Future medicine: from molecular pathways to the collective intelligence of the body

m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics

MOTIVATION

Spatial clustering is a key analytical technique for exploring spatial transcriptomics data. Recent graph neural network-based methods have shown promise in spatial clustering but face notable challenges. One significant issue is that analyzing the functions and complex mechanisms of organisms from a single scale is difficult and most methods focus exclusively on the single-scale representation of transcriptomic data, potentially limiting the discriminative power of extracted features for spatial domain clustering. Furthermore, classical clustering algorithms are often applied directly to latent representation, making it a worthwhile endeavor to explore a tailored clustering method to further improve the accuracy of spatial domain annotation.

RESULTS

To address these limitations, we propose m2ST, a novel dual multi-scale graph clustering method. m2ST first uses a multi-scale masked graph autoencoder to extract representations across different scales from spatial transcriptomic data. To effectively compress and distill meaningful knowledge embedded in the data, m2ST introduces a random masking mechanism for node features and uses a scaled cosine error as the loss function. Additionally, we introduce a tailored multi-scale clustering framework that integrates scale-common and scale-specific information exploration into the clustering process, achieving more robust annotation performance. Shannon entropy is finally utilized to dynamically adjust the importance of different scales. Extensive experiments on multiple spatial transcriptomic datasets demonstrate the superior performance of m2ST compared to existing methods.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BBKing49/m2ST.

Synechism 2.0: Contours of a new theory of continuity in bioengineering

The methodological principle of synechism, the all-pervading continuity first proposed by Charles Peirce in 1892, is reinvigorated in the present paper to prompt a comprehensive reevaluation of the integrated concepts of life, machines, agency, and intelligence. The evidence comes from the intersections of synthetic bioengineering, developmental biology, and cognitive and computational sciences. As a regulative principle, synechism, "that continuity governs the whole domain of experience in every element of it", has been shown to infiltrate fundamental issues of contemporary biology, including cognition in different substrates, embodied agency, collectives (swarm and nested), intelligence on multiple scales, and developmental bioelectricity in morphogenesis. In the present paper, we make explicit modern biology's turn to this fundamental feature of science in its rejection of conceptual binaries, preference for collectives over individuals, quantitative over qualitative, and multiscale applicability of the emerging hypotheses about the integration of the first principles of the diversity of life. Specifically, synechism presents itself as the bedrock for research encompassing biological machines, chimaeras, organoids, and Xenobots. We then review a synechistic framework that embeds functionalist, information-theoretic, pragmaticist and inferentialist approaches to springboard to continuum-driven biosystemic behaviour.

The Multiscale Wisdom of the Body: Collective Intelligence as a Tractable Interface for Next-Generation Biomedicine

The dominant paradigm in biomedicine focuses on genetically-specified components of cells and their biochemical dynamics, emphasizing bottom-up emergence of complexity. Here, I explore the biomedical implications of a complementary emerging field: diverse intelligence. Using tools from behavioral science and multiscale neuroscience, we can study development, regenerative repair, and cancer suppression as behaviors of a collective intelligence of cells navigating the spaces of possible morphologies and transcriptional and physiological states. A focus on the competencies of living material-from molecular to organismal scales-reveals a new landscape for interventions. Such top-down approaches take advantage of the memories and homeodynamic goal-seeking behavior of cells and tissues, offering the same massive advantages in biomedicine and bioengineering that reprogrammable hardware has provided information technologies. The bioelectric networks that bind individual cells toward large-scale anatomical goals are an especially tractable interface to organ-level plasticity, and tools to modulate them already exist. This suggests a research program to understand and tame the software of life for therapeutic gain by understanding the many examples of basal cognition that operate throughout living bodies.

Thoughts and thinkers: On the complementarity between objects and processes

We argue that "processes versus objects" is not a useful dichotomy. There is, instead, substantial theoretical utility in viewing "objects" and "processes" as complementary ways of describing persistence through time, and hence the possibility of observation and manipulation. This way of thinking highlights the role of memory as an essential resource for observation, and makes it clear that "memory" and "time" are also mutually inter-defined, complementary concepts. We formulate our approach in terms of the Free Energy Principle (FEP) of Friston and colleagues and the fundamental idea from quantum theory that physical interactions can be represented by linear operators. Following Levin (2024) [30], we emphasize that memory is, first and foremost, an interpretative function, from which the idea of memory as a record, at some level of accuracy, of past events is derivative. We conclude that the distinction between objects and processes is always contrived, and always misleading, and that science would be better served by abandoning it entirely.

Life, its origin, and its distribution: a perspective from the Conway-Kochen Theorem and the Free Energy Principle

We argue here that the Origin of Life (OOL) problem is not just a chemistry problem but is also, and primarily, a cognitive science problem. When interpreted through the lens of the Conway-Kochen theorem and the Free Energy Principle, contemporary physics characterizes all complex dynamical systems that persist through time as Bayesian agents. If all persistent systems are to some - perhaps only minimal - extent cognitive, are all persistent systems to some extent alive, or are living systems only a subset of cognitive systems? We argue that no bright line can be drawn, and we re-assess, from this perspective, the Fermi paradox and the Drake equation. We conclude that improving our abilities to recognize and communicate with diverse intelligences in diverse embodiments, whether based on familiar biochemistry or not, will either resolve or obviate the OOL problem.

Uncertainty minimization and pattern recognition in Volvox carteri and V. aureus

The field of diverse intelligence explores the capacity of systems without complex brains to dynamically engage with changing environments, seeking fundamental principles of cognition and their evolutionary origins. However, there are many knowledge gaps around a general behavioural directive connecting aneural to neural organisms. This study tests predictions of the computational framework of active inference based on the free energy principle in neuroscience, applied to aneural biological processes. We demonstrate pattern recognition in the green algae Volvox using phototactic experiments with varied light pulse patterns, measuring their phototactic bias as a readout for their preferential ability to detect and adapt to one pattern over another. Results show Volvox adapt more readily to regular patterns than irregular ones and even exhibit memory properties, exhibiting a crucial component of basal intelligence. Pharmacological and electric shock-based interventions and photoadaptation simulations reveal how randomized stimuli interfere with normal photoadaptation through a structured dynamic interplay of colony rotation and calcium-mediated photoreceptor-to-flagellar information transfer, consistent with uncertainty minimization. The detection of functional uncertainty minimization in an aneural organism expands concepts like uncertainty minimization beyond neurons and provides insights and novel intervention tools applicable to other living systems, similar to early learning validations in simpler neural organisms.

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

Stress sharing as cognitive glue for collective intelligences: A computational model of stress as a coordinator for morphogenesis

Individual cells have numerous competencies in physiological and metabolic spaces. However, multicellular collectives can reliably navigate anatomical morphospace towards much larger, reliable endpoints. Understanding the robustness and control properties of this process is critical for evolutionary developmental biology, bioengineering, and regenerative medicine. One mechanism that has been proposed for enabling individual cells to coordinate toward specific morphological outcomes is the sharing of stress (where stress is a physiological parameter that reflects the current amount of error in the context of a homeostatic loop). Here, we construct and analyze a multiscale agent-based model of morphogenesis in which we quantitatively examine the impact of stress sharing on the ability to reach target morphology. We found that stress sharing improves the morphogenetic efficiency of multicellular collectives; populations with stress sharing reached anatomical targets faster. Moreover, stress sharing influenced the future fate of distant cells in the multi-cellular collective, enhancing cells' movement and their radius of influence, consistent with the hypothesis that stress sharing works to increase cohesiveness of collectives. During development, anatomical goal states could not be inferred from observation of stress states, revealing the limitations of knowledge of goals by an extern observer outside the system itself. Taken together, our analyses support an important role for stress sharing in natural and engineered systems that seek robust large-scale behaviors to emerge from the activity of their competent components.

Nontraditional models as research tools: the road not taken

Historical reasons resulted in the almost exclusive use of a few species, most prominently Mus musculus, as the mainstream models in biomedical research. This selection was not based on Mus's distinctive relevance to human disease but rather to the pre-existing availability of resources and tools for the species that were used as models, which has enabled their adoption for research in health sciences. Unless the utilization and range of nontraditional research models expand considerably, progress in biomedical research will remain restricted within the trajectory that has been set by the existing models and their ability to provide clinically relevant information.

A platform to map the mind-mitochondria connection and the hallmarks of psychobiology: the MiSBIE study

Health emerges from coordinated psychobiological processes powered by mitochondrial energy transformation. But how do mitochondria regulate the multisystem responses that shape resilience and disease risk across the lifespan? The Mitochondrial Stress, Brain Imaging, and Epigenetics (MiSBIE) study was established to address this question and determine how mitochondria influence the interconnected neuroendocrine, immune, metabolic, cardiovascular, cognitive, and emotional systems among individuals spanning the spectrum of mitochondrial energy transformation capacity, including participants with rare mitochondrial DNA (mtDNA) lesions causing mitochondrial diseases (MitoDs). This interdisciplinary effort is expected to generate new insights into the pathophysiology of MitoDs, provide a foundation to develop novel biomarkers of human health, and integrate our fragmented knowledge of bioenergetic, brain-body, and mind-mitochondria processes relevant to medicine and public health.

Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue

Many studies on memory emphasize the material substrate and mechanisms by which data can be stored and reliably read out. Here, I focus on complementary aspects: the need for agents to dynamically reinterpret and modify memories to suit their ever-changing selves and environment. Using examples from developmental biology, evolution, and synthetic bioengineering, in addition to neuroscience, I propose that a perspective on memory as preserving salience, not fidelity, is applicable to many phenomena on scales from cells to societies. Continuous commitment to creative, adaptive confabulation, from the molecular to the behavioral levels, is the answer to the persistence paradox as it applies to individuals and whole lineages. I also speculate that a substrate-independent, processual view of life and mind suggests that memories, as patterns in the excitable medium of cognitive systems, could be seen as active agents in the sense-making process. I explore a view of life as a diverse set of embodied perspectives-nested agents who interpret each other's and their own past messages and actions as best as they can (polycomputation). This synthesis suggests unifying symmetries across scales and disciplines, which is of relevance to research programs in Diverse Intelligence and the engineering of novel embodied minds.

Electroceuticals: emerging applications beyond the nervous system and excitable tissues

Electroceuticals have evolved beyond devices manipulating neuronal signaling for symptomatic treatment, becoming more precise and disease modulating and expanding beyond the nervous system. These advancements promise transformative applications in arthritis, cancer treatment, tissue regeneration, and more. Here, we discuss these recent advances and offer insights for future research.

Wireless control of nerve growth using bipolar electrodes: a new paradigm in electrostimulation

Electrical activity underpins all life, but is most familiar in the nervous system, where long range electrical signalling is essential for function. When this is lost (e.g., traumatic injury) or it becomes inefficient (e.g., demyelination), the use of external fields can compensate for at least some functional deficits. However, its potential to also promote biological repair at the cell level is underplayed despite abundant in vitro evidence for control of neuron growth. This perspective article considers specifically the emerging possibility of achieving cell growth through the interaction of external electric fields using conducting materials as unwired bipolar electrodes, and without intending stimulation of neuron electrical activity to be the primary consequence. The use of a wireless method to create electrical interactions represents a paradigm shift and may allow new applications in vivo where physical wiring is not possible. Within that scheme of thought an evaluation of specific materials and their dynamic responses as bipolar unwired electrodes is summarized and correlated with changes in dynamic nerve growth during stimulation, suggesting possible future schemes to achieve neural growth using bipolar unwired electrodes with specific characteristics. This strategy emphasizes how nerve growth can be encouraged at injury sites wirelessly to induce repair, as opposed to implanting devices that may substitute the neural signals.

Collective intelligence: A unifying concept for integrating biology across scales and substrates

A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

Principled Limitations on Self-Representation for Generic Physical Systems

The ideas of self-observation and self-representation, and the concomitant idea of self-control, pervade both the cognitive and life sciences, arising in domains as diverse as immunology and robotics. Here, we ask in a very general way whether, and to what extent, these ideas make sense. Using a generic model of physical interactions, we prove a theorem and several corollaries that severely restrict applicable notions of self-observation, self-representation, and self-control. We show, in particular, that adding observational, representational, or control capabilities to a meta-level component of a system cannot, even in principle, lead to a complete meta-level representation of the system as a whole. We conclude that self-representation can at best be heuristic, and that self models cannot, in general, be empirically tested by the systems that implement them.

From reinforcement learning to agency: Frameworks for understanding basal cognition

Organisms play, explore, and mimic those around them. Is there a purpose to this behavior? Are organisms just behaving, or are they trying to achieve goals? We believe this is a false dichotomy. To that end, to understand organisms, we attempt to unify two approaches for understanding complex agents, whether evolved or engineered. We argue that formalisms describing multiscale competencies and goal-directedness in biology (e.g., TAME), and reinforcement learning (RL), can be combined in a symbiotic framework. While RL has been largely focused on higher-level organisms and robots of high complexity, TAME is naturally capable of describing lower-level organisms and minimal agents as well. We propose several novel questions that come from using RL/TAME to understand biology as well as ones that come from using biology to formulate new theory in AI. We hope that the research programs proposed in this piece shape future efforts to understand biological organisms and also future efforts to build artificial agents.