\chapter{A Deal with Life}
\begin{refsection}[bib/sivanov-dblp-mod.bib,bib/sivanov-extra.bib,bib/dealb.bib]

Life is one of the most beautiful things in the universe.  Arguably,
it is because we humans belong to the kingdom of Life that it
fascinates us so.  Beyond its intrinsic beauty to which our sensory
organs are attuned, it also deeply attracts us because of the
self-referentiality of its contemplation: when thinking about Life, we
often think about our interactions with it, and ultimately
about ourselves.

Self-referentiality is also a hurdle: it is intrinsically difficult to
conceive of oneself.  Even though theoretical computer science is no
substitute for philosophy, I enjoy taking Gödel's incompleteness
theorems\footnote{\url{https://en.wikipedia.org/wiki/Gödel's_incompleteness_theorems}}
and especially Hilbert's
\emph{Entscheidungsproblem}\footnote{\url{https://en.wikipedia.org/wiki/Entscheidungsproblem}}
and the halting
problem\footnote{\url{https://en.wikipedia.org/wiki/Halting_problem}}
as vivid examples: Turing's famous proof states that a Turing machine
cannot generally decide whether another Turing machine will ever halt.
Since abstract computing devices can be seen as distant
mathematizations of the human brain, this formal result hints that
entirely conceiving of our mind---and by extension of Life itself---is
borderline intractable.

The difficulty of self-referiantiality is also deeply disturbing,
especially because understanding how our bodies function within their
environments has so many essential implications: dealing with the
climate crisis, tackling diseases, improving the quality of life, to
only cite the foremost ones.  To avoid the worry of looking into the
mirror for too long, one can brutally build a wall between oneself and
``the rest'' of Life, and adopt what may be called the Engineer's
position: a living organism is a machine constituted out of mechanical
pieces, whilst the human disassembles, adjusts, and reassembles them
again, improved.

Modern biology, medicine, biotechnology illustrate the high
performance of the Engineer's approach, and this text is not
a criticism of mechanicism per se.  Nevertheless, its efficiency does
not entail total truthfulness, nor even exclusivity about truth.
In other words, mechanistic views allowing for impressive technical
achievements does not mean that these views fully reflect reality, nor
that mechanicism is the final stop on our journey to understanding
Life.  In my research, I aim for exploring different approaches to
Life and tools supporting such approaches.  I take particular
enthusiasm in thinking about striking \emph{a deal with Life}:
establishing \emph{mutually beneficial} interactions with living
systems.

Concluding deals as opposed to taking the Engineer's position resets
the power balance in our relationship with Life: instead of seeking to
control, hack, or otherwise dominate living organisms, the goal is to
further take into account their well-being.  I believe that
approaching Life from this viewpoint is essential if we are after true
solutions to fundamental problems such as the climate crisis or
complex diseases.  On a more philosophical note, the framework of
mutually beneficial interactions should remind us that our
intelligence in no way warrants an extraction of the human being into
an exceptional superior stance---we are part of Life, and we ought to
think and act accordingly.

\newpage

\section{Mechanicism: Where engineering meets biology}
\label{sec:mechanicism}

In the 20th century, biology was dramatically affected by physics and
engineering, and this has brought revolutionary advances in
understanding Life and interaction with
it~\cite{CornishBowdenCLSA2007,Glade22,Nicholson2019,Woese2004}.
Grounding the function of biological structures in the physical
reality allowed for convergence of worldview between physics and
biology, thereby conferring to the latter the gravitas of a ``real''
science.  A remarkable tool physics and engineering brought to biology
is reductionism---to understand a system, decompose it into parts,
understand each of the parts, and understand the interactions between
the parts to get back to the big picture.  Reductionism in turn
fostered the emergence of mechanicism, the modern proponents of which
``conceive of the cell as an intricate piece of machinery whose
organization reﬂects a pre-existing design, whose structure is wholly
intelligible in reductionistic terms, and whose operation is governed
by deterministic laws, rendering its behaviour predictable and
controllable—at least in principle.''\cite{Nicholson2019}

With all due recognition of the major advances yielded by reductionism
and mechanicism, it appears hard to believe that this is the final
stop on the way to understanding Life.  I recall first of all the
discussion in~\cite[page~2]{Woese2004} of reductionism as an
operational tool allowing to tackle complexity (empirical
reductionism), as opposed to the belief that it actually corresponds
to the organization of the living matter (fundamental reductionism).
Fundamental reductionism makes therefore an additional strong
assumption, which impacts the ``sense of what is important'':
molecular biology established the molecular level as fundamental, and
demoted the status of larger structures---e.g. organisms, ecosystems,
etc.  These are deemed emergent, and therefore less important,
secondary, directly derivable from more fundamental matters.

While the notion of emergence in natural sciences is fraught, and its
objective qualities can be debated (e.g.~\cite{RonaldSC99}), it has
the merit of putting in focus the hierarchy of scales.  It is
a hierarchy in the sense that, while physics teaches us that the whole
is always necessarily the sum of its parts (plus the interactions), it
is often irrelevant to put the whole away, and only peer at the
components.  It is therefore important to not always fall through to
the underlying levels, and specifically to avoid Laplace's daemon
abuse: the Laplace's daemon\footnote{Laplace's daemon is a thought
  experiment introducing an imaginary creature which knows exactly the
  positions and momenta of every atom in the universe.  The original
  conclusion conceived by of Pierre-Simon Laplace in 1814 is that this
  absolute knowledge should entail full knowledge of past and future
  positions of these particles~\cite{wikiLaplace}.  In modern days,
  Laplace's daemon is often used as a metaphor for absolute knowledge
  of the minutae of a complex system, down to its elementary
  particles.} cannot practically exist, but should it exist, it would
in no way have any influence on the fact that we as humans find it
extremely useful to operate with concepts situated at higher
scales\footnote{An informal inspiration for these observations comes
  from~\cite{Carroll}.}.  It is physics again, and statistical
mechanics in particular, that recalls this saliently by deeply relying
upon thinking about systems such as gasses in terms of macrostates
(volume, pressure, temperature) and microstates (positions and momenta
of all particles)~\cite{SusskindCourse,wikiEntropy}.  In other words,
while one might argue that microstates are more ``fundamental'' in
some way, it is of little practical importance, and addressing
multiple scales is still pertinent.

Fundamental reductionism as a belief is strongly related to
engineering, and specifically the practice of constructing complex
structures and mechanisms out of simpler building blocks.
The multiple ways in which engineering has been durably changing our
lives and our surroundings naturally fuels extending its reach beyond
human creation, onto living matter.  A spectacular manifestation is
the Machine Conception of the Cell (MCC) as introduced
in~\cite{Nicholson2019}: the cell is seen as an intricate machine,
somewhat similar to a computer, which makes it appropriate to use
engineering terms to designate the cellular components visible by
microscopy: molecular motors, Golgi apparatus, genetic program, pumps,
locks, keys, gates, circuitry, etc.  The choice of terms is in
principle contingent, and it is natural to use words evoking familiar
structures, but in practice this reinforces the belief in the
truthfulness of the engineering approach.  Indeed, scientific papers
ubiquitously summarize knowledge in the form of circuits or maps.
As stated in~\cite[page~6]{Mayer2009}, ``the typical ‘cartoons’ of
signaling pathways, with their reassuring arrows and limited number of
states [...] could be the real villain of the piece.'' The Wikipedia
page on molecular motors literally starts with the sentence
``Molecular motors are [...] molecular
\emph{machines}''\cite{wikiMotors} (the emphasis is mine), and
features several animations which would look appropriate in a book on
the construction of mechanical toys.  The last illustration---and
probably the most verbose---of the relationship between reductionism
and the Engineer's work I bring here is the very term
``biological engineering''.

In fact, widely admitted considerations easily uncover some flaws in
the belief in the fundamental nature of the MCC~\cite{Nicholson2019}.
To cite two of the most salient ones, the cell is a milieu which is
better described as liquid, rather than solid.  It is densely packed
with various molecules, which do not always strictly respect a certain
conformation, but rather continuously evolve across a spectrum of
shapes.  It being impossible for a human to observe the cellular
processes with the naked eye, the researcher is tempted to follow the
mindset suggested by the available technology conceived for conceiving
of and observing microscopic machines~\cite{Glade22}, a mindset which
also happens to be mainstream.  Unsurprisingly, if one looks for
machines, one finds machines, as the animation ``The Inner Life of the
Cell'' conveniently illustrates~\cite{lifeOfTheCell}.

Avoiding conceptual frameworks other than fundamental reductionism and
mechanicism not only forces our thinking into a certain box which
partially corresponds to reality, but also biases our methodology of
interactions with Life.  When one imagines the cell as a machine, one
expects mechanistic explanations, building upon strong causality.
When the computer screen shows a picture or a car modifies its
trajectory, it is always possible to indicate a satisfactory set of
causes.  This is because the engineers who built the device had
a specific intention in mind, which can be relatively easily unpacked.
Biological systems originated from spontaneous evolution, without
anyone human baking in specific goals, implying that causality is much
harder to establish convincingly.  Yet, reductionism and mechanicism
tempt the researches to only look for correlations which may be
interpreted as causal: ``It is much easier to write and publish
a paper suggesting Protein X is necessary for transmitting a signal
from A to B, than one showing that Protein X is one of many potential
components of a heterogeneous ensemble of signaling complexes that
together couple A to B.''~\cite{Mayer2009}.

While the Machine Conception of the Cell and similar mechanistic
points of view are not oblivious to the intrinsic noise of the
respective biological systems, seeing them as machines invites to
treating noise as a nuisance which the biological systems manage to
successfully combat in every moment of their existence.  However,
multiple indications exist that noise plays an essential role, as
a matter of fact making some processes possible.  We cite as an
example the Brownian ratchet model of intracellular transport, which
has been gaining considerable traction recently~\cite{Nicholson2019},
and which essentially consists in hypothesising that molecular motors
feature two distinct conformations of the energy landscape---a flat
one and a saw-toothed one.  By periodically switching between the two,
the motor buffeted by thermal fluctuations will tend to advance along
the cytoskeletal track it is attached to
(Figure~\ref{fig:ratchet-motor}).

\begin{figure}
  \centering

  \tikzstyle axis=[->]
  \tikzstyle movement=[-{Latex[width=1.2mm]},semithick]
  \tikzstyle landscape=[very thick,cap=round]
  \tikzstyle motor=[draw,circle,thick,minimum size=3.5mm]
  \tikzstyle motorFlip=[motor]
  \tikzstyle motorFlop=[motor,fill=black!40]
  \tikzstyle motorGhost=[motor,densely dotted]
  \newcommand{\landscapeXOff}{.2mm}
  \newcommand{\landscapeYOff}{1mm}
  \newcommand{\xLength}{56mm}
  \newcommand{\yLength}{11mm}
  \newcommand{\graphSkip}{\vspace{-3mm}}
  \newcommand{\stepLabOff}{-7mm}
  \begin{tikzpicture}
    \draw[axis] (0,0) --
    node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (1)}
    (0,\yLength)
    node[xshift=3mm] {$U$};
    \draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
    \draw[landscape] (\landscapeXOff,\landscapeYOff) -- +(52mm,0);

    \node[motorFlip] (motor) at (11mm,3mm) {};
    \node[motorGhost] at ($(motor)-(3.5mm,0)$) {};
    \node[motorGhost] at ($(motor)-(6mm,0)$) {};
    \node[motorGhost] at ($(motor)+(3.5mm,0)$) {};
    \node[motorGhost] at ($(motor)+(6mm,0)$) {};

    \coordinate[above=2mm of motor] (arrowAnchor);
    \draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-6mm,0);
    \draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(6mm,0);
  \end{tikzpicture}

  \graphSkip

  \begin{tikzpicture}
    \draw[axis] (0,0) --
    node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (2)}
    (0,\yLength)
    node[xshift=3mm] {$U$};
    \draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
    \draw[landscape] (\landscapeXOff,\landscapeYOff)
    -- ++(2mm,5mm) -- ++(11mm,-5mm)
    -- ++(2mm,5mm) -- ++(11mm,-5mm)
    -- ++(2mm,5mm) -- ++(11mm,-5mm)
    -- ++(2mm,5mm) -- ++(11mm,-5mm);

    \node[motorFlop] (motor) at (25.2mm,3.7mm) {};
    \coordinate[above=2mm of motor] (arrowAnchor);
    \draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-4.5mm,0);
    \draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(9mm,0);
  \end{tikzpicture}

  \graphSkip

  \begin{tikzpicture}
    \draw[axis] (0,0) --
    node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (3)}
    (0,\yLength)
    node[xshift=3mm] {$U$};
    \draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
    \draw[landscape] (\landscapeXOff,\landscapeYOff) -- +(52mm,0);

    \node[motorFlip] (motor) at (25.2mm,3mm) {};
    \node[motorGhost] at ($(motor)-(3.5mm,0)$) {};
    \node[motorGhost] at ($(motor)-(6mm,0)$) {};
    \node[motorGhost] at ($(motor)+(3.5mm,0)$) {};
    \node[motorGhost] at ($(motor)+(6mm,0)$) {};

    \coordinate[above=2mm of motor] (arrowAnchor);
    \draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-6mm,0);
    \draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(6mm,0);
  \end{tikzpicture}

  \graphSkip

  \begin{tikzpicture}
    \draw[axis] (0,0) --
    node[midway,xshift=\stepLabOff,minimum width=7mm] {\small (4)}
    (0,\yLength)
    node[xshift=3mm] {$U$};
    \draw[axis] (0,0) -- (\xLength, 0) node[yshift=-2mm,xshift=-1mm] {$x$};
    \draw[landscape] (\landscapeXOff,\landscapeYOff)
    -- ++(2mm,5mm) -- ++(11mm,-5mm)
    -- ++(2mm,5mm) -- ++(11mm,-5mm)
    -- ++(2mm,5mm) -- ++(11mm,-5mm)
    -- ++(2mm,5mm) -- ++(11mm,-5mm);

    \node[motorFlop] (motor) at (38.2mm,3.7mm) {};
    \coordinate[above=2mm of motor] (arrowAnchor);
    \draw[movement] ($(arrowAnchor)-(2mm,0)$) -- +(-4.5mm,0);
    \draw[movement] ($(arrowAnchor)+(2mm,0)$) -- +(9mm,0);
  \end{tikzpicture}

  \caption{A schematic illustration of the Brownian ratchet model of
    molecular motors.  A motor is shown as a circle
    (\protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlip,minimum
    size=2.5mm]{}; or
    \protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlop,minimum
    size=2.5mm]{};), and its energy landscape is shown as a thick line
    \protect\tikz[baseline,yshift=.2em]\protect\draw[landscape]
    (0,0) -- (2ex,0);.  The horizontal axis $x$ represents the motor's
    position on the cytoskeletal track, while the vertical axis $U$
    illustrates the motor's free energy.  The motor is hypothesized to
    feature two distinct potential energy landscapes, depending on its
    conformational state.  In the flip conformation
    \protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlip,minimum
    size=2.5mm]{};, the energy landscape is flat so the protein may
    slide freely in one of the two directions, with equal probability
    for both directions.  In the flop conformation
    \protect\tikz[baseline,yshift=1.2mm]\protect\node[motorFlop,minimum
    size=2.5mm]{};, the saw-tooth shape of the landscape favors the
    motor moving to the right, illustrated by a longer arrow pointing
    to the right.  When cycles of ATP hydrolysis make the motor
    periodically switch between the two conformations, thermal
    fluctuations will tend to push it to the right.  (The original
    figure is~\cite[Figure~4]{Nicholson2019}, itself a reproduction
    from~\cite{Kurakin2006}.)}
  \label{fig:ratchet-motor}
\end{figure}

\section{A Deal: Mutually beneficial interactions}
\label{sec:deals}

Seeing Life as an ensemble of machines biases how we expect to collect
profit from acting on it.  Machine means control: we are constantly
looking for knobs which we could turn this or that way, and which
could modify the behavior of the system to fit our needs and
expectations.  This can be seen both at the very practical level,
where bioengineers seek to modify bacteria to produce chemicals,
e.g.~\cite{berkleyBio}, and also at the theoretical level, where
researchers develop methodologies to support looking for the coveted
knobs, e.g.~\cite{PardoID21,Vogel2008,Zanudo2015}.  If we admit that
the reductionistic and mechanistic approach is not globally true, we
must accept that these knobs may not necessarily have a definitive
shape, but rather be a complex assemblage of factors, affecting the
trajectory of the system in multiple non-trivial ways, and possibly
shifting in time.  Finally, this control mindset introduces an
asymmetric relationship between the controller and the controlled,
which is unnatural biological context because both the controller and
the controlled are made out of the same kind of matter, and are
ultimately embedded in the same environment.
of a Deal with Life is to render the interactions \emph{mutually
  beneficial}: ideally, both systems engaging in the interaction
should benefit from it.  In practice, this should be translated into
joint maximization of a pair of functions measuring the utility of the
interaction for both parties, possibly with one of the two functions
being prioritized over the other.

\printbibliography[heading=subbibliography]

\end{refsection}

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