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Modeling the Dynamics of Life: New Frontiers in Biological Physics and Mechanics

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Modeling the Dynamics of Life: New Frontiers in Biological Physics and Mechanics is a special Collection of the npj Biological Physics and Mechanics journal, dedicated to the development and application of innovative models and theoretical methods that explore the complex dynamics of biological systems. This Collection seeks to highlight the transformative role of modeling approaches that combine principles from physics, mechanics, and biology, offering new insights into the fundamental processes that underpin life across multiple scales.

As biological systems are inherently complex, involving interactions across different levels of organization—ranging from molecules and cells to tissues and organisms—understanding their mechanics and dynamics requires sophisticated and interdisciplinary approaches. Modeling serves as a crucial tool to bridge the gaps between theory and experiment, enabling researchers to simulate biological phenomena, predict behavior, and test hypotheses that would otherwise be difficult to assess through empirical methods alone.

This special Collection invites contributions that focus on novel theoretical frameworks, computational techniques, and simulations that illuminate the physics and mechanics of biological systems. Submissions should emphasize how these models and methods advance our understanding of biological processes by addressing key challenges such as multiscale dynamics, nonlinearity, stochasticity, non-equilibrium activity, decision-making, parameter/model inference, self-organization and emergent behaviors. Areas of interest include, but are not limited to:

  • Mechanobiology: Models that explore the mechanical properties of cells, tissues, and organisms, shedding light on how mechanical forces influence biological function and interact with biochemical signals.
  • Multiscale, Multiphysics Modeling: Integrative models that connect processes at molecular, cellular, and tissue levels, and or combine several physical models (e.g., mechanics, electromagnetism), providing a holistic understanding of complex biological systems.
  • Stochastic Models of Biological Dynamics: Models that incorporate stochastic effects to better represent biological variability, to understand how randomness interacts with deterministic processes and how this affects reliability and function at various scales.
  • Active Matter in Biological Systems: Models of biological systems as active matter, focusing on understanding how non-equilibrium processes lead to emergent phenomena such as self-organisation, pattern formation, and collective motion in biological systems.
  • Models of Cellular Decision-Making: Models that investigate how cells use and integrate mechanical and biochemical cues to make decisions, for instance through the lens of information theory, probabilistic theory or machine-learning.
  • Fluid-Structure Interactions in Biology: Models that describe how biological entities, such as cells and tissues, interact with their surrounding fluid environments, capturing the interplay between rheology, shape and biological function.
  • Inference Methods: New models and inference methods to deduce mechanical properties and signaling pathways from experimental data. This includes using techniques like inverse modeling, data assimilation, or machine learning to infer forces, stresses, and signaling dynamics from observable outputs (e.g., cell shapes, movements, molecular markers).
  • Machine Learning in Biological Physics: Applications of machine learning models to infer mechanical properties from experimental data, predict tissue dynamics, or optimize models of biological processes.

We encourage submissions that reflect the diversity of approaches in this rapidly evolving field and contributions that challenge existing paradigms and open new avenues of inquiry. We also welcome favourably research that demonstrates the application of novel theoretical methods or computational tools, particularly those that integrate different scales or disciplines or that can be applied across various biological contexts, fostering practical advances in the field. Additionally, studies that incorporate experimental data to validate or refine their models are highly encouraged.

We hope that this Collection may contribute to push the boundaries of biological modeling, where physics and mechanics become integral tools for understanding life dynamics at every scale. 

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