\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 therefore 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. In this chapter, I introduce the Deal with Life: instead of looking to impact biological systems asymmetrically, surreptitiously lifting ourselves above the living matter, I propose to account for the fact that we act within complex feedback loops, which sometimes end up imposing the consequences of the actions on the actors. The principle 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} %%% Local Variables: %%% TeX-engine: luatex %%% TeX-master: "hdr" %%% End: