367 lines
19 KiB
TeX
367 lines
19 KiB
TeX
\chapter{A Deal with Life}
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\begin{refsection}[bib/sivanov-dblp-mod.bib,bib/sivanov-extra.bib,bib/dealb.bib]
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Life is one of the most beautiful things in the universe. Arguably,
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it is because we humans belong to the kingdom of Life that it
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fascinates us so. Beyond its intrinsic beauty to which our sensory
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organs are attuned, it also deeply attracts us because of the
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self-referentiality of its contemplation: when thinking about Life, we
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often think about our interactions with it, and ultimately
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about ourselves.
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Self-referentiality is also a hurdle: it is intrinsically difficult to
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conceive of oneself. Even though theoretical computer science is no
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substitute for philosophy, I enjoy taking Gödel's incompleteness
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theorems\footnote{\url{https://en.wikipedia.org/wiki/Gödel's_incompleteness_theorems}}
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and especially Hilbert's
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\emph{Entscheidungsproblem}\footnote{\url{https://en.wikipedia.org/wiki/Entscheidungsproblem}}
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and the halting
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problem\footnote{\url{https://en.wikipedia.org/wiki/Halting_problem}}
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as vivid examples: Turing's famous proof states that a Turing machine
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cannot generally decide whether another Turing machine will ever halt.
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Since abstract computing devices can be seen as distant
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mathematizations of the human brain, this formal result hints that
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entirely conceiving of our mind---and by extension of Life itself---is
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borderline intractable.
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The difficulty of self-referiantiality is also deeply disturbing,
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especially because understanding how our bodies function within their
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environments has so many essential implications: dealing with the
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climate crisis, tackling diseases, improving the quality of life, to
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only cite the foremost ones. To avoid the worry of looking into the
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mirror for too long, one can brutally build a wall between oneself and
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``the rest'' of Life, and adopt what may be called the Engineer's
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position: a living organism is a machine constituted out of mechanical
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pieces, whilst the human disassembles, adjusts, and reassembles them
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again, improved.
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Modern biology, medicine, biotechnology illustrate the high
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performance of the Engineer's approach, and this text is not
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a criticism of mechanicism per se. Nevertheless, its efficiency does
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not entail total truthfulness, nor even exclusivity about truth.
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In other words, mechanistic views allowing for impressive technical
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achievements does not mean that these views fully reflect reality, nor
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that mechanicism is the final stop on our journey to understanding
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Life. In my research, I aim for exploring different approaches to
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Life and tools supporting such approaches. I take particular
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enthusiasm in thinking about striking \emph{a deal with Life}:
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establishing \emph{mutually beneficial} interactions with living
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systems.
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Concluding deals as opposed to taking the Engineer's position resets
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the power balance in our relationship with Life: instead of seeking to
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control, hack, or otherwise dominate living organisms, the goal is to
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further take into account their well-being. I believe that
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approaching Life from this viewpoint is essential if we are after true
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solutions to fundamental problems such as the climate crisis or
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complex diseases. On a more philosophical note, the framework of
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mutually beneficial interactions should remind us that our
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intelligence in no way warrants an extraction of the human being into
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an exceptional superior stance---we are part of Life, and we ought to
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think and act accordingly.
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\newpage
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\section{A short glance on reductionism and mechanicism}
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\label{sec:mecha}
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In the 20th century, biology was dramatically affected by physics and
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engineering, and this has brought revolutionary advances in
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understanding Life and interaction with
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it~\cite{CornishBowdenCLSA2007,Glade22,Nicholson2019,Woese2004}.
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Grounding the function of biological structures in the physical
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reality allowed for convergence of worldview between physics and
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biology, thereby conferring to the latter the gravitas of a ``real''
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science. A remarkable tool physics and engineering brought to biology
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is reductionism---to understand a system, decompose it into parts,
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understand each of the parts, and understand the interactions between
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the parts to get back to the big picture. Reductionism in turn
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fostered the emergence of mechanicism, the modern proponents of which
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``conceive of the cell as an intricate piece of machinery whose
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organization reflects a pre-existing design, whose structure is wholly
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intelligible in reductionistic terms, and whose operation is governed
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by deterministic laws, rendering its behaviour predictable and
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controllable—at least in principle.''\cite{Nicholson2019}
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With all due recognition of the major advances yielded by reductionism
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and mechanicism, it appears hard to believe that this is the final
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stop on the way to understanding Life. I recall first of all the
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discussion in~\cite[page~2]{Woese2004} of reductionism as an
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operational tool allowing to tackle complexity (empirical
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reductionism), as opposed to the belief that it actually corresponds
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to the organization of the living matter (fundamental reductionism).
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Fundamental reductionism makes therefore an additional strong
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assumption, which impacts the ``sense of what is important'':
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molecular biology established the molecular level as fundamental, and
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demoted the status of larger structures---e.g. organisms, ecosystems,
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etc. These are deemed emergent, and therefore less important,
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secondary, directly derivable from more fundamental matters.
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While the notion of emergence in natural sciences is fraught, and its
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objective qualities can be debated (e.g.~\cite{RonaldSC99}), it has
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the merit of putting in focus the hierarchy of scales. It is
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a hierarchy in the sense that, while physics teaches us that the whole
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is always necessarily the sum of its parts (plus the interactions), it
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is often irrelevant to put the whole away, and only peer at the
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components. It is therefore important to not always fall through to
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the underlying levels, and specifically to avoid Laplace's daemon
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abuse: the Laplace's daemon\footnote{Laplace's daemon is a thought
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experiment introducing an imaginary creature which knows exactly the
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positions and momenta of every atom in the universe. The original
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conclusion conceived by of Pierre-Simon Laplace in 1814 is that this
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absolute knowledge should entail full knowledge of past and future
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positions of these particles~\cite{wikiLaplace}. In modern days,
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Laplace's daemon is often used as a metaphor for absolute knowledge
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of the minutae of a complex system, down to its elementary
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particles.} cannot practically exist, but should it exist, it would
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in no way have any influence on the fact that we as humans find it
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extremely useful to operate with concepts situated at higher
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scales\footnote{An informal inspiration for these observations comes
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from~\cite{Carroll}.}. It is physics again, and statistical
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mechanics in particular, that recalls this saliently by deeply relying
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upon thinking about systems such as gasses in terms of macrostates
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(volume, pressure, temperature) and microstates (positions and momenta
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of all particles)~\cite{SusskindCourse,wikiEntropy}. In other words,
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while one might argue that microstates are more ``fundamental'' in
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some way, it is of little practical importance, and addressing
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multiple scales is still pertinent.
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Fundamental reductionism as a belief is strongly related to
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engineering, and specifically the practice of constructing complex
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structures and mechanisms out of simpler building blocks.
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The multiple ways in which engineering has been durably changing our
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lives and our surroundings naturally fuels extending its reach beyond
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human creation, onto living matter. A spectacular manifestation is
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the Machine Conception of the Cell (MCC) as introduced
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in~\cite{Nicholson2019}: the cell is seen as an intricate machine,
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somewhat similar to a computer, which makes it appropriate to use
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engineering terms to designate the cellular components visible by
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microscopy: molecular motors, Golgi apparatus, genetic program, pumps,
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locks, keys, gates, circuitry, etc. The choice of terms is in
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principle contingent, and it is natural to use words evoking familiar
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structures, but in practice this reinforces the belief in the
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truthfulness of the engineering approach. Indeed, scientific papers
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ubiquitously summarize knowledge in the form of circuits or maps.
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As stated in~\cite[page~6]{Mayer2009}, ``the typical ‘cartoons’ of
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signaling pathways, with their reassuring arrows and limited number of
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states [...] could be the real villain of the piece.'' The Wikipedia
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page on molecular motors literally starts with the sentence
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``Molecular motors are [...] molecular
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\emph{machines}''\cite{wikiMotors} (the emphasis is mine), and
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features several animations which would look appropriate in a book on
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the construction of mechanical toys. The last illustration---and
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probably the most verbose---of the relationship between reductionism
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and the Engineer's work I bring here is the very term
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``biological engineering''.
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In fact, widely admitted considerations easily uncover some flaws in
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the belief in the fundamental nature of the MCC~\cite{Nicholson2019}.
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To cite two of the most salient ones, the cell is a milieu which is
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better described as liquid, rather than solid. It is densely packed
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with various molecules, which do not always strictly respect a certain
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conformation, but rather continuously evolve across a spectrum of
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shapes. It being impossible for a human to observe the cellular
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processes with the naked eye, the researcher is tempted to follow the
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mindset suggested by the available technology conceived for conceiving
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of and observing microscopic machines~\cite{Glade22}, a mindset which
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also happens to be mainstream. Unsurprisingly, if one looks for
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machines, one finds machines, as the animation ``The Inner Life of the
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Cell'' conveniently illustrates~\cite{lifeOfTheCell}.
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Avoiding conceptual frameworks other than fundamental reductionism and
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mechanicism not only forces our thinking into a certain box which
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partially corresponds to reality, but also biases our methodology of
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interactions with Life. When one imagines the cell as a machine, one
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expects mechanistic explanations, building upon strong causality.
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When the computer screen shows a picture or a car modifies its
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trajectory, it is always possible to indicate a satisfactory set of
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causes. This is because the engineers who built the device had
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a specific intention in mind, which can be relatively easily unpacked.
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Biological systems originated from spontaneous evolution, without
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anyone human baking in specific goals, implying that causality is much
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harder to establish convincingly. Yet, reductionism and mechanicism
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tempt the researches to only look for correlations which may be
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interpreted as causal: ``It is much easier to write and publish
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a paper suggesting Protein X is necessary for transmitting a signal
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from A to B, than one showing that Protein X is one of many potential
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components of a heterogeneous ensemble of signaling complexes that
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together couple A to B.''~\cite{Mayer2009}.
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While the Machine Conception of the Cell and similar mechanistic
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points of view are not oblivious to the intrinsic noise of the
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respective biological systems, seeing them as machines invites to
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treating noise as a nuisance which the biological systems manage to
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successfully combat in every moment of their existence. However,
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multiple indications exist that noise plays an essential role, as
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a matter of fact making some processes possible. We cite as an
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example the Brownian ratchet model of intracellular transport, which
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has been gaining considerable traction recently~\cite{Nicholson2019},
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and which essentially consists in hypothesising that molecular motors
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feature two distinct conformations of the energy landscape---a flat
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one and a saw-toothed one. By periodically switching between the two,
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the motor buffeted by thermal fluctuations will tend to advance along
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the cytoskeletal track it is attached to
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(Figure~\ref{fig:ratchet-motor}).
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\begin{figure}
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\centering
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\tikzstyle axis=[->]
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\tikzstyle movement=[-{Latex[width=1.2mm]},semithick]
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\tikzstyle landscape=[very thick,cap=round]
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\tikzstyle motor=[draw,circle,thick,minimum size=3.5mm]
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\tikzstyle motorFlip=[motor]
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\tikzstyle motorFlop=[motor,fill=black!40]
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\tikzstyle motorGhost=[motor,densely dotted]
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\newcommand{\landscapeXOff}{.2mm}
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\newcommand{\landscapeYOff}{1mm}
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\newcommand{\xLength}{56mm}
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\newcommand{\yLength}{11mm}
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\newcommand{\graphSkip}{\vspace{-3mm}}
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\newcommand{\stepLabOff}{-7mm}
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\begin{tikzpicture}
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\draw[axis] (0,0) --
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node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (1)}
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(0,\yLength)
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node[xshift=3mm] {$U$};
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\draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
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\draw[landscape] (\landscapeXOff,\landscapeYOff) -- +(52mm,0);
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\node[motorFlip] (motor) at (11mm,3mm) {};
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\node[motorGhost] at ($(motor)-(3.5mm,0)$) {};
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\node[motorGhost] at ($(motor)-(6mm,0)$) {};
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\node[motorGhost] at ($(motor)+(3.5mm,0)$) {};
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\node[motorGhost] at ($(motor)+(6mm,0)$) {};
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\coordinate[above=2mm of motor] (arrowAnchor);
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\draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-6mm,0);
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\draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(6mm,0);
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\end{tikzpicture}
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\graphSkip
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\begin{tikzpicture}
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\draw[axis] (0,0) --
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node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (2)}
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(0,\yLength)
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node[xshift=3mm] {$U$};
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\draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
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\draw[landscape] (\landscapeXOff,\landscapeYOff)
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-- ++(2mm,5mm) -- ++(11mm,-5mm)
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-- ++(2mm,5mm) -- ++(11mm,-5mm)
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-- ++(2mm,5mm) -- ++(11mm,-5mm)
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-- ++(2mm,5mm) -- ++(11mm,-5mm);
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\node[motorFlop] (motor) at (25.2mm,3.7mm) {};
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\coordinate[above=2mm of motor] (arrowAnchor);
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\draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-4.5mm,0);
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\draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(9mm,0);
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\end{tikzpicture}
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\graphSkip
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\begin{tikzpicture}
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\draw[axis] (0,0) --
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node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (3)}
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(0,\yLength)
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node[xshift=3mm] {$U$};
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\draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
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\draw[landscape] (\landscapeXOff,\landscapeYOff) -- +(52mm,0);
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\node[motorFlip] (motor) at (25.2mm,3mm) {};
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\node[motorGhost] at ($(motor)-(3.5mm,0)$) {};
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\node[motorGhost] at ($(motor)-(6mm,0)$) {};
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\node[motorGhost] at ($(motor)+(3.5mm,0)$) {};
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\node[motorGhost] at ($(motor)+(6mm,0)$) {};
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\coordinate[above=2mm of motor] (arrowAnchor);
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\draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-6mm,0);
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\draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(6mm,0);
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\end{tikzpicture}
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\graphSkip
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\begin{tikzpicture}
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\draw[axis] (0,0) --
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node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (4)}
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(0,\yLength)
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node[xshift=3mm] {$U$};
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\draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
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\draw[landscape] (\landscapeXOff,\landscapeYOff)
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-- ++(2mm,5mm) -- ++(11mm,-5mm)
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-- ++(2mm,5mm) -- ++(11mm,-5mm)
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-- ++(2mm,5mm) -- ++(11mm,-5mm)
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-- ++(2mm,5mm) -- ++(11mm,-5mm);
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\node[motorFlop] (motor) at (38.2mm,3.7mm) {};
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\coordinate[above=2mm of motor] (arrowAnchor);
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\draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-4.5mm,0);
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\draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(9mm,0);
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\end{tikzpicture}
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\caption{A schematic illustration of the Brownian ratchet model of
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molecular motors. A motor is shown as a circle
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(\protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlip,minimum
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size=2.5mm]{}; or
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\protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlop,minimum
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size=2.5mm]{};), and its energy landscape is shown as a thick line
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\protect\tikz[baseline,yshift=.2em]\protect\draw[landscape]
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(0,0) -- (2ex,0);. The horizontal axis $x$ represents the motor's
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position on the cytoskeletal track, while the vertical axis $U$
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illustrates the motor's free energy. The motor is hypothesized to
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feature two distinct potential energy landscapes, depending on its
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conformational state. In the flip conformation
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\protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlip,minimum
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size=2.5mm]{};, the energy landscape is flat so the protein may
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slide freely in one of the two directions, with equal probability
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for both directions. In the flop conformation
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\protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlop,minimum
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size=2.5mm]{};, the saw-tooth shape of the landscape favors the
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motor moving to the right, illustrated by a longer arrow pointing
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to the right. When cycles of ATP hydrolysis make the motor
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periodically switch between the two conformations, thermal
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fluctuations will tend to push it to the right. (The original
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figure is~\cite[Figure~4]{Nicholson2019}, itself a reproduction
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from~\cite{Kurakin2006}.)}
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\label{fig:ratchet-motor}
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\end{figure}
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Seeing Life as an ensemble of machines biases how we expect to collect
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profit from acting on it. Machine means control: we are constantly
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looking for knobs which we could turn this or that way, and which
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could modify the behavior of the system to fit our needs and
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expectations. This can be seen both at the very practical level,
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where bioengineers seek to modify bacteria to produce chemicals,
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e.g.~\cite{berkleyBio}, and also at the theoretical level, where
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researchers develop methodologies to support looking for the coveted
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knobs, e.g.~\cite{PardoID21,Vogel2008,Zanudo2015}. If we admit that
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the reductionistic and mechanistic approach is not globally true, we
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must therefore accept that these knobs may not necessarily have
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a definitive shape, but rather be a complex assemblage of factors,
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affecting the trajectory of the system in multiple non-trivial ways,
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and possibly shifting in time. Finally, this control mindset
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introduces an asymmetric relationship between the controller and the
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controlled, which is unnatural biological context because both the
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controller and the controlled are made out of the same kind of matter,
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and are ultimately embedded in the same environment.
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In this chapter, I introduce the Deal with Life: instead of looking to
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impact biological systems asymmetrically, surreptitiously lifting
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ourselves above the living matter, I propose to account for the fact
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that we act within complex feedback loops, which sometimes end up
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imposing the consequences of the actions on the actors. The principle
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of a Deal with Life is to render the interactions \emph{mutually
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beneficial}: ideally, both systems engaging in the interaction
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should benefit from it. In practice, this should be translated into
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joint maximization of a pair of functions measuring the utility of the
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interaction for both parties, possibly with one of the two functions
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being prioritized over the other.
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\printbibliography[heading=subbibliography]
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\end{refsection}
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%%% Local Variables:
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%%% TeX-engine: luatex
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%%% TeX-master: "hdr"
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%%% End:
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