Biology subjects

Koseska, A.

Publications and source records attributed to Koseska, A..

10 recordsLinked to original sources

bioRxiv2026

Structured connectivity for structured sequential computations

Cortical computations emerge from the coordinated activity of recurrent neural circuits and are often described through population dynamics and neuronal selectivity. However, how the backbone of the computations - the circuit connectivity - gives rise to these dynamics and organizes the computations, remains largely unknown. Mixed selectivity, which can arise without structured connectivity, has been proposed as a substrate for flexible computation, but alone it does not account particularly for sequential computations, where distinct selectivities have been observed. Here, we investigate this problem using the delayed match-to-category (DMC) task, which requires categorization, memory maintenance, comparison, and decision-making, and in which pure, mixed, and time-varying selectivity have been observed. Using explicit connectivity models, we identify circuit motifs that generate these selectivity types and interconnect them to implement the computations required for behavior. Within these motifs, mixed selectivity neurons implement comparison via localized, distributed, or hybrid representations, whereas time-varying selectivity neurons gate the activation and timing of output. Recurrent networks trained from random initial connectivity develop similar motifs, demonstrating that learning leads to structured connectivity to support computations. The framework generalizes to extended task variants, revealing reusable circuit principles that mechanistically link connectivity, neuronal selectivity, and sequential computation.

neuroscience↗

bioRxiv2026

Near-critical slow dynamics enable flexible temporal computations and generalization

Although neural activity often evolves along low-dimensional manifolds, such descriptions do not explain the dynamical mechanisms that generate, constrain, and stabilize computation. Identifying these mechanisms is essential for predicting responses to perturbations, understanding generalization to untrained signals, and explaining how similar computations arise from distinct circuit implementations. Here we use recurrent neural networks trained on an interval timing task as a model system to uncover the dynamical mechanisms of neural computation. We show that, despite converging to highly diverse attractor architectures, trained networks share a conserved transient dynamics. During learning, networks self-organize near dynamical bifurcations, forming structured ghost sets of slow points characterized by graded spectra of near-zero eigenvalues. These slow sets form a dynamical scaffold that constrains trajectory evolution. Inputs transiently reconfigure the vector field and reposition activity within this scaffold, while the underlying slow set governs subsequent dynamics. As a result, temporal computation is implemented through structured transient evolution rather than convergence to fixed points or persistent activity states. The extent of the slow sets predicts generalization to unseen temporal intervals, and networks lacking such organization fail to extrapolate reliably. To test sufficiency, we construct a minimal dynamical system endowed with analogous slow set geometry that reproduces interval timing without learning, providing a benchmark for identifying the essential dynamical ingredients of temporal computation. Together, these results identify structured slow transients as a candidate dynamical mechanism for temporal computation, provide a mechanistic interpretation of slow low-dimensional manifolds as emergent consequences of underlying state-space structure, and suggest that computational capacity in near-critical systems arises from the organization of transient flow rather than attractor states alone.

neuroscience↗

bioRxiv2025

Homeorhetic regulation of cellular phenotype

How cells translate growth factor (GF) signals into context-specific phenotypes remains a fundamental question in cell biology. The classical view holds that cells translate a constant concentration of a GF with specific chemical identity to a steady state activation of the underlying signaling pathway, resulting in a defined phenotypic response. However, recent findings suggest that even a single GF, when presented in a pulsatile manner, drives differential phenotypic responses depending on the frequency of stimulation. To reconcile these views, we introduce a novel conceptual framework of "signaling homeorhesis". Unlike homeostasis, homeorhesis describes the stable evolution of signaling trajectories over time. Defining this concept quantitatively using a dynamical systems framework, we use mathematical models of the Epidermal Factor Growth Receptor (EGFR) and the Tropomyosin receptor kinase A (TrkA) networks, as well as available experimental temporal protein activity recordings in PC-12 cells to demonstrate that cells classify GF signals to unique signaling trajectories that encode for distinct cell phenotypes, irrespective of the GF identity. We thereby propose that the cellular phenotype is determined in real-time, as the cell actively interprets the growth factor signals from its environment.

cell biology↗

bioRxiv2023

Non-asymptotic transients away from steady states determine cellular responsiveness to dynamic spatial-temporal signals

Majority of the theory on cell polarization and the understanding of cellular sensing and responsiveness to localized chemical cues has been based on the idea that non-polarized and polarized cell states can be represented by stable asymptotic switching between them. The existing model classes that describe the dynamics of signaling networks underlying polarization are formulated within the framework of autonomous systems. However these models do not simultaneously capture both, robust maintenance of polarized state longer than the signal duration, and retained responsiveness to signals with complex spatial-temporal distribution. Based on recent experimental evidence for criticality organization of biochemical networks, we challenge the current concepts and demonstrate that non-asymptotic signaling dynamics arising at criticality uniquely ensures optimal responsiveness to changing chemoattractant fields. We provide a framework to characterize non-asymptotic dynamics of systems state trajectories through a non-autonomous treatment of the system, further emphasizing the importance of (long) transient dynamics, as well as the necessity to change the mathematical formalism when describing biological systems that operate in changing environments.

biophysics↗

bioRxiv2021

Cells use molecular working memory to navigate in changing chemoattractant fields

In order to migrate over large distances, cells within tissues and organisms rely on sensing local gradient cues. These cues however are multifarious, irregular or conflicting, changing both in time and space. Here we find that single cells utilize a molecular mechanism akin to a working memory, to generate persistent directional migration when signals are disrupted by temporally memorizing their position, while still remaining adaptive to spatial and temporal changes of the signal source. Using dynamical systems theory, we derive that these information processing capabilities are inherent for protein networks whose dynamics is maintained away from steady state through organization at criticality. We demonstrate experimentally using the Epidermal growth factor receptor (EGFR) signaling network, that the memory is maintained in the prolonged receptors activity via a slow-escaping remnant, a dynamical "ghost" of the attractor of the polarized signaling state, that further results in memory in migration. As this state is metastable, it also enables continuous adaptation of the migration direction when the signals vary in space and time. We therefore show that cells implement real-time computations without stable-states to navigate in changing chemoattractant fields by memorizing position of disrupted signals while maintaining sensitivity to novel chemical cues.

biophysics↗

bioRxiv2020

Cell-cell communication through FGF4 generates and maintains robust proportions of differentiated cell fates in embryonic stem cells

During embryonic development and tissue homeostasis, reproducible proportions of differentiated cell types are specified from populations of multipotent precursor cells. Molecular mechanisms that enable both robust cell type proportioning despite variable initial conditions in the precursor cells, as well as the re-establishment of these proportions upon perturbations in a developing tissue remain to be characterized. Here we report that the differentiation of robust proportions of epiblast-like and primitive endoderm-like cells in mouse embryonic stem cell cultures emerges at the population level through cell-cell communication via a short-range FGF4 signal. We characterize the molecular and dynamical properties of the communication mechanism, and show how it controls both robust cell type proportioning from a wide range of experimentally controlled initial conditions, as well as the autonomous re-establishment of these proportions following the isolation of one cell type. The generation and maintenance of reproducible proportions of discrete cell types is a new function for FGF signaling that may operate in a range of developing tissues.

developmental biology↗

bioRxiv2019

Robustness and timing of cellular differentiation through population-based symmetry breaking

During mammalian development, cell types expressing mutually exclusive genetic markers are differentiated from a multilineage primed state. These observations have invoked single-cell multistability view as the dynamical basis of differentiation. However, the robust regulative nature of mammalian development is not captured therein. Considering the well-established role of cell-cell communication in this process, we propose a fundamentally different dynamical treatment in which cellular identities emerge and are maintained on population level, as a novel unique solution of the coupled system. Subcritical systems organization here enables symmetry-breaking to be triggered by cell number increase in a timed, self-organized manner. Robust cell type proportions are thereby an inherent feature of the resulting inhomogeneous solution. This framework is generic, as exemplified for early embryogenesis and neurogenesis cases. Distinct from mechanisms that rely on pre-existing asymmetries, we thus demonstrate that robustness and accuracy necessarily emerge from the cooperative behaviour of growing cell populations during development.

systems biology↗

bioRxiv2019

Optimal biochemical information processing at criticality

How cells utilize surface receptors for chemoreception is a recurrent question spanning between physics and biology over the past few decades. However, the dynamical mechanism for processing time-varying signals is still unclear. Using dynamical systems formalism to describe criticality in non-equilibrium systems, we propose generic principle for temporal information processing through phase-space trajectories using dynamic transient memory. In contrast to short-term memory, dynamic memory generated via ghost attractor enables signal integration depending on stimulus history, and thus balance between stability and plasticity in receptor responses. We propose that self-organization at criticality can arise through fluctuation-sensing mechanism, illustrated for the experimentally established epidermal growth factor sensing system. This framework applies irrespective of the intrinsic node dynamics or network size, as we show using also a basic neuronal model. Processing of non-stationary signals, a feature previously attributed only to neuronal networks, thus uniquely emerges for biochemical networks organized at criticality.

systems biology↗

bioRxiv2018

A synthetic morphogenic perceptory cell

Reconstitution of artificial cells capable of transducing extracellular signals into cytoskeletal changes is a challenge in synthetic biology that will reveal fundamental principles of non-equilibrium phenomena of cellular morphogenesis and information processing. Here, we generated a life-like Synthetic Morphogenic Membrane System (SynMMS) by encapsulating a dynamic microtubule (MT) aster and a light-inducible signaling system driven by GTP/ATP chemical potential into cell-sized vesicles. The biomimetic design of the light-induced signaling system embodies the operational principle of morphogen induced Rho-GTPase signal transduction in cells. Activation of synthetic signaling promotes membrane-deforming growth of MT-filaments by dynamically elevating the membrane-proximal concentration of tubulin. The resulting membrane deformations enable the recursive coupling of the MT-aster with the signaling system, creating global self-organized morphologies that reorganize towards external light cues in dependence on prior sensory experience that is stored in the dynamically maintained morphology. SynMMS thereby signifies a step towards bio-inspired engineering of self-organized cellular morphogenesis.

synthetic biology↗

bioRxiv2018

Interdependence of EGFR with PTPs on juxtaposed membranes generates a growth factor sensing and responding network

The proto-oncogenic epidermal growth factor receptor (EGFR) is a tyrosine kinase whose sensitivity to growth factors (GFs) and signal duration determines cellular behavior. We resolve how EGFRs response to epidermal growth factor (EGF) originates from dynamically established recursive interactions with spatially organized protein tyrosine phosphatases (PTPs). Opposed genetic PTP perturbations enabled identification of receptor-like PTPRG/J at the plasma membrane (PM) and endoplasmic reticulum (ER) associated PTPN2 as the major EGFR dephosphorylating activities. Imaging spatial-temporal PTP reactivity then revealed that vesicular trafficking establishes a spatially-distributed negative feedback with PTPN2 that determines signal duration, whereas single cell dose-response analysis uncovered a ROS-mediated toggle switch between autocatalytically activated monomeric EGFR and the tumor suppressor PTPRG that governs EGFRs sensitivity to EGF. Vesicular recycling of monomeric EGFR unifies the interactions with these PTPs on juxtaposed membranes, dynamically generating a network architecture that can sense and respond to time varying growth factor signals.

systems biology↗