Ecological firewalls for synthetic biology

Abstract

It has been recently suggested that engineered microbial strains could be used to protect ecosystems from undesirable tipping points and biodiversity loss. A major concern in this context is the potential unintended consequences, which are usually addressed in terms of designed genetic constructs aimed at controlling overproliferation. Here we present and discuss an alternative view grounded in the nonlinear attractor dynamics of some ecological network motifs. These ecological firewalls are designed to perform novel functionalities (such as plastic removal) while containment is achieved within the resident community. That could help provide a self-regulating biocontainment. In this way, engineered organisms have a limited spread while—when required—preventing their extinction. The basic synthetic designs and their dynamical behavior are presented, each one inspired in a given ecological class of interaction. Their possible applications are discussed and the broader connection with invasion ecology outlined.

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iScience
Article
Ecological firewalls for synthetic biology
Blai Vidiella,
Ricard Sole
´
ricard.sole@upf.edu
Highlights
Population control of
synthetic strains can be
achieved by engineering
ecological links
We introduce ecological
firewalls, inspired in four
types of ecological
interactions
Our firewalls are shown to
maintain diversity while
performing designed
functions
Ecological firewalls will
help to tackle future
bioremediation strategies
Vidiella & Sole´ , iScience 25,
104658
July 15, 2022 ª2022 The
Author(s).
https://doi.org/10.1016/
j.isci.2022.104658
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iScience
Article
Ecological firewalls for synthetic biology
Blai Vidiella
1,2,3
and Ricard Sole
´
1,2,4,5,
*
SUMMARY
It has been recently suggested that engineered microbial strains could be used to
protect ecosystems from undesirable tipping points and biodiversity loss. A ma-
jor concern in this context is the potential unintended consequences, which are
usually addressed in terms of designed genetic constructs aimed at controlling
overproliferation. Here we present and discuss an alternative view grounded in
the nonlinear attractor dynamics of some ecological network motifs. These
ecological firewalls are designed to perform novel functionalities (such as plastic
removal) while containment is achieved within the resident community. That
could help provide a self-regulating biocontainment. In this way, engineered or-
ganisms have a limited spread while—when required—preventing their extinc-
tion. The basic synthetic designs and their dynamical behavior are presented,
each one inspired in a given ecological class of interaction. Their possible applica-
tions are discussed and the broader connection with invasion ecology outlined.
INTRODUCTION
Since the dawn of synthetic biology, scientists have been designing and modifying living systems in diverse
ways, sometimes breaking the constraints imposed by evolution. In this context, synthetic biology is the last
revolution within technological evolution: it deals with the creation of novel functional designs grounded in
the use of living matter [Benner and Sismour, 2005;Cameron et al., 2014]. This emerging discipline has been
instrumental in providing new tools to interrogate nature [Lim, 2010;Brophy and Voigt, 2014] and develop
new biomedical applications [Ruder et al., 2011;Weber and Fussenegger, 2011;Wu et al., 2019] while
expanding the reach of possible biology [Elowitz and Lim, 2010]. The success of the field is highlighted by
the continuous expansion of its application domain toward higher-level problems, including the design of syn-
thetic ecosystems [Weber et al., 2007;Mee and Wang, 2012;Grobkopf and Soyer, 2014;De Roy et al., 2013;
Widder et al., 2016;Jaramillo, 2017], biocomputation beyond simple circuits [Purnick and Weiss, 2009;Regot
et al., 2011;Macı
´a et al., 2012;Grozinger et al., 2019], nonlinear dynamics in cells [Levine et al., 2013;Vidiella
et al., 2021] or multicellular designs [Davies, 2008;Toda et al., 2020;Duran-Nebreda et al., 2021]. Despite
the major challenges ahead, everything seems to suggest that there is plenty of room for exploration and
development. To a large extent, the success of this field has been tied to its deep connection with systems
biology. Right from its initial steps, synthetic designs and their theoretical description (from Boolean networks
to differential equations) came together. Over the last decade, major advances have been made within the
context of engineered microbiomes thanks to the merging between the two disciplines [Widder et al., 2016;
Leggieri et al., 2021]. Not surprisingly, community ecology—a traditional systems science—has inspired
many relevant ideas within microbiome research, where concepts from population dynamics are easily trans-
lated into the microbiota context [Costello et al., 2012]. Can such ideas be also used to address the problem
of climate change, where releasing modified microbes could be a potential future scenario?
It has been recently suggested that synthetic biology might actually help to protect, restore or ’’terraform’’
extant ecosystems that can be in danger of experiencing catastrophic transitions [Sole
´, 2015;de Lorenzo
et al., 2016;Conde-Pueyo et al., 2020]. The rapid acceleration of Anthropogenic impacts on our biosphere,
in particular in relation of marine and freshwater pollution, carbon dioxide removal, and biodiversity decay,
has ignited a debate concerning the need of usingsynthetic biology as an emerging approach to these prob-
lems [Sole
´, 2015;Piaggio et al., 2017;Goold et al., 2018;Xiong et al., 2018;DeLisi et al., 2020;Rylott and Bruce,
2020;Cleves et al., 2018;Levin et al., 2017;van Oppen and Blackall, 2019;Coleman and Goold, 2019]. However,
too often the emphasis is placed on the bottom, microscopic level, i.e., how genetic designs are made. In this
context, our understanding of how synthetic designs can work within cell populations has been rapidly
improving, particularly when dealing with microbial systems. However, what can be said about their impact
on community dynamics? Is this an uncertain domain where little can be predicted or controlled?
1
ICREA-Complex Systems
Lab, Universitat Pompeu
Fabra, Dr Aiguader 88, 08003
Barcelona, Spain
2
Institut de Biologia
Evolutiva, CSIC-UPF, Pg
Maritim de la Barceloneta 37,
08003 Barcelona, Spain
3
Centre de Recerca
Matema
`tica, Campus de
Bellaterra, Edifici C, 08193
Cerdanyola del Valles, Spain
4
Santa Fe Institute, 1399
Hyde Park Road, Santa Fe,
NM 87501, USA
5
Lead contact
*Correspondence:
ricard.sole@upf.edu
https://doi.org/10.1016/j.isci.
2022.104658
iScience 25, 104658, July 15, 2022 ª2022 The Author(s).
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1
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A major issue of deployed engineered microorganisms concerns the potential implications for local eco-
systems and unknown evolutionary outcomes [Schmidt and de Lorenzo, 2012,2016]. As a consequence
of an almost complete lack of experimental evidence (because of moratoria and the absence of a proper
theoretical framework) this issue has been very often discussed in non-scientific terms. The ethical debate
has been prominent (as it should be) but not substantiated by evidence. Field interventions aimed at
ecosystem restoration involving inoculation-based techniques [Bowker, 2007]ortheuseofurbanorganic
waste [Pascual et al., 1999] are seldom questioned despite the fact that they actually bring multiple exotic
microorganisms (even a whole microbiome) to the given habitat. Instead, the delivery of a single engi-
neered microorganism strain is typically received with extreme caution.
Biocontainment designs are a big issue within synthetic biology [Lee et al., 2018] as it was originally in the
past century in relation to recombinant DNA technology. In this context, most proposed strategies deal with
engineering tools that would minimize the interactions between synthetic strains and the potential life forms
around it. Practical scenarios include microbes designed to degrade a given toxic substrate [de Lorenzo,
2008;Nikel and Lorenzo, 2021], modify the gut [Foo et al., 2017] or plant microbiomes [Ke et al., 2021] or cya-
nobacteria capable of improving soil [Vidiella et al., 2020], to mention just a few. The key argument is that a
properly defined genetic firewall could in principle provide a source of containment that is predictable
[Wang and Zhang, 2019]. These genetic firewalls require some amount of orthogonality in relation to living
designs as we know. Two major, conceivable routes to synthetic life are de novo cellular design or successive
alienation of designed bacterial strains using in vitro evolution. The latter would allow a process of directed
codon emancipation leading artificials cell equipped with an unnatural genetic code [Budisa, 2014].
In this paper, we want to address the previous questions in relation to the problem of population contain-
ment of synthetic organisms deployed in extant ecosystems. We start with the recent proposal of terrafor-
mation as applied to our biosphere [Sole
´, 2015;Conde-Pueyo et al., 2020;Sole
´et al., 2015,2018]. The key
concept here is that, ideally starting from a member of the resident community, a synthetic strain can be
obtained to be reintroduced to perform a function while being constrained to a limited spread within
the given habitat. This would include a broad range of goals, from removal of undesirable molecules (as
in classic bioremediation) to the stabilization of endangered communities (such as soils in drylands). This
scenario raises immediate questions regarding unintended consequences, often to be compared with
those involving invasive species. In general, the assumption is that invaders will be harmful and thus de-
signed ‘‘invaders’’ should also be detrimental to community organization or diversity. Is that the case?
To properly address this question, we need to move to a systems-level picture.
The lessons from invasion ecology tell us a different story. First of all, invasion is usually constrained by commu-
nity structure [Lockwood et al., 2013]. Typically, most invaders fail to get established. On the other hand, many
historical examples of invaders have to do with species capable of fast growth facilitated by the lack of negative
feedback (caused, for example by the disappearance of top predators) and the presence of a degraded resi-
dent community (caused, for example, by grazing). Importantly, we also know of other situations where the
invader actually helps to sustain diversity and create opportunities for other species to survive [Marris, 2013;
Wright et al., 2014;Pearce, 2016]. Can we design ecological webs where the introduced strains become effec-
tively controlled by ecosystem-level interactions? Once again, lessons from the study and manipulation of
ecological communities provide a positive answer. In particular, the work on tipping points and alternative
states in ecosystems revealed that a limited set of potential ‘‘attractors’’ are possible and that the right manip-
ulations can shift the system from a degraded state to a healthy one [Scheffer et al., 1993;Scheffer, 2009]. More
generally, some universal properties of ecosystem organization guarantee the presence of a well-definedset of
robust attractor states [Holling, 1973]. In a nutshell, as a nonlinear dynamical system, every ecosystem has a
finite number of possible equilibrium states (or attractors) for a given set of parameters. The stability of each
possible attractor is essentially determined by the nature of the interactions among the different species
and sometimes only one attractor is possible. If that is the case, a very interesting possibility emerges: can
an adequate design of synthetic interactions create a robust attractor state from which we cannot escape? If
yes, can such engineering approach preserve species diversity? If that were the case, confinement would be
the outcome of an ecological firewall resulting from the nature of ecological dynamics.
RESULTS
In the following sections, the concept of ecological firewalls is made explicit by considering a set of specific,
potentially testable examples and how models predict the expansion or control of each firewall. None of
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them involves a synthetic strain capable of triggering positive, uncontrolled feedback loops and can
become extinct if functional traits (tied to their desired bioremediation goal) are not fit enough. In that
case, it can transform into the original wild type or simply become extinct by competition. As shown in
the next examples, this does not require special genetic orthogonal designs. Instead, the firewalls are ob-
tained from a properly designed set of ecological interactions that include resource-consumer dynamics,
mutualism, parasitism, niche construction and indirect cooperation. To provide a unified picture of our re-
sults in theoretical terms, we consider a population dynamics set of models where the multidimensional
problem is collapsed to a single-species, one-dimensional equation.
Ecological firewalls
Five classes of ecological firewalls (EFWS) will be discussed that can be used to build novel synthetic com-
munities under controlled conditions. The main goal here is to show that the synthetic population will be
maintained in a self-controlled level associated with a suitable attractor state but can also scale up with the
underlying problem (such toxin levels or plastic waste).
A given EFW will be described as a specific subnetwork of species interactions constructed to work within a
larger web structure (a natural community) where a given functionality needs to be performed. Because of
the specific topology of the interactions associated with each subnetwork, we use the term ‘‘motif’’ to refer
to them (as presented in [Sole
´et al., 2015]). We do not suggest specific genetic constructs (and thus no
explicit candidates are proposed) and instead assume their basic functional traits that are introduced in
a population-level description. In this context, our goal is to show that the attractor dynamics resulting
from the EFW is consistent with the containment goals that we aim to implement.
Toprovideaunifyingpictureofadiverserangeofcasestudies,wechosetodefinepopulationmodelsthat
capture the dynamics of the synthetic strain as a single variab le. That means a dimensional reduction from a
multispecies ecosystem model to the minimalist picture that helps understanding the outcome of each
EFW in intuitive terms.
Synthetic resource-consumer firewall
The first example of our systematic approach to ecological firewalls deals with the vast amount of anthro-
pogenic xenobiotics (such as oil spills, plastic, or recalcitrant chemicals) and the difficulties associated with
their safe removal. It was early suggested that synthetic biology could be the key for advanced avenues
beyond classical bioremediation strategies [Zobell, 1946;Atlas, 1978]. Here we use the systems ecology
approach to define the conditions of effectiveness and safety that should be at work.
The goal of this EFW is to stabilize a community of synthetic microorganisms able to degrad ea given anthro-
pogenic substrate that is being injected inside the system at a given rate (Figure 1A). We want the synthetic
to become part of the community at intermediate levels of the xenobioticwhile it becomes dominant at high
levels. In additionally, when the levels of the substrate decay below some limits, the synthetic strain should
disappear. In other words, if removal is efficient enough, once the function has been performed, the intro-
duced strain gets extinct. In this context, our EFW acts as a ‘‘function-and-die’’(FAD) motif [Sole
´et al., 2015].
The motif properties are summarized in Figure 1. The model is inspired by classical models of resource-con-
sumer dynamics but with an extra component representing the synthetic strain that will perform the bioreme-
diation task. Here Rdescribes the amount of xenobiotic (such as polyethylene) which is generated at a rate r,
decays at a rate drand is removed from the system under the action of the synthetic strain (i.e., The term
hSR). The model starts from a whole community Cof microorganisms defined by means of a set of npopula-
tions, i.e., C=fCmgwith m=1;2::::; nindicating species index (Figure 1A). Each one grows with a given rate
riwhile competing with the synthetic strain Sfor some available (non-xenobiotic) resources and space (Fig-
ure 1B), growing at a rate r. The previous rules can be summarized by the following set of differential equations:
dR
dt =rhSR drR
dS
dt =rRS dsSSFR;S;Cm
dCi
dt =riCidiCiCiFR;S;Cm
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In these equations the decay and death rates are indicated by dj. The flux term Fis introduced to include
the competition among Sand Cand is usually interpreted as an average fitness when total population size is
conserved [Nowak, 2006]. More precisely, if we assume that S+P
k
Ckis constant (constant population
constraint, CPC), we have dS+P
k
Ckdt =0 or, in other words, dS=dt +d P
j
Cj!,dt =0. This allows
(as we will see in our different examples) to explicitly find Fand solve our models.
This multidimensional system is difficult to analyze in all its complexi ty (see SM) but we can take a shortcut by
considering that the resident community can be safely represented using average rates, i. e. assuming that
rihrcand that dizdfor all i=1;.;n. Under this homogeneity assumption and using C=P
k
Ck,wehave:
dR
dt =rhSR dR
dS
dt =rRS dSSFðS;CÞ
dC
dt =rcCdCCFðS;CÞ
and using a normalized sum S+C=1, we find:
FðS;CÞ=ðrRrcÞS+ðrcdÞ:(Equation 2)
The system is reduced to a two-dimensional set of equations including only the synthetic and the resource
components, namely
dS
dt =ðrRrcÞSð1SÞ
dR
dt =rhSR dR
This two-dimensional system can be studied using standard methods of phase-plane analysis (see Box 1). A
further simplification of our model allows us to reduce it to a one-dimensional dynamical system. In this
case, we assume that resources rapidly equilibrate (compared with cell populations), and thus we can
use dR=dtz0.Thisisarathercommonapproximationindynamicalsystemsthatinthiscasegivesafunc-
tional coupling between the resource and the synthetic population: RðSÞzr=ðhS+dÞ.
Figure 1. Resource-consumer (‘‘function and die’’) firewall
This motif is sketched in (a) and would be associated for example to plastic waste (b) where a synthetic strain Swould
compete for space on plastic surfaces with the resident community. Depending on the input rate of the resource (r)the
synthetic organism will be more or less prevalent (c). The continuous line indicates the three regimes of stable states of the
synthetic strain, from failure to establish itself (when ris too small), to overcoming the ecosystem when the input rate is
really high, through a region of coexistence between the synthetic organism (S) and the original community (C). Notice
that this happens even when the replication rate of the synthetic strain is very small compared to the original community
growth (in this particular case rC=10r). The set of parameters used here are d=0:1, rc=0:5, r=0:05, and h=0:1,
respectively.
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Using this expression, we obtain a single differential equation for the synthetic population, namely:
dS
dt zfðSÞ=Sð1SÞrr
hS+drc(Equation 3)
For this we have three fixed points, namely (i) no synthetic, I, e, S=0, (ii) only-synthetic community, i. e.
S=1 and (iii) the coexistence point where the synthetic strain achieves an intermediate level:
S=rrdrc
hrc
(Equation 4)
The stability of these fixed points is determined by the sign of
lðSÞ=df ðSÞ
dS (Equation 6)
[Strogatz, 1994] evaluated at each S.AgivenfixedpointSwill be stable provided that lðSÞ<0(andun-
stable if lðSÞ<0). For our system, we have
lðSÞ=ð12SÞrr
hS+drcrrSð1SÞ
ðhS+dÞ2(Equation 7)
The stability of the fixed point where the synthetic fails to establish itself (S=0) is obtained under the
inequality
lðS=0Þ=rr
drc<0 (Equation 8)
In this case, the pollutant Rwill achieve a steady level R=r=d. On the other hand, the dominant synthetic
state (S=1) will be stable when
Box 1. Mathematical systems biology of ecological firewalls
The reduction from a high-dimensional model to a simplified, even one-dimensional equation requires different types
of assumptions that we summarize here using the resource-consumer firewall as a case study. In general, the stability of
the fixed points associated with a d-dimensional dynamical system dXi=dt =fiðX1;.;XdÞis determined by evaluating
the eigenvalues of the (d3d) Jacobian matrix (J), associated with the linearized system dxi=dt =Pd
jJijxjnamely:
Jij =v
_
Xi
vXjX
(Equation 1)
and evaluated in each fixed point X
k. The set of eigenvalues lkis then determined from DetJ =ðJijÞ
ldij, where dij is the Kronecker’s delta [Strogatz, 1994]. For the FAD motif, we have Xi˛fR;S;Cg.Once
the CPC constraint has been applied to the original set of equations, and before we assume rapid dy-
namics for the resource R, the resulting d=2 dynamical system involves three fixed points: X=ðS;
RÞ˛fð0;r=dÞ;ð1;r=ðd+hÞÞ;ððrdcdrÞ=rh;rc=rÞg. Their stability can be studied using its associated
232 Jacobian matrix:
JS;R=
2
6
6
6
6
6
4
v
vSdS
dt v
vRdS
dt 
v
vSdR
dt v
vRdR
dt 
3
7
7
7
7
7
5
For our specific set of equations, this reads
JS;R=2
4
ð1SÞSrðRrrcÞð12SÞ
ShdRh3
5
The fixed points will be stable if DetðJðXÞÞ <0and D=TrðJðXÞÞ =ðv
_
X1=vX1+v
_
X2=vX2ÞðXÞ>0.Adetailedanalysis
shows that the first point where no synthetic is established will occur provided that rc>rr=d, whereas the exclusion point
where only the synthetic persists is obtained under the condition rc<rr=ðd+hÞ. See SM for a detailed derivation.
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lðS=1Þ=rcrr
h+d<0 (Equation 9)
This can be achieved if the rate of pollutant input is higher than a critical value rc.Where
rc=rc
rðh+dÞ(Equation 10)
and now, Rwill reach a constant value R=r=ðh+dÞ. The location of the stable states for our synthetic strain
is indicated in Figure 1C by means of a continuous line. We can appreciate that three well-defined phases
exist.
An alternative, complementary and often more intuitive way of studying the stability of these dynamical sys-
tems is grounded on the use of the so called potential function VðSÞ. For a general system dS=dt =fðSÞ,it
is defined as (see Box 2):
VðSÞ=ZfðSÞdS (Equation 11)
and it can be shown that the minima (maxima) of VðSÞcorrespond to the stable (unstable) fixed points. In
this paper we will transform all the examples into one-dimensional differential equations to provide a uni-
fied picture based on the properties of VðSÞ.
For our FAD firewall, the potential function reads
VðSÞ=rr
h2ðd+hÞd
hlogðhS+dÞS
+rcS21
2+rr
2hS
3
Box 2. Potential functions
A very common metaphor of a stable ecological state considers a physical analogy, where a marble (representing
population size) is located at the bottom of a valley [Holling, 1973;Scheffer, 2009;Sole´ , 2011]. If perturbed, the marble
will move around but eventually will settle back to the bottom state again. In contrast, a marble located on a peak in
this landscape might stay there but the slightest perturbation makes it run away: the peak corresponds to an unstable
state. Is there a way to define this landscape in some rigorous mathematical way? For a one-dimensional dynamical
system defined as dS=dt =fðSÞ,withfðSÞ˛C1ðUÞ(i. e. fand its derivative ar e continuous on the relevant set U3R)the
system is said to derive from a potential function VðSÞ[Arnold, 2013;Strogatz, 1994;Sole´ , 2011]ifwecanwritethe
dynamical system as:
dS
dt =dV
dS (Equation 5)
i. e. when the changes in the state of the system obey a gradient response: the steeper the derivative in
the right hand side, the larger the damping in the opposite direction. If we move away from Stoward
S+s(where sis small) the change in the potential with swill be DV=s=ðVðS+sÞVðSÞÞ=s.IfDV>0
means that the potential grows (we are in a valley) and thus, from (Equation 6)therighthandsideof
(Equation 5) is negative: growth is inhibited. The opposite occurs if we are at an unstable point. From
the previous equation, it is easy to see that
VðSÞ=ZdS
dt dS =ZfðSÞdS
At a given S,using(Equation 7) the first and second derivatives of Vare:
dVðSÞ
dS S
=fðSÞ d2VðSÞ
dS2!S
=lðSÞ;
respectively. The first derivative is zero (as it should be for an extremum of the function) and the second
will be positive (negative) if the point is stable (unstable), consistently with a minimum (maximum) of the
potential.
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In Figures 1C and 1D we use this potential function (and the information on stability gathered from lðSÞ)to
capture the different equilibrium states associated with the synthetic population. In order to normalize the
potentials (their exact values are irrelevant) we plot the normalized version (to be used in all the plots
hereafter)
UðSÞ=VðSÞminrfVðSÞg
maxrfVðSÞg  minrfV ðSÞg (Equation 12)
In the Figure 1B diagram, the behavior of the potential is displayed for different rates of pollutant input r
(vertical axis). Bright and dark colors indicate higher and lower values of the potential, respectively. Darkest
areas are thus associated with stable states of the synthetic population. Three snapshots of the VðSÞplots
are also displayed in Figure 1C. As we can see, low levels of input (and thus low concentrations of R)are
unable to sustain the synthetic, whereas intermediate values of rcreate the conditions for a stabilized
coexistence attractor where both the synthetic and the resident community coexist. The dominance of
the synthetic population can only occur at very high levels of waste concentrations.
The power of this simple model approach is illustrated by its application to a specific (but very relevant)
case scenario associated with the dynamics of ocean plastic debris under a growing trend of
plastic deployment, presented in [Sole
´et al., 2017]. It was shown that a resource-consumer model
similar to the one discussed here (but with a time-dependent input rate, i. e. r=rðtÞ) could explain a
counterintuitive pattern displayed by sampled marine plastic concentrations, namely the lack of a
growing trend despite the almost exponential rate of debris dumping. Assuming that a microbial
compartment exploits the plastic substrate, two predictions were made. The first is that plastic concen-
trations would be much smaller than the expected from the uncontrolled scenario (i.e., a microbial-free
context). Secondly, the model also predicted that the population dynamics of the synthetic strain SðtÞ
would positively correlate with plastic debris input rate. Both predictions have been recently confirmed
by two different global metagenomic analyses. They include the detection of widespread presence of
plastic-degrading microbes [Alam et al., 2020] and their correlation with pollution trends [Zrimec
et al., 2021]. The fact that plastic-degrading enzymes have been evolving and allowed for plastic contain-
ment (at least at some level) further supports the viability and scalability of the terraformation scenarios
discussed here.
Synthetic mutualistic firewall
Cooperative interactions are particularly useful when designing ecological firewalls. On one hand coop-
erative loops are known to be relevant to maintain reliable behavior and help foster diversity, although
they involve desirable dynamical properties and enhance stability. In some systems, synthetic circuits
have been proposed in order to help the system to avoid undesired shifts. The design of cooperative
loops based on the creation of auxotrophic interactions has shown the reliable response of the designed
consortia [Johns et al., 2016;Amor et al., 2017;Rodrı
´guez Amor and Dal Bello, 2019]. This is illustrated by
drylands [Sole
´, 2015;Maestre et al., 2017;Sole
´et al., 2018;Conde-Pueyo et al., 2020]. In these arid and
semiarid ecosystems, the goal of terraformation would be a synthetic design able to improve the condi-
tions for the community driving it to a healthier situation while preserving community diversity. One way
of obtaining such a result would be to use cyanobacteria from the resident community and engineer it to
produce a molecule capable of improving soil moisture. This could be achieved by engineering some
cyanobacteria already present within the community. By doing so, an improvement of soil quality would
help the vegetation cover to be more stable and in return higher vegetation cover could also help
improve the quality of the soil where the synthetic microorganism lives. A mutualistic dependency has
been engineered.
The firewall motif associated with our mutualistic design is depicted in Figure 2A (same notation than in the
previous EFW). As pointed out above, arid and semiarid lands are one of the possible ecological contexts
where this EFW would be helpful (Figure 2B). Depending on the parameters of synthetic replication (r)and
the increase of the positive symbiosis with the community (h) measures the strength of mutualistic ex-
changes. As shown in the following sections, the mutualistic design allows us to maintain or even increase
diversity. Hereafter, as in previous sections, we consider the constraint P
k
Ck+S=1.
The multispecies model, as sketched in Figure 2A, can be described by the following system of n+1dif-
ferential equations:
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dS
dt = r+X
n
k
ekhkCk!SdSSFðS;CÞ
dCi
dt =ri
c+hiSCidiCiCiFðS;CÞ
Here the parameters ekstand for the gain associated with Sas a result of mutualistic exchanges with each
species from C. If we assume that hkh;dk
cdand eke, a new coarse-grained set of two equations for
the synthetic-resident pair can be written, namely:
dS
dt =rCS dSSFðS;CÞ
dC
dt =ðrchSÞCdCCFðS;CÞ
We end up again with a one-dimensional dynamical model:
FðS;CÞ=hð1+eÞð1SÞS+ðrrcÞS+rcd
whereweusedC=1S. This allows us to obtain a third-order dynamical equation:
dS
dt =Sð1SÞðLSÞ
Wherewedefine
L=rrc+he
hð1+eÞ(Equation 13)
The fixed points are thus: (i) S= 0 (no synthetic), (ii) S= 1 (synthetic dominates), (iii) S=L(species coexis-
tence). The stability of each Sis now determined by the sign of
lðSÞ=ð12SÞrrC+he
hð1+eÞ+Sð23SÞ
For the first fixed point, it will be stable (and no synthetic will be present) if
r+eh <rC
The dominant synthetic community will instead occur if rh>rC, which we can also write as a threshold
condition:
h>hc=rCr(Equation 14)
Figure 2. Synthetic mutualist firewall
(A) Schematic representation of the interactions between the original community (C) and the synthetic mutualist organism
(S). One of the possible applications of this motif is the Terraformation of semiarid ecosystems (b). Depending on the
parameters of synthetic replication (r) and the increase of the positive symbiosis with the community (1 +e). Different
stable ecosystem configurations can be obtained (see panels c and d). As mutualism increases, the competition between
the synthetic strain and the original community diminishes. Reaching a coexistence regime, an ecosystem where both are
present. The parameters used for these simulations are r=0.1,r=0.5,e=2.0andd=0.1.
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Both conditions are the boundaries of existence, the coexistence fixed point, which is the most common
one. The associated potential VðSÞ:
VðSÞ=LS2S
31
2+S31
3S
4(Equation 15)
The normalized potential UðSÞis displayed in Figures 2C and 2D. The relevant parameter is now the effi-
ciency of the cooperative interaction, h. Two major phases are now observed. If the strength of the coop-
erative link is weak (below a threshold value, I. e. h<hc), the synthetic train is unable to persist. Instead,
once the threshold is overcome, a robust coexistence attractor is found.
The mutualistic firewall illustrates very well our main point concerning EFW behavior. It is defined not in
terms of a given construct carried by a modified cell but as a designed network motif. The positive input
provided by the synthetic mutualist to the whole community can be exemplified by the drylands case study:
improving moisture levels can immediately improve the state of the components of C. As a consequence,
community level properties (such as soil carbon) can also improve to the benefit of the synthetic cooper ator
[Sole
´, 2015;Sole
´et al., 2015].
The robustness of such design makes the coexistence attractor an inevitable outcome. More importantly,
because cooperation requires a balance of two positive, dependent exchanges, none of the components
ofthesystemwilldisplacethesecond.
Synthetic parasitic firewall
Although coexistence is one of our design goals here, we might also want to guarantee that the synthetic
population remains low. An EFW that favors this goal would help to preserve the resident community on
high population levels. In the previous example, the mutualistic feedback typically pushes the synthetic to-
ward higher population values.
Along with competition (which we have implicitly introduced by means of the constant population constraint)
and cooperation, parasitism is another main component of the space of ecological interactions. What if a
parasitic-like interaction is considered as part of an EFW? Parasites are a dominant class of life forms that are
characterized by taking advantage of a given species while not bringing back any benefit. They can in fact
be disruptive to the stability of a given species. However, we need to consider this possibility too and, as shown
in the following sections, a parasitic interaction is actually a good design principle for another kind of EFW.
Specifically, consider now the scenario where the synthetic strain performs a function while it takes advan-
tage of the resident community from which it exploits a given set of resources while it acts as an inhibitor (for
example, by means of an excreted molecule). The basic network motif for this firewall is depicted in Fig-
ure 3A: the synthetic strain exploits some good from Cwhich it requires to replicate. One case study would
involve to engineer an organism to fix CO
2
using concrete as scaffold from civil infrastructures (w hose cracks
can host dozens of bacterial species). This is o ne particularly important outcome of the Anthropocene: after
water, concrete is the second-most-used substance in the world. Our engineered organism may be able to
profit from the metabolic reactions done by the residentcommunity using concrete as a substrate but needs
to make sure not to become extinct (otherwise, its engineered function would be lost). This can be ensured
by actively repelling the other organisms, by means for example of bacteriostatic antibiotics.
These interactions can be introduced withinanother kind of EFW, where we again represent our community
Cas a set of species Ckthat sustain the engineered organism by providing it with a given repertoire of nu-
trients or key metabolites. However, we do not ask the synthetic strain to provide anything in return. The
question here is under what conditions the parasitic strain will be established in a stable manner.
The new set of equations reads:
dS
dt =r X
n
m=1
rmCm!SdSSFðCk;SÞ
dCk
dt =rk
chkSCkCkFðCk;SÞ
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The terms rkCxSand hkCkSstand for the advantages provided to Sfrom the community and the inhibitory
effect of the synthetic population on the resident community, respectively. Assuming that P
k
Ck=Cand
homogeneity in the parameters, we have now:
dS
dt =rCS dSSFðS;CÞ
dC
dt =ðrchSÞCdCCFðS;CÞ
Using C=1-S, the flow term reads:
FðS;CÞ=ð1SÞðrchS+rSÞd(Equation 16)
and our one-dimensional system is:
dS
dt =Sð1SÞrrc
rhS(Equation 17)
We have obtained again a simple model that has a factored form, with the fixed points: (i) S=0, (ii) S=1,
(iii) S=ðrrcÞ=ðrhÞ. The stability is determined from
lðSÞ=ð1SÞðrrCÞ+Sð23SÞðhrÞ
For the first fixed point, the existence condition is simply given by r<rc, whereas the condition for com-
plete invasion is r<h. The coexistence fixed point, its stability is achieved if two inequalities are
met altogether, namely
r>rcr<3h+4rc
7(Equation 18)
The potential function associated with this EFW now reads:
VðSÞ= S2
2S3
3!ðrrcÞ S3
3S4
4!ðhrÞ
In Figure 3CUis shown as a function of the inhibition strength measured by the hparameter along with
three representative snapshots (Figure 3D). By contrast with the mutualistic case, the valleys of the poten-
tial landscape behave in a rather different way. For small h, the parasitic strain is unable to survive and the
S=0 state is the only stable one. When the parasitic link grows, a short range of parameters is consistent
with a full dominance of S, but again as soon as a threshold value hcis achieved, a coexistence point is also
Figure 3. Synthetic parasite firewall
(A) The schematic representation of the interactions between the original community (C) and the synthetic parasite
organism (S). The target community here could be a diverse microbial ecosystem such as the gut microbiome (b,
photograph by Ana Berges). Two important parameters for the synthetic parasite are how much profit it takes from the
ecosystem (r) and how much it inhibits the community (h). Depending on these two parameters. Different stable
configurations can be obtained (see panel c). If his small, the original community gets rid of the synthetic. For a small
range of parameters, the synthetic can become dominant. However, when h>rcoexistence is the only stable state. The
parameters used for this study are: rC=0.5andr=0.25.
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generated. As we can see, the Spopulation decays in size with larger hbut nevertheless coexists with the
resident community.
Synthetic stigmergy firewall
Although competition and cooperation are two well-known kinds of ecological interactions, less atten-
tion has been paid within synthetic and systems biology to niche construction processes as the one
explored here. The next EFW deals with the phenomenon of stigmergy, i.e., the feedback between a
given population that generates a material scaffold (an insect nest, for example) that then acts on the
individuals, modifying their behavior. It is because of stigmergy that collective intelligence in social in-
sects’ works: coordinated, global activity emerges among individual agents thanks to the formation
(and interactions with) an external substrate [Theraulaz and Bonabeau, 1999]. Only recently the
potential relevance of this concept has been acknowledged within the context of microbial colonies
[Gloag et al., 2013,2016] where stigmergy would be responsible for collective coordination of microbial
assemblies.
This is a more sophisticated motif that takes advantage of the multicellular behavior exhibited by bacterial
communities. In particular, we can use their well-known niche construction strategy based on biofilm for-
mation [Flemming et al., 2016]. This is not a minor topic, because it has been est imated that a large majority
of bacteria on the planet live in biofilms. Such a strategy can be one of the most optimal ways of cooper-
ation in environments such as rivers or the surface of the ocean (on plastic debris). Because of their prev-
alence in both natural and anthropogenic env ironments and their robust structural properties (that favor for
example division of labor and enhanced persistence), they are becoming a major target for synthetic
biology [Tran and Prindle, 2021;Kassinger and van Hoek, 2020].
In our model, the biofilm is represented by the state variable Bproduced by Sat a rate h. At the same time,
Scompetes for the resources with the resident community C. A maximum biofilm population (the carrying
capacity) is assumed, to be associated with finite surface cover. As usual, we consider this maximum size
normalized to one. In Figure 4A the network motif that defines our firewall is sketched.
We will show in the following sections that, despite the different design principles introduced in this EFW,
its population dynamics are very close to the EFW presented in the previous section. One relevant biore-
mediation scheme where synthetic populations could be specially relevant concerns the potential for
removing undesirable molecules from freshwatere nvironments (including both natural and urban contexts,
Figure 4B) or helping degrade plastic waste.
Figure 4. Synthetic stigmergy firewall
(A) The schematic representation of the interactions between the original community (C) and the synthetic mutualist
organism (S). In some environments, the synthetic organisms could face physical aversions, i.e. the flow in a river or
sewage (b). In this case, the effect is mediated by the synthetic organism replication (r), the biofilm production (h)thus
reducing individual death while also limiting the growth (g) and noxious effect of being encapsulated. Depending on
these characteristics, different ecosystem’s configurations can be obtained (c) as different levels of biofilm production are
used (h). Low-level synthetic coexistence largely dominates the parameter space. The other parameters are: rC=0.5,r=
0.25, d=0.2andg=0.5.
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In general, creating a biofilm is a costly process that runs against the engineered strain, except when it pro-
vides a shelter against environmental fluctuations (Figure 4A). The biofilm acts as an extra component
(ECM, for extracellular matrix) that both limits the growth of the synthetic strain (because of metabolic
trade-offs) while it reduces its effective death rate.
The equations used in our model (which, as before, is nonspatial) are defined as follows:
dB
dt =hSdB
dS
dt =rð1BÞSd
1+gBSSFðS;fCkgÞ
dCk
dt =rk
cCkdkCkCkFðS;fCkgÞ
where the biofilm is represented by the state variable Bproduced at hrate by the synthetic strain S.The
positive effects of Bare introduced by the parameter g. Specifically, the gBin the equation for dS=dt in-
troduces an inability to reduce mortality. At the same time, Shave to compete for the resources with the
original community C. Following the assumption of constant population constraint used above and
assuming homogeneous constant rates for the resident species, it can be easily shown that the resulting
two-dimensional model reads (using C+S=1):
dB
dt =hSdB
dS
dt =Sð1SÞðrð1BÞrC+dð1GðBÞÞÞ
where GðxÞstands for the function
GðxÞ=1
1+gx(Equation 19)
The fixed points of this system, are: (i) X
0=ð0;0Þ, (ii) X
1=ð1;h=dÞ, and a coexistence attractor with a rather
cumbersome expression (see SM). From the previous equations, if we assume a fast dynamics of the biofilm
variable (dB=dtz0) then, we obtain the following expression for B,namely
Bzh
dS(Equation 20)
which allows us to obtain our final, one-dimensional form for the synthetic population dynamics:
dS
dt =½rð1yÞrc+dð1GðyÞÞð1SÞS(Equation 21)
with y=hS=d. The three fixed points are: (i) S=0, (ii) S=1 and a pair of lengthy coexistence points (iii)
S
G(see SM for their exact form).
The stability analysis indicates that the synthetic will fail to establish if
lð0Þ=rrC<0 (Equation 22)
i.e., the extinction of the synthetic strain depends on being unable to replicate as fast as the resident com-
munity (C). Moreover, the stability of the synthetic exclusion fixed point depends on the sign of
lð1Þ=rCd0
B
@11
1+hg
d1
C
A(Equation 23)
In this case, dominance of the synthetic is obtained if the rate of biofilm production is greater than a critical
value hc,where
hc=d
g 11
1rC
d!(Equation 24)
For this system, the potential function Vinvolves also a lengthy expression (see SM) but its behavior can be
appreciated in Figures 4B and 4C where we display how it changes with the biofilm production parameter h
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is increased. As we can see, a low-Scoexistence attractor dominates the parameter space, where higher
ECM production, along with the nonlinear interactions associated with biofilm formation, provides the
desired firewall for the synthetic population. As mentioned earlier, one relevant application of this EFW
is environmental bioremediation, where our synthetic strain would help with the removal hazardous com-
ponents (from drugs in waste waters to heavy metals) that would end up safely stored in the extracellular
matrix of the biofilm (see [Tran and Prindle, 2021] and references cited).
Indirect cooperation firewall
Let us consider now an important scenario connected to many instances of environmental
degradation associated with toxic spills. In many cases, such spills can extend over long periods of
time, damaging life in persistent ways. Each year, thousands of chemical spills occur in many places
all around the planet.
Toxic spills are particularly damaging to coastal waters and rivers. Their effects are many and affect diverse
scales. They kill wildlife, damage habitat and can wreak havoc on local economies as key resources (such as
fish) become contaminated. Very often, the costs of dealing with the cleaning of large spills can be stag-
gering. Moreover, another large class of related problems is eliminating pharmaceuticals and endocrine
disruptors from trophic chains [de Lorenzo, 2017]. The ecological firewall described here would be ad-
dressed to these class or environmental problems.
The EFW includes three main compartments, sketched in Figure 5A. These are, as before, the resident
community C(here the extant set of species representing the contaminated ecosystem), the synthetic
strain Sand the toxin concentration T. Here we consider that the toxin has no negative effects on the
synthetic strain (a parameterized rate only shifts the location of threshold values). This could be the
case, for example, of a synthetic bacterium that uses the toxin as a carbon source.
The EFW is in this case described by the following set of equations:
d
dt T=rbTS dT
dS
dt =rSdSSFS;Cj
dCk
dt =rk
chkTCkdkCkCkFS;Cj
Figure 5. Indirect cooperation firewall
In (a) we summarize the EFW motif. Interactions between the original community (C) and the synthetic organism (S)are
coupled by the degradation of an incoming toxin (T) resulting, for example, from a toxic spill (b, photograph by Gerry
Broom). Here the toxin input rate is our relevant parameter (r). The amount of toxin is minimal when the complete
ecosystem is synthetic and maximum when the whole ecosystem is the original community. The system displays full
coexistence behavior all over the isr axis, as shown in the normalized potential (c-d). The parameters used here are h=1.0,
b= 20.0, rC=1.0,r=0.9,andd=0.1.
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As defined by the model, the toxin enters the ecosystem at a rate rand is actively removed by the synthetic
strain at a rate b. The parameter set hkweights the negative impact of Ton each species within the com-
munity.Wewilllookatras our key parameter to test its impact on the kinds of attractors displayed by the
model.
What kind of ecological interaction class is involved here? Let us notice an important feature of this EFW:
because the synthetic strain has an inhibitory effect on the toxin (driving its removal) Ti tself inhibits the resi-
dent community C, the overall effect of Son Cis positive. This is known within ecology as an indirect mutu-
alism [Morin, 1999]. In other words, the net exchange between the engineered strain and our resident spe-
cies is a cooperative one. Besides, we have seen from previous examples that mutualism provides the basis
for stable coexistence. Is that the case here?
Using the constant population constrain (S+P
k
Ck=1) and the assumption of homogeneous coefficients,
FðS;CÞis simply
FðS;CÞ=rc+ðrrcÞShTð1SÞd(Equation 25)
which allows us to write down the reduced system
Figure 6. Parameter spaces and phases for the EFW
In these five phase diagrams we display the domains associated with the three phases allowed by the minimal one-dimensional models. The parameters
associated with each EFW are the following: a resource-consumer (d=0.1,h=0.5,andr= 0.1), b synthetic mutualistic (ε= 1.0), c synthetic parasitism (h=
0.01), dself-containment (d= 0.25, and g=0.5),andesynthetic indirect cooperator (b= 20.0, h=1.0,d= 0.1). The original community growth rate is fixed at
rC= 0.5. The diagrams on the upper right sketch the qualitative classes of potential functions associated with each phase.
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dT
dt =rbTS dT
dS
dt =Sð1SÞðrrc+hTÞ
As a final step, we consider the scenario where the toxin concentration has a rapid equilibration dynamics.
Using dT=dtz0, our one-dimensional model reads:
dS
dt =Sð1SÞrrc+rh
bS+d(Equation 26)
Once again the two fixed points S=0andS=1 are present, along with a coexistence point
S=1
brh
rcrd(Equation 27)
which will be positive provided that the rate of toxin production is:
r>r
c=ðrcrÞd
h(Equation 28)
which will be always fulfilled if rc>r. In this case, as illustrated by Figures 5C and 5D, the potential function
V(see SM for its specific form) displays a single valley associated with a stable coexistence point for all the
range of toxin input values.
The mathematical nature of this EFW is responsible for the dominant presence of a stable coexistence
state. Because of the mutualistic input created through the chain of inhibitions
SxTxC(Equation 29)
the synthetic strain helps to sustain the community while performing the toxic degradation task. These are
two faces of the same motif, and illustrate the importance of considering the effects of nonlinear dynamics
on the performance of designed strains. The improved removal of the toxin with increasing production
rates is the obvious, direct consequence of the designed EFW. In addition, the ecological, systems-level
features of population interactions are responsible for another, highly desirable outcome: maintaining
biodiversity in place. Notice that, if the toxin levels decay, the synthetic strain also decays but remains pre-
sent at low population values. In this way, we define an EFW that promotes diversity while also sustaining
the engineered strain in place when toxin levels are very small.
This EFW (as well as others described in this paper) can be connected with other designs, such as the bio-
film formation system. In this context, removal of toxins by biofilms (as we pointed out) would be part of a
more complex consortium of designed bacteria. Future work sh ould consider both the spatial extensions of
these basic EFW as well as potential combinations.
DISCUSSION
In this paper we suggest an alternative path to containment strategies for synthetic microorganisms. The
usual approach is based on a top-down gene circuit design, where biosafety is designed at the cellular
level and different mechanisms for constraining spread as well as horizontal gene transfer have been sug-
gested. These include the use of host-construct dependencies, conditional plasmid replication or the
requirement for a specific metabolite to be present. Although there is no doubt that refactoring of
the existing genetic code offers many advantages, our suggestion provides a different (bottom-up)
source of control.
Although there is a general view of engineered microorganisms as similar to invasive species that can cause
major harm, invasion ecology shows us that in fact the success of invaders—when they succeed—is very
often limited [Davis, 2012;Lockwood et al., 2013;Hui and Richardson, 2017]. Most invaders that end up
as members of the resident community remain reduced to smal l populations. The community thus is shifted
to a new ecological state where diversity is maintained. In the previous examples, we have shown—using
some assumptions—that the global state achieved by the engineered community can be made compatible
with coexistence between the synthetic and the resident species poo l. It is even possible to design the EFW
in such a way that coexistence occurs at low levels of the engineered microorganisms. In most cases, the
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presence of a toxic component largely influences the attractor state, and once removed, the synthetic can
go away with it or remain at very low concentrations.
Each EFW shows three basic ‘‘phases’’ associated with the failure of the synthetic (S=0), its dominance
(S=1) and, more g enerally, its coexistence (S˛ð0;1Þ). The results described in previous sections are sum-
marized for the five EFW, in Figure 6. The qualitative form of the potential function Vfor each phase is also
depicted (top right picture). In each case (a-e) the parameter space includes the difference between repli-
cation rates of the synthetic versus the resident (i.e., rrcalong with the control parameter chosen for
each example. The success of each design is captured by the sum of two phases: the coexistence phase
and the community replacement phase (yellow). Coexistence is feasible in a large area for all EFW and
particularly dominant for the mutualistic and stigmergic firewalls. It is important to remind that, since we
consider that each synthetic interacts with Cas a whole (i.e., with all members of the resident community)
the full community replacement would be in reality an artifact of this assumption. In a realistic description,
where interactions are necessarily limited to a subset of the ecosystem, the impact of the synthetic would
also be limited.
Although our ecological, systems-level EFW approachis presented here by contrast with genetic firewalls,they
are certainly not exclusive strategies. One can benefit from the other to make interventions as controllable as
possible. Moreover, we are assuming that the synthetic is highly robust and able to thrive under the given con-
ditions offered by the resident network and the surrounding environment. This might well not be the case,
as illustrated by the failure of exotic strains when they invade microbial communities. Transient phenomena
can be at work and our previous EFW might need to consider the possibility of a limited time span before
the synthetic strain decays. This can be an advantage to add further control to the system and also reminds
us that many invaders in the wild have a dynamic of early expansion followed by extinction or major decay.
We have not pointed toward specific genetic circuits underlying the EFW designs, such connections can be
made and tentative classes of circuit designs could be proposed. Such a connection between genetic
circuits and population responses has been done for example within the context of synthetic collective
intelligence [Sole
´et al., 2016]. In this context, we can also envision more sophisticated EFW that use multi-
cellular consortia using different, communicating strains. Finally, in relation to the mathematical modeling
used here, future work will require the use of stochastic dynamics, a better and finer definition of models
including other relevant variables (perhaps considering a more realistic introduction of community struc-
ture), the explicit use of spatial degrees of freedom and additional efforts to calibrate relevant parameters.
Moreover, the nature of the physical environment and the integration between species and their material
worlds deserves special attention. An important case study that illustrates this point is provided by soils and
the emergent patterns that arise from the interaction between plants, the soil microbiome and abiotic soil
characteristics [Crawford et al., 2011]. To a large extent, soils can be understood in fact as extended com-
posite phenotypes, i. e. as expressions of the effects of genes through the effects of organisms [Phillips,
2009;Neal et al., 2020].
Above all, these EFW should be used to test future terraformation strategies under a microcosm/meso-
cosm framework, where the presence and reliability of the previous attractor states could be studied under
realistic conditions.
Limitations of the study
The models developed here are limited to a specific mathematical framework, namely deterministic,
continuous sets of differential equations. Because we adopt a low-dimensional description of each ecolog-
ical firewall, the heterogeneous nature of realistic ecosystems is not included, nor their intrinsic stochastic-
ity. Prospective studies should also consider the use of these ingredients along with a connection between
the cell/circuit level of design and its impact on large-scale population dynamics.
STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:
dKEY RESOURCES TABLE
dRESOURCE AVAILABILITY
BLead contact
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BMaterials availability
BData and code availability
dMETHOD DETAILS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.isci.2022.104658.
ACKNOWLEDGMENTS
The authors thank Nuria Conde-Pueyo, Josep Sardanye
´s, James Sharpe, Ian Malcolm, Fernando Maestre,
Miguel Berdugo, Daniel Amor, Victor Maull, and Victor de Lorenzo for discussions and support regarding
the CSL Terraformation project. R.S. thanks the members of the Madonna consortium for stimulating dis-
cussions. B.V. thanks Regis Ferreire and Roy Sleator as well as the other participants to ‘‘Life in closed sys-
tems’’ at the ASU Biosphere 2 facility for their useful comments. We also thank Santa Fe Institute where this
work was initiated. B.V. and R.S. have been funded by the PR01018-EC-H2020-FET-Open MADONNA proj-
ect. R.S. thanks the FIS2015-67616-P grant, and the support of Secretaria d’Universitats i Recerca del De-
partament d’Economia i Coneixement de la Generalitat de Catalunya. B.V. has been also funded by grant
RYC-2017-22243.
AUTHOR CONTRIBUTIONS
R.S. conceived the project and wrote the manuscript. B.V. performed the numerical simulations and data
analysis. Both authors contributed to the development of the models, theory, and conceptualization.
DECLARATIONS OF INTERESTS
The authors declare no competing interests.
Received: January 7, 2022
Revised: April 30, 2022
Accepted: June 17, 2022
Published: July 15, 2022
REFERENCES
Alam, I., Aalismail, N., Martin, C., Kamau, A.,
Guzma
´n-Vega, F.J., Jamil, T., Momin, A.A.,
Acinas, S.G., Gasol, J.M., Arold, S.T., et al. (2020).
Rapid evolution of plastic-degrading enzymes
prevalent in the global ocean. Preprint at bioRxiv.
https://doi.org/10.1101/2020.09.07.285692.
Amor, D.R., Montan
˜ez, R., Duran-Nebreda, S.,
and Sole
´, R. (2017). Spatial dynamics of synthetic
microbial mutualists and their parasites. PLoS
Comput. Biol. 13.https://doi.org/10.1371/
journal.pcbi.1005689.
Arnold, V.I. (2013). Mathematical Methods of
Classical Mechanics (Springer-Verlag).
Atlas, R.M. (1978). Microorganisms and
petroleum pollutants. Bioscience 28, 387–391.
https://doi.org/10.2307/1307454.
Benner, S.A., and Sismour, A.M. (2005). Synthetic
biology. Nat. Rev. Genet. 6, 533–543.
Bowker, M.A. (2007). Biological soil crust
rehabilitation in theory and practice: an
underexploited opportunity. Restor. Ecol. 15,
13–23. https://doi.org/10.1111/j.1526-100x.2006.
00185.x.
Brophy, J.A.N., and Voigt, C.A. (2014). Principles
of genetic circuit design. Nat. Methods 11,
508–520.
Budisa, N. (2014). Xenobiology, new-to-nature
synthetic cells and genetic firewall. Curr. Org.
Chem. 18, 936–943. https://doi.org/10.2174/
138527281808140616154301.
Nemhauser, J.L., Torii, K.U., and Collins, J.J.
(2014). A brief history of synthetic biology. Nat.
Rev. Microbiol. 12, 381.
Cleves, P.A., Strader, M.E., Bay, L.K., Pringle, J.R.,
and Matz, M.V. (2018). Crispr/cas9-mediated
genome editing in a reef-building coral. Proc.
Natl. Acad. Sci. USA 115, 5235–5240. https://doi.
org/10.1073/pnas.1722151115.
Coleman, M.A., and Goold, H.D. (2019).
Harnessing synthetic biology for kelp forest
conservation
1
. J. Phycol. 55, 745–751.
Conde-Pueyo, N., Vidiella, B., Sardanye
´s, J.,
Berdugo, M., Maestre, F.T., de Lorenzo, V., and
Sole
´, R. (2020). Synthetic biology for
terraformation lessons from mars, earth, and the
microbiome. Life 10, 14. https://doi.org/10.3390/
life10020014.
Costello, E.K., Stagaman, K., Dethlefsen, L.,
Bohannan, B.J.M., and Relman, D.A. (2012). The
application of ecological theory toward an
understanding of the human microbiome.
Science 336, 1255–1262.
Crawford, J.W., Deacon, L., Grinev, D., Harris,
J.A., Ritz, K., Singh, B.K., and Young, I. (2012).
Microbial diversity affects self-organization of the
soil–microbe system with consequences for
function. J. R. Soc. Interface 9, 1302–1310.
Davies, J.A. (2008). Synthetic morphology:
prospects for engineered, self-constructing
anatomies. J. Anat. 212, 707–719.
Davis, M.A. (2012). Invasion Biology (Oxford
University Press).
de Lorenzo, V. (2008). Systems biology
approaches to bioremediation. Curr. Opin.
Biotechnol. 19, 579–589. https://doi.org/10.1016/
j.copbio.2008.10.004.
de Lorenzo, V. (2017). Seven microbial bio-
processes to help the planet. Microb. Biotechnol.
10, 995–998.
de Lorenzo, V., Marlie
`re, P., and Sole
´, R. (2016).
Bioremediation at a global scale: from the test
tube to planet earth. Microb. Biotechnol. 9,
618–625.
De Roy, K., Marzorati, M., Van den Abbeele, P.,
Van de Wiele, T., and Boon, N. (2014). Synthetic
microbial ecosystems: an exciting tool to
understand and apply microbial communities.
Environ. Microbiol. 16, 1472–1481.
ll
OPEN ACCESS
iScience 25, 104658, July 15, 2022 17
iScienc
e
Article

DeLisi, C., Patrinos, A., MacCracken, M., Drell, D.,
Annas, G., Arkin, A., Church, G., Cook-Deegan,
R., Jacoby, H., Lidstrom, M., et al. (2020). The role
of synthetic biology in atmospheric greenhouse
gas reduction: prospects and challenges.
BioDesign Research 2020, 1–8. https://doi.org/
10.34133/2020/1016207.
Duran-Nebreda, S., Pla, J., Vidiella, B., Pin
˜ero, J.,
Conde-Pueyo, N., and Sole
´, R. (2021). Synthetic
lateral inhibition in periodic pattern forming
microbial colonies. ACS Synth. Biol. 10, 277–285.
Elowitz, M., and Lim, W.A. (2010). Build life to
understand it. Nature 468, 889–890.
Flemming, H.-C., Wingender, J., Szewzyk, U.,
Steinberg, P., Rice, S.A., and Kjelleberg, S. (2016).
Biofilms: an emergent form of bacterial life. Nat.
Rev. Microbiol. 14, 563–575.
Foo, J.L., Ling, H., Lee, Y.S., and Chang, M.W.
(2017). Microbiome engineering: current
applications and its future. Biotechnol. J. 12,
1600099. https://doi.org/10.1002/biot.
201600099.
Gloag, E.S., Javed, M.A., Wang, H., Gee, M.L.,
Wade, S.A., Turnbull, L., and Whitchurch, C.B.
(2013). Stigmergy: a key driver of self-
organization in bacterial biofilms. Commun.
Integr. Biol. 6, e27331. https://doi.org/10.4161/
cib.27331.
Gloag, E.S., Turnbull, L., Javed, M.A., Wang, H.,
Gee, M.L., Wade, S.A., and Whitchurch, C.B.
(2016). Stigmergy co-ordinates multicellular
collective behaviours during myxococcus xanthus
surface migration. Sci. Rep. 6.https://doi.org/10.
1038/srep26005.
Goold, H., Wright, P., and Hailstones, D. (2018).
Emerging opportunities for synthetic biology in
agriculture. Genes 9, 341. https://doi.org/10.
3390/genes9070341.
Grozinger, L., Amos, M., Gorochowski, T.E.,
Carbonell, P., Oyarzu
´n, D.A., Stoof, R.,
Fellermann, H., Zuliani, P., Tas, H., Gon
˜i-Moreno,
A., et al. (2019). Pathways to cellular supremacy in
biocomputing. Nat. Commun. 10.https://doi.
org/10.1038/s41467-019-13232-z.
Großkopf, T., and Soyer, O.S. (2014). Synthetic
microbial communities. Curr. Opin. Microbiol.
18, 72–77. https://doi.org/10.1016/j.mib.2014.
02.002.
Holling, C.S. (1973). Resilience and stability of
ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23.
https://doi.org/10.1146/annurev.es.04.110173.
000245.
Hui, C., and Richardson, D.M. (2017). Invasion
Dynamics (Oxford University Press).
Jaramillo, A. (2017). Synthetic biology:
engineered stable ecosystems. Nat. Microbiol. 2.
https://doi.org/10.1038/nmicrobiol.2017.119.
Johns, N.I., Blazejewski, T., Gomes, A.L., and
Wang, H.H. (2016). Principles for designing
synthetic microbial communities. Curr. Opin.
Microbiol. 31, 146–153. https://doi.org/10.1016/j.
mib.2016.03.010.
Kassinger, S.J., and van Hoek, M.L. (2020). Biofilm
architecture: an emerging synthetic biology
target. Synth. Syst. Biotechnol. 5, 1–10.
Han, S.W., Wang, B., and Yoshikuni, Y. (2021).
Microbiome engineering: synthetic biology of
plant-associated microbiomes in sustainable
agriculture. Trends. Biotechnol. 39, 244.
Lee, J.W., Chan, C.T.Y., Slomovic, S., and Collins,
J.J. (2018). Next-generation biocontainment
systems for engineered organisms. Nat. Chem.
Biol. 14, 530–537.
Leggieri, P.A., Liu, Y., Hayes, M., Connors, B.,
Seppa
¨la
¨, S., O’Malley, M.A., and Venturelli, O.S.
(2021). Integrating systems and synthetic biology
to understand and engineer microbiomes. Annu.
Rev. Biomed. Eng. 23, 169–201.
Levin, R.A., Voolstra, C.R., Agrawal, S., Steinberg,
P.D., Suggett, D.J., and van Oppen, M.J.H.
(2017). Engineering strategies to decode and
enhance the genomes of coral symbionts. Front.
Microbiol. 8, 1220. https://doi.org/10.3389/
fmicb.2017.01220.
Levine, J.H., Lin, Y., and Elowitz, M.B. (2013).
Functional roles of pulsing in genetic circuits.
Science 342, 1193–1200.
Lim, W.A. (2010). Designing customized cell
signalling circuits. Nat. Rev. Mol. Cell. Biol. 11,
393–403.
Lockwood, J.L., Hoopes, M.F., and Marchetti,
M.P. (2013). Invasion Ecology (Wiley-Blackwell).
Macı
´a, J., Posas, F., and Sole
´, R.V. (2012).
Distributed computation: the new wave of
synthetic biology devices. Trends. Biotechnol. 30,
342–349.
Maestre, F.T., Sole
´, R., and Singh, B.K. (2017).
Microbial biotechnology as a tool to restore
degraded drylands. Microb. Biotechnol. 10,
1250–1253.
Marris, E. (2013). Rambunctious Garden: Saving
Nature in a Post-wild World, Bloomsbury.
Mee, M.T., and Wang, H.H. (2012). Engineering
ecosystems and synthetic ecologies. Mol. Biosyst.
8, 2470. https://doi.org/10.1039/c2mb25133g.
Morin, P.J. (1999). Community Ecology (John
Wiley & Sons).
Neal, A.L., Bacq-Labreuil, A., Zhang, X., Clark,
I.M., Coleman, K., Mooney, S.J., Ritz, K., and
Crawford, J.W. (2020). Soil as an extended
composite phenotype of the microbial
metagenome. Sci. Rep. 10.https://doi.org/10.
1038/s41598-020-67631-0.
Nikel, P.I., and Lorenzo, V. (2021). Metabolic
engineering for large-scale environmental
bioremediation. Metabolic Engineering 13,
859–890.
Nowak, M.A. (2006). Evolutionary Dynamics:
Exploring the Equations of Life (Belknap Press of
Harvard University Press).
Pascual, J.A., Garcı
´a, C., and Hernandez, T.
(1999). Lasting microbiological and biochemical
effects of the addition of municipal solid waste to
an arid soil. Biol. Fertil. Soils 30, 1–6. https://doi.
org/10.1007/s003740050579.
Pearce, F. (2016). The New Wild: Why Invasive
Species Will Be Nature’s Salvation.
Phillips, J.D. (2009). Soils as extended composite
phenotypes. Geoderma 149, 143–151. https://
doi.org/10.1016/j.geoderma.2008.11.028.
Piaggio, A.J., Segelbacher, G., Seddon, P.J.,
Alphey, L., Bennett, E.L., Carlson, R.H., Friedman,
R.M., Kanavy, D., Phelan, R., Redford, K.H., et al.
(2017). Is it time for synthetic biodiversity
conservation? Trends. Ecol. Evol. 32, 97–107.
https://doi.org/10.1016/j.tree.2016.10.016.
Purnick, P.E.M., and Weiss, R. (2009). The second
wave of synthetic biology: from modules to
systems. Nat. Rev. Mol. Cell. Biol. 10, 410–422.
Regot, S., Macia, J., Conde, N., Furukawa, K.,
Kjelle
´n, J., Peeters, T., Hohmann, S., de Nadal, E.,
Posas, F., Sole
´, R., et al. (2011). Distributed
biological computation with multicellular
engineered networks. Nature 469, 207–211.
Rodrı
´guez Amor, D., and Dal Bello, M. (2019).
Bottom-up approaches to synthetic cooperation
in microbial communities. Life 9, 22. https://doi.
org/10.3390/life9010022.
Ruder, W.C., Lu, T., and Collins, J.J. (2011).
Synthetic biology moving into the clinic. Science
333, 1248–1252.
Rylott, E.L., and Bruce, N.C. (2020). How synthetic
biology can help bioremediation. Curr. Opin.
Chem. Biol. 58, 86–95. https://doi.org/10.1016/j.
cbpa.2020.07.004.
Scheffer, M. (2009). Critical Transitions in Nature
and Society (Princeton University Press).
Scheffer, M., Hosper, S., Meijer, M.-L., Moss,
B., and Jeppesen, E. (1993). Alternative
equilibria in shallow lakes. Trends. Ecol. Evol.
8, 275–279. https://doi.org/10.1016/0169-
5347(93)90254-m.
Schmidt, M., and de Lorenzo, V. (2012). Synthetic
constructs in/for the environment: managing the
interplay between natural and engineered
biology. FEBS (Fed. Eur. Biochem. Soc.) Lett. 586,
2199–2206.
Schmidt, M., and de Lorenzo, V. (2016). Synthetic
bugs on the loose: containment options for
deeply engineered (micro)organisms. Curr. Opin.
Biotechnol. 38, 90–96. https://doi.org/10.1016/j.
copbio.2016.01.006.
Sole
´, R.V. (2011). Phase Transitions (Princeton
University Press).
Sole
´, R. (2015). Bioengineering the biosphere?
Ecol. Complex. 22, 40–49. https://doi.org/10.
1016/j.ecocom.2015.01.005.
Sole
´, R., Amor, D.R., Duran-Nebreda, S., Conde-
Pueyo, N., Carbonell-Ballestero, M., and
Montan
˜ez, R. (2016). Synthetic collective
intelligence. Biosystems 148, 47–61. https://doi.
org/10.1016/j.biosystems.2016.01.002.
Sole
´, R., Fontich, E., Vidiella, B., Duran-Nebreda,
S., Montan
˜ez, R., Pin
˜ero, J., and Valverde, S.
(2017). The paradox of constant oceanic plastic
debris: evidence for evolved microbial
biodegradation?.
Sole
´, R.V., Montan
˜ez, R., and Duran-Nebreda, S.
(2015). Synthetic circuit designs for earth
terraformation. Biol. Direct 10.https://doi.org/10.
1186/s13062-015-0064-7.
ll
OPEN ACCESS
18 iScience 25, 104658, July 15, 2022
iScienc
e
Article

Sole
´, R.V., Montan
˜ez, R., Duran-Nebreda, S.,
Rodriguez-Amor, D., Vidiella, B., and Sardanye
´s,
J. (2018). Population dynamics of synthetic
terraformation motifs. R. Soc. Open Sci. 5,
180121. https://doi.org/10.1098/rsos.180121.
Strogatz, S.H. (1994). Nonlinear Dynamics and
Chaos: With Applications to Physics, Biology,
Chemistry, and Engineering (Perseus Books).
Theraulaz, G., and Bonabeau, E. (1999). A brief
history of stigmergy. Artif. Life. 5, 97–116.
Toda, S., McKeithan, W.L., Hakkinen, T.J.,
Lopez, P., Klein, O.D., and Lim, W.A. (2020).
Engineering synthetic morphogen systems that
can program multicellular patterning. Science
370, 327–331.
Tran, P., and Prindle, A. (2021). Synthetic biology
in biofilms: tools, challenges, and opportunities.
Biotechnol. Prog. 37.https://doi.org/10.1002/
btpr.3123.
van Oppen, M.J.H., and Blackall, L.L. (2019). Coral
microbiome dynamics, functions and design in a
changing world. Nat. Rev. Microbiol. 17, 557–567.
Vidiella, B., Guillamon, A., Sardanye
´s, J., Maull,
V., Pla, J., Conde, N., and Sole
´, R. (2021).
Engineering self-organized criticality in living
cells. Nat. Commun. 12.https://doi.org/10.1038/
s41467-021-24695-4.
Vidiella, B., Sardanye
´s, J., and Sole
´, R.V. (2020).
Synthetic soil crusts against green-desert
transitions: a spatial model. R. Soc. Open Sci. 7,
200161. https://doi.org/10.1098/rsos.200161.
Wang, F., and Zhang, W. (2019). Synthetic
biology: recent progress, biosafety and
biosecurity concerns, and possible solutions.
J. Biosaf. Biosecurity. 1, 22–30. https://doi.org/10.
1016/j.jobb.2018.12.003.
Weber, W., Daoud-El Baba, M., and
Fussenegger, M. (2007). Synthetic ecosystems
based on airborne inter- and intrakingdom
communication. Proc. Natl. Acad. Sci. USA 104,
10435–10440. https://doi.org/10.1073/pnas.
0701382104.
Weber, W., and Fussenegger, M. (2011).
Emerging biomedical applications of synthetic
biology. Nat. Rev. Genet. 13, 21–35.
Widder, S., Allen, R.J., Pfeiffer, T., Curtis, T.P.,
Wiuf, C., Sloan, W.T., Cordero, O.X., Brown, S.P.,
Momeni, B., Shou, W., et al.; Isaac Newton
Institute Fellows (2016). Challenges in microbial
ecology: building predictive understanding of
community function and dynamics. ISME J. 10,
2557–2568.
Wright, J.T., Byers, J.E., DeVore, J.L., and Sotka,
E.E. (2014). Engineering or food? mechanisms of
facilitation by a habitat-forming invasive
seaweed. Ecology 95, 2699–2706. https://doi.
org/10.1890/14-0127.1.
Wu, M.-R., Jusiak, B., and Lu, T.K. (2019).
Engineering advanced cancer therapies with
synthetic biology. Nat. Rev. Cancer. 19, 187–195.
https://doi.org/10.1038/s41568-019-0121-0.
Xiong, J.-Q., Kurade, M.B., and Jeon, B.-H. (2018).
Can microalgae remove pharmaceutical
contaminants from water? Trends. Biotechnol. 36,
30–44.
Zobell, C.E. (1946). Action of microo
¨rganisms on
hydrocarbons. Bacteriol. Rev. 10, 1–49.
Zrimec, J., Kokina, M., Jonasson, S., Zorrilla, F.,
and Zelezniak, A. (2021). Plastic-degrading
potential across the global microbiome
correlates with recent pollution trends. mBio 12.
https://doi.org/10.1128/mbio.02155-21.
ll
OPEN ACCESS
iScience 25, 104658, July 15, 2022 19
iScienc
e
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STAR+METHODS
KEY RESOURCES TABLE
RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources should be directed to and will be fulfilled by the lead con-
tact, Ricard Sole
´(ricard.sole@upf.edu).
Materials availability
No materials were newly generated for this paper.
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No data and codes were newly generated for this paper.
METHOD DETAILS
A more detailed compilation of different alternatives to case studies is provided in the Data S1,relatedto
STAR Methods.
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Software and algorithms
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