Is the cell really a machine?

Open problems in synthetic multicellularity

Multicellularity is one of the major evolutionary transitions, and its rise provided the ingredients for the emergence of a biosphere inhabited by complex organisms. Over the last decades, the potential for bioengineering multicellular systems has been instrumental in interrogating nature and exploring novel paths to regeneration, disease, cognition, and behaviour. Here, we provide a list of open problems that encapsulate many of the ongoing and future challenges in the field and suggest conceptual approaches that may facilitate progress.

Biological mechanisms contradict AI consciousness: The spaces between the notes

The presumption that experiential consciousness requires a nervous system and brain has been central to the debate on the possibility of developing a conscious form of artificial intelligence (AI). The likelihood of future AI consciousness or devising tools to assess its presence has focused on how AI might mimic brain-centered activities. Currently, dual general assumptions prevail: AI consciousness is primarily an issue of functional information density and integration, and no substantive technical barriers exist to prevent its achievement. When the cognitive process that underpins consciousness is stipulated as a cellular attribute, these premises are directly contradicted. The innate characteristics of biological information and how that information is managed by individual cells have no parallels within machine-based AI systems. Any assertion of computer-based AI consciousness represents a fundamental misapprehension of these crucial differences.

Modeling relaxation experiments with a mechanistic model of gene expression

BACKGROUND

In the present work, we aimed at modeling a relaxation experiment which consists in selecting a subfraction of a cell population and observing the speed at which the entire initial distribution for a given marker is reconstituted.

METHODS

For this we first proposed a modification of a previously published mechanistic two-state model of gene expression to which we added a state-dependent proliferation term. This results in a system of two partial differential equations. Under the assumption of a linear dependence of the proliferation rate with respect to the marker level, we could derive the asymptotic profile of the solutions of this model.

RESULTS

In order to confront our model with experimental data, we generated a relaxation experiment of the CD34 antigen on the surface of TF1-BA cells, starting either from the highest or the lowest CD34 expression levels. We observed in both cases that after approximately 25 days the distribution of CD34 returns to its initial stationary state. Numerical simulations, based on parameter values estimated from the dataset, have shown that the model solutions closely align with the experimental data from the relaxation experiments.

CONCLUSION

Altogether our results strongly support the notion that cells should be seen and modeled as probabilistic dynamical systems.

Quantum modeling simulates nutrient effect of bioplastic polyhydroxyalkanoate (PHA) production in Pseudomonas putida

Polyhydroxyalkanoates (PHAs) could be used to make sustainable, biodegradable plastics. However, the precise and accurate mechanistic modeling of PHA biosynthesis, especially medium-chain-length PHA (mcl-PHA), for yield improvement remains a challenge to biology. PHA biosynthesis is typically triggered by nitrogen limitation and tends to peak at an optimal carbon-to-nitrogen (C/N) ratio. Specifically, simulation of the underlying dynamic regulation mechanisms for PHA bioprocess is a bottleneck owing to surfeit model complexity and current modeling philosophies for uncertainty. To address this issue, we proposed a quantum-like decision-making model to encode gene expression and regulation events as hidden layers by the general transformation of a density matrix, which uses the interference of probability amplitudes to provide an empirical-level description for PHA biosynthesis. We implemented our framework modeling the biosynthesis of mcl-PHA in Pseudomonas putida with respect to external C/N ratios, showing its optimization production at maximum PHA production of 13.81% cell dry mass (CDM) at the C/N ratio of 40:1. The results also suggest the degree of P. putida's preference in channeling carbon towards PHA production as part of the bacterium's adaptative behavior to nutrient stress using quantum formalism. Generic parameters (kD, kN and theta θ) obtained based on such quantum formulation, representing P. putida's PHA biosynthesis with respect to external C/N ratios, was discussed. This work offers a new perspective on the use of quantum theory for PHA production, demonstrating its application potential for other bioprocesses.

Biological computations: Limitations of attractor-based formalisms and the need for transients

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, for e.g. via cell surface receptors or sensory organs. By integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.

Molecular Ratchets and Kinetic Asymmetry: Giving Chemistry Direction

Over the last two decades ratchet mechanisms have transformed the understanding and design of stochastic molecular systems-biological, chemical and physical-in a move away from the mechanical macroscopic analogies that dominated thinking regarding molecular dynamics in the 1990s and early 2000s (e.g. pistons, springs, etc), to the more scale-relevant concepts that underpin out-of-equilibrium research in the molecular sciences today. Ratcheting has established molecular nanotechnology as a research frontier for energy transduction and metabolism, and has enabled the reverse engineering of biomolecular machinery, delivering insights into how molecules 'walk' and track-based synthesisers operate, how the acceleration of chemical reactions enables energy to be transduced by catalysts (both motor proteins and synthetic catalysts), and how dynamic systems can be driven away from equilibrium through catalysis. The recognition of molecular ratchet mechanisms in biology, and their invention in synthetic systems, is proving significant in areas as diverse as supramolecular chemistry, systems chemistry, dynamic covalent chemistry, DNA nanotechnology, polymer and materials science, molecular biology, heterogeneous catalysis, endergonic synthesis, the origin of life, and many other branches of chemical science. Put simply, ratchet mechanisms give chemistry direction. Kinetic asymmetry, the key feature of ratcheting, is the dynamic counterpart of structural asymmetry (i.e. chirality). Given the ubiquity of ratchet mechanisms in endergonic chemical processes in biology, and their significance for behaviour and function from systems to synthesis, it is surely just as fundamentally important. This Review charts the recognition, invention and development of molecular ratchets, focussing particularly on the role for which they were originally envisaged in chemistry, as design elements for molecular machinery. Different kinetically asymmetric systems are compared, and the consequences of their dynamic behaviour discussed. These archetypal examples demonstrate how chemical systems can be driven inexorably away from equilibrium, rather than relax towards it.

Bio-analog dissipative structures and principles of biological behavior

The place of living organisms in the natural world is a nearly perennial question in philosophy and the sciences; how can inanimate matter yield animate beings? A dominant answer for several centuries has been to treat organisms as sophisticated machines, studying them with the mechanistic physics and chemistry that have given rise to technology and complex machines. Since the early 20th century, many scholars have sought instead to naturalize biology through thermodynamics, recognizing the precarious far-from-equilibrium state of organisms. Erwin Bauer was an early progenitor of this perspective with ambitions of "general laws for the movement of living matter". In addition to taking a thermodynamic perspective, Bauer recognized that organisms are fundamentally behaving systems, and that explaining the physics of life requires explaining the origins of intentionality, adaptability, and self-regulation. Bauer, like some later scholars, seems to advocate for a "new physics", one that extends beyond mechanics and classical thermodynamic, one that would be inclusive of living systems. In this historical review piece, we explore some of Bauer's ideas and explain how similar concepts have been explored in modern non-equilibrium thermodynamics and dissipative structure theory. Non-living dissipative structures display end-directedness, self-maintenance, and adaptability analogous to organisms. These findings also point to an alternative framework for the life sciences, that treats organisms not as machines but as sophisticated dissipative structures. We evaluate the differences between mechanistic and thermodynamic perspectives on life, and what each theory entails for understanding the behavior of organisms.

Engineering is evolution: a perspective on design processes to engineer biology

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.

Debunking the idea of biological optimisation: quantitative biology to the rescue

The idea that plants would be efficient, frugal or optimised echoes the recurrent semantics of 'blueprint' and 'program' in molecular genetics. However, when analysing plants with quantitative approaches and systems thinking, we instead find that plants are the results of stochastic processes with many inefficiencies, incoherence or delays fuelling their robustness. If one had to highlight the main value of quantitative biology, this could be it: plants are robust systems because they are not efficient. Such systemic insights extend to the way we conduct plant research and opens plant science publication to a much broader framework.

A Holistic Perspective on Living Aggregate

Aggregate is one of the most extensive existing modes of matters in the world. Besides the research objectives of inanimate systems in physical science, the entities in life science can be regarded as living aggregates, which are far from being thoroughly understood despite the great advances in molecular biology. Molecular biology follows the research philosophy of reductionism, which generally reduces the whole into parts to study. Although reductionism benefits the understanding of molecular behaviors, it encounters limitations when extending to the aggregate level. Holism is another epistemology comparable to reductionism, which studies objectives at the aggregate level, emphasizing the interactions and synergetic/antagonistic effects of a group of composed single entities in determining the characteristics of a whole. As a representative of holism, aggregation-induced emission (AIE) materials have made great achievements in the past two decades in both physical and life science. In particular, the unique properties of AIE materials endow them with in situ and real-time visual methods to investigate the inconsistency between microscopic molecules and macroscopic substances, offering researchers excellent toolkits to study living aggregates. The applications of AIE materials in life science are still in their infancy and worth expanding. In this Perspective, we summarize the research progress of AIE materials in unveiling some phenomena and processes of living systems, aiming to provide a general research approach from the viewpoint of holism. At last, insights into what we can do in the near future are also raised and discussed.

Seven knowledge gaps in modern biogerontology

About a year ago, members of the editorial board of Biogerontology were requested to respond to a query by the editor-in-chief of the journal as to what one question within their field of ageing research still needs to be asked and answered. This editorial is inspired by the wide range and variety of questions, ideas, comments and suggestions received in response to that query. The seven knowledge gaps identified in this article are arranged into three main categories: evolutionary aspects of longevity, biological survival and death aspects, and heterogeneity in the progression and phenotype of ageing. This is not an exhaustive and exclusive list, and may be modified and expanded. Implications of these knowledge gaps, especially in the context of ongoing attempts to develop effective interventions in ageing and longevity are also discussed.

Patterns Lead the Way to Far-from-Equilibrium Materials

The universe is a complex fabric of repeating patterns that unfold their beauty in system-specific diversity. The periodic table, crystallography, and the genetic code are classic examples that illustrate how even a small number of rules generate a vast range of shapes and structures. Today, we are on the brink of an AI-driven revolution that will reveal an unprecedented number of novel patterns, many of which will escape human intuition and expertise. We suggest that in the second half of the 21st century, the challenge for Physical Chemistry will be to guide and interpret these advances in the broader context of physical sciences and materials-related engineering. If we succeed in this role, Physical Chemistry will be able to extend to new horizons. In this article, we will discuss examples that strike us as particularly promising, specifically the discovery of high-entropy and far-from-equilibrium materials as well as applications to origins-of-life research and the search for life on other planets.

The feasibility of artificial consciousness through the lens of neuroscience

Interactions with large language models (LLMs) have led to the suggestion that these models may soon be conscious. From the perspective of neuroscience, this position is difficult to defend. For one, the inputs to LLMs lack the embodied, embedded information content characteristic of our sensory contact with the world around us. Secondly, the architectures of present-day artificial intelligence algorithms are missing key features of the thalamocortical system that have been linked to conscious awareness in mammals. Finally, the evolutionary and developmental trajectories that led to the emergence of living conscious organisms arguably have no parallels in artificial systems as envisioned today. The existence of living organisms depends on their actions and their survival is intricately linked to multi-level cellular, inter-cellular, and organismal processes culminating in agency and consciousness.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

Advances in science and engineering often reveal the limitations of classical approaches initially used to understand, predict, and control phenomena. With progress, conceptual categories must often be re-evaluated to better track recently discovered invariants across disciplines. It is essential to refine frameworks and resolve conflicting boundaries between disciplines such that they better facilitate, not restrict, experimental approaches and capabilities. In this essay, we address specific questions and critiques which have arisen in response to our research program, which lies at the intersection of developmental biology, computer science, and robotics. In the context of biological machines and robots, we explore changes across concepts and previously distinct fields that are driven by recent advances in materials, information, and life sciences. Herein, each author provides their own perspective on the subject, framed by their own disciplinary training. We argue that as with computation, certain aspects of developmental biology and robotics are not tied to specific materials; rather, the consilience of these fields can help to shed light on issues of multiscale control, self-assembly, and relationships between form and function. We hope new fields can emerge as boundaries arising from technological limitations are overcome, furthering practical applications from regenerative medicine to useful synthetic living machines.

Increased gene expression variability hinders the formation of regional mechanical conflicts leading to reduced organ shape robustness

To relate gene networks and organ shape, one needs to address two wicked problems: i) Gene expression is often variable locally, and shape is reproducible globally; ii) gene expression can have cascading effects on tissue mechanics, with possibly counterintuitive consequences for the final organ shape. Here, we address such wicked problems, taking advantage of simpler plant organ development where shape only emerges from cell division and elongation. We confirm that mutation in VERNALIZATION INDEPENDENCE 3 (VIP3), a subunit of the conserved polymerase-associated factor 1 complex (Paf1C), increases gene expression variability in Arabidopsis. Then, we focused on the Arabidopsis sepal, which exhibits a reproducible shape and stereotypical regional growth patterns. In vip3 sepals, we measured higher growth heterogeneity between adjacent cells. This even culminated in the presence of negatively growing cells in specific growth conditions. Interestingly, such increased local noise interfered with the stereotypical regional pattern of growth. We previously showed that regional differential growth at the wild-type sepal tip triggers a mechanical conflict, to which cells resist by reinforcing their walls, leading to growth arrest. In vip3, the disturbed regional growth pattern delayed organ growth arrest and increased final organ shape variability. Altogether, we propose that gene expression variability is managed by Paf1C to ensure organ robustness by building up mechanical conflicts at the regional scale, instead of the local scale.

The machine-organism relation revisited

This article addresses some crucial assumptions that are rarely acknowledged when organisms and machines are compared. We begin by presenting a short historical reconstruction of the concept of "machine." We show that there has never been a unique and widely accepted definition of "machine" and that the extant definitions are based on specific technologies. Then we argue that, despite the concept's ambiguity, we can still defend a more robust, specific, and useful notion of machine analogy that accounts for successful strategies in connecting specific devices (or mechanisms) with particular living phenomena. For that purpose, we distinguish between what we call "generic identity" and proper "machine analogy." We suggest that "generic identity"-which, roughly stated, presumes that some sort of vague similarity might exist between organisms and machines-is a source of the confusion haunting many persistent disagreements and that, accordingly, it should be dismissed. Instead, we endorse a particular form of "machine analogy" where the relation between organic phenomena and mechanical devices is not generic but specific and grounded on the identification of shared "invariants." We propose that the machine analogy is a kind of analogy as proportion and we elucidate how this is used or might be used in scientific practices. We finally argue that while organisms are not machines in a generic sense, they might share many robust "invariants," which justify the scientists' use of machine analogies for grasping living phenomena.

There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing"-the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Proteins turn "Proteans" - The over 40-year delayed paradigm shift in structural biology: From "native proteins in uniquely defined configurations" to "intrinsically disordered proteins"

The current millennium brought up a revolutionary paradigm shift in molecular biology: many operative proteins, rather than being quasi-rigid polypeptide chains folded into unique configurations - as believed throughout most of the past century - are now known to be intrinsically disordered, dynamic, pleomorphic, and multifunctional structures with stochastic behaviors. Yet, part of this knowledge, including suggestions about possible mechanisms and plenty of evidence for the same, became available by the 1950s and 1960s to remain then nearly forgotten for over 40 years. Here, we review the main steps toward the classic notions about protein structures, as well as the neglected precedents of present views, discuss possible explanations for such long oblivion, and offer a sketch of the current panorama in this field.

Neurobiological reduction: From cellular explanations of behavior to interventions

Scientific reductionism, the view that higher level functions can be explained by properties at some lower-level or levels, has been an assumption of nervous system analyses since the acceptance of the neuron doctrine in the late 19th century, and became a dominant experimental approach with the development of intracellular recording techniques in the mid-20th century. Subsequent refinements of electrophysiological approaches and the continual development of molecular and genetic techniques have promoted a focus on molecular and cellular mechanisms in experimental analyses and explanations of sensory, motor, and cognitive functions. Reductionist assumptions have also influenced our views of the etiology and treatment of psychopathologies, and have more recently led to claims that we can, or even should, pharmacologically enhance the normal brain. Reductionism remains an area of active debate in the philosophy of science. In neuroscience and psychology, the debate typically focuses on the mind-brain question and the mechanisms of cognition, and how or if they can be explained in neurobiological terms. However, these debates are affected by the complexity of the phenomena being considered and the difficulty of obtaining the necessary neurobiological detail. We can instead ask whether features identified in neurobiological analyses of simpler aspects in simpler nervous systems support current molecular and cellular approaches to explaining systems or behaviors. While my view is that they do not, this does not invite the opposing view prevalent in dichotomous thinking that molecular and cellular detail is irrelevant and we should focus on computations or representations. We instead need to consider how to address the long-standing dilemma of how a nervous system that ostensibly functions through discrete cell to cell communication can generate population effects across multiple spatial and temporal scales to generate behavior.

Artifacts and levels of abstraction

The purpose of this article is to show how the comparison or analogy with artifacts (i.e., systems engineered by humans) is foundational for the idea that complex neuro-cognitive systems are amenable to explanation at distinct levels, which is a central simplifying strategy for modeling the brain. The most salient source of analogy is of course the digital computer, but I will discuss how some more general comparisons with the processes of design and engineering also play a significant role. I will show how the analogies, and the subsequent notion of a distinct computational level, have engendered common ideas about how safely to abstract away from the complexity of concrete neural systems, yielding explanations of how neural processes give rise to cognitive functions. I also raise worries about the limitations of these explanations, due to neglected differences between the human-made devices and biological organs.

Resolving teleology’s false dilemma

This paper argues that the account of teleology previously proposed by the authors is consistent with the physical determinism that is implicit across many of the sciences. We suggest that much of the current aversion to teleological thinking found in the sciences is rooted in debates that can be traced back to ancient natural science, which pitted mechanistic and deterministic theories against teleological ones. These debates saw a deterministic world as one where freedom and agency is impossible. And, because teleological entities seem to be free to either reach their ends or not, it was assumed that they could not be deterministic. Mayr’s modern account of teleonomy adheres to this basic assumption. Yet, the seeming tension between teleology and determinism is illusory because freedom and agency do not, in fact, conflict with a deterministic world. To show this, we present a taxonomy of different types of freedom that we see as inherent in teleological systems. Then we show that our taxonomy of freedom, which is crucial to understanding teleology, shares many of the features of a philosophical position regarding free will that is known in the contemporary literature as ‘compatibilism’. This position maintains that an agent is free when the sources of its actions are internal, when the agent itself is the deterministic cause of those actions. Our view shows that freedom is not only indispensable to teleology, but also that, contrary to common intuitions, there is no conflict between teleology and causal determinism.

The essence of life revisited: how theories can shed light on it

Disagreement over whether life is inevitable when the conditions can support life remains unresolved, but calculations show that self-organization can arise naturally from purely random effects. Closure to efficient causation, or the need for all specific catalysts used by an organism to be produced internally, implies that a true model of an organism cannot exist, though this does not exclude the possibility that some characteristics can be simulated. Such simulations indicate that there is a limit to how small a self-organizing system can be: much smaller than a bacterial cell, but around the size of a typical virus particle. All current theories of life incorporate, at least implicitly, the idea of catalysis, but they largely ignore the need for metabolic regulation.

Organization needs organization: Understanding integrated control in living organisms

Organization figures centrally in the understanding of biological systems advanced by both new mechanists and proponents of the autonomy framework. The new mechanists focus on how components of mechanisms are organized to produce a phenomenon and emphasize productive continuity between these components. The autonomy framework focuses on how the components of a biological system are organized in such a way that they contribute to the maintenance of the organisms that produce them. In this paper we analyze and compare these two accounts of organization and argue that understanding biological organisms as cohesively integrated systems benefits from insights from both. To bring together the two accounts, we focus on the notions of control and regulation as bridge concepts. We start from a characterization of biological mechanisms in terms of constraints and focus on a specific type of mechanism, control mechanisms, that operate on other mechanisms on the basis of measurements of variables in the system and its environment. Control mechanisms are characterized by their own set of constraints that enable them to sense conditions, convey signals, and effect changes on constraints in the controlled mechanism. They thereby allow living organisms to adapt to internal and external variations and to coordinate their parts in such a manner as to maintain viability. Because living organisms contain a vast number of control mechanisms, a central challenge is to understand how they are themselves organized. With the support of examples from both unicellular and multicellular systems we argue that control mechanisms are organized heterarchically, and we discuss how this type of control architecture can, without invoking top-down and centralized forms of organizations, succeed in coordinating internal activities of organisms.

Brains as Computers: Metaphor, Analogy, Theory or Fact?

Whether electronic, analog or quantum, a computer is a programmable machine. Wilder Penfield held that the brain is literally a computer, because he was a dualist: the mind programs the brain. If this type of dualism is rejected, then identifying the brain to a computer requires defining what a brain “program” might mean and who gets to “program” the brain. If the brain “programs” itself when it learns, then this is a metaphor. If evolution “programs” the brain, then this is a metaphor. Indeed, in the neuroscience literature, the brain-computer is typically not used as an analogy, i.e., as an explicit comparison, but metaphorically, by importing terms from the field of computers into neuroscientific discourse: we assert that brains compute the location of sounds, we wonder how perceptual algorithms are implemented in the brain. Considerable difficulties arise when attempting to give a precise biological description of these terms, which is the sign that we are indeed dealing with a metaphor. Metaphors can be both useful and misleading. The appeal of the brain-computer metaphor is that it promises to bridge physiological and mental domains. But it is misleading because the basis of this promise is that computer terms are themselves imported from the mental domain (calculation, memory, information). In other words, the brain-computer metaphor offers a reductionist view of cognition (all cognition is calculation) rather than a naturalistic theory of cognition, hidden behind a metaphoric blanket.

Naturalising Agent Causation

The idea of agent causation—that a system such as a living organism can be a cause of things in the world—is often seen as mysterious and deemed to be at odds with the physicalist thesis that is now commonly embraced in science and philosophy. Instead, the causal power of organisms is attributed to mechanistic components within the system or derived from the causal activity at the lowest level of physical description. In either case, the ‘agent’ itself (i.e., the system as a whole) is left out of the picture entirely, and agent causation is explained away. We argue that this is not the right way to think about causation in biology or in systems more generally. We present a framework of eight criteria that we argue, collectively, describe a system that overcomes the challenges concerning agent causality in an entirely naturalistic and non-mysterious way. They are: (1) thermodynamic autonomy, (2) persistence, (3) endogenous activity, (4) holistic integration, (5) low-level indeterminacy, (6) multiple realisability, (7) historicity, (8) agent-level normativity. Each criterion is taken to be dimensional rather than categorical, and thus we conclude with a short discussion on how researchers working on quantifying agency may use this multidimensional framework to situate and guide their research.

Mechanisms of macromolecular reactions

During the past two decades, philosophers of biology have increasingly turned their attention to mechanisms of biological phenomena. Through analyses of mechanistic proposals advanced by biologists, the goal of these philosophers is to understand what a mechanism is and how mechanisms explain. These analyses have generally focused on mechanistic proposals for phenomenon that occur at the cellular or sub-cellular level, such as synapse firing, protein synthesis, or metabolic pathway operation. Little is said about the mechanisms of the macromolecular reactions that underpin these phenomena. These reactions comprise a diverse family of reaction types, and include protein folding, macromolecular complex formation, receptor-ligand interactions, and enzyme catalysis. In this paper, I develop an account of mechanism that focuses exclusively on macromolecular reactions. I begin by reviewing how mechanism is understood in enzymology, and how mechanistic concepts of enzymology apply to macromolecular reactions in general. We will see that the mechanism of a macromolecular reaction is most accurately described as a progression of reaction intermediates, where the evolution of intermediates, from one to the next, is characterized by an energetic coupling between chemistry and protein dynamics. I then make the case that this description necessitates a grounding in a process ontology. To describe the mechanism by which a macromolecular reaction occurs is to describe a process.

The genetic control paradigm in biology: What we say, and what we are entitled to mean

We comment on the article by Keith Baverstock (2021) and provide critiques of the concepts of genetic control, genetic blueprint and genetic program.

A New Story of the Three Magi: Scaffolding Proteins and lncRNA Suppressors of Cancer

Scaffolding molecules exert a critical role in orchestrating cellular response through the spatiotemporal assembly of effector proteins as signalosomes. By increasing the efficiency and selectivity of intracellular signaling, these molecules can exert (anti/pro)oncogenic activities. As an archetype of scaffolding proteins with tumor suppressor property, the present review focuses on MAGI1, 2, and 3 (membrane-associated guanylate kinase inverted), a subgroup of the MAGUK protein family, that mediate networks involving receptors, junctional complexes, signaling molecules, and the cytoskeleton. MAGI1, 2, and 3 are comprised of 6 PDZ domains, 2 WW domains, and 1 GUK domain. These 9 protein binding modules allow selective interactions with a wide range of effectors, including the PTEN tumor suppressor, the β-catenin and YAP1 proto-oncogenes, and the regulation of the PI3K/AKT, the Wnt, and the Hippo signaling pathways. The frequent downmodulation of MAGIs in various human malignancies makes these scaffolding molecules and their ligands putative therapeutic targets. Interestingly, MAGI1 and MAGI2 genetic loci generate a series of long non-coding RNAs that act as a tumor promoter or suppressor in a tissue-dependent manner, by selectively sponging some miRNAs or by regulating epigenetic processes. Here, we discuss the different paths followed by the three MAGIs to control carcinogenesis.

Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype-phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes.

CBC-Clock Theory of Life - Integration of cellular circadian clocks and cellular sentience is essential for cognitive basis of life

Cellular circadian clocks represent ancient anticipatory systems which co-evolved with the first cells to safeguard their survival. Cyanobacteria represent one of the most ancient cells, having essentially invented photosynthesis together with redox-based cellular circadian clocks some 2.7 billion years ago. Bioelectricity phenomena, based on redox homeostasis associated electron transfers in membranes and within protein complexes inserted in excitable membranes, play important roles, not only in the cellular circadian clocks and in anesthetics-sensitive cellular sentience (awareness of environment), but also in the coupling of single cells into tissues and organs of unitary multicellular organisms. This integration of cellular circadian clocks with cellular basis of sentience is an essential feature of the cognitive CBC-Clock basis of cellular life.

Synthetic living machines: A new window on life

Increased control of biological growth and form is an essential gateway to transformative medical advances. Repairing of birth defects, restoring lost or damaged organs, normalizing tumors, all depend on understanding how cells cooperate to make specific, functional large-scale structures. Despite advances in molecular genetics, significant gaps remain in our understanding of the meso-scale rules of morphogenesis. An engineering approach to this problem is the creation of novel synthetic living forms, greatly extending available model systems beyond evolved plant and animal lineages. Here, we review recent advances in the emerging field of synthetic morphogenesis, the bioengineering of novel multicellular living bodies. Emphasizing emergent self-organization, tissue-level guided self-assembly, and active functionality, this work is the essential next generation of synthetic biology. Aside from useful living machines for specific functions, the rational design and analysis of new, coherent anatomies will greatly increase our understanding of foundational questions in evolutionary developmental and cell biology.

From Wingspread to CLARITY: a personal trajectory

In the three decades since endocrine disruption was conceptualized at the Wingspread Conference, we have witnessed the growth of this multidisciplinary field and the accumulation of evidence showing the deleterious health effects of endocrine-disrupting chemicals. It is only within the past decade that, albeit slowly, some changes regarding regulatory measures have taken place. In this Perspective, we address some historical points regarding the advent of the endocrine disruption field and the conceptual changes that endocrine disruption brought about. We also provide our personal recollection of the events triggered by our serendipitous discovery of oestrogenic activity in plastic, a founder event in the field of endocrine disruption. This recollection ends with the CLARITY study as an example of a discordance between 'science for its own sake' and 'regulatory science' and leads us to offer a perspective that could be summarized by the motto attributed to Ludwig Boltzmann: "Nothing is more practical than a good theory".

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

Biomolecular Basis of Cellular Consciousness via Subcellular Nanobrains

Cells emerged at the very beginning of life on Earth and, in fact, are coterminous with life. They are enclosed within an excitable plasma membrane, which defines the outside and inside domains via their specific biophysical properties. Unicellular organisms, such as diverse protists and algae, still live a cellular life. However, fungi, plants, and animals evolved a multicellular existence. Recently, we have developed the cellular basis of consciousness (CBC) model, which proposes that all biological awareness, sentience and consciousness are grounded in general cell biology. Here we discuss the biomolecular structures and processes that allow for and maintain this cellular consciousness from an evolutionary perspective.

Shaping therapeutic trajectories in mental health: Instructive vs. permissive causality

We are currently facing the challenge of improving treatments for psychiatric disorders such as major depression. Notably, antidepressants have an incomplete efficacy, mostly due to our limited knowledge of their action. Here we present a theoretical framework that considers the distinction between instructive and permissive causality, which allows formalizing and disentangling the effects exerted by different therapeutic strategies commonly used in psychiatry. Instructive causality implies that an action determines a specific effect while permissive causality allows an action to take effect or not. We posit that therapeutic strategies able to improve the quality of the living environment or the ability to face it, including changes in lifestyle and psychotherapeutic interventions, rely mainly on instructive causality and thus shape the individual's ability to face the psychopathology and build resilience. By contrast, pharmacological treatments, such as selective serotonin reuptake inhibitors, act primarily through a permissive causality: they boost neural plasticity, i.e. the ability of the brain to change itself, and therefore allow for instructive interventions to produce beneficial effects or not. The combination of an instructive and a permissive action represents the most promising approach since the quality of the living environment can shape the path leading to mental health while drug treatment can increase the likelihood of achieving such a goal.

Plants Show Us the Light

In a recent article, Arp et al. (Science 2020, 368, 1490-1495) propose a new theory as to why plants are green: plants prioritize the management of light fluctuations over maximal efficiency. Beyond plant science, this conclusion may inspire our sustainability strategies, to shift our societal goals from performance to resilience.

Imaging the living brain: An argument for ruthless reductionism from olfactory neurobiology

Should theories of "higher-level" cognitive effects originate in "lower-level" molecular mechanisms? This paper supports reductionist explanations of sensory perception via molecular mechanisms in neurobiology. It shows that molecular and cellular mechanisms must constitute the material foundation to derive better theories and models for neuroscience. In support of "bottom-up theorizing", I explore the recent application of a new real-time molecular imaging technique (SCAPE microscopy) to mixture coding in olfaction. Seemingly emergent "higher-level" psychological effects in odor perception, irreducible to the physical stimulus, are linked back to underlying molecular mechanisms at the receptor level. The SCAPE study has notable theoretical impact. It provides a possible answer to the neurocomputational challenge in olfaction from combinatorial coding at the periphery: how does the brain discriminate different complex mixtures from widespread and overlapping receptor activation? The failure of previous reductionist structure-odor explanations is shown to reside in misconceptualizations of the critical causal elements involved. Causally fundamental features are not of parts independently of a mechanism. Components and their relevant features are units via their causal role within a mechanism. Here, new technologies allow revisiting our understanding of the ontology and levels of organization of a system.

Kinetic coherence underlies the dynamics of disordered proteins

The dynamics of two proteins of similar size, the globular lysozyme and the intrinsically disordered Huntingtin interacting protein, has been simulated in three states resembling a globule, a pre-molten globule, and a molten globule. A coherence time τ has been defined, measuring the delay in the display of a stochastic behaviour after a perturbation of the system. This time has been computed for two sets of collective variables: the projection of the phase point onto the positions and momenta subspaces (τ_r and τ_p), and the principal components (PCs) of positions q and momenta π produced by a covariance analysis in these subspaces (τ_q and τ_π). In all states τ_p ≈ 3.5τ_r, and τ_π ≈ 3.5τ_q. The coherence times of individual PCs, τ^(l)_q and τ^(l)_π, have also been computed, and τ^(l)_π > τ^(l)_q in all states. The prevalence of τ_p over τ_r, or of τ_π over τ_q, drives the dynamics of the protein over a time range of ≈1–2 ps; moreover, a hidden synchronism appears to raise the momenta subspace's coherence above that of its individual PCs. In the transition of lysozyme to the molten globule the τ^(l)_q decrease but, unexpectedly, the τ^(l)_π increase; after this transition τ_p ≈ 5τ_r and τ_π ≈ 5τ_q. A gain of kinetic coherence accompanies thus the loss of structural coherence caused by the denaturation of the protein in the transition from globule to molten globule. The increase of the τ^(l)_π does not take place in the analogous transition of the Huntingtin protein. These results are compared with those of a similar analysis performed on three pseudo-proteins designed by scrambling the primary sequence of the Huntingtin interacting protein, and on two oligopeptides. The hidden synchronism appears to be a generic property of these polypeptides. The τ^(l)_π spectrum is similar in denaturated and in intrinsically disordered biomolecules; but the gain of kinetic coherence as a result of denaturation seems to be a specific property of the biologically functional lysozyme.

In the phase space of a globular or intrinsically disordered protein, the momenta's dynamics is less chaotic than the coordinates' dynamics. When a protein is denaturated, a gain in kinetic coherence accompanies the loss of structural coherence.

Expression Partitioning of Duplicate Genes at Single Cell Resolution in Arabidopsis Roots

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene - its site of expression and thus the initial locus of its function - occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.

Stochasticity in microbiology: managing unpredictability to reach the Sustainable Development Goals

Pure (single) cultures of microorganisms and mixed microbial communities (microbiomes) have been important for centuries in providing renewable energy, clean water and food products to human society and will continue to play a crucial role to pursue the Sustainable Development Goals. To use microorganisms effectively, microbial engineered processes require adequate control. Microbial communities are shaped by manageable deterministic processes, but also by stochastic processes, which can promote unforeseeable variations and adaptations. Here, we highlight the impact of stochasticity in single culture and microbiome engineering. First, we discuss the concepts and mechanisms of stochasticity in relation to microbial ecology of single cultures and microbiomes. Second, we discuss the consequences of stochasticity in relation to process performance and human health, which are reflected in key disadvantages and important opportunities. Third, we propose a suitable decision tool to deal with stochasticity in which monitoring of stochasticity and setting the boundaries of stochasticity by regulators are central aspects. Stochasticity may give rise to some risks, such as the presence of pathogens in microbiomes. We argue here that by taking the necessary precautions and through clever monitoring and interpretation, these risks can be mitigated.

The Role of Stochasticity in the Origin of Epigenetic Variation in Animal Populations

Epigenetic mechanisms such as DNA methylation modulate gene expression in a complex fashion are consequently recognized as among the most important contributors to phenotypic variation in natural populations of plants, animals, and microorganisms. Interactions between genetics and epigenetics are multifaceted and epigenetic variation stands at the crossroad between genetic and environmental variance, which make these mechanisms prominent in the processes of adaptive evolution. DNA methylation patterns depend on the genotype and can be reshaped by environmental conditions, while transgenerational epigenetic inheritance has been reported in various species. On the other hand, DNA methylation can influence the genetic mutation rate and directly affect the evolutionary potential of a population. The origin of epigenetic variance can be attributed to genetic, environmental, or stochastic factors. Generally less investigated than the first two components, variation lacking any predictable order is nevertheless present in natural populations and stochastic epigenetic variation, also referred to spontaneous epimutations, can sustain phenotypic diversity. Here, potential sources of such stochastic epigenetic variability in animals are explored, with a focus on DNA methylation. To this day, quantifying the importance of stochasticity in epigenetic variability remains a challenge. However, comparisons between the mutation and the epimutation rates showed a high level of the latter, suggesting a significant role of spontaneous epimutations in adaptation. The implications of stochastic epigenetic variability are multifold: by affecting development and subsequently phenotype, random changes in epigenetic marks may provide additional phenotypic diversity, which can help natural populations when facing fluctuating environments. In isogenic lineages and asexually reproducing organisms, poor or absent genetic diversity can hence be tolerated. Further implication of stochastic epigenetic variability in adaptation is found in bottlenecked invasive species populations and populations using a bet-hedging strategy.

Extracellular ionic fluxes suggest the basis for cellular life at the 1/f ridge of extended criticality

The criticality hypothesis states that a system may be poised in a critical state at the boundary between different types of dynamics. Previous studies have suggested that criticality has been evolutionarily selected, and examples have been found in cortical cell cultures and in the human nervous system. However, no one has yet reported a single- or multi-cell ensemble that was investigated ex vivo and found to be in the critical state. Here, the precise 1/f noise was found for pollen tube cells of optimum growth and for the physiological ("healthy") state of blood cells. We show that the multi-scale processes that arise from the so-called critical phenomena can be a fundamental property of a living cell. Our results reveal that cell life is conducted at the border between order and disorder, and that the dynamics themselves drive a system towards a critical state. Moreover, a temperature-driven re-entrant state transition, manifest in the form of a Lorentz resonance, was found in the fluctuation amplitude of the extracellular ionic fluxes for the ensemble of elongating pollen tubes of Nicotiana tabacum L. or Hyacintus orientalis L. Since this system is fine-tuned for rapid expansion to reach the ovule at a critical temperature which results in fertilisation, the core nature of criticality (long-range coherence) offers an explanation for its potential in cell growth. We suggest that the autonomous organisation of expansive growth is accomplished by self-organised criticality, which is an orchestrated instability that occurs in an evolving cell.

What Is a Hologenomic Adaptation? Emergent Individuality and Inter-Identity in Multispecies Systems

Contemporary biological research has suggested that some host-microbiome multispecies systems (referred to as "holobionts") can in certain circumstances evolve as unique biological individual, thus being a unit of selection in evolution. If this is so, then it is arguably the case that some biological adaptations have evolved at the level of the multispecies system, what we call hologenomic adaptations. However, no research has yet been devoted to investigating their nature, or how these adaptations can be distinguished from adaptations at the species-level (genomic adaptations). In this paper, we cover this gap by investigating the nature of hologenomic adaptations. By drawing on the case of the evolution of sanguivory diet in vampire bats, we argue that a trait constitutes a hologenomic adaptation when its evolution can only be explained if the holobiont is considered the biological individual that manifests this adaptation, while the bacterial taxa that bear the trait are only opportunistic beneficiaries of it. We then use the philosophical notions of emergence and inter-identity to explain the nature of this form of individuality and argue why it is special of holobionts. Overall, our paper illustrates how the use of philosophical concepts can illuminate scientific discussions, in the trend of what has recently been called metaphysics of biology.

Thermodynamic Limits and Optimality of Microbial Growth

Understanding microbial growth with the use of mathematical models has a long history that dates back to the pioneering work of Jacques Monod in the 1940s. Monod’s famous growth law expressed microbial growth rate as a simple function of the limiting nutrient concentration. However, to explain growth laws from underlying principles is extremely challenging. In the second half of the 20th century, numerous experimental approaches aimed at precisely measuring heat production during microbial growth to determine the entropy balance in a growing cell and to quantify the exported entropy. This has led to the development of thermodynamic theories of microbial growth, which have generated fundamental understanding and identified the principal limitations of the growth process. Although these approaches ignored metabolic details and instead considered microbial metabolism as a black box, modern theories heavily rely on genomic resources to describe and model metabolism in great detail to explain microbial growth. Interestingly, however, thermodynamic constraints are often included in modern modeling approaches only in a rather superficial fashion, and it appears that recent modeling approaches and classical theories are rather disconnected fields. To stimulate a closer interaction between these fields, we here review various theoretical approaches that aim at describing microbial growth based on thermodynamics and outline the resulting thermodynamic limits and optimality principles. We start with classical black box models of cellular growth, and continue with recent metabolic modeling approaches that include thermodynamics, before we place these models in the context of fundamental considerations based on non-equilibrium statistical mechanics. We conclude by identifying conceptual overlaps between the fields and suggest how the various types of theories and models can be integrated. We outline how concepts from one approach may help to inform or constrain another, and we demonstrate how genome-scale models can be used to infer key black box parameters, such as the energy of formation or the degree of reduction of biomass. Such integration will allow understanding to what extent microbes can be viewed as thermodynamic machines, and how close they operate to theoretical optima.

Keeping the Proportions of Protein Complex Components in Check

How do cells maintain relative proportions of protein complex components? Advances in quantitative, genome-wide measurements have begun to shed light onto the roles of protein synthesis and degradation in establishing the precise proportions in living cells: on the one hand, ribosome profiling studies indicate that proteins are already produced in the correct relative proportions. On the other hand, proteomic studies found that many complexes contain subunits that are made in excess and subsequently degraded. Here, we discuss these seemingly contradictory findings, emerging principles, and remaining open questions. We conclude that establishing precise protein levels involves both coordinated synthesis and post-translational fine-tuning via protein degradation.