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Search · four archives
11 papers · ranked by Valyu relevance
Zsófia Bujtár, Björn Goldenbogen, Jana Wolf
Muscle regeneration relies on the coordinated activation of muscle stem cells, whose fate decisions are regulated by intracellular gene expression dynamics and intercellular coupling via the Notch-Dll1 signaling pathway. Central components of this regulatory network include the transcriptional repressor Hes1, its…
Yiming Cheng, Yan Li
Chimeric antigen receptor (CAR) T-cell therapies undergo rapid in vivo expansion followed by contraction and variable long-term persistence after a single infusion, yielding cellular kinetic (CK) profiles that differ fundamentally from conventional small-molecule and biologic pharmacokinetics. Piecewise, phase-based CK…
Cheyenne N. Jarman, Taal Levi, Mark Novak
Applications of machine learning in ecology are rapidly expanding. Symbolic regression is gaining particular attention for its success in reverse-engineering human-readable explanatory population models, including the logistic growth and Lotka-Volterra equations, from simulated and laboratory-based population time…
Leonardo Demarchi
Although many animals rely on visual information to navigate, optic flow is inherently ambiguous as it confounds information about motion speed and object distance. As a result, the visual feedback produced by a given motor command is context-dependent and requires an appropriately adapted response. Recent experiments…
Jie Deng, Xinyu Zhang, Xuchang Zhang, Xing Yang
Coupled diffusion–reaction partial differential equations (PDEs) describe biochemical network dynamics but are difficult to solve for realistic multi-species systems without combining mechanism and data. We present a multi-stage physics-informed neural network (PINN) for multi-species diffusion–reaction PDEs and apply…
Nikos I. Kavallaris, Farrukh Javed
We introduce a mechanistic, nonlocal tumour-growth model designed specifically to capture explosive dynamics that are not adequately explained by standard logistic reaction–diffusion descriptions. The motivation is empirical: the universal scaling law reported in [1] provides compelling cross-sectional evidence of…
Zhiwei Zhang, Shuwang Li, John Lowengrub, Steven M. Wise
We present a fast, unconditionally energy-stable numerical scheme for simulating vesicle deformation under osmotic pressure using a phase-field approach. The model couples an Allen–Cahn equation for the biomembrane interface with a variable-mobility Cahn–Hilliard equation governing mass exchange across the membrane.…
Mika Ohkawa, Ying Joey Zhou, Saskia Haegens, Matin Jafarian
Learning new information in the presence of distracters and changing conditions requires the ability to adapt. In the brain, this adaptive capability has been linked to dynamic interactions between attention and working memory, which enable the selective filtering of irrelevant input while preserving behaviorally…
Denizhan Pak, Randall D. Beer
Organisms must manage a trade-off between robustness and flexibility as they enact adaptive behaviors. One way organisms achieve this is by navigating a network of quasi-stable behavioral states. Evidence for such behavioral states has been observed in many organisms, and new methods for detecting these states have…
Tsubasa Sukekawa, Shin-Ichiro Ei
Mass-conserved reaction-diffusion systems are used as mathematical models for various phenomena such as cell polarity. Numerical simulations of this system present transient dynamics in which multiple stripe patterns converge to spatially monotonic patterns. Previous studies indicated that the transient dynamics are…
Navid Akbari, Kai Mason, Aaron Gruber, Wilten Nicola
Spiking Neural Networks (SNNs) have the potential to replicate the brain’s computational efficacy by explicitly incorporating action potentials or “spikes”, which is not a feature of most artificial neural networks. However, training SNNs is difficult due to the non-differentiable nature of the most common spiking…