Sadjad Arzash

Howey Physics Building

Georgia Institute of Technology

Atlanta, GA

I am a postdoctoral researcher in the School of Physics at the Georgia Institute of Technology, working with Prof. Shiladitya Banerjee. I study the physics of soft and living matter, asking how materials ranging from biopolymer networks to whole tissues acquire their mechanical properties, and how living systems learn, adapt, and organize themselves. My work combines statistical physics, computational modeling, and ideas from optimization and machine learning.

Before Georgia Tech, I was a postdoc working jointly with Prof. M. Lisa Manning (Syracuse University) and Prof. Andrea J. Liu (University of Pennsylvania), where I studied collective cell behavior and rigidity transitions in epithelial tissues. In 2021, I received my Ph.D. in chemical engineering from Rice University under the supervision of Prof. Fred C. MacKintosh, focusing on the rheology and mechanical critical phenomena of biopolymer networks. My Ph.D. thesis is available here.

My recent research reframes tissue mechanics through the lens of physical learning. I show how epithelial tissues can store mechanical memory, tune their own elasticity, and drive morphogenetic processes such as convergent extension through simple, local feedback rules, connecting the physics of tunable matter with developmental biology and unsupervised learning. Where living tissues rely on such local rules, global optimization offers a complementary strategy: the same physical learning framework can be used to train novel mechanical materials for a specific target function, a direction that connects soft matter physics with machine learning and engineering design. See my research and publications for more.

selected publications

A minimal in vitro assay for cell intercalation highlights the importance of interfacial tension and migratory forces

Ruppel, Artur, Misiak, Vladimir, Arzash, Sadjad, Selma-Herrador, Daniel, Boudou, Thomas, Manning, M. Lisa, Fagotto, François, and Balland, Martial

Nature Physics Jun 2026

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Cell intercalation, the dynamic exchange of cellular neighbors, is fundamental to embryonic morphogenesis, tissue homeostasis, and wound healing. Despite extensive study in complex tissues, the minimal mechanical requirements driving intercalation remain poorly understood due to confounding tissue level interactions. Here, we present a novel morphogenesis-on-chip assay utilizing micropatterned cell quadruplets. This system isolates the elementary unit of intercalation while enabling quantitative force and shape measurements. Cross-shaped micropatterns generate stable four cell configurations in MDCK epithelial cells. Surprisingly, these cells spontaneously undergo T1 transitions autonomously. We combined live imaging with force inference and traction force microscopy, which revealed that intercalation emerges from two distinct mechanisms: interfacial tension dynamics and differential cell migration. Specifically, we show a correlation between central junction shrinkage and increased relative tension. Similarly, we show a correlation between central junction shrinkage and migratory forces. We successfully adapted the assay to Xenopus mesoderm cells, revealing conserved mechanical principles across cell types. Furthermore, experimentally derived effective energy landscapes closely match theoretical vertex model predictions, and suggest a dominant role for migratory forces in driving intercalation. This confirms that our minimal system recapitulates the fundamental physics of intercalation. This approach provides the first quantitative framework for studying intercalation mechanics in isolation and establishes a versatile platform for investigating morphogenetic processes.
Learning Epithelial Elasticity via Local Tension Remodeling

Arzash, Sadjad, and Banerjee, Shiladitya

bioRxiv Dec 2025

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Biological materials, like epithelial tissues, exhibit remarkable adaptability to mechanical stresses, dynamically remodeling their structure in response to external and internal forces. A key challenge is understanding how these tissues store a memory of past mechanical stimuli. Here, we investigate this memory using an active Vertex Model of epithelial sheets incorporating a local, mechanosensitive tension-remodeling rule where junctional tension updates depend on strain, acting as a slow, history-dependent variable. We demonstrate three hallmark mechanical consequences of this memory mechanism. First, a localized, short contractile cue permanently reprograms the global shear modulus, with the direction of change (stiffening or softening) controlled by the tension remodeling rate. Second, the tissue stores a long-range mechanical memory: a prior stimulus at one site modulates the tissue’s response to a subsequent, distant stimulus, mediated by coupling across the entire junctional network. Finally, we show that simple cyclic bulk deformation acts as a training protocol that autonomously tunes the tissue’s constitutive properties, including programming the Poisson ratio to auxetic (negative) values. These findings position epithelial mechanics within the framework of unsupervised physical learning, identifying the mechanosensitive remodeling rates as powerful control parameters for designing programmable tissue-scale rheology.
Epithelial convergent extension as a tuning process

Arzash, Sadjad, Liu, Andrea J., and Manning, M. Lisa

bioRxiv Nov 2025

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Self-tuning, the ability of disordered systems to develop desired collective behaviors by tuning internal couplings in response to feedback, has recently emerged as a powerful framework for understanding adaptation in amorphous solids, mechanical metamaterials, and electrical networks. These systems can learn desired responses, encode memory, and robustly reorganize under repeated stimuli, much like artificial neural networks but without requiring processors to adjust their weights. Here, we extend this paradigm to morphogenesis and show that the epithelium can be viewed as tunable matter and that epithelial convergent extension (CE) can be understood as a self-tuning process. Using a vertex model with active interfacial tensions, we systematically compare distinct tension-update strategies, including externally imposed shear, global gradient descent optimization, and decentralized local feedback rules. We find that while all methods can generate tissue elongation, only local orientation- and length-sensitive rules reproduce key experimental features of CE, such as supracellular actomyosin pattern formation, cell shape changes, and junctional alignment. In contrast, global optimization produces homogeneous tension patterns and mechanically fragile states. By interpreting CE through the lens of tuning, our framework bridges the physics of tunable matter with developmental biology, revealing how simple, local rules enable tissues to efficiently orchestrate complex morphogenetic outcomes through decentralized mechanical adaptation.
Universality in the Mechanical Behavior of Vertex Models for Biological Tissues

Damavandi, Ojan Khatib^*, Arzash, Sadjad^*, Lawson-Keister, Elizabeth, and Manning, M. Lisa

* Co-first author with Ojan Khatib Damavandi.

PRX Life Jul 2025

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Simple vertex models, where the cell shape is defined as a network of edges and vertices, have made useful predictions about the collective behavior of confluent biological tissues, including rigidity transitions. Quite a few different versions of vertex models have appeared in the literature, and they propose substantial differences in how the mechanical energy depends on vertex positions, yet all seem to make correct predictions. To understand how this is possible, we search for universality in the emergent mechanical behavior-including the shear modulus defined in the limit of zero-strain rate and the viscoelastic response at finite strain rates-of six different vertex models. We identify a class of models with a well-defined shear modulus, and demonstrate that these models all exhibit a crossover from a soft or floppy regime to a stiff regime. We find that the observed cell shape index (the ratio of the cell perimeter to the square root of the cell area) is a good observable order parameter for the crossover, even though the input model parameter that controls the crossover is different in each model. We also find that the finite-strain-rate viscoelastic response of all models exhibits a universal scaling with frequency, following the Zener model in the rigid phase and Burgers model in the fluid phase. The observation that a geometric observable like cell shape governs the transition, rather than a topological observable such as coordination number, suggests that second-order rigidity may be a good framework for explaining this universality. It generates an empirical prediction that a broad class of vertex models with second-order rigidity transitions should also exhibit these universal mechanical features, and helps to explain why many different vertex models are able to robustly predict these features in experiments.
Rigidity of epithelial tissues as a double optimization problem

Arzash, Sadjad, Tah, Indrajit, Liu, Andrea J., and Manning, M. Lisa

Physical Review Research Feb 2025

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How do cells tune emergent properties at the scale of tissues? One class of such emergent behaviors are rigidity transitions, in which a tissue changes from a solidlike to a fluidlike state or vice versa. Here we introduce a way for a tissue described by a vertex model to tune its rigidity by using "tunable degrees of freedom." We use the vertex model elastic energy as a cost function and the cell stiffnesses, target shapes, and target areas as different sets of degrees of freedom describing cell-cell interactions that can be tuned to minimize the cost function. We show that the rigidity transition is unaffected when cell stiffnesses are treated as tunable degrees of freedom. When preferred shapes or areas are treated as tunable degrees of freedom, however, induced spatial correlations in target cell shapes or areas shift the rigidity transition. These observations suggest that tissues can coordinate changes in cell-scale properties, treated here as tunable degrees of freedom, to achieve desired tissue-scale behaviors.
Finite size effects in critical fiber networks

Arzash, Sadjad, Shivers, Jordan L., and MacKintosh, Fred C.

Soft Matter Jul 2020

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Fibrous networks such as collagen are common in physiological systems. One important function of these networks is to provide mechanical stability for cells and tissues. At physiological levels of connectivity, such networks would be mechanically unstable with only central-force interactions. While networks can be stabilized by bending interactions, it has also been shown that they exhibit a critical transition from floppy to rigid as a function of applied strain. Beyond a certain strain threshold, it is predicted that underconstrained networks with only central-force interactions exhibit a discontinuity in the shear modulus. We study the finite-size scaling behavior of this transition and identify both the mechanical discontinuity and critical exponents in the thermodynamic limit. We find both non-mean-field behavior and evidence for a hyperscaling relation for the critical exponents, for which the network stiffness is analogous to the heat capacity for thermal phase transitions. Further evidence for this is also found in the self-averaging properties of fiber networks.