces a reduction of the following output voltage V3,
which again lead to an increase of V1. The model results in regular oscil-
lations in the three output voltages of the electronic circuit and is known

数字 5. The diagram is a sketch of the electronic version of the Repressilator
(Buldú et al. 2007, p. 3508).

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484

Biological Control Materialized

in electrical engineering as a ring oscillator (displaying the engineering
origin of the Repressilator) (数字 6).

The electronic Repressilator shows that robust oscillations are possible
despite of the presence of noise. But how could this result be transferred into the
context of biology? The problem becomes one of relating the parameters of the
electronic circuit to the genetic circuit, and it has so far remained unsolved.
因此, it seems that there are limitations to the widespread practice in
synthetic biology of drawing analogies between electronic and genetic circuits.
The electronic Repressilator turned out to be a valuable model in the exploration
of possible network designs, 然而, due to its combination of experimental
ﬂexibility and implementation of noise in the electronic components. An exam-
ple of this line of work is the interdisciplinary collaboration between Michael
Elowitz, his long-time collaborator physicist Jordi García-Ojalvo, and mathe-
matician Steven Strogatz. They constructed a mathematical model of a popula-
tion of Repressilators, coupled by quorum sensing (García-Ojalvo et al. 2004).
Quorum sensing is the common way of how bacteria communicate with each
other by exchanging molecules. Following Strogatz’s interest in synchronization
of oscillatory systems such as the blinking of ﬁreﬂies, García-Ojalvo et al. 用过的
the mathematical model together with a computer simulation to explore con-
ditions under which the coupled Repressilators would synchronize. 甚至
though the model predicted that “a diverse and noisy community of such
genetic oscillators interacting through quorum-sensing mechanism should
self-synchronize in a robust way” (García-Ojalvo et al. 2004, p. 10955), 这
attempt of constructing a synthetic system of coupled Repressilators failed.
Buldú et al. (2007) succeeded, 然而, in constructing an electronic version
of the population of Repressilators that was able to exhibit synchronized

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数字 6. The diagram shows the oscillations in voltages in the electronic
circuit (Buldú et al. 2007, p. 3508).

科学观点

485

oscillations. 尽管如此, given the problem of relating the electronic and
biochemical parameters, they could not answer the question of why the
synthetic system failed to synchronize.

总结, the Repressilator showed that it was possible to build, 根据
to engineering and physical principles, a biological circuit able to oscillate. 如何-
曾经, as these oscillations turned out to be noisy, the Elowitz lab went on to study
the sources of noise, 一方面, and the possibility of robust oscillations and
the synchronization of oscillating bacteria, 在另一. While the former studies
were performed with synthetic systems, the latter were carried out with electronic
circuits and mathematical modeling and simulation. 还, elsewhere in synthetic
biology a lot of effort was put into designing robust genetic circuits. Here espe-
cially the work of the Hasty lab at the University of California San Diego needs to
be mentioned. Hasty and his co-workers were able to construct a robust oscilla-
tory genetic circuit by paying close attention to the biochemical parameters, ba-
sically involving an unintended interaction between the synthetic genetic circuit
and the host cell (Cookson et al. 2009; Stricker et al. 2008).

最后, Elowitz and his co-workers also addressed the role of noise in biolog-
ical organization. In several studies, the members of the lab showed that noise is
not a nuisance in biology but has a functional meaning (Süel et al. 2007; Eldar
and Elowitz 2010). Noise, in the form of stochastic ﬂuctuations, triggers as well
as controls processes in cells. In addressing the functional meaning of noise, 这
Elowitz lab drew inspiration from the study of neurodynamics and lasers, 男人-
tioning also the classic experiments by Spudich and Koshland (1976) on non-
genetic variability of the bacterial chemotaxis. Spudich and Koshland had
concluded that “nongenetic variability would be a preferred mechanism for ac-
commodation to random ﬂuctuations in the environment and genetic variability
the preferred mechanism for accommodation to long lasting environmental
changes” (1976, p. 470). With the beneﬁt of new technologies, the Elowitz
lab was able to study such nongenetic variability in the form of stochastic ﬂuc-
tuations within living cells. 重要的, their exploration on noise led to a de-
velopment of an experimental-cum-conceptual framework in which noise and
control became intertwined and redeﬁned.

5. Discussion and Conclusions
The exploratory research program on biological control and noise by the
Elowitz lab and its collaborators portrays an intricate fabric of mathematical
modeling and simulation, intracellular measurement, and the construction of
synthetic genetic circuits and electronic circuits. We deem multiple modeling
and the combination of different epistemic means a central scientiﬁc practice
that merits more research from the exploratory perspective. That contemporary
modeling practices typically employ multiple models and triangulate them

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Biological Control Materialized

with other epistemic activities is something that has been recognized and ana-
lyzed by the philosophical discussion surrounding robustness. 然而, robust-
ness analysis has been focused on convergence; on either the use of multiple
related models to causally isolate a core mechanism producing certain phenom-
ena (Knuuttila and Loettgers 2011), or on the use of independent epistemic
方法 (型号, 实验, and observations) to “triangulate the existence
and character of a common phenomenon, object or result” (Wimsatt 2007,
p. 43). Although both of these aspects of robustness analysis are present in
our case study, the notion of exploratory modeling better captures how the
Elowitz lab and its collaborators studied biological control, and noise. In line
with robustness analysis, the scientists were using multiple, materially distinct
and thus partially independent epistemic means, in order to study whether the
simple network design could create robust oscillations at the molecular level.
然而, this triangulation process is better conceived of as providing an initial
proof of principle, in the form of the Repressilator model that then led into
further exploration into the sources and role of noise in biological control. 在
this explorative process, materially different models were used rather in a dia-
logue with each other than only as an attempt to isolate a core causal mechanism
common to all of them. 相比之下, the models studied were different material
realizations (digital, synthetic, and electronic) of a particular circuit design.3

Massimi (2018) discusses multiple models as means of exploration, and casts,
而且, exploratory modeling in a modal idiom. She presents a case in Beyond
Standard Model (BMS) 物理, where different models perform the exploratory
function in charting and carving out the space of possibilities (Massimi 2018,
p. 349). The Elowitz lab’s research on biological control was also importantly
modal in that it was motivated by the question of whether the kind of feedback
systems already familiar from physics and engineering, and theorized since the
early 1960s (例如, Jacob and Monod 1961) could be realizable in biological or-
ganisms. Synthetic biology ﬁnally gave means to scientists to study such possi-
ble general design principles of biological organization within living cells. 这
underlying additive character of Massimi’s “perspectival modeling” does not
easily accommodate our example, 然而. In her construal, perspectival models
provide each a partial account of the phenomenon of interest, where the “explor-
atory function is performed jointly” (Massimi 2018, p. 350). The multiple
modeling process of the Elowitz lab had more of an unfolding character. 他们的
models and measurements functioned as kinds of target systems/research ob-
jects for each other in the investigation of the entangled, and largely unknown
phenomena of noise and control. In this process the phenomenon of interest,

3. The epistemic importance of the different materialities and formats of models has

also been studied by e.g., Chandrasekharan and Nersessian (2011); Vorms (2012).

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科学观点

487

biological control, became reassessed, as researchers realized that noise might
play a crucial role in it.

最后, the synthetic biology program of the Elowitz lab provides in-
sight into the discussion of models as experiments. 的建设
synthetic constructs—synthetic genetic circuits and synthetic intracellular
measuring devices—each functioning within individual, and multiplying,
bacterial cells, provides the particular novelty of the research practice of the
Elowitz lab. For considering the experimental character of models, syn-
thetic genetic circuits seem especially exciting, as they seem difﬁcult to
place either in the category of modeling or that of experimentation. 这
comparison of synthetic genetic circuits to the electronic circuits on the
一只手, and to the synthetic intracellular measuring devices on the other,
helps to identify the model-like and experimental features of synthetic ge-
netic circuits.

第一的, while both electronic and synthetic genetic circuits are material model
systems used experimentally to study biological control, we seem more inclined
to treat such electronic circuits as models than synthetic circuits. 为什么? 这
answer appears to be that because electronic circuits are implemented in another
medium than the biological systems of interest, they are more readily called
型号. 例如, Rheinberger (2015) distinguishes what he calls “prepara-
tions” from models on this basis: while preparations “participate in the materi-
ality of the object of knowledge in question,” models do not (p. 325). 我们不
consider this a very substantial point. Both electronic circuits and synthetic cir-
cuits can be considered models in that they are tightly constrained and (到一个
程度) self-contained constructions designed to explore certain pertinent theo-
retical and empirical questions (Knuuttila 2011, 2017). Neither of them aims
to represent some particular naturally occurring target systems, they rather pres-
ent and study very rudimentary and highly hypothetical design principles, ten-
tatively applying to actual and possible non-actual biological systems alike.
Such a program is in line with the grander vision of synthetic biology: “the ex-
pansion of biology from a discipline that focuses on natural organisms to one
that includes potential organisms” (Elowitz and Lim 2010, p. 889). This modal
character of synthetic biology is precisely one of the most important reasons for
approaching it from the exploratory perspective (Knuuttila forthcoming;
Knuuttila and Koskinen 2020; Gelfert 2016, 2018).

The comparison of the Repressilator to the synthetic measuring device, 这
noise sensor, further underlines its model-like character. In contrast to the Re-
pressilator, the noise sensor was not constructed to fulﬁll some speciﬁc biological
function and, 最后, to exhibit at least partially independent behavior
from the rest of the cell. In designing the Repressilator, 相比之下, the scientists
aimed for a synthetic module that would, by being able to create new behavior,
provide a tool for the exploration of possible design principles of biological

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Biological Control Materialized

有机体 (see above). The noise sensor as opposed to the Repressilator did not
have a dynamic of its own. The independent functioning of the Repressilator
was crucial for its role as an exploratory tool, whereas the noise sensor was
implemented as an integral part of the cell and was supposed to be responsive
to various conditions taking place in it. 因此, although the Repressilator and
the noise sensor were both parts of the cell, they were considered different
kinds of things with only the former having a proper model-like character.
然而, 同时, the material make-up of the Repressilator, 它是
being of the same stuff as the naturally occurring genetic circuits, and fur-
thermore implemented in the natural cell environment, gave it a highly
experimental character. 像这样, it was exposed to the (largely unknown)
constraints of naturally evolved biological systems which explains how the
researchers reacted to the unexpected results. The irregular oscillations
were taken more seriously than any particular results derived from math-
ematical models would have been, spawning a new research program on
noise. The fusion of the model-like and experimental features of the Re-
pressilator speaks in favor of considering it from a broad exploratory per-
观望的. There is no need to dress such human epistemic artifacts as
synthetic genetic circuits in either the experimental or modeling guise,
as they can function in both modes, even simultaneously.

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.2307/j.ctv1pncnrh

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