## 1. Introduction



Systems biology and synthetic biology are relatively recent
interdisciplinary approaches that aim to improve our ability to
understand, predict, and control living systems. They both capitalize
on large-scale data production in genomics and related fields and
explore new paths of cross-fertilization between biology and
non-biological disciplines such as physics, computer science,
mathematics, chemistry, and engineering. Systems and synthetic biology
are often described as 'sister-disciplines' or
'cousins', focusing on the complementary and interrelated
aims of *understanding* (systems biology) and
*designing* (synthetic biology) living systems. Accordingly,
they have been associated with knowledge-driven versus
application-driven epistemologies, or with the aims of analysis versus
synthesis. Philosophers of science examining the research practice
have, however, argued that understanding and design are often
interdependent, and that no simple distinction between basic and
applied science can be made in this context (Kastenhofer 2013a,b;
Knuuttila & Loettgers 2013a; O'Malley 2009). Moreover, the
notions of systems and synthetic biology cover broad and highly
diverse spectra of interdisciplinary hybrids (Calvert & Fujimura
2011; Gramelsberger et al. 2013). This entry will therefore aim to
reach a balance between displaying the methodological flexibility and
variability within these fields and highlighting common themes.


### 1.1 Research Practices in Systems Biology



Systems biology can be interpreted as a response to limitations of
research strategies investigating molecules and pathways in isolation.
To clarify the significance of their approach, systems biologists
often stress the need to complement or go beyond what is perceived as
reductionist strategies in molecular biology (De Backer et al. 2004;
van Regenmortel 2004). The contrast between systems biology and
molecular biology should not be overstated (see also
 Section 4.1).
 However, the contrasting comparison reveals how systems biology is
motivated by the need for a theoretical framework that is better
suited for studying the dynamics and organization of many
interconnected components (Boogerd et al. 2007; Kitano 2001).



Molecular biology has been extremely successful in generating
knowledge on biological mechanisms through the twin strategies of
decomposition and localization of component parts and molecular
operations (Bechtel & Richardson 1993 [2010]). But the very
success of the detailed study of molecular pathways has also revealed
dynamic interfaces and crosslinks between processes and components
that were previously assigned to distinct mechanisms or subsystems. To
account for a vast number of interacting components and multiple
feedback loops dynamic modeling is needed (Bechtel & Abrahamsen
2012). Network modeling and computational simulations in systems
biology allow for the study of larger integrated systems and thus
provide strategies for *recomposing* the findings in the
context of larger systems (Bechtel 2016; Gross & Green 2017).
*Systems* are in this context generally understood as large
networks of integrated components exhibiting non-linear dynamics.
However, there is no unified view of what constitutes a
systems-approach (Calvert 2010; Calvert & Fujimura 2011). As
clarified below, systems biology comprises different research
practices with different ties to molecular biology, genomics, as well
as to non-biological disciplines (see also Krohs & Callebaut
2007).



Systems biology research is sometimes divided into a
*systems-theoretical* and a *pragmatic* stream
(O'Malley and Dupre 2005). The former is historically
related to the initial use of the term 'systems biology'
in 1968, denoting the merging of systems theory and biology
(Mesarovic
 1968).[1]
 This definition has been taken up as providing a theoretical vision
and reorientation also for modern systems biology (Wolkenhauer &
Mesarovic 2005; Drack & Wolkenhauer 2011).
Systems-theoretical proponents view systems biology as an opportunity
to revive important theoretical questions that for half a century
stood in the shade of the success of experimental biology. Examples of
such questions are what characterizes living systems and whether
generic organizational principles can be identified (see
 Section 3.2).
 In contrast, the *pragmatic stream* views systems biology as a
successor of genomics and as a powerful extension of molecular
biology. This stream is sometimes referred to as molecular systems
biology (De Backer et al. 2010), and practitioners within this field
relate the emergence of systems biology to the production of data
within genomics and other high-throughput technologies from the late
1990s and onwards (Guttinger & Dupre 2016).



Krohs and Callebaut (2007) consider omics-disciplines a third
dimension or root of systems biology due to the impact of data-rich
modeling strategies on the development of the
 approach.[2]
 Omics fields allow for large-scale measurements of relations between
vast number of molecular components (e.g., genes or associated protein
concentrations). Since modeling tools in molecular biology have
limited capacity for handling large quantitative datasets, researchers
turned to the exploration and further development of mathematical and
computational strategies developed in engineering and physics
(Alberghina & Westerhoff 2005).



Although not all systems biologists put equal emphasis on the
prospects of big data, there is a general acceptance in the community
that mathematical and computational approaches are required to account
for biological complexity. The first international conference on
systems biology was held in 2000 (at the first Systems Biology
Institute in Tokyo), and thematic journal issues and dedicated
journals quickly followed (Kitano 2002a,b). Since then, many systems
biology research institutions have emerged, and large-scale
international collaborations have been initiated.


#### 1.1.1 Network Approaches in Systems Biology



What is distinctive about systems biology can in part be understood
through the characteristics and implications of different styles of
representation. Whereas textbooks and research papers in molecular and
cell biology typically contain mechanistic diagrams tracking
interactions between specific genes and proteins, representations in
systems biology often display interactions between a vast number of
molecular components as abstract networks of interconnected nodes and
links. This difference in representational style is epistemically
significant, because it highlights an increasing focus on the
*organizational structure* of the system as a whole.



By conducting correlation-based studies of how multiple genes and
proteins are associated with each other, and how these are connected
to specific biological processes, systems biology research has
revealed common patterns in biological networks such as scale-free
network architectures and multi-level hierarchies (Ravasz et al.
2002). Systems biologists often distinguish between two major classes
of networks based on their connectivity distribution (how components,
or *nodes*, in the network are connected to other nodes).
*Exponential* and *scale-free network* structures are
illustrated on
 Figure 1
 (for details on the differences, see Albert et al. 2000). An
exponential network is largely homogeneous with approximately the same
number of links per node, and it is unlikely to find nodes with many
links. In contrast, scale-free networks are inhomogeneous. Most nodes
have only a few links but some nodes (called hubs) have a large number
of
 connections.[3]



![[subfigre a labeled 'Exponential' and subfigure b labeled 'Scale-free']](Figure1.jpg)

Figure 1: An example of a network
representation in systems biology, showing the difference between an
*exponential network* and a *scale-free network*. Red
nodes are the five nodes with most links, green nodes are the first
neighbors of red nodes (from Albert et al. 2000: 379, reprinted by
permission from Macmillan Publishers Ltd: Nature, copyright 2000).




Interestingly, many real-world networks such as social networks, the
World Wide Web, and regulatory networks in biology display scale-free
architectures. This raises important questions about whether
real-world networks can be related to more generic *structural
types* with characteristic functional capacities. For example, the
scale-free structure makes the average path length between any two
nodes in the network small, and this structure has therefore been
associated with capacities for coordinated regulation throughout the
network (Albert et al. 2000). Moreover, scale-free networks exhibit a
high error tolerance, understood as robustness against failure of
random nodes and links (e.g., random gene deletion). These functional
properties are not shared by exponential networks, and illustrate some
advantages of the scale-free structure. However, the functional
importance of hubs in scale-free networks at the same time results in
fragility to attacks on central notes (Albert et al. 2000;
Barabasi & Oltvai 2004). Similarly, a multi-layered network
structure called a *bow-tie network* connects many inputs and
outputs through a central core (the knot of the bow-tie) that consists
of a small number of elements (Kitano & Oda 2006). This structure
has been associated with efficient information flow, but also with
fragility towards perturbations of the intermediate nodes in the core
of the network.



Some systems biologists examine network architectures in the hope that
smaller functional units or patterns can be identified that are akin
to modules in electronic networks (Hartwell et al. 1999). One example
of pattern-detection within large datasets has received special
attention from philosophers of science, namely research on so-called
*network motifs*. Network motifs are defined as "patterns
of interaction that recurs in a network in many contexts" (Alon
2007: 268). By comparing the gene regulatory network of the bacterium
*E. coli* to random networks, it was discovered that a few
circuit patterns occurred more frequently than expected by chance
(Shen-Orr et al. 2002). Statistically significant circuits were
defined as network motifs. Two examples of network motifs are shown in
 Figure 2,
 called coherent and incoherent feedforward loops (cFFL and iFFL).



![[two diagrams. The top one is labeled 'Coherent FFL' and has an X with an arrow to the right pointing to a Y another arrow to the right goes from Y to a Z; this arrow is labeled 'Indirect'. The Z has a (+) to its left and also below. A third arrow goes from X down then to the right before going up to the Z; this arrow is labeled 'Direct'. The bottom diagram is the same except the overall label is 'Incoherent FFL', the (+) to the left of Z is replaced by (-), and the line from Y to Z is not an arrow but rather ends in vertical bar]](Figure2.svg)

Figure 2: Two examples of network
motifs, the coherent and incoherent feedforward loop (cFFL and iFFL).
The diagrams represent the regulation of a gene Z, via a direct path
between a transcription factor X to Z, and an indirect connection from
X via another gene or transcription factor Y to Z. Arrows represent
gene activation (+) and a vertical bar inactivation (-). In a coherent
feedforward loop (cFFL), all connections are activating. In the
incoherent feedforward loop (iFFL), the path from Y to Z inhibites
gene expression. (Adapted from Alon 2007: 47).




Akin to common circuit types in electronic networks, it was
hypothesized that the recurring wiring patterns may indicate
generalizable functional behaviors. Mathematical analysis suggested
that the cFFL may function as a sign-sensitive delay element that
filters out noisy inputs for gene activation (e.g., sporadic levels of
nutrients in the environment). In contrast, the regulatory function of
the iFFL was hypothesized to be an accelerator that creates a rapid
pulse of gene expression in response to an activation signal. These
predicted functions have been experimentally demonstrated in living
bacteria (Mangan et al. 2003). Moreover, similar and additional
network motifs have been found in other species and in other
regulatory networks important to cell-signaling, metabolism, and
developmental processes (Alves & Sorribas 2011; Peter &
Davidson 2015; Tyson & Novak 2010). Such results have
sparked interesting debates on whether network motifs could be seen as
*design principles*, whose functions are generalizable across
different biological contexts (see
 Section 2.3).



Philosophers and scientists alike have debated the implications of
research focusing on generic properties of biological networks, rather
than the properties of specific molecular components (e.g.,
Barabasi 2002; Bechtel 2015a,b; Craver 2016). Among the
controversial issues is how the enhanced focus on topological
properties relates to more traditional mechanistic explanations in
biology
 (Section 4.2).
 A related issue is whether functional capacities can be derived from
an analysis of network architectures alone. Some systems biologists
and philosophers have pointed to the limitations and possible biases
associated with inferences of functions from automated
pattern-detection in datasets (Calvert 2012; Keller 2005; Krohs 2012;
see also Prill et al. 2010). Specifically, some have called for a more
dynamic approach that can account for the context of the whole network
and how it changes over time.



Network models are also used to study *temporal* aspects of
coordinated processes in cells and the evolutionary dynamics and
stability of gene regulatory networks (de Lichtenberg et al. 2005;
Hogeweg 2012; Knight & Pinney 2009). Of scientific as well as
philosophical relevance is whether and how network approaches can
extend mechanistic strategies by identifying how multiple biological
processes are
  

organized and coordinated in time (Green et al. 2018; Bechtel 2020),
or, alternatively, provide new types of explanations (Kostic et
al. 2020; Suarez & Deulofeu 2019). This topic will be
further discussed in Section 4.2.


#### 1.1.2 Research Objectives and Recent Developments



Whereas some engineering-inspired systems biologists search for
functional units or modules in biological networks, another line of
research emphasizes the need for a 'global' approach
inspired by dynamical systems theory (Huang et al. 2009; Jaeger &
Crombach 2012). Drawing on Waddington's idea of the epigenetic
landscape of cell differentiation, the global approach represents
dynamical states in a network through vectors, attractors, and
trajectories in a state space (Huang 2012; for a philosophical
analysis, see Fagan 2012). The framework explores how inherent
mathematical properties of the network architectures may constrain
developmental and evolutionary processes, in a way analogous to how
laws of motion constrain the possible planetary movements (Goodwin et
al. 1993). The global approach is sometimes highlighted as being in
opposition to mechanistic or "modular" engineering
approaches (Huang 2011; Fagan 2016). Controversies of this kind can
reveal interesting differences in research aims as well as different
standards for models and explanation present in systems biology
 (Section 5.1).



Some systems biologists describe their approach as one occupied with
*functional*, rather than evolutionary questions (Boogerd et
al. 2007; Hofmeyr 2007). By stressing the autonomy of functional
biology, beyond discussions of selected effects, philosophy of systems
biology can help balance the extensive emphasis on evolutionary
biology in philosophy of biology (Pradeu 2016). At the same time,
*evolutionary systems biology* presents a new research
approach aiming to extend both systems biology and evolutionary
biology
 (Section 3.3).



Another important extension of systems biology is the emergence of
*systems medicine*, the medical application of systems biology.
Systems biology has from the outset been associated with ambitions to
solve grand societal challenges. Examples are the aims to provide a
better understanding of complex diseases like cancer, and the vision
of developing patient-specific models that will allow for
individualized disease prediction and prevention
 (Section 5.3).



The aforementioned aims are often pursued through large-scale
international collaborations where many research teams provide data
and models to be integrated in complex models of whole cells (e.g.,
*The Virtual Cell Project*), whole organs (e.g., the
*Virtual Liver Project*), and even the whole human body (e.g.,
*The Virtual Physiological Human* and *the Physiome
Project*). Such projects raise new exciting questions for
philosophy of science concerning strategies for model building and
model validation (Carusi 2014). Moreover, they provide new sources for
philosophical discussions on reduction and explanation
 (Section 4)
 and on the societal applications of big data biology
 (Section 5).


### 1.2 Research Practices in Synthetic Biology



The term synthetic biology was used already in the 1970s and 1980s,
but the development of synthetic biology in its modern context is also
connected to genomics (Morange 2009; O'Malley 2009; Gelfert
2013). In the wake of big data production around year 2000,
comprehensive gene regulatory networks could be constructed that
allowed for more systematic and more ambitious design projects. Just
like systems biologists commonly define their approach in opposition
to the focus on isolated molecules or pathways in molecular biology,
so is synthetic biology sometimes framed as a system-oriented approach
that moves beyond isolationist approaches in genetic engineering
(O'Malley et al. 2008). Also in this context, however, the ties
to precursors are diverse, and there are different views on whether
modular approaches are compatible with biological complexity (see
below).



The term synthetic biology became an official label with the first
Synthetic Biology Conference in 2004, highlighting the
BioBrickTM standard as a paradigm example of the
engineering aim to design living systems through assembling of modular
parts (Endy 2005; Bensaude-Vincent 2013). But the relation to
engineering is complex, and synthetic biology displays a plurality of
epistemic aims, visions and methodological frameworks.


#### 1.2.1 Aims and Visions



A central research aim in synthetic biology is to modify gene
regulatory pathways so as to control biochemical reactions and produce
biochemicals of societal value. For example, genetically modified
microorganisms can work as biosensors that detect toxic chemicals or
help clean up contaminated soil and water by metabolizing toxic
compounds (Khalil & Collins 2010). Many efforts are also directed
towards improvements of biomedical research and clinical practices,
e.g., through diagnostic tools, and personalized cancer
treatments (Christiansen 2016b). Products of synthetic biology that
are already commercially available include biomedical compounds used
in treatments against diabetes and malaria, nitrogen fertilizers, and
flavouring substances for "bleeding" plant-based burgers
(Voigt 2020). Most prominently, the fast development of effective
synthetic mRNA vaccines against Covid-19 points to the potential
of synthetic biology in supporting "pandemic
preparedness" (Vickers and Freemont 2022), although these
developments draw on molecular techniquest that precede the field of
synthetic biology. 



Much of synthetic biology research can be characterized as
application-oriented innovations that utilize and modify biological
structures. But not all practices fit this description. Some synthetic
biologists pursue a basic science approach to understand the origin of
life or the conditions for minimal life
 (Section 3.2).
 Others are driven by the wish to create artificial life in the lab,
inspired by von Neumann's (1966) vision of a self-reproducing
automaton. This aim may be achieved through the design of macroscale
'living technology' (Bedau 2009). Examples are 3D printers
that can print biological tissue structures and perhaps--with
time--also replicate
 themselves.[4]
 Thus, synthetic biology is an umbrella term covering very diverse
research practices.


#### 1.2.2 Knowledge-Making Practices in Synthetic Biology



Examining the diversity of knowledge-making practices in synthetic
biology, O'Malley et al. (2008) suggest a distinction between
*DNA-based device construction*, *genome-driven cell
engineering*, and *protocell creation*.



*DNA-based device construction*, or what Benner and Sismour
(2005) characterize more broadly as the engineering trend, explores
the extent to which interchangeable and functionally distinct
components can be designed and implemented in a modular fashion. The
BioBrick Foundation is a paradigmatic example of this approach
(biobricks.org). The guiding principles for this approach are
standardization, decoupling, and abstraction of parts and functional
descriptions (Endy 2005). Examples of DNA-based devices are the
repressilator (Elowitz & Leibler 2000, see
 Section 2.1),
 the synthetic toggle switch (Gardner et al. 2000; see
 Section 2.3),
 as well as production of synthetic artemisinin for antimalarial drugs
(Keasling 2010). The orientation towards functional devices and
machine-like control is also explicit in the so-called iGEM
competition (Genetically Engineered Machine) where student teams
compete in the task of constructing genetically engineered biological
systems to address societal problems. Whether the ideal of
constructing standardized and replaceable parts is compatible with
biological complexity is an ongoing topic of debate
 (Sections 2
 and
 3).



*Genome-driven cell engineering* in synthetic biology departs
from the modularity assumption of DNA-based device construction by
focusing on the functioning of whole genomes or whole cells
(O'Malley et al. 2008). This practice includes transplantation
of foreign or modified genomes into empty 'chassis cells'
to acquire new functions (Chan et al. 2005), and synthesis of
"minimal genomes" (Andrianantoandro et al. 2006; Simons
2021). The notion of a minimal genome highlights the idea that
existing organisms may have genomes more complex than necessary for
the basic functions of survival and reproduction. The human parasite
*Mycoplasma genitalium* has been considered a model organism
for the exploration of such questions due to its small genome, and it
has also been the target for the first whole-cell model in systems
biology (Karr et al. 2012). In 2010, scientists at the J. Craig Venter
Institute (JCVI) created a synthetic genome of another bacterial
species of the same genus, *Mycoplasma mycoides* (Gibson et al.
2010). From the synthetic DNA sequence, the researchers could partly
control the production of new *Mycoplasma mycoides* cells. This
genome was further reduced to a working approximation for the simplest
synthetic minimal genome (JCVI-syn 3.0), allowing cells to function
and reproduce with only 473 genes (Hutchison et al. 2016; see also
Sung et al. 2016). At the same time as lower boundaries for biological
complexity are explored, other synthetic biologists aim to manipulate
and control the functions of genomes of more complex species, such as
yeast and algae (Calvert & Frow 2015; Georgianna & Mayfield
2012; see Simons 2021).



The third category of synthetic biology, *protocell creation*,
takes on the ambitious aim to construct simple approximations of
living cells *de novo* (O'Malley et al. 2008).
Researchers within this stream are often interested in the fundamental
question of what life is, and the notion of applied science may not be
an appropriate description of this research approach (see
 Section 3.2).
 Historically, the theoretical discussion of what life is has often
been coupled to wet-lab synthesis of compounds associated with early
or minimal
 life.[5]
 Synthetic biology takes this approach further through synthesis and
manipulation of vesicles resembling primitive cells, as well as
through advanced computational simulations of gene regulatory networks
(Luisi 2006; Kauffman 2015). This research practice connects to
projects within genome-driven cell engineering investigating the
minimal requirements for a living system to function, survive, and
reproduce. Research on protocells may focus primarily on understanding
the origin of life as we know it, or aim to create synthetic life
forms in other and simpler ways (Rasmussen et al. 2008).



Deplazes (2009) has suggested the following extended classification of
practices within synthetic biology: i) bioengineering, ii) synthetic
genomics, iii) protocell synthetic biology, iv) unnatural molecular
biology, and v) *in silico* approaches (see
 Figure 3).
 While the categories of work on protocells and synthetic genomes
overlap with the categorization by O'Malley et al. (2008),
DNA-based device construction is here further divided into
*bioengineering* (exemplified by assembly standards like
BioBricksTM) and *unnatural molecular biology*.
Unnatural molecular biology aims to create systems with different
components, e.g., artificial nucleic acids based on a different coding
system. Philosophers have debated whether the term
'unnatural' distinguishes practices in synthetic biology
from other bioengineering approaches (Lewens 2013; Preston 2013). But
the emphasis on synthetic artifacts underscores the affinity to
synthetic chemistry and perhaps also to the 'chemist style of
thinking', emphasizing synthesis and invention in addition to
analysis and discovery (Bensaude-Vincent 2009, 2013, 2015; see also
Bursten 2020, ed.).



![[5 circles arranged in a pentagon each connected to all the others by lines. The circles in clockwise order are labeled 'Bioengineering', 'In silico synthetic biology', 'Synthetic genomics', 'Unnatural molecular biology', and 'Protocell synthetic biology'. The In slico... circle has labeled arrows on all the lines to the others: To all the arrows are labeled 'Computer design' {for Bioengineering there is also a reverse arrow 'New metabolic pathways'}. Bioengineering also has another arrow pointing to Protocell... with the label 'New metabolic pathways'. The Synthetic ... circle has an arrow to Bioengineering labeled 'Minimal chassis organisms' and to Protocell... labeled 'Synthetic genomes'; it has an arrow to and from Unnatural... labeled 'Unnatural genome fragments'. Unnatural has arrows to Bioengineering and Protocell labeled 'Unnatural genome fragments'. ]](Figure3.png)

Figure 3: Schematic representation of
the five categories of synthetic biology and their connections. (From
Deplazes 2009: 431, Copyright 2009 by John Wiley & Sons, Inc.
reprinted by permission of John Wiley & Sons, Inc.).




Deplazes' fifth category, *in silico* synthetic biology,
focuses on the development and practical implementation of computer
simulations. Deplazes argues that *in silico* synthetic
modeling should be a distinct category because many of the
computational models have "little or no direct reference to
living organisms" (Deplazes 2009: 430). As
 Figure 3
 illustrates, computational design is an important part
of research practices in synthetic biology. But the
characteristics of various roles of simulations can shed new light on
philosophical discussions of the relation between models and target
systems, and between modeling, experimentation, and synthesis
(Knuuttila & Loettgers 2013a,b; Carusi et al. 2012).



Abstracting from the variety of research practices, a distinctive
feature of synthetic biology is that it aims "to go beyond mere
modeling and to treat biological systems as fully constructible
objects" (O'Malley 2009: 381; see also Schyfter 2013).
Thus, synthetic biology opens new philosophical questions concerning
the relation between synthesis and analysis, between artificial and
natural target systems, and between machine and organism (Holm &
Powell 2013; Nordman 2015).


## 2. Design Approaches in Biology


### 2.1 Designing and Understanding Life



Systems and synthetic biology offer new opportunities to reconsider
the prospects and limitations of engineering approaches for biological
research (Braillard 2015; Calcott et al. 2015). Engineering approaches
are often considered inadequate to grasp biological complexity.
Specifically, design thinking has often been associated with
adaptationism, i.e., the assumption that biological traits are
optimally "designed" by natural selection (Orzack &
Forber 2010). Accordingly, critics have argued that the engineering
approach may lead to a simplified understanding of evolution as driven
solely by natural selection (Lynch 2007). But whereas some approaches
in systems biology can be described as adaptationist (Green 2014), it
cannot be assumed that design approaches always entail adaptationist
implications. Systems biologists may adopt a 'thin' notion
of design focusing primarily on the relations between structures and
functions (Green et al. 2015b). Because research in systems and
synthetic biology often is often dissociated from evolutionary
considerations, these research practices may force philosophers to
reexamine the concepts of biological function and design beyond
traditional aetiological accounts (Holm 2012; Preston 2008; see also
Mossio et al. 2009).



In this context, it is debatable whether the thin notion of design is
compatible with biological complexity. Some scholars have raised
concerns that the machine-view of organisms (and use of design
metaphors) may lead to a neglect of distinct biological features such
as autonomy, evolvability, and intrinsic purposiveness (Nicholson
2019; see also Jacob 1977; Kogge & Richter 2013; McLeod and
Nerlich 2017). Design approaches may implicitly enforce the view that
operations and outputs of specific parts are stable and predictable
across different contexts. In biology, context-sensitivity and
degeneracy between genotype-phenotype-relations may provide severe
limitations to the utility of this heuristic (Boudry & Pigliucci
2013; Guttinger 2013; see also
 Section 3.1).
 Yet, as outlined in the following sections, the ties to engineering
are multi-faceted in both systems and synthetic biology.


### 2.2 Reverse and Forward Engineering



Feynman's 'last blackboard' statement in 1988 that
"what I cannot create, I do not understand" is often cited
in the context of synthetic biology and has sparked philosophical
reflections on the relation between understanding and design
(O'Malley 2009; Holm 2012; Calvert & Frow 2013). Although
synthetic biology is distinct from systems biology in the explicit aim
to design synthetic systems, it is often described as a practice
relying on systems biology as a theoretical foundation for design
(Barrett et al. 2006).



Synthetic and systems biology are sometimes described through the
complementary aims of forward and reverse engineering. *Forward
engineering* refers to the design of systems with novel functions,
often by drawing on an existing high-level model (e.g., an existing
code or living cell). Forward engineering, or re-engineering, can lead
to more complex systems, but simplicity may also be pursued through
implementation of a more abstract coding language or through reduction
or rewiring of (genetic) circuits. *Reverse engineering* refers
to the aim to understand the functioning of a system
'backwards' by examining an existing system in contexts
where consulting a design protocol is not an option. The search for
recurring network motifs
 (Section 1.1.1)
 is an example of how systems biologists may reverse engineer the
structure of a system to understand its functional capacities. Because
functional features such as robustness are not only important to the
survival of organisms but also to design problems in engineering,
reverse engineering of organizational features that underpin such
capacities is an important aim in both fields (Csete & Doyle 2002;
Hartwell et al. 1999; Kitano 2004; Stelling et al. 2004).



Reverse and forward engineering approaches may challenge the
philosophical perspective on how-possibly-models, which are often
taken to be stepping-stones for how-actually explanations. In systems
and synthetic biology, however, how-possibly models can be explanatory
in their own right - in explaining *how possibly* biological
functions can be realized in natural or artificial systems (Green
2015; Koskinen 2017). In engineering, reverse engineering is an
activity geared towards the identification of generic features of
system design, or *design principles*, that can be reused in
other systems. Similarly, reverse engineering in systems biology aims
to identify *biological design principles* that can increase
the understanding of structure-function relations across different
systems (Csete & Doyle 2002; Doyle & Stelling 2006; Voit
2003), and potentially also be used in the design of novel systems in
synthetic biology (Koskinen 2019).


### 2.3 Design Principles in Biology



Systems and synthetic biology not only adopt mathematical tools from
electronic and control engineering but also a conceptual framework in
which biological functions are understood in terms of control
principles and generic representations such as feedback control
(Gramelsberger 2013; MacLeod & Nersessian 2013a,b). The search for
functional principles, with applications to different systems in
biology and engineering, dates back to the ideals of cybernetics and
biochemical systems theory (Savageau 1976; Wiener 1948). Negative
feedback control has long been a central principle in mechanical and
electronic engineering to maintain stable concentrations and minimize
fluctuations. An important insight from cybernetics is that the same
formalization can also describe biological processes, e.g., where a
reaction is inhibited as a result of accumulation of the product of
the same reaction. An abstract model of negative feedback can
therefore denote a functional principle that holds for a larger class
of systems (Green & Wolkenhauer 2013).



Characteristic of the search for design principles in modern systems
biology is that the strategy is enforced through automated
pattern-detection in network models based on large biological
datasets, as exemplified by the search for network motifs
 (Section 1.1.1).
 The hope is that simple control principles, using e.g., positive and
negative feedback, can make up basic building blocks of biological
functioning such as filters, switches, oscillators, amplifiers,
detectors etc. (Alon 2007; Tyson et al. 2003). One such example is the
so-called toggle-switch, a simple regulatory circuit that is
considered a design principle in both systems and synthetic
biology.



Like a toggle-switch that turns an electrical device ON or OFF,
genetic toggle-switches have been found to regulate many biological
processes such as the cell cycle of budding yeast and developmental
processes in fruit flies (Tyson et al. 2003; Jaeger & Crombach
2012). A genetic toggle-switch is a double-negative feedback loop that
shifts the system between two distinct stable states of gene
expression--allowing synthesis of one protein while another is
repressed. Synthetic biologists have succeeded in constructing a
synthetic toggle switch in *E. coli* (Gardner et al. 2000), and
the toggle switch is now considered part of the design composites or
'computational templates' for building new synthetic
systems (Choffnes et al. 2011; Humphreys 2004). Recent work in
synthetic biology also involve RNA-based regulatory genetic switches,
called *riboswitches*, that can control gene expression by
binding to specific ligands (Mehrshahi et al. 2020). The dynamic
behavior of genetic toggle-switches have also been investigated in
studies of parameter spaces for robust functioning of network motifs
(Tyson & Novak 2010), and in approaches combining the
modeling framework of dynamical systems theory with reverse
engineering of gene regulatory networks from experimental data (Huang
et al. 2009; Jaeger & Crombach 2012). Because of the abstract
nature of proposed design principles, an interesting philosophical
question is how such strategies relate to discussions about the
possibilities of laws-like generalizations in biology.



Biological systems are often taken to be too contingent, diverse, and
context-dependent to allow for derivation of laws or general
principles akin to those found in physics (Burian et al. 1996). The
quest for design principles and generic topological features in
biology is therefore interesting test cases to explore such issues
(Green 2015b; Moreno and Suarez 2020). A key issue in this
debate is whether generalizable functions can be inferred from
organizational structures, relatively independently of cellular,
organismal, and environmental contexts. This issue has often been
discussed as a question of *multiple realizability,* i.e.
whether a higher-level state or property is relatively independent of
the causal details or properties at a lower level because the
system's structure determines the function. Systems biology bring
new light to these debates, e.g., by discussing the extent to
which network motifs provide concrete examples of this
phenomenon (Fang 2020). Multiple realizability of
functions via network motifs has also been argued to provide a design
heuristic explored in synthetic biology, e.g., when engineering
minimal genetic systems and artificial biochemical systems (Koskinen
2019). The search for network motifs and other functional units make
apparent that assumptions of modularity are not completely abandoned.
Rather, the notion of modularity may be reconfigured via strategies
for *structural* decomposition of larger networks (Serban
2020). However, some systems biologists and philosophers have
questioned whether functional 'units' can be meaningfully
isolated from the workings of the network as a whole (DiFrisco and
Jaeger 2019; Huang 2011; Isalan et al. 2008), thus casting new light
on debates on reductionism and holism in the life sciences. A related
question is how the enhanced focus on generic features of biological
networks relates to mechanistic accounts of biological explanation
 (Section 4.2).


## 3. Revisiting Biological Complexity



Engineering is often promoted through the idea of rational design.
Some synthetic biologists have adopted a similar vision of synthetic
organisms as structures that can be assembled via replaceable and
standardized components, akin to Lego-bricks (front cover of
*Science*, 2 September 2011 [333(6047)]). Yet, philosophers
have argued that the actual research practices in synthetic and
systems biology paint a different picture (Knuuttila & Loettgers
2013a, 2014).


### 3.1 Reverse and Forward Tinkering?



Rather than following a fixed design protocol, research in synthetic
biology often progresses through what is taken to be the opposite of
rational design, namely *kludging*: a solution that is
*k*lumsy, *l*ame, *u*gly, *d*umb but
*g*ood *e*nough (O'Malley 2009, 2011b). The
research process can perhaps be described as *reverse and
forward* *tinkering*, in which opportunities for modular
decomposition and recomposition are explored in iterative cycles to
arrive at piecemeal solutions. Some have argued that synthetic biology
exhibits a reflexive double bind to engineering, because the design
assumptions guiding the construction of the synthetic systems are
continuously reevaluated (Knuuttila & Loettgers 2013a). That is,
synthetic biology does not only progress through similarities between
biology and engineering. Through the exploration of engineering
approaches, important differences (or *negative analogies*)
between biological and engineered systems are discovered (Knuuttila
& Loettgers 2014)



An example of a possible negative analogy discussed in this context is
the role of noise in biological versus engineered systems. In
engineering, oscillations are often associated with noise and lack of
precision, but oscillatory dynamics are characteristic of many
biological processes including metabolism and day and night rhythms
(Knuuttila & Loettgers 2013a; see also Bechtel 2016). To study
oscillations in gene expression, synthetic biologists at the Elowitz
lab constructed a synthetic genetic circuit called the
*repressilator* (Elowitz & Leibler 2000). The repressilator
was designed to test whether the oscillatory dynamics of gene
expression could be mathematically described via connected feedback
loops in a simple regulatory circuit and implemented in a synthetic
model.



Interestingly, important insights resulted from the repressilator
because it *failed* to produce regular oscillations as
predicted by the underlying mathematical model. The mismatch between
the mathematical and synthetic model led to a reevaluation of the
assumptions underlying the deterministic mathematical model (Knuuttila
& Loettgers 2013a, 2014). Subsequent investigation of the dynamics
of a stochastic version of the same model suggested that noise may be
an inherent feature of oscillations in gene expression in living
cells. The exploration of the functional role of noise and the
potentials for stochastic modeling has since developed into an
important research program of interest to biologists and engineers
alike (Briat et al. 2016; Munsky et al. 2009; O'Malley 2011b).
The repressilator and similar examples also sparked debates on whether
design procedures in synthetic biology need to account for cellular
contexts, distributed organization of functions, and other features
that are unlike modular machines (Guttinger
 2013).[6]



To what extent the ideal of precision engineering is pursued in
synthetic biology is therefore debatable. Some have stressed the
centrality of the engineering perspective because the construction of
synthetic models is guided by knowledge on which design principles can
be expected to create specific dynamic behaviors (e.g., Gramelsberger
2013). Others have emphasized that synthetic modeling, as a kind of
*material recomposition* strategy, offer material constraints
that always involve open-ended aspects (Knuuttila & Loettgers
2013b). The latter view highlights exploratory features of the
triangulation of mathematical, computational, and synthetic
approaches. The notion of *exploratory experimentation* has
also been used to characterize research processes in systems biology,
where scholars search for structural and functional patterns in
biological networks without being driven by a specific biological
hypothesis (O'Malley & Soyer 2012; Steinle 1997). Research
on network motifs
 (Section 1.1.1)
 is one example where engineering approaches were applied in an
exploratory fashion to generate, rather than test, hypotheses on
biological functions (Alon 2007). Also in this context, the analysis
may lead to a reevaluation of design assumptions, e.g., whether the
functions of network motifs are independent of the network context
they are embedded in (Isalan et al. 2008).



It should also be noted that synthetic biologists often aim to produce
biological technology that is more machine-like than natural organisms
(Kastenhofer 2013a,b; O'Malley 2011b). An alternative response
to biological complexity may therefore be to make the designed system
simpler, for instance through genetic rewiring that ensures a higher
degree of modularity (Knuuttila & Loettgers 2013a). The explicit
aim to simplify synthetic models has been interpreted as
acknowledgment of biological complexity, and it is therefore debated
whether the criticism of the machine-view in synthetic biology is
misguided (cf., Holm 2015; Nicholson 2013). The heuristic value of
design approaches is, however, an ongoing topic of debate in both
systems and synthetic biology.


### 3.2 Defining and Creating Minimal Life



Unlike DNA-based device construction and genome-driven cell
engineering, synthetic biologists in the practice of *protocell
creation* engage in classical theoretical discussions on what life
is, and how it originated and evolved (Luisi 2006; O'Malley et
al. 2008). This research stream connects to discussions of the
fundamental properties of life in systems biology (Cornish-Bowden
2006; Letelier et al. 2011; Wolkenhauer and Hofmeyr 2007) and
philosophy (Bich 2010; Bich & Damiano 2007; Bedau & Cleland
2010; Moreno & Mossio 2015).



One strand of research attempts to design minimal cells resembling
early cellular life-forms. Inspired by Maturana and Varela's
(1972/1980) formulation of autopoiesis, some synthetic biologists
experiment on conditions for minimal cells through chemical models
that combine the continuous formation and destruction of the
compartment (the cell membrane) with metabolic processes inside the
cell (e.g., Zepik et al. 2001). This research program specializes in
synthesis of lipid compartments, such as vesicles, to study
coordinated processes that possibly resemble self-maintenance in the
pre-biotic world (Luisi 2006). Moreover, vesicles have been used to
experiment on how catalytic molecules such as ribozymes and peptides
could become entrapped in lipid compartments and create precursors for
metabolic circuits (de Souza et al. 2014).



An important research goal is to provide a model for the fundamental
features that distinguish living systems from the non-living. So far,
however, there is no consensus on the defining features of life.
Whether this should be seen as a disappointment, indicating the
uselessness of attempts to define life, is an ongoing issue of
philosophical debate. Different views on this question may depend on
different interpretations of what the project of *defining
life* entails in philosophy and science, e.g., whether the aim is
to establish demarcate a natural kind (Cleland 2012) or to provide
operational definitions of practical use in model and theory
construction (Bich & Green 2018; Luisi 1998). More specific topics
of debate are whether definitions of life must include Darwinian
evolution, how evolution and units of selection should be defined, and
whether it is fruitful to categorize all the varieties of life forms
under one definition (Dupre & O'Malley 2009; Laland
et al. 2009; Ruiz-Mirazo et al. 2010; Szostak et al. 2001)



Systems biologists engaging in debates on the properties of life often
stress the relevance of insights from classical philosophers. Examples
are references to Aristotle or Kant in discussions on whether our
current notion of causation can adequately capture how parts and
wholes in living systems stand in reciprocal causes and effect
relationships (Drack & Wolkenhauer 2011; Hofmeyr 2017). Other
important sources of inspiration are Rosen's formalization of
metabolism-repair systems, (mathematical) general systems theory, and
category theory (e.g., Drack 2015; Rosen 1991; Mesarovic et al.
2004; Letelier et al. 2011). The systems-theoretical stream is also
influenced by mathematical biophysics (Rashevsky 1961),
Waddington's theoretical biology (Fagan 2012), Biochemical
Systems Theory (Hofmeyr 2007; Savageau 1976), and classical
systems-oriented physiology (Noble 2008).



Systems-theoretical and organizational accounts often highlight the
functional roles of constraints, autonomy, and causal closure of the
processes of self-maintenance (Moreno & Mossio 2015; Wolkenhauer
& Hofmeyr 2007). While also stressing the utility of the search
for general principles, some prefer the term *organizing
principles* instead of design principles (Green & Wolkenhauer
2013; Mesarovic et al. 2004). This choice of terminology signals
the view that understanding living systems requires a
systems-theoretical approach in a broader sense than cybernetics. An
intriguing question in this context is whether a different kind of
formal or mathematical framework is required--one that is adapted
to biology rather than to engineering or physics (Bich & Damiano
2008; Gramelsberger 2017; Rosen 1991).



Another debated issue in discussions about the fundamental properties
of life is the relative priority of autonomy, openness and
collaboration. Whereas much research in philosophy and origin of life
has focused on individual organisms, recent insights from microbiology
and synthetic biology have extended the focus on individual cells to
also include the interactions of cell colonies and pangenomic
populations (Dupre & O'Malley 2009; Bich & Green
2018; O'Malley et al. 2019). For example, synthetic models can
be used to investigate the dynamics of colonies of giant oleate-based
vesicles under hypothesized prebiotic conditions. For example, an
experiment conducted by Carrara and colleagues (2012) showed that that
the permeability of the vesicles to catalysts and macromolecules were
much higher for the colonies, compared to individual vesicles. In some
cases, the colonies also attracted each other and exchanged material.
The results raise important questions about whether life has arisen
from 'cooperative mechanisms' between prebiotic systems,
and whether we need to rethink ontological assumptions about what
constitutes a 'unity of life'. Synthetic models can also
be used to study the role of structural and cognitive interactions for
biological identity and autonomy (Bich 2020). Meanwhile, research on
minimal genomes and imagined artificial life forms can explore what is
biologically possible beyond what is found in natural systems
(Koskinen 2017; Knuttila & Koskinen 2021). Synthetic biology,
understood as a technoscience exploring the *design* of minimal
and alternative life forms, thus has potential to address questions
about universal biology beyond the historical question about how life
originated (Simons 2021).


### 3.3 Evolutionary Systems Biology



Systems biology is often defined as an approach primarily concerned
with functional questions (Boogerd et al. 2007). Evolutionary systems
biology (ESB) explicitly seeks to expand this focus through
investigations of the evolutionary dynamics of interacting networks
(Soyer 2012). ESB aims to put the results of systems biology in a
proper evolutionary context and also to enhance and extend current
evolutionary biology. Specifically, ESB aims to develop more detailed
models of genotype-phenotype maps to study causal changes over
evolutionary time-scales and to enhance the predictive potential of
evolutionary models. These aims are pursued by combining methods from
molecular and evolutionary biology with *in silico* evolution,
comparative genomic analysis, as well as with dynamical modeling of
network architectures (Bapteste and Papale 2021; Soyer &
O'Malley 2013).



ESB can be considered a meeting ground not only for systems biology
and evolutionary biology but also for the integrated study of
developmental phenomena in an evolutionary perspective (O'Malley
et al. 2015). Systems biology complements statistical sequence
analysis in population genetics and studies of constraints on
evolutionary pathways in Evolutionary Developmental biology
(EvoDevo) through *in silico* modelling of large data-sets
(Jaeger & Crombach 2012; Kim et al. 2011; see also Brigandt 2015).
Some projects also draw on synthetic modelling of intracellular
networks to investigate how the evolutionary potential of a lineage is
affected by specific combinations of mutations (Palmer & Kishony
2013).



Evolutionary network modeling contributes to conceptual development in
evolutionary biology, e.g., by throwing new light on discussions on
the unit of selection (Bapteste and Papale 2021). Another important
line of investigation in ESB examines the evolutionary background for
global network architectures, modular structures, and recurring
network motifs
 (Section 1.1.1;
 see also Krohs 2009). The structures may have arisen due to natural
selection, but ESB also takes seriously the possibilities that
patterns can arise as side-effects of genomic evolution and drift, or
from a combination of selective and non-selective factors (Steinacher
& Soyer 2012). An example of an insight from simulations of
evolving yeast networks is that hierarchical structures with an
overabundance of network motifs (feedforward loops) can emerge by
*non-selective tinkering* with promoter regions (Cordero &
Hogeweg 2006). In extended models, the integrated effects of selective
and non-selective factors can be studied at multiple levels (Hogeweg
2012). With the emphasis on pattern-generation and the potential for
future evolutionary change, ESB takes up Hugo de Vries'
challenge to explain not only the *survival* of the fittest but
also the *arrival* of the fittest (Wagner 2012, 2014). The hope
is that evolutionary simulations can extend evolutionary biology by
providing a forward-looking account.



Evolutionary biology has traditionally been considered as a historical
discipline that provides explanations without giving predictions. ESB
offers an opportunity to develop more detailed models of evolving
genotype-phenotype relations and to explore whether some evolutionary
pathways are predictable given enough information about the network
structures, dynamic states, and environmental conditions. Unlike
forward engineering in synthetic biology, the aim is not to shape new
designs but to understand the possible trajectories of change under
different conditions. That is, ESB goes beyond historical accounts in
exploring the potential for evolvability or innovation--what some
have called the 'tinkering potential' of system evolution
(Koonin & Wolf 2010). Research on evolvability can, however, also
inform and inspire functional analysis and design in synthetic biology
(Calvert & Frow 2015; Lewens 2013). Thus, also in this context,
the relation between biology and engineering must also be
reexamined.



The phenomena of tinkering and contingency in evolution are typically
taken to highlight the *limitations* of engineering approaches
in biology (Jacob 1977; Lynch 2007). Interestingly, however, some
systems biologists and philosophers emphasize that a non-adaptationist
framework can be reached through further cross-fertilization of
biology and engineering (Calcott et al. 2015). ESB and synthetic
biology can provide a broader perspective on engineering by
considering how design often operates through modification of existing
systems. For instance, there are interesting similarities between
*evolvability* in biology and a *diachronic*
*engineering goal* in software engineering, where the aim is to
design systems that can be adapted to serve future functional
requests, without having to rewrite the code from scratch (Calcott
2014; see also Sole and Valverde 2020).



The ambition to predict evolutionary trajectories, even for simple
organisms, may turn out to be extremely difficult. But ESB provides
new ways to investigate whether evolutionary pathways follow some
recognizable dynamical patterns. Simulations of how gene regulatory
networks change over evolutionary time-scales can be coupled to
experimental evolution in bacteria or insects. For instance, the
development of bacterial antibiotic resistance has been shown to be
constrained by interactions between different mutations, which may
make evolutionary biology informative also for the development and
combination of antibiotics used in medical treatment (Palmer &
Krishony 2013; Weinreich et al. 2006).



Some proponents expect that ESB will uncover regularities or stable
evolutionary patterns across various types of networks and species.
Such regularities may not resemble universal laws, but the hope is
that some general principles underlying evolutionary dynamics can be
identified, referred to as "laws of evolution" (Koonin
2011), "evolutionary design principles" (Steinacher &
Soyer 2012) or "generic principles" of evolution (Jaeger
& Monk 2013). ESB thus offers excellent cases for debating the
possibility of turning evolutionary biology into a more predictive
field with generalizable principles (Lobkovsky & Koonin 2012; Papp
et al. 2011). Moreover, it is a rich field for discussing the
implications of an 'extended modern synthesis' that
combines molecular, mathematical, computational, synthetic, and
population genetic approaches to evolution (Pigliucci 2007;
O'Malley et al. 2015).


## 4. Revisiting Classical Philosophical Questions



With the emphasis on systems and interacting networks, both systems
and synthetic biology explicitly engage in one of the oldest
philosophical discussions on the relationship between the whole and
its parts, or between holism and reductionism. This section examines
how classical questions are reframed in the new light of strategies
for large-scale data production and dynamic modeling.


### 4.1 Reductionism and the Sum of the Parts



As mentioned in the introduction, proponents of systems biology often
explicitly define their approach in contrast to reductionist
strategies in molecular biology. Molecular biology is depicted as a
field studying molecular components and pathways in isolation, whereas
systems biology integrates the pieces of the puzzle in the context of
the system as a whole (van Regenmortel 2004; Keller 2005; Kitano
2002a,b). The contrast between molecular biology and systems biology
is often overstated, and much of systems biology research is also
focused on specific molecular difference-makers (De Backer et al.
2010; Gross 2017; O'Malley & Dupre 2005). However,
systems biology may give a novel interpretation of Aristotle's
dictum that *the whole is more than the sum of the parts* by
specifying what *more* means in the context of contemporary
biology.


#### 4.1.1. Modular and Bottom-Up Reductionism



When systems biologists criticize reduction in molecular biology, the
issue at stake is typically the limitations of studying biological
parts or modules in isolation. Because the target of the criticism
often differs from the more traditional philosophical focus on
reduction of higher-level to lower-level explanations (Brigandt &
Love 2017), systems biologists can be said to oppose what has been
termed *modular reductionism* (Gross & Green 2017). An
anti-reductionist stance towards modular reductionism needs not reject
the idea that living systems can be modelled and explained *bottom
up*. For instance, while global approaches within these fields
reject the modularity assumption, some keep the focus on genomes and
molecular networks as the primary determinants of biological functions
(O'Malley et al.
 2008).[7]



Many systems biologists have, however, also argued against reduction
of higher-level models and explanations, and there is an ongoing
debate about how far genomics, proteomics, etc. will take us in
solving complex problems like understanding cancer (cf.,
Barabasi et al. 2011; Hood et al. 2015). Similarly, researchers
involved in projects aiming to simulate multiscale structures like the
human heart emphasize the need to include macroscale parameters as
they provide functionally important constraints on the behavior of
microscale processes (Bassingthwaighte et al. 2009; Kohl & Noble
2009). An interesting reframing of Aristotle's dictum in this
discussion is that living systems at the same time are *more*
and *less* than the sum of the parts (see Hofmeyr 2017; Noble
2012). In other words, the system as a whole constrains the degree of
freedom of lower-level parts and provides a functional organization of
these that are required for some system capacities (see below).



Given the increasing emphasis on comprehensive multiscale models,
systems biology research may have a unique potential for philosophical
insights considering the explanatory role of macroscale properties and
top-down effects. Systems biologists for instance point to how enzyme
activity is constrained by the chemical environment and the cellular
context (Hofmeyr 2017), or how the biophysical properties of muscle
fibers and cell membranes provide functional constraints on ionic
oscillations central to the generation of heart rhythms (Noble 2012).
Interpreting top-down effects as constraining relations may exemplify
what philosophers of science have called 'medium downward
causation' (Emmeche et al. 2000), which interprets downward
causation as boundary conditions. Noble (2012) explicitly endorses
such a view when arguing that downward causation is necessary by
pointing out how equations describing the kinetics of ion channels in
cardiac modeling cannot be solved without defining the boundary
conditions (e.g., the cell voltage). Multi-scale modeling may thus
help to give a more concrete mathematical reinterpretation of the
controversial notion of downwards causation (see also Ellis et al.
2011; Green 2018). Moreover, discussions of downward causation in
systems biology have practical as well as theoretical implications,
e.g., for experimental design in cancer research on studies of cancer
as a genetic or tissue-based disease (Bertolaso 2011; Soto et al.
2008; see also
 Section 5.3).


#### 4.1.2 Emergence and Predictability



Discussions of downward causation are often connected to debates about
whether biological systems exhibit *emergent properties* (cf.,
Alberghina & Westerhoff 2005; Boogerd et al. 2005, 2007; Kolodkin
et al. 2011). Emergence in the context of systems biology typically
means that systems properties cannot be explained from the
properties of parts. Discussions can center on epistemic questions
about whether systems properties can be predicted from analyses of
lower-level constituents (epistemological emergence) to debates on
whether multilevel systems are fundamentally irreducible (ontological
emergence).



Some systems biologists are optimistic that current limitations in the
predictive capacity of models can be overcome by increasing the
complexity of computational models, thus presenting a epistemic
departure from the methodological principle of Ockham's razor
(Kolodkin & Westerhoff 2011). Others stress that biological
complexity forces life scientists to draw on abstract and idealized
models (Hofmeyr 2017; Gross 2017; 2019). These debates center on
fundamental questions about how far biological research can be taken
by integrating more parameters and data points (Kolodkin et al. 2012),
and how far we can 'extend ourselves' through
computational approaches (Humphreys 2004; Vermeulen 2011). But they
also concern ontological questions about whether there are principled
limitations to our ability to predict and control living systems
"bottom-up" (Bassingthwaighte et al. 2009; Green 2018;
Noble 2012; Moreno and Suarez 2020). Large-scale modeling
projects such as the *Virtual Cell, the Physiome Project*, and
the *Virtual Physiological Human* offer exciting cases for
philosophical analysis of the prospects and challenges associated with
parameterizing and validating complex models (Carusi et al. 2012;
Carusi 2014; Hunter et al. 2013). Ultimately, such projects may push
the boundaries for prediction and control in the life science, or may
reveal deeper challenges of biological complexity.


### 4.2 Explanatory Pluralism



Systems biology also brings new insights to discussions of scientific
explanation. In particular, network analysis in systems biology have
further sparked discussion about the roles of mathematical
abstractions to identify organizational features, counterfactual
dependencies, and generalizable aspects of biological systems. The
integration of different disciplinary inputs and emphasis on
mathematical modeling make systems biology a particularly interesting
case for discussions of whether the mechanistic account can fully
capture the diversity of explanatory practices in contemporary
biology.



Mechanistic accounts initially emphasized the differences between the
explanatory ideal of covering laws in physics and explanations in
molecular biology (Craver & Tabery 2015). Mechanistic explanations
cite how biological functions arise from the interactions and
organization of component parts or entities (Bechtel & Richardson
1993; Machamer et al. 2000; Glennan 2002). Since research in systems
biology is often integrated with research in molecular and cell
biology, some have argued that many cases in systems biology can
readily be seen as an extension of mechanistic research (Boogerd et
al. 2013; Richardson & Stephan 2007). But although abstract
modeling is compatible with mechanistic research, it is a contested
issue whether abstract models are developed towards research aims that
are distinct from mechanistic explanations. While some contend that
mathematical models are primarily used as inputs to or heuristics for
mechanistic explanations, i.e., as mechanistic schemas (Darden 2002;
Matthiessen 2015), others interpret the emphasis on quantitative and
dynamic aspects as a departure from mechanistic explanations. One
argument in support of the latter is that the modeling process does
not proceed from abstract to more detailed models, but in the opposite
direction, suggesting that mechanistic details may be vehicles for
more generic explanations rather than the other way around (Braillard
2010; Serban & Green 2020). A second argument refers to the
difficulty of reconstructing a causal story from the abstract
mathematical models (Issad & Malaterre 2015). Systems biology may
therefore also offer cases of non-mechanistic explanations.



*Topological* or *structural* explanations emphasize how
networks architectures generically determine dynamic behaviors,
independently of the causal details of the network (Huneman 2010;
Kostic 2020; Kostic et al. 2020; Suarez &
Deulofeu 2019). Mathematical analysis of network structures underlying
biological robustness has been argued to exemplify this explanatory
goal (Jones 2014). Another type of non-mechanistic explanations is
what Wouters (2007) calls *design explanations,* first outlined
in the context of comparative physiology. Design explanations do not
describe how a biological function is causally produced but instead
clarify why a given design (and not an alternative design) is present.
They do so by pointing to constraints on the possible designs that
make some designs good, some suboptimal, and others impossible. The
relevance of design explanations for systems biology lies in the
interest to specify relations between functional capacities (e.g.,
robustness) and system organization (e.g., integral feedback control)
via design principles that are independent of specific contexts of
implementation (Braillard 2010; Boogerd 2017, see also Shinar &
Feinberg 2011 for a candidate example from systems biology). Design
explanations highlight explanatory features similar to what Batterman
and Rice (2014) call *minimal model explanations* (see also
Gross 2015). These explanatory accounts have been interpreted as
varieties of *constraint-based explanations* (Green and Jones
2016), because they prioritize physical and formal constraints at the
expense of mechanistic details. Aside from highlighting differences in
the epistemic aims of explanation, some accounts also provide an
*ontological justification* for different explanatory
strategies, i.e., that higher-level phenomena are characterized by a
functional loss of and autonomy from mechanistic details at lower
levels (Batterman and Green 2021; Moreno and Suarez 2020).



Another approach is to consider explanations in systems biology as
mergers of different types of explanations. Some accounts highlight
the compatibility of different explanatory aims as ingredients in a
pluralistic approach to biological explanation (Brigandt et al. 2018;
Mekios 2015). One way to clarify the compatibility is that different
approaches highlight *constitutive* or *causal* aspects
(Fagan 2015). Distinguishing between these can possibly help identify
the distinct contributions of causal mechanistic modeling and
mathematical analysis of network dynamics. Whereas causal relations
are often prioritized in mechanistic accounts, Fagan (2015) suggests a
joint account of *collaborative explanations* that combine
descriptions of the spatial organization and the causal relations in
the system. The increasing reliance on formal modeling in systems
biology also call for an examination of the relation between
*mathematical* and *mechanistic explanations* (Baker
2005; Brigandt 2013; Mekios 2015). Some have argued that an updated
account of *dynamic mechanistic explanations* is required,
highlighting the importance of mathematical and computational modeling
for understanding non-linear dynamics, cyclic organization, and
complex feedback relations (Bechtel & Abrahamsen 2011, 2012;
Brigandt 2013). In this context, idealized models are important for
understanding the organization of mechanisms within a larger network
structure (Bechtel 2015b; Levy & Bechtel 2013; van Eck &
Wright
 2021).[8]



Although much, or perhaps most, of the philosophical work on systems
biology has focused on explanation, one cannot assume that explanation
is the sole aim of systems biology research (MacLeod & Nersessian
2015; Kastenhofer 2013a,b). Design principle may not only explain how
biological functions are realized but also allow for explorations of
whether the same function could be realized in other or simpler ways
(Briat et al. 2016). Thus, synthetic and systems biology also call for
philosophers to examine other epistemic aims, such as prediction,
control, and design, and well as theoretical and practical interests
in understanding the minimal requirements for biological functions and
life itself.


## 5. Social and Societal Implications


### 5.1 Interdisciplinary Integration, Collaboration and Education



Large-scale interdisciplinary collaboration is increasingly
highlighted as a necessary requirement for addressing the grand
challenges of modern science and society (Vermeulen 2010; Calvert
2010; Andersen 2016). Interdisciplinarity is a hallmark of systems and
synthetic biology, and both approaches offer rich sources for
philosophical analysis of the epistemic features of interdisciplinary
integration (O'Malley & Soyer 2012), interdisciplinary
identities (Calvert 2012), and of large-scale collaboration across
different universities (Calvert & Frow 2015; for examples of
large-scale projects see the list of internet resources below).



Studying the prospects and challenges of interdisciplinary integration
also offers a window to the cognitive processes involved in
interdisciplinary problem-solving, where research cannot rely on a
single theoretical framework (MacLeod & Nersessian 2013a,b).
Through this process, different epistemic ideals operating in
contemporary science often become apparent. While combining different
conceptual frameworks can lead to innovation and scientific progress,
the existence of different epistemic cultures can also pose challenges
for interdisciplinary collaboration (Calvert & Fujimura 2011;
Carusi 2011; Kastenhofer 2007). Philosophical analysis has been
proposed to help facilitate collaboration in situations where
epistemic standards clash. Philosophy of science can help explicate
the differences and the backgrounds for these, and uncover
'blind spots' considering the aims, values, constraints,
and challenges of other disciplines (Nersessian 2017).



One divide that is commonly highlighted is the gap between
experimental biologists and modelers with a background in engineering,
mathematics, or physics. Empirical analyses of collaborations between
modelers and experimentalists in systems biology show that these two
groups often have different views on what kinds of data and models are
most relevant (Joaquium et al. 2019; Nersessian 2017; Rowbottom 2011).
For instance, modelers may not be interested in studying biological
systems at the same level of detail as experimental biologists, and
experimentalists may find highly abstract and idealized models
inspired by physics or engineering misleading (MacLeod 2018). From the
perspective of engineers or physicists, the preoccupation with
molecular details involves a risk of missing out on dynamic patterns
that could be captured with more generic models, while
experimentalists may find that such generic models often lack
specificity or merely reproduce current biological knowledge. These
differences may in part be grounded in different explanatory standards
as also discussed in Section 4.2 (Fagan 2016; Green et al. 2015a).



Making explanatory standards and values more explicit, and clarifying
the relative prospects and limitations of different research
strategies, are ways in which philosophy of science could contribute
to science and science education. At the same time, collaborative
practices in synthetic and systems biology may call for an extension
of the scope of philosophical analysis. Whereas philosophers
traditionally have focused on logical and argumentative relations in
scientific texts, understanding collaborative practices in systems and
synthetic biology may further require the exploration of the
aesthetic, cognitive, and creative dimensions of scientific practice
(Carusi 2011; Ginsberg et al. 2015). Important questions are which
aspects underlie choices of visualization strategies, and how the
representation of biological data through diagrams, heat maps, network
models, or state spaces influence biological reasoning and
collaboration (Abrahamsen & Bechtel 2015; Carusi 2012; Jones &
Wolkenhauer 2012).


### 5.2 Biology in the Digital Age



The emergence of systems biology and synthetic biology in the 21th
century is to a large extent driven by the development of techniques
and strategies for producing, digitalizing, collecting, disseminating,
and standardizing data. A first step in these research practices is
often to consult online databases or bio-ontologies that are used for
data sharing, data integration, annotation, and curation of
experimental datasets (Leonelli 2016). For instance, the search for
network motifs
 (Section 1.1.1)
 started with the development of network models based on data
downloaded from the database RegulonDB as well as other sources
(Shen-Orr et al. 2002). Numerous databases, bio-ontologies, and
software tools have been developed, emphasizing the ideals of Open
Data and large-scale collaborative efforts (Leonelli 2013). These
practices raise questions as to whether some projects within systems
and synthetic biology may be described as 'big science'
(Vermeulen 2010), and about the epistemic and social implications of
data-intensive research practices and collaborations.



One debated issue is the extent to which big data will radically
change or even revolutionize scientific methods, shifting the focus
from hypothesis-driven inquiry to data-driven discoveries (Mazzocchi
2015). Whereas some have seen data-intensive strategies as a
theory-free approach (Allen 2001), others have argued that
theory plays different, although not less important, roles in
data-intensive science (Leonelli 2016). In particular, big data
biology presents new opportunities for eliminative inferences and
exploratory experiments (Ratti 2015). Moreover, since the aim of
bio-ontologies is to provide a standardized language and repository
for shared information, choices concerning vocabulary and
categorizations become important philosophical topics, including
whether standardization is compatible with local data uses (Leonelli
2016). Important philosophical questions include how choices
concerning collection, sampling, standardization, visualization, and
interpretation of data affect the result of the analysis.
Specifically, the results of correlation-based methods such as GWAS
are sensitive to sampling procedures and specific statistical
thresholds, and the clinical validity and utility of genetic
biomarkers is a debated issue (e.g., Hey 2015; Maher 2008; McPherson
and Tybjaerg-Hansen 2016).



Finally, data-intensive practices also have implications for the
institutional organization of science, as exemplified by the push for
open access on one hand and commercial interests in big data on the
other (Calvert & Frow 2015; Kastenhofer 2013a,b; Leonelli 2013,
2014; Royal Society 2012; Vermeulen 2011). Data are often produced and
compiled without specific aims in mind and are used and reused for
multiple purposes, and scholars have stressed the need to reconsider
the relation between data and evidence, and between basic and applied
science. Moreover, as contexts for ownership of data change, also
reward-systems in terms of authorship and credits may need to be
reconsidered (Ankeny & Leonelli 2015).


### 5.3 Applications and Ethical Issues



Systems biology and synthetic biology are expected to help address
societal challenges with payoffs for biomedical research, health care,
and environmental resource management. While offering new exciting
potentials, concerns have also been raised about the social and
ethical implications of the new techno-scientific strategies.



Systems medicine, the medical application of systems biology, aims for
major breakthroughs towards understanding of complex diseases such as
cancer and heart conditions (Voit & Brigham 2008; Wolkenhauer et
al. 2013). Ambitious projects within systems biology such as the
*Virtual Physiological Human* raise intriguing questions about
how computational modeling will influence future biomedical research
(Kohl & Noble 2009; Kolodkin et al. 2011; Hunter et al. 2013).
Multi-scale cardiac models are already being developed with important
implications for the potential to address physiological variability,
and for philosophical discussions of model validation and the relation
between experiment and simulation (Carusi et al. 2012; Carusi
2014).



Developments in systems and synthetic biology have encouraged
philosophers to revisit traditional conceptions of disease as
dysfunctional states (Holm 2013, 2014), or broken mechanisms (Gross
2011). Rather than focusing on the function or dysfunction of specific
genes or molecular mechanisms, network approaches to disease may
suggest a more dynamic approach to disease that accounts for an
increasing number of integrated processes (Del Sol et al. 2010; Huang
et al. 2009). Cancer research is a major priority of systems biology
research, and exemplifies a field where data-intensive approaches may
be needed to address biological complexity (Plutynski 2021).
Developments in cancer genomics have revealed an astonishing diversity
of tumor types, suggesting that finer-grained disease-categories and
more targeted treatments may be needed. This line of research has
opened new research venues, such as the development of multi-action
remedies targeting numerous disease-related pathways identified via
genomic analysis. The discovered diversity of mutations identified in
sequenced tumors has, however, also led to heated debates on whether
the behavior and identity of cancer cells can fully be understood at
the molecular level. Some have stressed the need to move beyond
gene-centric or cell-centric approaches and to consider cancer as a
problem of tissue-organization (Soto & Sonnenschein 2011).
Although some systems biological explanations of cancer illustrate the
possibility of integrating "top-down" and
"bottom-up" approaches (Bertolaso 2016; Plutynski and
Bertolaso 2018), systems biology research also displays a variety and
sometimes conflicting approaches to cancer. These debates not only
have philosophical implications for discussions about reductionism but
also practical implications for how cancer should be approached
experimentally and treated in clinical practice (Bertolaso 2011; Soto
et al. 2008).



One of the important problems in contemporary medicine is how to
account for patient-specific variation. Systems medicine is sometimes
highlighted as a route to personalized or precision medicine (Hood et
al. 2015). The aim to personalize medicine is as old as the medical
profession itself, but it is now promoted through data-intensive
computational strategies with exciting new potentials (Tutton 2014).
Proponents are optimistic that large-scale modeling of
patient-specific data can result in a paradigm-shift from a
one-size-fits-all model in traditional medicine, toward a more
efficient systemic approach that can tailor disease prevention,
diagnosis, and treatment to individual patients. Against this
optimism, some have raised concerns about uncertainties and potential
harmful results associated with genetic risk profiling, including the
risk of overdiagnosis and unnecessary medicalization of healthy
individuals (Green & Vogt 2016; Vogt et al. 2016, 2019).
Philosophers and social scientists have also critically examined how
risk information is communicated to the public by companies
capitalizing on personalized genomics, and discussed how such
information may affect health outcomes and perceptions of health
(Bartol 2013; Prainsack 2017; Reydon et al. 2012). More generally,
systems medicine has spurred debates about whether the vision to
predict and control future health outcomes is compatible with a
humanistic approach to medicine and social complexity (Tutton 2014;
Vogt et al. 2014). Moreover, combination of systems medicine and
synthetic biology applications opens also for what O'Malley
(2011a) termed *constructive personalized* medicine, where
predictive modeling and synthetic interventions are combined.
Promising technologies for these purposes now include CRISPR-Cas9 gene
editing, organ-on-chip models, 3D bioprinting and organoid-based
models for replacement therapies. While creating unprecedented
potentials for disease treatment, they also raise ethical questions
about artificial chimerism and human enhancement (Pio-Lopez 2021).



In the context of synthetic biology, discussions often center on
whether the attempt to create synthetic organisms and the use of
design metaphors can compromise our perspective on and respect for
life (Christiansen 2016a; Douglas et al. 2013; McLeod and Nerlich
2017). Other ethical debates consider the risk of negative
consequences of specific synthetic applications. Discussions often
involve so-called dual-use problems where the benefits of genetically
modified organisms are weighted against potential problems and risks.
Examples are biohazards associated with intentional or unintentional
release of synthetic pathogens, or the possibility that synthetic
production of biomedical compounds opens for efficient and inexpensive
production of illegal drugs (Christiansen 2016b). Concerns have also
been raised about the commercialization of biotechnologies. Examples
are applications of broad patents concerning minimal genomes or
synthetic genes (O'Malley et al. 2008), and considerations about
structural injustice if industrial production via synthetic organisms
outcompete conventional farming in developing countries (Christiansen
2016b). Thus, the strong push to address societal problems through
systems and synthetic biology gives rise to new exciting possibilities
but also calls for critical examination from philosophers and social
scientists.


## 6. Concluding Remarks



Systems and synthetic biology are often described as distinguishable
in their emphasis on analysis versus synthesis, but the ties to
engineering approaches are complex and multi-faceted in both fields.
Systems and synthetic biology have been addressed together in this
entry because many scientific and philosophical issues overlap. Both
approaches draw inspiration from engineering, mathematics, physics,
and computer science, but in various different ways and towards the
different aims of understanding, predicting, and modifying biological
systems. Accordingly, both approaches provide excellent sources for
philosophical discussions of interdisciplinary integration,
particularly the implications of the increasing embedding of
mathematical and computational approaches in biology. Some practices
encourage philosophers to revisit classical philosophical topics such
as reductionism and scientific explanation, but systems and synthetic
biology also broaden the scope of philosophical topics to include
data-intensive practices and modeling strategies towards the aims of
design and control.



Since this entry has examined the philosophical implications of both
systems biology and synthetic biology, much attention has been given
to the implications of engineering approaches, including the quest for
so-called design principles. It is, however, important to note that
systems biology and synthetic biology cover a broad spectrum of
research practices, some of which are more strongly inspired by
chemistry or physics than engineering. This entry should thus be taken
as an attempt to only cover some ground of the vast philosophical
landscape that these research fields reach into. With the rapid
development of both approaches and their explicit aim to address grand
scientific and societal challenges, exciting times lie ahead for
philosophy of biology.