arXiv subjects

Mohammadreza Bahadorian

Publications and source records attributed to Mohammadreza Bahadorian.

3 recordsLinked to original sources

arXiv2025

TopoSPAM: Topology grounded Simulation Platform for morphogenesis and biological Active Matter

We present a topology grounded, multiscale simulation platform for morphogenesis and biological active matter. Morphogenesis and biological active matter represent keystone problems in biology with additional, far-reaching implications across the biomedical sciences. Addressing these problems will require flexible, cross-scale models of tissue shape, development, and dysfunction that can be tuned to understand, model, and predict relevant individual cases. Current approaches to simulating anatomical or cellular subsystems tend to rely on static, assumed shapes. Meanwhile, the potential for topology to provide natural dimensionality reduction and organization of shape and dynamical outcomes is not fully exploited. TopoSPAM combines ease of use with powerful simulation algorithms and methodological advances, including active nematic gels, topological-defect-driven shape dynamics, and an active 3D vertex model of tissues. It is capable of determining emergent flows and shapes across scales.

physics.bio-ph↗

arXiv2021

ANDOR and beyond: dynamically switchable logic gates as modules for flexible information processing in biochemical regulatory networks

An information processing circuit must be flexible to perform multiple tasks: biochemical information processing circuits in living systems can have multiple functions, requiring them to perform distinct computational tasks in different scenarios. Here, we describe a tunable dynamical system that performs different logical operations (such as AND, OR, and XOR) depending on which basin of attraction the dynamics resides in. We analyze the robustness of these dynamically switchable logic gates, and characterize the trade-off between reliability and efficiency in their function.

q-bio.MN↗

arXiv2020

The gift of gab: probing the limits of dynamic concentration-sensing across a network of communicating cells

Many systems in biology and beyond employ collaborative, collective communication strategies for improved efficiency and adaptive benefit. One such paradigm of particular interest is the community estimation of a dynamic signal, when, for example, an epithelial tissue of cells must decide whether to react to a given dynamic external concentration of stress signaling molecules. At the level of dynamic cellular communication, however, it remains unknown what effect, if any, arises from communication beyond the mean field level. What are the limits and benefits to communication across a network of neighbor interactions? What is the role of Poissonian vs. super Poissonian dynamics in such a setting? How does the particular topology of connections impact the collective estimation and that of the individual participating cells? In this letter we construct a robust and general framework of signal estimation over continuous time Markov chains in order to address and answer these questions. Our results show that in the case of Possonian estimators, the communication solely enhances convergence speed of the Mean Squared Error (MSE) of the estimators to their steady-state values while leaving these values unchanged. However, in the super-Poissonian regime, MSE of estimators significantly decreases by increasing the number of neighbors. Surprisingly, in this case, the clustering coefficient of an estimator does not enhance its MSE while reducing total MSE of the population.

nlin.AO↗