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Our Research

The tissues in our body are made up of different types of cells that communicate with each other to perform joint functions. Tissues differ greatly from each other in the composition of the cells, spatial arrangement, and function.

Can we uncover principles that are common across different tissues? What are the characteristics of the communication between cells that are critical to the proper functioning of the tissue? What diseases may develop if these characteristics are not maintained?


In our research, we combine concepts from nonlinear dynamics, complex systems theory, optimality control, and machine learning to uncover the universal principles of tissues in health and disease.     ​

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Design principles of wound healing and fibrosis

Fibrosis is a pathology of excessive scarring which causes morbidity and mortality worldwide. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. We recently developed a theoretical framework for the cell circuit between myofibroblasts - the scar producing cells and macrophages - the recruited immune cells. The mathematical framework predicts two types of fibrosis - hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. Moreover, we used the model to predict new therapeutic target for reducing fibrosis. We are working closely with experimental labs to test our theoretical predictions in different tissues including heart, lung and liver. 

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Related publications:

Principles of Cell Circuits for Tissue Repair and Fibrosis

Miri Adler, Avi Mayo, Xu Zhou, Ruth A Franklin, Matthew L Meizlish, Ruslan Medzhitov, Stefan Kallenberger, and Uri Alon

iScience, 2020

An autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in nonalcoholic steatohepatitis

Wang et al.

Science Translational Medicine, 2023

Circuit to target approach defines an autocrine myofibroblast loop that drives cardiac fibrosis

Shoval Miyara*, Miri Adler* et al.

bioRxiv, 2023

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Principles of division of labor at the tissue and population level

Advances in single-cell gene expression measurement techniques provide a view into the diversity between individual cells of the same type. An emerging observation is that differentiated cells often show a continuum of gene expression profiles. In order to help interpret these continua of gene expression profiles, we use a theoretical approach considering that cells need to perform multiple tasks. Because no cell can optimally perform all of these tasks at once, cells in multi-cellular organisms must divide labor. Continuum of gene expression in single-cell data can thus be interpreted as a continuum of specializations where individual cells perform different tasks depending on their spatial position, environmental conditions and other factors. Learn more about our recent work on optimal division of labor in Miri's talk at the LMRL 2021 workshop.

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Related publications:

Continuum of Gene-Expression Profiles Provides Spatial Division of Labor within a Differentiated Cell Type

Miri Adler, Yael Korem Kohanim , Avichai Tendler, Avi Mayo, and Uri Alon

Cell Systems, 2019

Controls for Phylogeny and Robust Analysis in Pareto Task Inference

Miri Adler*, Avichai Tendler*, Jean Hausser*, Yael Korem, Pablo Szekely, Noa Bossel, Yuval Hart, Omer Karin, Avi Mayo, and Uri Alon

Molecular Biology and Evolution, 2022

Emergence of division of labor in tissues through cell interactions and spatial cues

Miri Adler*, Noa Moriel*, Aleksandrina Goeva*, Inbal Avraham-Davidi, Simon Mages, Taylor S. Adams, Naftali Kaminski, Evan Z. Macosko, Aviv Regev, Ruslan Medzhitov and Mor Nitzan

Cell Reports, 2023

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Design principles of cell circuits for tissue homeostasis


Tissue processes involve communication between several cell types by means of diverse secreted factors and cell contact signals. In order to help make sense of this complexity, theoretical models coupled with experimental evidence can play an important role. We use this combined approach to address questions in tissue biology including how healthy tissues maintain proper composition and spatial organization of their constituent cell types. Our goal is to reveal design principles of cell communication circuits that govern tissue homeostasis.

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Related publications:

Circuit Design Features of a Stable Two-Cell System

Xu Zhou*, Ruth A Franklin*, Miri Adler*, Jeremy B Jacox, Will Bailis, Juston A Shyer, Richard Flavell, Avi Mayo, Ruslan Medzhitov & Uri Alon

Cell, 2018

Endocytosis as a Stabilizing Mechanism for Tissue Homeostasis

Miri Adler, Avi Mayo, Xu Zhou, Ruth A Franklin, Jeremy B Jacox, Ruslan Medzhitov, and Uri Alon

PNAS, 2018

Microenvironmental Sensing by Fibroblasts Controls Macrophage Population Size

Xu Zhou*, Ruth A Franklin*, Miri Adler, Trevor S Carter, Emily Condiff, Taylor S Adams, Scott Pope, Naomi H Philip, Matthew L Meizlish, Naftali Kaminski, and Ruslan Medzhitov

PNAS, 2022

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Network dynamics

Network hyper-motifs and their emergent properties

Networks are built from nodes connected by edges. Nodes are the 'atoms' of networks. Just as atoms can combine into molecules that have new properties, nodes combine into network motifs (Milo et al., 2002). Network motifs are recurrent arrangements of nodes and edges that have certain dynamic properties such as oscillations and multi-stability and can provide useful input-output relations. Network motifs are the building blocks of complex networks. However, it is unclear how these building-block patterns are combined in real networks and what dynamic properties can emerge from their combinations. To address this, we define hyper-motifs: multiple network motifs that are direclty joined in the network. We developed a computational method to identify hyper-motifs in real networks and found that each type of network (social, neuronal, electronic, gene regulatory etc.) is enriched in some hyper-motifs, while other hyper-motifs are under-represented compared to random networks. We use the hyper-motif framework to explore behavior and structure of complex networks in complex tissue processes such as embryonic development.

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Fold-change detection in biological systems

Many biological systems, from bacteria to human cells respond to relative (rather than absolute) changes in input, as we respond to relative changes in light, smell or sound. This fold-change detection (FCD) property provides cells with exquisite ability to sense across many scales of signal. FCD is implemented by specific feed-forward and feedback regulatory circuits that are repeatedly used by nature. Exploring these circuits is important in order to understand decision making of cells in response to environmental signals. We explore network topologies that can provide an FCD dynamical response.

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