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Hyun Youk

Publications and source records attributed to Hyun Youk.

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Predictability can be dynamically constructed in deterministic systems

A deterministic system's future can be fixed from the start yet remain unpredictable because of chaos or computational irreducibility. Here we reveal another source of unpredictability for non-chaotic deterministic systems: collective entities making the future legible are initially absent but dynamically constructed. In a generalized cellular automaton, disordered lattices evolved into static configurations, rectilinear waves, or spiral waves. Although initial configurations fixed each fate, machine-learning models did no better than randomly guessing fates from them. Recoding cell states geometrically revealed self-organization of prediction-enabling topological entities: vortices, non-contractible-loop strings, and, most generally, a winding field describing how same-state regions wrap the lattice. As the winding field self-organized, initially unpredictable fates became increasingly legible. Static and rectilinear-wave fates became progressively predictable, whereas spiral-wave fate became accurately predictable only near wave formation. These results establish that self-organization can build not merely order but entities that make certain futures visible, revealing a gap between determinism and practical predictability.

physics.bio-ph

Self-Organization Dynamics Beyond Equilibrium: Discreteness, Computation, and Rules of Life

Living systems self-organize in ways that conventional physical frameworks-based on forces, energies, and continuous fields-cannot fully capture. Processes like gene regulation and cellular decision-making involve rule-based logic and computational interactions. Here, I introduce the concept of non-equilibrium capacity (NEC) to denote the finite capacity of living systems to generate and sustain life-associated dynamics-the very capacity that defines viability-and whose irreversible loss constitutes death. I argue that two lines of inquiry are especially promising for understanding why this capacity is inevitably lost. First, experiments that slow or suspend all cellular processes reveal "low speed limits" below which life collapses. Second, generalized cellular automata-where cells interact over diffusion-defined neighborhoods and obey discrete rules-provide a framework to understand how order emerges or persists. Together, these approaches suggest a new grammar of biology that complements energy-based physics and explains how living systems sustain and ultimately lose their NEC.

q-bio.OT

Predictive landscapes hidden beneath biological cellular automata

To celebrate Hans Frauenfelder's achievements, we examine energy(-like) "landscapes" for complex living systems. Energy landscapes summarize all possible dynamics of some physical systems. Energy(-like) landscapes can explain some biomolecular processes, including gene expression and, as Frauenfelder showed, protein folding. But energy-like landscapes and existing frameworks like statistical mechanics seem impractical for describing many living systems. Difficulties stem from living systems being high dimensional, nonlinear, and governed by many, tightly coupled constituents that are noisy. The predominant modeling approach is devising differential equations that are tailored to each living system. This ad hoc approach faces the notorious "parameter problem": models have numerous nonlinear, mathematical functions with unknown parameter values, even for describing just a few intracellular processes. One cannot measure many intracellular parameters or can only measure them as snapshots in time. Another modeling approach uses cellular automata to represent living systems as discrete dynamical systems with binary variables. Quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g., Cellular Potts Model). But numerous biological features, in current practice, are qualitatively described rather than quantitatively (e.g., gene is (highly) expressed or not (highly) expressed). Cellular automata governed by verbal rules are useful representations for living systems and can mitigate the parameter problem. However, they can yield complex dynamics that are difficult to understand because much of the existing mathematical tools and theorems apply to continuous but not discrete dynamical systems. Recent studies found ways to overcome this challenge by discovering a predictive "landscape" that yield low-dimensional representations of cellular automata dynamics. We review these studies.

q-bio.QM

Statistical dynamics of spatial-order formation by communicating cells

Communicating cells can coordinate their gene expressions to form spatial patterns. 'Secrete-and-sense cells' secrete and sense the same molecule to do so and are ubiquitous. Here we address why and how these cells, from disordered beginnings, can form spatial order through a statistical mechanics-type framework for cellular communication. Classifying cellular lattices by 'macrostate' variables - 'spatial order paramete' and average gene-expression level - reveals a conceptual picture: cellular lattices act as particles rolling down on 'pseudo-energy landscapes' shaped by a 'Hamiltonian' for cellular communication. Particles rolling down represent cells' spatial order increasing. Particles trapped on the landscapes represent metastable spatial configurations. The gradient of the Hamiltonian and a 'trapping probability' determine the particle's equation of motion. This framework is extendable to more complex forms of cellular communication.

q-bio.QM

Progress towards quantitative design principles of multicellular systems

Living systems, particularly multicellular systems, often seem hopelessly complex. But recent studies have suggested that beneath this complexity, there may be unifying quantitative principles that we are only now starting to unravel. All cells interact with their environments and with other cells. Communication among cells is a primary means for cells to interact with each other. The complexity of these multicellular systems, due to the large numbers of cells and the diversity of intracellular and intercellular interactions, makes understanding multicellular systems a daunting task. To overcome this challenge, we will likely need judicious simplifications and conceptual frameworks that can reveal design principles that are shared among diverse multicellular systems. Here we review some recent progress towards developing such frameworks.

q-bio.MN

Autocrine signaling and quorum sensing: Extreme ends of a common spectrum

"Secrete-and-sense cells" can communicate by secreting a signaling molecule while also producing a receptor that detects the molecule. The cell can potentially "talk" to itself ("self-communication") or talk to neighboring cells with the same receptor ("neighbor-communication"). The predominant forms of secrete-and-sense cells are self-communicating "autocrine cells" that are largely found in animals, and neighbor-communicating "quorum sensing cells" that are mostly associated with bacteria. While assumed to function independent of one another, recent studies have discovered quorum sensing organs and autocrine signaling microbes. Moreover, similar types of genetic circuits control many autocrine and quorum sensing cells. We outline these recent findings and explain how autocrine and quorum sensing are two sides of a many-sided "dice" created by the versatile secrete-and-sense cell.

q-bio.CB

Molecular-level tuning of cellular autonomy controls the collective behaviors of cell populations

A rigorous understanding of how multicellular behaviors arise from the actions of single cells requires quantitative frameworks that bridge the gap between genetic circuits, the arrangement of cells in space, and population-level behaviors. Here, we provide such a framework for a ubiquitous class of multicellular systems - namely, "secrete-and-sense cells" that communicate by secreting and sensing a signaling molecule. By using formal, mathematical arguments and introducing the concept of a phenotype diagram, we show how these cells tune their degrees of autonomous and collective behavior to realize distinct single-cell and population-level phenotypes; these phenomena have biological analogs, such as quorum sensing or paracrine signaling. We also define the "entropy of population," a measurement of the number of arrangements that a population of cells can assume, and demonstrate how a decrease in the entropy of population accompanies the formation of ordered spatial patterns. Our conceptual framework ties together diverse systems, including tissues and microbes, with common principles.

q-bio.MN

The Molecular Diffusion of Ice Crystals of Various Shapes

The growth by molecular diffusion (Reynold's number (Re) = 0) of ice crystals of different shapes, represented by the Sherwood number (Sh) is calculated using an electrical analog which relates capacity (C) to Sh. Although experimental data on dependence of Sh on Re for various ice crystals of interest in cloud physics have been previously obtained, extrapolation of the data to smaller particles for Re=0 has been unreliable. We present a simple computational algorithm for computing Sh at Re=0 for various crystals of interest, which will allow proper coverage over the whole Re range applicable to ice crystals. The method we present can be applied to any crystal of rectilinear shape. The approach was as follows: the model crystal is positioned in a box and an electric field is applied between the crystal and the box, simulating the initial growth stage of ice crystals. Using a finite Cartesian grid system of variable lattice separations, the corners of ice crystal are assigned to particular lattice points, thereby defining the geometry of the crystal. A discrete version of Gauss' flux law is developed and used for this lattice system. Sh (at Re=0) is obtained for hexagonal plates, hexagonal columns, broad branched crystals, stellar crystals, and capped columns. Our calculations reveal that our simple computational algorithm provides the values of Sh for these shapes to within 5% error from the values obtained through estimates in previous studies.

physics.ao-ph