Biology subjects

Bahadorian, M.

Publications and source records attributed to Bahadorian, M..

3 recordsLinked to original sources

bioRxiv2023

Multifractal signatures in ossification reveal a disordered-to-ordered transition in mineral pattern

Developing biological systems can exhibit both dynamic pattern formation and cross-scale interactions. Multiscale relationships are critical in the establishment of these patterns but remain poorly understood. Classification of mineral pattern in bone is a quintessential example. One approach to quantifying these patterns relies upon statistical self-similarity and, in particular, monofractal analysis. However, simple monofractal characterisations fail to capture the complexity of multiscale interactions in developing biological systems. Here we show that multifractal techniques, effectively capture the complex patterns of self-similarity in a dimensionally reduced, usable way. Further, we show that a simple generative model of ossification in the mouse skull, coupled with multifractal methods indicates a primary role of collagen density in pattern establishment and predicts the existence of a sharp boundary in pattern complexity.

biophysics↗

bioRxiv2021

ANDOR and beyond: dynamically switchable logic gates as modules for flexible information processing

Understanding how complex (bio-)chemical pathways and regulatory networks may be capable of processing information in efficient, flexible, and robust ways is a key question with implications touching fields across biology, systems biology, biochemistry, synthetic biology, dynamical systems theory, and network science. Considerable effort has been focused on the identification and characterization of structural motifs in these signaling networks, and companion efforts have instead sought to cast their operation as controlled by dynamical modules that appear out of dynamical correlations during information processing. While both these approaches have been successful in many examples of biological information processing, cases in which the signaling or regulatory network exhibits multi-functionality or context dependence remain problematic. We here propose a small set of higher-order effective modules that simultaneously incorporate both network structure and the attendant dynamical landscape. In so doing, we render effective computational units that can perform different logical operations based purely on the basin of attraction in which the network dynamics resides or is steered to. These dynamically switchable biochemical logic gates require fewer chemical components or gene products overall than their traditional analogs where static, separate gates are used for each desired function. We demonstrate the applicability and limits of these flexible gates by determining a robust range of parameters over which they correctly operate and further characterize the resilience of their function against intrinsic noise of the constituent reactions using the theory of large deviations. We also show the capability of this framework for general computations by designing a binary adder/subtractor circuit composed of only six components.

biophysics↗

bioRxiv2019

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.

systems biology↗