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The Multiple Paths to Multiple Life

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We argue for multiple forms of life realized through multiple different historical pathways. From this perspective, there have been multiple origins of life on Earth—life is not a universal homology. By broadening the class of originations, we significantly expand the data set for searching for life. Through a computational analogy, the origin of life describes both the origin of hardware (physical substrate) and software (evolved function). Like all information-processing systems, adaptive systems possess a nested hierarchy of levels, a level of function optimization (e.g., fitness maximization), a level of constraints (e.g., energy requirements), and a level of materials (e.g., DNA or RNA genome and cells). The functions essential to life are realized by different substrates with different efficiencies. The functional level allows us to identify multiple origins of life by searching for key principles of optimization in different material form, including the prebiotic origin of proto-cells, the emergence of culture, economic, and legal institutions, and the reproduction of software agents.
The levels of life. All life-forms follow a simultaneous trajectory within the three parallel state spaces or levels governed by material properties, constraint surfaces, and optimization principles. In L1, each phylogeny illustrates a possible evolutionary trajectory, each of which is associated with a different material origin. A history-centric approach to life equates life with a complete phylogenetic history. Extant-centric approaches seek commonality across the terminal branches of phylogenies. All points in L1 map many-to-one to points in L2. The set of points in L2 describes the space of physical constraints to include the limitations of physical laws. Evolved constraints are the sub-set of points in L2 that we describe as the physics of living systems. All points in L1 and L2 obey action or optimization principles that are defined by the set of points in L3. A small set of optimization principles such as the maximization of fitness and related concepts define the space of living action principles in L3. A principle-centric approach to life defines life in terms of the entry and restriction of a material trajectory within L1 that is constrained in L2 and only moving within the restricted space of living optimization principles in L3. Each material phylogeny in L1 is likely to be different across the universe, but can still map onto similar or identical sets of physical constraints in L2. For example, the blue and red phylogenies in L1 map onto the same set of constraints in L2. These in turn project into the space of the living in L3. In addition, living systems might produce non-living descendants. Here, we have shown in orange how a putative AI might originate from the terminal biotic branch of the green phylogeny and venture outside biology to be governed by the constraints of engineering in L2 and non-living optimization principles in L1. The reverse is also possible where abiotic materials give rise through biotechnology to new biotic life-forms. The non-unique trajectories through L1-L3 allow for the possibility of multiple life (Color figure available online; Image credit: Mesa Schumacher)
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Journal of Molecular Evolution (2021) 89:415–426
https://doi.org/10.1007/s00239-021-10016-2
REVIEW
The Multiple Paths toMultiple Life
ChristopherP.Kempes1· DavidC.Krakauer1
Received: 28 July 2020 / Accepted: 8 June 2021 / Published online: 12 July 2021
© The Author(s) 2021
Abstract
We argue for multiple forms of life realized through multiple different historical pathways. From this perspective, there
have been multiple origins of life on Earth—life is not a universal homology. By broadening the class of originations, we
significantly expand the data set for searching for life. Through a computational analogy, the origin of life describes both the
origin of hardware (physical substrate) and software (evolved function). Like all information-processing systems, adaptive
systems possess a nested hierarchy of levels, a level of function optimization (e.g., fitness maximization), a level of constraints
(e.g., energy requirements), and a level of materials (e.g., DNA or RNA genome and cells). The functions essential to life
are realized by different substrates with different efficiencies. The functional level allows us to identify multiple origins of
life by searching for key principles of optimization in different material form, including the prebiotic origin of proto-cells,
the emergence of culture, economic, and legal institutions, and the reproduction of software agents.
Introduction: Life isEverywhere
An ongoing scientific challenge has been to create a general
theory of life that integrates our empirical understanding of
biology with logical principles that might transcend it (Cle-
land 2019; Goldenfeld and Woese 2011; Goldenfeld etal.
2017; Walker etal. 2017; Walker 2017; Davies and Walker
2016; Walker etal. 2018). The search for principles that
are not dependent on evolved constraints and biochemical
materials has been intriguing, but has not yet led to complete
theories of how to identify, quantify, or create life (Langton
1984; von Neumann 1966; Langton etal. 1992, 1994; Küp-
pers 1990; Yockey 2005; Walker and Davies 2013). Meet-
ing this challenge would help to address several of the most
interesting questions facing the natural sciences and biology
in relation to questions of generality and universality. These
would include the following: (1) how do biotic mechanisms
emerge from abiotic ones, (2) how can we be sure that we
have found life if it is materially different from life on Earth,
and by extension, how do we verify that an environment is
truly lifeless, for example, in a sample of ice from Encela-
dus?, and (3) how do we in general understand the range of
possibilities for the origin and maintenance of life?
From an evolutionary perspective, the central challenge
for defining life has been the need to make a distinction
between describing known evolutionary trajectories while
establishing a full possibility space for life (Scharf etal.
2015). No one wants to restrict the science of life to one
current realization on Earth, and prior work has exhorted
origins of life researchers to study “the onset of the various
organizational phenomena that we associate with the living
world” (Scharf etal. 2015). We define life as the union of
two crucial energetic and informatic processes producing
an autonomous system that can metabolically extract and
encode information from the environment of adaptive/sur-
vival value and propagate it forward through time (Krakauer
etal. 2020). We provide a new perspective on the origin of
life by arguing that life has emerged many times on Earth
and that there are many forms of extant life coexisting on a
variety of physical substrates. To help explain this position,
we organize theories of life into three dominant perspectives:
extant centric, history centric, and principle centric.
The Extant-centric approach focuses on characteris-
tics and comparisons among existing life. This was the
first focus of biology as a discipline. The History centric
focuses on the specific evolutionary trajectories that lead
to extant life including Earth’s specific origin of life and its
conserved molecular traits. The Principle centric focuses
Handling Editor: Aaron Goldman.
* Christopher P. Kempes
ckempes@santafe.edu
David C. Krakauer
krakauer@santafe.edu
1 The Santa Fe Institute, SantaFe, NM, USA
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416 Journal of Molecular Evolution (2021) 89:415–426
1 3
on generalizations of life in terms of shared properties of
all possible evolutionary trajectories and all possible ori-
gins of life. In each case, a focus should be interpreted as
a perspective that prioritizes a certain style of work and
effort.
Most agree with the need for moving from a extant- or
history-centric perspective on life to a principle-centric
one. However, this perspective remains under-explored—
for practical reasons—and its implications have not been
fully appreciated. The natural tendency is to associate life
with Earth life, often restricting mechanisms supporting
life to those mechanisms universal across Terran species,
and, as has recently been discovered, organisms that share a
common molecular ancestor. From a living-principles-first
perspective, life can be defined independently from its con-
tingent evolutionary history in terms of a suite of adaptive
functions. For example, the way that macroscopic functions
or features of organisms can be understood independently
from their molecular or developmental mechanisms (e.g., as
exemplified by the optimal properties of a variety of vascular
networks of plants and mammals in Savage etal. 2004; West
and Brown 2005). And by analogy, the way that effective
software can be described using a logic that is different, and
in many cases independent, from the details of its hardware
support.
This view of life naturally opens up the possibilities for
many origins in many different systems. It is also a view-
point that revives a classic natural history perspective that
categorizes biology by form and function in distinction to
the modern evolutionary synthesis and molecular biology
revolution that categorizes life based on lineage. While
these earlier perspectives lack the unifying framework of
evolution by natural selection, they recognized functional
similarities and what we think of in terms of surprising bio-
logical homoplasy. We wish to generalize these similarities
into ingredients for a theory of life. It could be that a focus
on the evolution of life has blinded us to additional general
principles of life.
For the principles-centric definition of life, there may be
many origins of different types of life along an evolutionary
trajectory. Some trajectories may even transition from liv-
ing to non-living optimized states before giving rise to life
again. We would argue that autonomous digital computers
are an example of this possibility: they are created by life
initially as non-living information-processing machines, but
may later provide the substrate for new types of life such
as through evolutionary simulations, a rather rudimentary
example, and autonomous A.I., a more complicated exam-
ple. Importantly, computers might eventually expand our
conception of life where the human-transistor system in
aggregate resides within the space of the living andwhere
neither could persist independently akin to many extant obli-
gate mutualisms.
Somewhat surprisingly, this approach suggests that con-
trary to the wide-spread belief that life has a single chemical
origin and basis (history-centric), life has in fact evolved
many times on Earth. Biological life at the biochemical level
might have a unique provenance, but higher-level aggrega-
tions with emergent living features do not.
This forces us to distinguish between the idea of an origin
and the fact of a first occurrence. This relates very naturally
to the evolutionary concepts of analogy and homology. Life
itself is typically considered ancestral to all of biology and
thereby the ultimate homology, whereas we argue somewhat
counter-intuitively that life should be thought of as analo-
gous, or more technically as homoplastic—a set of traits that
have been gained or lost independently in separate lineages
over the course of evolution. Life should be thought of as a
special class of convergent evolution. The multiple origins
of life on Earth happen to have a common historical trajec-
tory in LUCA. As has been noted (Walker 2020), if new life
were created in a computer or in a laboratory, those specific
substrates are setup by humans and create a causal link with
LUCA.
Scharf etal. (2015) first presented an argument along
similar lines to those here by proposing a classification of
life based on historical, synthetic, and universal properties,
with subfields defined by the overlap among these catego-
ries. They suggest convincingly that there could be many
paths from an abiotic to biotic Earth with various potential
bottlenecks, convergences, and branching points. We add the
many multiple transitions from the living to the non-living
and back to the living (e.g., from modern human society to
solid-state devices to software-based computer viruses). And
that these multiple transitions take place over a range of dif-
ferent levels in the life hierarchy. This implies that there is
a huge richness of types of life that emerge at the principles
(or universal as in Scharf etal. 2015) level, and that there
are already observations of multiple origins of life on Earth
when we adopt the appropriate theoretical lens, to include
many products of cultural evolution. This is distinct from
the perspective that characterizing life is “not in explaining
the states themselves, but instead the paths” (Walker 2017)
as we are interested in theories that identify the homoplasy
of evolutionary endpoints.
A Spectrum ofLiving Processes
The definition of life as an autonomous system that can
metabolically extract and encode information from the
environment of adaptive/survival value and propagate it
forward through time does not make use of ideas of replica-
tion or compartmentalization but builds on recent efforts
to place categorical features of life, such as individual-
ity, onto a quantitative spectrum. The key idea is to relate
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417Journal of Molecular Evolution (2021) 89:415–426
1 3
life to information theoretic measures of autonomy which
describes the information in a system’s past that is trans-
mitted independently of the environment into the system’s
future (Krakauer etal. 2009, 2020). In this way, life is able to
encompass a variety of evolving systems, all of which can be
recognized by their ability to efficiently and reliably propa-
gate adaptive information from the past into the future. We
do not define life as any evolving system because many of
these will not possess autonomy or individuality but obtain
their functional features entirely through external constraints
and design (e.g., simple rolling stones that reduce friction
through erosion or complicated examples of human built
architecture from pyramids to sky scrapers).
In order to illustrate why such a theory of life needs to be
foundational, consider the following taxonomic spectrum:
virus, bacterium, multicellular animal, ecosystem, planet.
Now ask which of these systems represents life? Almost
every biologist on the planet would agree that the bacte-
rium and the multicellular organism are living. Viruses
have proven more controversail because they possess a
minimum combination of autonomy in metabolic capabil-
ity and coding capacity (e.g. Villarreal 2004). But all of
the arguments that one uses to exclude viruses, are true of
many bacterial species, such as obligate symbionts. What
about individual cells in the multicellular organism, or the
distinction between germline and somatic cells in those same
organisms? Is it only the whole multicellular body that is
alive? Can obligate predators be considered life since their
metabolism is not fully autonomous? If one accepts that both
cell and whole bodies are forms of life, then why wouldn’t
both the individual and ecosystem be a form of life? These
are all well-known debates that highlight how hard it has
been to agree on the discovery of new life that possesses
neither cells or bodies. The use of phosphine as a possible
biosignature has already proven to be a controversial topic
(e.g., Sousa-Silva etal. 2020; Cockell etal. 2020), but harder
debates lurk ahead for life that could look radically different.
The problem is that we cannot agree on the answers to the
question of living bacterium versus virus precisely because
we don’t have a fundamental theory that can quantitatively
assign “livingness” to an autonomous dynamical system.
The problem of relying on lists is that lists never add up to
processes.
In this context it is useful to relate the idea of life to the
idea of computational processes. These connections have
been explored in the setting of general perspectives on life
(Walker and Davies 2013). Here we are not suggesting that
life is a computation but that the division of matter and
logic in universal computation—what has been called “The
Beginning of Infinity” (Deutsch 2011)—is precisely the type
of step that needs to be taken to broaden our study of liv-
ing phenomena and move beyond lists of charactersistics
toward functional processes. This approach also resembles in
several ways the brain-mind and genotype-phenotype binary
oppositions, both of which stress the critical distinction
between the material and the codical or functional domains,
while allowing for significant co-dependencies between the
two. We highlight several recent efforts which introduce a
quantitative spectrum for various categorical features of life,
such as individuality (Krakauer etal. 2009, 2020), agency
(Kolchinsky and Wolpert 2018), or how much assembly an
object requires (Marshall etal. 2017a, 2021; Murray etal.
2018).
Living Across Levels
Our aim is to move toward generalized concepts and metrics
for life rather than the commitment to specific characteristics
or implementations (Goldenfeld and Woese 2011; Golden-
feld etal. 2017; Walker etal. 2017; Walker 2017; Davies and
Walker 2016; Walker etal. 2018). Our strategy is to intro-
duce a layered or multi-level structure for thinking about
life inspired by Marr’s levels of information-processing for
vision (Marr 1982) (a deeper investigation into what sepa-
rates mind from brain and rather like the separation made
between phenotype and genotype). Marr’s approach to dis-
tinguishing layers of information-processing (Marr 1982)
is a useful analogy for illustrating the type of theory that
we want to build, albeit with a greater dependence among
the levels than Marr considered. Marr suggested that all
information-processing architectures possess three essential
levels. A computational or functional level that describes
the computational problem. For example, identifying an
object in a visual scene or isolating odorants in a complex
biochemical mixture. A subvening algorithmic or proce-
dural level that realizes iteratively the desired computation.
For example, deep convolutional neural networks or histo-
grams of oriented gradients. And a foundational hardware-
implementation level that supports the software realizing
a computation. For example, a general purpose computer,
a field programmable gate array, or a graphics processing
unit. All three levels are required, whereas the composition
of each level can be substituted with a working alternative.
Critically each of these levels interacts through fundamental
constraints of architecture and thermodynamics. In Table3,
we explore how we might map between computational and
biological structures and processess at each level.
For life we introduce three comparable levels: an optimi-
sation level; a constraints level, and a material level. These
are outlined in Table1 and defined below. This approach is
justified by the widely held premise that life be understood in
terms of adaptive information. The hierarchy follows directly
from this assumption and makes no strong claim that life is
a computation. Furthermore these ontological levels should
not be confused with physical-spatial levels. For example,
optimization takes place at many physical levels from basic
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418 Journal of Molecular Evolution (2021) 89:415–426
1 3
molecular mechanisms through to ecosystem engineering.
In this way there can be vast numbers of nested realizations
of these three levels. A few examples are listed in Table2.
Level 3: optimization Life is required to maximize fit-
ness, minimize the dissipation of metabolic free energy, effi-
ciently encode adaptive information, and achieve strategic
stability in the face of competitors (e.g., Walker and Davies
2013). The abstract frameworks at this level include the logi-
cal elements of the problem, measures of information, free
energy, algorithmic complexity, and geometry. The biologi-
cal theories that address these frameworks include, popula-
tion and quantitative genetics, evolutionary game theory, and
adaptive dynamics.
Level 2: constraints General principles of the physi-
cal/material world impose largely unavoidable constraints
on what is being optimized at Level 3 (Schrodinger 1944;
Goldenfeld etal. 2017; Goldenfeld and Woese 2011; Walker
2017; Kempes etal. 2019; Bialek 2012; Kaneko 2006;
Walker etal. 2018). These include architecture (dimension,
topology, conservation laws) and design principles. Biologi-
cal theories that touch on these constraints include reaction-
diffusionsystems and pattern formation (Turing 1952), allo-
metric scaling laws (Schmidt-Nielsen and Knut 1984; Niklas
1994; Savage etal. 2004; West and Brown 2005), canaliza-
tion through regulatory interactions, mendelian segregation
and its violations, the central dogma and its violations, and
information aggregation mechanisms to include population
coding and winner take all dynamics.
Level 1: materials The physical and chemical properties
of matter are felt and impose limitations on the scope of
Level 2 and 3. These include much of inorganic and organic
chemistry, principles of kinematics, self-assembly, and bio-
physical laws. Biological theories at this level include the
cell theory, molecular dynamics and protein folding, cell-
sorting dynamics, and a variety of mesoscopic laws such as
Lewis’ law (Lewis 1928).
Figure1 provides an illustration of how these three levels
relate to one another, where one can see clearly interrelated
evolutionary trajectories at each of the three levels. Classic
evolutionary processes are realized in L1 describing the ori-
gin and diversification of lineages. All evolutionary motion
in L1 is constrained by both physical conservation laws (e,g,
conservation of energy) and evolved constraints (e.g., allom-
etry) described as acceptable paths through the space of L2.
And paths through L1 and L2 are guided by principles in
L3 (e.g., natural selection). The extant-centric perspective
on life involves inferences made from comparisons among
all terminal branches of a tree, typically in L1, whereas the
history-centric perspective encompasses an entire evolution-
ary tree in L1. L2 and L3 coarse grain the trajectories in
L1 and L2 and represent a decoherent history of life—that
Table 1 Universal versus
contingent theories at three
levels of analysis
Level in hiearchy Abstract theories Biological theories
Level 3: optimization Variational/action principles Natural selection
Neutral theory
Individuality
Agency
Level 2: constraints Conservation laws Allometric scaling
Geometry and topology Molecular packing
Maximum entropy principle Maximum entropy ecol-
ogy and neuroscience
Pattern formation Reaction-diffusionmor-
phogenesis
Level 1: materials Chemical bonds Protein folding
Chemical kinetics Gene expression dynamics
Table 2 How the mechanism of
encapsulation can be described
at three levels of analysis
Level in hiearchy Example: “Encapsulation”
Computational principle Biological mechanism
Level 3: optimization Logical scope: local/global Compartments/modularity
Algorithms Self-organization
Level 2: constraints Type inference Cross reactivityand
specificity
Function blocks Regulatory circuits
Level 1: materials Protected memory DNA packaging
Transistors Enzymes
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419Journal of Molecular Evolution (2021) 89:415–426
1 3
Fig. 1 The levels of life. All life-forms follow a simultaneous tra-
jectory within the three parallel state spaces or levels governed by
material properties, constraint surfaces, and optimization principles.
In L1, each phylogeny illustrates a possible evolutionary trajectory,
each of which is associated with a different material origin. A his-
tory-centric approach to life equates life with a complete phylogenetic
history. Extant-centric approaches seek commonality across the ter-
minal branches of phylogenies. All points in L1 map many-to-one to
points in L2. The set of points in L2 describes the space of physical
constraints to include the limitations of physical laws. Evolved con-
straints are the sub-set of points in L2 that we describe as the physics
of living systems. All points in L1 and L2 obey action or optimiza-
tion principles that are defined by the set of points in L3. A small set
of optimization principles such as the maximization of fitness and
related concepts define the space of living action principles in L3.
A principle-centric approach to life defines life in terms of the entry
and restriction of a material trajectory within L1 that is constrained
in L2 and only moving within the restricted space of living optimiza-
tion principles in L3. Each material phylogeny in L1 is likely to be
different across the universe, but can still map onto similar or identi-
cal sets of physical constraints in L2. For example, the blue and red
phylogenies in L1 map onto the same set of constraints in L2. These
in turn project into the space of the living in L3. In addition, living
systems might produce non-living descendants. Here, we have shown
in orange how a putative AI might originate from the terminal biotic
branch of the green phylogeny and venture outside biology to be gov-
erned by the constraints of engineering in L2 and non-living optimi-
zation principles in L1. The reverse is also possible where abiotic
materials give rise through biotechnology to new biotic life-forms.
The non-unique trajectories through L1-L3 allow for the possibility
of multiple life (Color figure available online; Image credit: Mesa
Schumacher)
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420 Journal of Molecular Evolution (2021) 89:415–426
1 3
is, families of fine-grained histories in L1 map onto fewer
trajectories or points in L2 and L3.
This framework highlights the complicated connec-
tions among the levels. First, and most simply, the rates
of evolution in each level will drastically differ. Typically,
large changes will occur in L1 that do not change the con-
straints that these materials follow in L2 or the optimi-
zation principles in L3. For example, body mass might
change relatively quickly across generations or taxa, but
the scaling of mass with metabolism will be largely invari-
ant. Contrariwise, small changes in L1 might lead to large
shifts in L3. For example, mutations that influence the
body plan or the rate of mutation can change the way that
selection operates on populations. For example, a genome
can undergo selection for specific GC content by selecting
among synonymous codons with no change to the overall
phenotype, except in the environmental requirements of
the organism (e.g. Mann etal. 2010). This would be a
material constraint of the environment imposing selection
on the genotype where the selection for whole organism
characteristics influences the genotype independently from
the phenotype. The properties of the organism change but
not through the genotype-to-phenotype mapping since that
is preserved at the level of the amino acid coding.
L2and L3 are most directly connected to universal
abstract and mathematical principles and thus the non-
living universe. L2introduces anisotropies and biases on
L3 through energetic and informational constraints, and
while somewhat contingent, these will always appear in one
form or another. L3 principles describes variational princi-
ples, one of which is evolution by natural selection, which
is required by any form of life. And L1is the most path-
dependent, contingent, and constrained by L2 and L3.
Asserting universality at L1would be equivalent to
describing life as uniquely materially realizable, through a
one-to-one mapping from L1 to L2 to L3. This would be
analogous in the cultural domain to studying the evolution of
one language as opposed to the evolution of languages more
broadly. We need to consider some version of all three levels
in order to explain the origins of Igbo, French, or Japanese,
where both physical constraints of sound production and
perception interact with optimization that either minimizes
the time or energy to produce a signal.
A common perspective is that L1 is the most universal
since it is closest to the material basis of the universe which
need obey physical law. For example, Smith and Morow-
itz suggest that core metabolism can be understood as the
most likely autocatalytic network given non-equilibrium
thermodynamic considerations and environmental compo-
sitions, and that these networks are not arbitrary (Smith and
Morowitz 2004; Morowitz and Smith 2007). This makes
the particular combinations at this level, such as a biochem-
istry, exemplary of what all of life is likely to look like.
However, we should be careful to extract the principles from
this example—such as finding the most likely autocatalytic
network conditioned on an environment—and situate those
principles in the huge space of the chemical combinations
of various abiotic environments and planetary conditions in
order to understand the full range of material possibilities .
The most illustrative examples of this hierarchy are the
connections between L1 and L2. For example, life harnesses
many energetic gradients for useful anabolism via many L1
mechanisms. But all of these conform to the laws of thermo-
dynamics and no cell will be found to contain more internal
structure than can be accounted for by the total free energy
available from the environment (Schrodinger 1944; Morow-
itz 1955). This result is well-known and illustrates a general
L2 principle, in this case the laws of thermodynamics, real-
ized on many L1 instances.
As discussed previously, some biological phenomena
require explicit consideration of all three levels. For exam-
ple, allometric scaling laws manifest because of specific L1
architectures under specific L2 constraints, with near perfect
L3 optimization. Indeed, we expect many rich biological
concepts to be defined by a “strange tangle” of the three
levels, because the three levels will unavoidably coevolve.
Similarly, it has been suggested that while all of life’s prop-
erties require material instantiation (L1) and obey energetic
constraints (L2), the classes of informational systems that
emerge (L3) in terms of optimized representation, infor-
mation storage, and processing, obey more general laws
independent of the underlying material aspects (Davies and
Walker 2016; Walker 2017; Krakauer 2017; Krakauer and
Jansen 2002). While life’s information storage and process-
ing systems are often based on different material composi-
tions (material level), each of these achieves greater effi-
ciency or robustness through principles that are very general,
such as error correction, sparse coding, and fractal architec-
tures (Flack 2017; Davies and Walker 2016; Walker 2017;
Krakauer 2017; Krakauer and Jansen 2002; Smith 2008;
Cronin etal. 2006; Kempes etal. 2019).
Within this framework we would define life as certain
hyper-regions of L3. All of which need to be able to support
adaptive histories. The shape of these hyper-regions may be
quite tortuous and there may be non-overlapping regions that
each represent life, but the main idea is that we want to allow
for scenarios where something can be defined as living with
various combinations of values along the high-dimensional
axes of L3. For example, something could be far out on the
“intelligence” or information capacity axis, but close to the
origin on the “robustness” axis and still be counted as living.
Something else could have relatively minimal intelligence
and have very high robustness and also be living. The goal
of future work is to identify the high-dimensional surface of
minimum requirements for life in L3.
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421Journal of Molecular Evolution (2021) 89:415–426
1 3
Universal Life Analogized toUniversal Computation
In considering principles-centric perspectives on life, a use-
ful analogy to make is to the idea of computation and its
somewhat scale-independent features. It is perfectly accu-
rate to say that transistors compute, CPUs compute, and
computer networks compute. Every one of these performs a
function, realized by an algorithm, supported by hardware.
Every element in this list possesses all levels L1–L2–L3. In
every case we are applying the same L3 logical principle
(traditionally the Church–Turing principle (Smith 2020))
and at each level we observe a different range of L1–L2
computational power, efficiency, constraints, and range of
applications (Davis 2018).
We acknowledge that without the lowest physical element
many of the higher-order structures would not exist. Indeed,
all of L3 can only exist on physical matter. And in particular
environments defined by specific L2 constraints there may
be very narrow ranges of L1 that allow an L3 to be realized.
But, we do not say that only transistors compute and that all
higher-order computations are merely downstream instances
of the binary operations of a transistor. Every level can be
understood as a computation to the extent that each level
can be described in the language of L3 somewhat indepen-
dently of the language of L1–L2. Not allowing for this would
represent an extreme form of computational reductionism
and severely limit the scope of both hardware and software
engineering—your PC is every bit as much a computer as
its logic gates, they just compute different functions, and the
same idea generalizes to the network of computers forming
an internet. This physical hierarchy is critical to effective
scientific computation (Brandt 2002).
Furthermore, at this point we also distinguish, as others
have (Walker and Davies 2013), between two broad classes
of computer—analog and digital—which differ with respect
to both hardware and software and reflect a fundamental dif-
ference of design in their use of continuous versus discrete
variables and differential versus discontinuous hardware
elements—differences in L1 and L2. Nevertheless, both are
able to realize the property of Turing completeness (Bournez
etal. 2013) the critical feature at L3.
Tracking this analogy back to life we should not confuse
microscopic material properties with macroscopic logical
capabilities. Or the first occurrence of a living mechanism
with the origin of alternative living mechanisms. By avoid-
ing these traps we might identify the many cases where
“life” has evolved and the common conditions that support
every instance. We should also be comfortable with one type
of life living upon another. Proposing that cultural evolution
is a type of life implemented on a collection of humans is not
radically different from considering a Turing complete soft-
ware or internet implemented on several Turing complete
computers or even Turing incomplete computers.
A key idea is the need to focus on “the separation of phys-
ical embodiment from ability” and on whether a system can
imitate cellular function (similar to another computational
analogy, the Turing test) independent of size and composi-
tion (Cronin etal. 2006). While we support this perspective
our argument makes a distinction between the theoretical
challenge of agreeing upon and defining the set of living fea-
tures and the experimental challenge of embodying specific
cellular characteristics in various materials.
The computational example also helps to illustrate the
interrelation of the levels. If one wants to implement a spe-
cific algorithm on a specified scale of data with a desired
runtime, then there will be serious requirements for an L1
that can dissipate enough heat to avoid melting components.
This could manifest as both architecture and materials solu-
tions under a dominant L2 constraint of heat dissipation.
Similarly, if cells want to avoid the error threshold at a par-
ticular temperature this may constrain which molecules can
be used for information storage. There will be certain types
of L1 that can only be understood from the perspective of
what L3 principle they are implementing and under what
L2 constraints they have been subject to. The signature of
life in L1 requires conditioning on a specific L3 and L2. The
trick of spotting life is to realize that a general L3 principle
is being implemented on an L1 material and that the par-
ticular implementation reflects a set of L2 constraints. L1
becomes a special type of material when L3 optimizations
occur under specific L2 constraints. Some of these corre-
spondences are described in Table2.
Hardware, Software, Mechanisms, andFunctions
Computer science is not hardware independent and is much
concerned with the hardware requirements of particular
algorithms, or the construction of algorithms given hard-
ware constraints (Steiner and Athanas 2005). Distinguishing
between hardware and software provides for synergies such
as the use of GPUs to support deep learning architectures
and training. The universality of computer languages cre-
ates a significant degree of freedom when coding a problem.
By analogy, for living systems, we might expect to see
common constraints from L2 intervening on many different
materials and designs. For example, network structures that
most effectively distribute metabolic resources or propagate
information.
This is not, however, a hard constraint or “law” of nature
as different lineages have discovered different means of solv-
ing universal problems. When it comes to life the standard
biological perspective tends to focus on a single or a limited
number of ways of realizing particular biological functions
(e.g., RNA and DNA for heredity, a universal genetic code,
ATP for energy). This viewpoint draws a unique path from
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422 Journal of Molecular Evolution (2021) 89:415–426
1 3
L1through to L3. The standard model for biological origina-
tion is therefore rather narrow and might miss the essence
of a variety of evolved biological processes by mapping
function (software) too readily onto substrate (hardware).
Recent advances in reprogramming the genetic code nicely
illustrates the practical value of code pluralism (Chin 2017).
When we consider inheritance more broadly we find a
variety of mutational and transference mechanisms that
includes horizontal gene transfer, epigenetics, RNA inter-
ference, and parasexual recombination. Each represents a
variety of material mechanisms for managing the tension
between information preservation and adaptation (Jablonka
and Lamb 2014). Thereby expanding the class of substrates
that can support a given function.
Hence questions about the requirements of, for example,
information storage, transmission and function are all gen-
eral question about functions required by life at an appropri-
ate level. Questions about what information-processing and
storing molecules are likely to emerge out of a given geo-
logic scenario are specific questions about the L1 hardware
required to enable life.
Once we generalize this kind of dichotomy toward a hier-
archy of life, we expand the number of mechanisms that
might support life. For example, our designed digital com-
puters use entirely different hardware than cells and require
no evolved cellular biomolecules, yet there are considerable
overlaps with life in terms of the concepts of information
storage, error-prone signaling, and information-processing
atL2 andL3. This overlap is one of the justifications for
exploring the possibility and diversity of Artificial Life
(Bedau etal. 2000).
The hardware software dichotomy is a universal feature
of any systems that can be described through a functional-
codical language and a physico-mechanical language. It is
therefore a central concept for biology and the origin of life
which, through this lens, is the manifestation of software in
hardware.
Levels, Lists, Axioms, andGeneralizations
Much of the focus in the effort to define life has centered
on lists of characteristics (e.g., Trifonov 2011; Kolb 2007;
Benner 2010; Bains etal. 2014) , or what we refer to as,
mechanical axioms. However, for most of these axioms we
find exceptions, and this creates the need for more univer-
sal principles of life (Cleland 2019; Goldenfeld and Woese
2011; Goldenfeld etal. 2017; Walker etal. 2017; Walker
2017; Davies and Walker 2016; Walker etal. 2018; Kolb
2007; Cleland 2012; Benner 2010; Bains etal. 2014).
Replication is one of the most oft-cited “mechani-
cal axioms” of life (Trifonov 2011). Additional axioms
include endogenous metabolism, a container or semi-
permeable interface, and the ability to evolve. If we take
replication as an example of a L1 physical feature, we find
that in most cases it is a proxy for the essential L2 require-
ment that life requires a means of forestalling entropy pro-
duction (England 2015). Replication is more often than not
a means of persistence (Pascal etal. 2013), including the
exclusion of rivals from shared resources, or the way in
which variation through imperfect copying is introduced
into a population fueling natural selection. It is possible to
observe all of these features without replication, and also
at multiple levels of organization (Boerlijst and Hogeweg
1995). Entities perfectly able to repair regulatory circuitry
and avoid death (e.g., from predation, the consumption of
essential resources by competitors, or allelopathy) have
no need for replication in order to persist. In a perfectly
stable environment organisms don’t have the need to adapt
and thus no replication requirement as a means of intro-
ducing heritable variation. It should be noted that even
when adaptation is necessary it can be achieved in numer-
ous ways—from epigenetic modification to developmen-
tal plasticity–without requiring an error-prone copying
process.
A good example of repair without replication is found in
the field of error-correcting codes. These make extensive use
of redundancy to ensure that messages are not degraded. No
computer scientist would describe redundancy based correc-
tion as replication and at no level in hardware or software
does “replication” take place. Error correction is in fact a
simple computation not unlike performing a summation. It is
typically the Boolean “OR” function, which is the opposite
of replication as these logical mappings always map from
a larger redundant code, e.g., 10, 01, and 11 to the smaller
output 1.
Through this example we see that entropy resistance is
possible without replication and that replication is really a
sub-set of persistence mechanisms associated with adapt-
ing to changing environments. Thereby we can in principle
replace a key feature of two of the most common mechanical
axioms of life, replication as a mechanism of stability, with a
broader suite of mechanisms promoting persistence.
Similarly, and more generally, matter and energy are nec-
essary prerequisites for life. Both material and energetic con-
straints imposed on organisms can be highly informative and
predictive, such as through their manifestations in allometry.
But neither is sufficient for determining whether something
is living. After-all, material and energetic constraints are an
essential part of the abiotic universe and the key ingredients
for all of physical theory.
Finding the truly essential principles for a universal the-
ory of life is a challenging and open question. For exam-
ple, the process of adaptation by natural selection has been
generalized to many systems including biological species,
cultures, languages, and technology (Krakauer 2011). Adap-
tation through natural selection (L3) requires mechanisms
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423Journal of Molecular Evolution (2021) 89:415–426
1 3
(L1–L2) that enable information from the environment to be
encoded in the memories of an agent. Memories are stored
using a variety of different error-correcting codes all exploit-
ing structured redundancies (L3) but in materials as diverse
as DNA, epigenetic marks, synaptic boutons, and solid state
transistors (all L1).
By combining the L3 optimisation principle of natu-
ral selection with L3 principles of error correction there
emerges a new L3 principle—the error threshold (Eigen
1971). The error threshold is the maximum error rate that
can be achieved in an evolving system such that the fittest
lineage is preserved. This new limit can then be mapped onto
any system in the class of differentially propagated objects
that are mutable, provided that one understands the unique
mechanisms of information storage, variability, and the util-
ity value of the information.
In cells this list of features includes L1 properties such
as biochemistry of the genome, the mutation rate during
genome replication, and the total length of the genotype. In
cultural evolution one can map the same dynamical process
onto a set of L1 level written words, the likelihood of cor-
rectly learning and transmitting spoken words, the total size
or vocabulary of the language (Nowak etal. 1999).
In this way we find a new emergent L3 principle that
provides a way of grouping apparently unrelated phenom-
ena into a class of information dynamics that obey a shared
dissipation principle. This adherence to a principle could
become a new axiom for a broader sense of life.
This is why we believe that the L1–L2 the mechanical
axioms of life need to be expanded and generalized to prin-
ciple-centric L3 descriptions in order for us to be able to
understand, detect, and construct life in any context in the
universe.
From Life toLife Equivalence
Our focus is the search for a universal theory of life (Cleland
2019; Goldenfeld and Woese 2011; Goldenfeld etal. 2017;
Walker etal. 2017; Walker 2017; Davies and Walker 2016;
Walker etal. 2018), where we have argued that a variety of
conceptual approaches are likely to broaden what we con-
sider to be an origin of life and cause us to rethink many of
the classic “mechanical axioms” of life. One of our main
approaches was to compare theories of life to the theories
of physics and computing. By pursuing analogies between
life and computing we naturally arrive at the profound ques-
tion of universality. Modern computers are both program-
mable (can be configured to compute a variety of functions)
and universal (compute all functions in a given class). Both
ideas have their origins in Turing and Church’s proofs of
the Entscheidungs problem in which they show that it is not
possible to solve algorithmically—i.e., compute –all state-
ments in first order logic. In these proofs Turing and Church
rigorously introduce the concepts of algorithms, computa-
tion, and their physical implementation. The idea of Turing
equivalence captures the set of all computing machines that
can simulate one another (bi-simulation).
The idea of bi-simulation can expand our thinking about
life because to the extent that life can be described in prin-
ciples that are logical and algorithmic, it is worth deter-
mining to what degree the functions of life can be sup-
ported by hardware that is universal or, by analogy with
Turing equivalent, “life-equivalent”. Using the framework
developed here, such an equivalence would be a principle-
centric L3 description. To be concrete, multiple materials
in L1 would be life-equivalent if they all mapped through
L2 into the same space of the living in L3.
This is obviously a very challenging problem but
there are insights both positive and negative that can be
gleaned from the computational domain. Since the publi-
cation of Turing and Church’s seminal papers it has been
discovered that a rather large and unlikely class of dis-
crete dynamical systems and software systems are Turing
equivalent, including The Game of Life, the computer
games Minecraft and Minesweeper, most commonly used
computer languages from Lisp to Python, tag systems,
extended L Systems, Feynman machines, and random
access machines. If such a diversity of systems are univer-
sal one might wonder what value the concept contributes
to our understanding of each one.
The positive value of equivalence has been to identify
the shared properties of each of these systems, to include
discrete states, memory of state, programmable states, reli-
able state transition functions, and termination criteria.
This means that at this point we have a very strong idea of
how to build computers and with what level of efficiency
they will operate.
The negative implication of equivalence is precisely its
generality. If life is rare in the universe and our life equiva-
lence principles indicate that many different materials can
produce persistence, competition, adaptation, and evolv-
ability, how are we to reconcile these truths?
It is our contention that the origin of life is more com-
mon and multiple than typically thought. At least at the
level of equivalence principles. That is not to say that the
rather unique history of life on Earth is common. The
particular chemistry supporting life’s first appearance on
Earth might in fact be a rather rare form of universal life
machine and this is why attempts at full prebiotic synthesis
have proven so challenging. We wish to make clear that the
difficulty of instantiating life in the contingent biochemis-
try of Earth history should not be confused with the more
general problem of instantiating life. In addition, it may be
the case that certain systems make it much easier for life
to originate than others. The human world may be a great
example of this concept where intelligence, culture, social
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424 Journal of Molecular Evolution (2021) 89:415–426
1 3
structures, and digital computers all act as ready substrates
for an explosion of many new origins of new life.
Discussion
We have argued that the emerging perspective of life is
one that shifts focus from history and particular material
instantiations (L1) to more general levels of shared con-
straints (L2) and universal classes of optimisation (L3).
In line with this thinking, previous work has argued that
much of our understanding of life should be focused on
transitions in information, algorithms, and computational
hierarchies (Walker and Davies 2013). The ultimate theory
of life will certainly have ingredients from abstract theo-
ries of engineering, computation, physics (Walker 2017),
and evolution, but we expect will also require new per-
spectives and tools, just as theories of computation have.
Once materials and constraints at L1–L2 come into
existence capable of supporting L3, then L3 can recruit
new kinds of L1–L2 to generate diverse forms of life. For
example, artificial life is supported by radically different
materials and constraints than organically evolved life.
However, organically evolved life came first, i.e, the first
L3 needed to be supported by organic macromolecules.
This suggests a possible theory of accelerating life pro-
duction, whereby new L3 levels arrive at an increasing
pace. There is of course evidence for this. Material culture
is relatively recent in biological terms: stone tools first
appeared just under two million years ago, cave art around
seventy thousand years ago, pre-cuneiform writing around
five thousand years ago, and movable type around five
hundred years ago. Boolean logic was invented less than
two hundred years ago and the first universal computer was
built just over seventy years ago. The birth of computers
obviously required all of these prior cultural inventions to
exist to be at all possible. The history of culture is a his-
tory of dependency, so called implicational scaling, and
one of acceleration.
Our claim is that we will be able to tell that we have
a new theory of life when it is able to reveal to us many
origins and many types of life. It should be able to high-
light life as the ultimate homoplasy (convergence) rather
than homology, where life is discovered repeatedly from
many different trajectories. It should be able to define what
is shared among all of the living endpoints of many tra-
jectories and be able to assign to any system or process
a degree of “livingness”. At this point we do not know
whether our framework implies that the space of the living
in L3 has rather blurry boundaries, or whether the bound-
ary is sharp, and degrees-of-livingness should be meas-
ured in terms of their distance to this boundary. We sus-
pect that these boundaries will depend very much on the
nature of the changes in L1. For example, a fatal knock-
out mutation in L1 causes a discontinuous change in L3.
Either way, many recent efforts have begun to construct
metrics for a spectrum of living characteristics. For exam-
ple, quantifications of the assembly required for objects
(Marshall etal. 2017a, 2021; Murray etal. 2018), informa-
tion theoretic decompositions of individuality (Krakauer
etal. 2020), causal boundaries of living systems (Marshall
etal. 2017b), physical assessments of the agency of sys-
tems (Kolchinsky and Wolpert 2018), and the processes of
acquiring functional information (Lachmann and Walker
2019) have all been recently proposed and have promis-
ing future directions. Similarly, other recent efforts have
elucidate general constraints atL2, such as the connection
between fundamental energetics and cellular physiology
and evolutionary processes (Savage etal. 2004; West and
Brown 2005; DeLong etal. 2010; Lane and Martin 2010;
Kempes etal. 2012; Lynch and Marinov 2015; Kempes
etal. 2016, 2019; Ilker and Hinczewski 2019).
It is from the astrobiological perspective that our argu-
ments in favor of principles will demonstrate their great-
est value as we search for evolutionary sequelae off-world.
These are likely to include, principles as wide-ranging as
self-organized criticality, characteristics ofhighly optimized
network structures, evidence for the maximization of mutual
information, the emergence of multiple characteristic adap-
tive times scales, and wide-spread structural convergences.
Table 3 Disciplinary attitudes
to the three levels of analysis Level in hiearchy Typical rank emphasis of research area(1 is highest importance)
Natural his-
tory
Molecular
biology
Mathematical
biology
Biochemical
origins
Princi-
ples of
life
Level 3: optimization 2 3 1 2 1
Level 2: constraints 1 2 2 3 x
Level 1: materials 3 1 3 1 y
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425Journal of Molecular Evolution (2021) 89:415–426
1 3
TableDescriptions
In the following tables we consider the interpretation of each
of the three levels of analysis for living systems through (1)
General theories and abstractions versus Biological Theo-
ries; (2) the relationships between computational principles
and biological mechanisms; and (3) the rank order of empha-
sis placed on each level by different fields and disciplines,
from highest emphasis = 1 to lowest emphasis = 3. In the
final column of Table3, physical theory ranks
x
=
3;y
=
2
,
whereas biophysical theory ranks
x
=
2;y
=
3
.
Open Access This article is licensed under a Creative Commons Attri-
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as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
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permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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© 2022, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1037/hum0000284
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The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab. The search for life in the universe is difficult due to issues with defining signatures of living systems. Here, the authors present an approach based on the molecular assembly number and tandem mass spectrometry that allows identification of molecules produced by biological systems, and use it to identify biosignatures from a range of samples, including ones from outer space.
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Organisms are subject to the laws of physics, so the process of evolution by genetic variation and natural selection is constrained by these fundamental laws. Classic and recent studies of the biophysical limits facing organisms have shown how fundamental physical constraints can be used to predict broad-scale relationships between body size and organismal biomechanics and physiology. These relationships often take the form of power laws across a wide range of body sizes for organisms sharing a common body plan. However, such biophysical perspectives have not been fully connected with the detailed dynamics of evolution by natural selection, nor with the variation between species around the central scaling relationships. Here we first discuss what a general biophysical theory of evolution would require and provide a mathematical framework for constructing such a theory. We discuss how the theory can predict not only scaling relationships, but also of identifying the types of tradeoffs made by different species living in particular niches. In addition, we discuss how a key higher-order requirement of a biophysical theory of evolution is its ability to predict asymptotic behavior and the limits of a particular body plan. We use several examples to illustrate how dominant physical constraints can be used to predict the minimum and maximum body sizes for a particular body plan, and we argue that prediction of these limits is essential for identifying the dominant physical constraints for a given category of organisms. Our general framework proposes that a major portion of fitness should be the overlay of how all traits of a particular body plan interact with fundamental physical constraints. To illustrate this concept, we investigate multiple physical limits on particular traits, such as insect legs, and show how the interaction of a number of traits determines the size limits on entire body plans, such as those of vascular plants. We use bacteria as an example of the shifts in which physiological traits and physical constraints are most limiting at various organism sizes. Finally, we address the effects of environmental conditions and ecological interactions in determining which of the physical constraints faced by organisms are most likely to affect their growth, survival, and reproduction, and hence their fitness. We consider such ecological effects on our examples of bacteria, insects, mammals and trees, and we nest the constraints-perspective in the broader picture of evolutionary processes.
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Shannon information theory provides various measures of so-called syntactic information, which reflect the amount of statistical correlation between systems. By contrast, the concept of 'semantic information' refers to those correlations which carry significance or 'meaning' for a given system. Semantic information plays an important role in many fields, including biology, cognitive science and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper, we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. 'Causal necessity' is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while 'maintaining existence' is defined in terms of the system's ability to keep itself in a low entropy state. We also use recent results in non-equilibrium statistical physics to analyse semantic information from a thermodynamic point of view. Our framework is grounded in the intrinsic dynamics of a system coupled to an environment, and is applicable to any physical system, living or otherwise. It leads to formal definitions of several concepts that have been intuitively understood to be related to semantic information, including 'value of information', 'semantic content' and 'agency'.
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Cambridge Core - History of Astronomy and Cosmology - The Quest for a Universal Theory of Life - by Carol E. Cleland
Article
Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. We validate this result from physical principles through a general growth model and show it holds to excellent approximation for experimental parameters drawn from a wide range of species.