Transfer Learning for New Disease Treatment
Understanding how to guide cells from diseased to healthy states is a longstanding challenge in biomedicine. In a recent PNAS publication, we developed an approach that uses transfer learning to predict how genes can be perturbed to reprogram cells and promote prespecified healthy behavior. We estimated the perturbation responses from large-scale gene expression datasets and found additive response combinations that achieve the desired behavior, allowing knowledge from one setting to inform another. The approach has broad potential for therapeutic development, offering a scalable data-driven pathway to design interventions for a variety of diseases. You can read more about this work here.
Creating Programmable Microfluidic Networks
Microfluidic systems are now being designed with precision as miniaturized fluid manipulation devices that can execute increasingly complex tasks. However, their operation often requires numerous external control devices. In our recent Nature article, we design microfluidic networks whose behavior can be harnessed to create a fluid analogue of Braess’s paradox and switch the direction of internal flows solely by manipulating the input and/or output pressures. These findings can be used to create built-in control mechanisms, thereby facilitating the development of portable systems and enabling novel applications, ranging from wearable healthcare technologies to deployable space systems.
Demonstrating Converse Symmetry Breaking
Symmetry breaking—the phenomenon in which the symmetry of a system is not inherited by its stable states—underlies pattern formation, superconductivity, and numerous other effects. Theoretical work has established the possibility of converse symmetry breaking, a phenomenon in which the stable states are symmetric only when the system itself is not. This includes scenarios in which interacting entities are required to be non-identical in order to exhibit identical behavior, such as in reaching consensus. In our recent Nature Physics article, we present an experimental demonstration of this phenomenon using a network of electromechanical oscillators. An animated summary of the work is available here and you can view our cover on the issue here.
Videos
Cascade Vulnerability of the North American Power Grid
Janus Oscillator Networks
Levitation of Heavy Particles in Downward Flows
Network Analog of the Butterfly Effect
Fractal Geometry of Doubly Transient Chaos
Outreach: What is Entropy?
Outreach: Syncing Up Without Sameness
Outreach: Self-Siphoning Beads
Recent Publications
J.L. Ocampo-Espindola, C. Bick, A.E. Motter, and I.Z. Kiss,
Frequency Synchronization Induced by Frequency Detuning,
arXiv:2505.04714
B. Kuznets-Speck, B.K. Ogonor, T.P. Wytock, and A.E. Motter,
Generative prediction of causal gene sets responsible for complex traits,
bioRXiv:10.1101/2025.04.17.649405
A.N. Montanari, A.E.D. Barioni, C. Duan, and A.E. Motter,
Optimal flock formation induced by agent heterogeneity,
arXiv:2504.12297v1
Y. Shao, J.-R. Angilella, and A.E. Motter,
Emergent oscillations and chaos in non-compliant microfluidic networks,
Phys. Rev. Fluids 10, 054401 (2025).
doi:10.1103/PhysRevFluids.10.054401
arXiv:2505.00068
A.N. Montanari, C. Duan, and A.E. Motter,
Duality between controllability and observability for target control and estimation in networks,
IEEE Transactions on Automatic Control (2025).
doi:10.1109/TAC.2025.3552001
arXiv:2401.16372
A.N. Montanari, C. Duan, and A.E. Motter,
On the Popov-Belevitch-Hautus tests for functional observability and output controllability,
Automatica 174, 112122 (2025).
doi:10.1016/j.automatica.2025.112122
arXiv:2402.03245
A. Haber, F. Molnar, and A.E. Motter,
Global network control from local information,
Chaos 34, 123166 (2024).
doi:10.1063/5.0239177
arXiv:2501.03331
Z.G. Nicolaou, F. Jiang, and A.E. Motter,
Metamaterials with negative compressibility highlight evolving interpretations and opportunities,
Nature Communications 15, 8573 (2024).
doi:10.1038/s41467-024-52853-x
arXiv:2410.07489
Y. Zhao, T.P. Wytock, K.A. Reynolds, and A.E. Motter,
Irreversibility in bacterial regulatory networks,
Science Advances 10(35), eado3232 (2024).
doi:10.1126/sciadv.ado3232
arXiv:2409.04513v1 [
T.P. Wytock, and A.E. Motter,
Cell reprogramming design by transfer learning of functional transcriptional networks,
Proc. Natl. Acad. Sci. USA 121(11), e2312942121 (2024).
doi:10.1073/pnas.2312942121
arXiv:2403.04837v1
A.N. Montanari, C. Duan, and A.E. Motter,
Target Controllability and Target Observability of Structured Network Systems,
IEEE Control Systems Letters 7, 3060-3065 (2023).
doi:10.1109/LCSYS.2023.3289827
arXiv:2309.14263v1
Z.G. Nicolaou, S.B. Nicholson, A.E. Motter, and J.R. Green,
Prevalence of multistability and nonstationarity in driven chemical networks,
The Journal of Chemical Physics 158(22), 225101 (2023).
doi:10.1063/5.0142589
arXiv:2306.09408v1
C. Duan, T. Nishikawa, and A.E. Motter,
Prevalence and scalable control of localized networks,
Proc. Natl. Acad. Sci. USA 119(32), e2122566119 (2022).
doi:10.1073/pnas.2122566119 - Supplemental Material
arxiv:2208.05980
Adilson E. Motter
Photo by Eileen Molony
Professor Motter's research is focused on the dynamical behavior and control of complex systems and networks. Current projects include quantum networks, machine learning applications to network problems, and data-driven discovery in network science. More...
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YouTube Channel
Postdoctoral Positions
The Motter group is currently recruiting postdoctoral researchers in mechanical metamaterial networks, quantum network science, and other areas of complex systems and networks.
“Janus Bunch” Complexity Explorable
Explore the wealth of dynamical states exhibited in a network of Janus oscillators.
Group News
May 2024: The Center for Network Dynamics hosts the “Brain Architecture and Computing 2024” workshop.
May 2024: Jorin T. Graham presents the public lecture “Do Cicadas Have a Choirmaster?”.
January–February 2024: Arthur Montanari delivers the 2024 Sievert Prize Lecture Series, titled “Networks: From Brains and Quantum Internet to Climate Change”.
September 2023: Northwestern launches the Center for Network Dynamics, directed by Prof. Adilson Motter.
October 2022: Adilson E. Motter receives the Senior Scientific Award from the Complex Systems Society.
September 2022: Adilson E. Motter and Renaud Lambiotte chair the 2022 SIAM Workshop on Network Science.
September 2022: Ana Elisa D. Barioni and Thomas Mckenzie-Smith are awarded Data Science Fellowships from Northwestern University’s Data Science Initiative.
July 2022: Adilson E. Motter starts his term as President of the Network Science Society.
March 2022: Yuanzhao Zhang is awarded the 2021 Dissertation Award in Statistical and Nonlinear Physics.
Selected Press
Bacterial cells transmit memories to offspring
Northwestern Now (August 28, 2024)
http://...
From a Point to a Torus: Unveiling Emergent Dynamics with Higher-order Bifurcations
SIAM News (July 5, 2024)
http://...
Transfer learning paves the way for new disease treatments
Northwestern Now (March 4, 2024)
http://...
Failures in large networks can be prevented with local focus
Northwestern Now (August 8, 2022)
http://...
T&D HIL for Demand Response
FLEXLAB (November, 2021)
http://...
New tool untangles complex dynamics on hypergraphs
Santa Fe Institute (October 26, 2021)
http://...
