Research interests

We work on problems in quantitative biology, non-equilibrium dynamics, and theoretical computer science.   A frequent question is how collective dynamics in physical and biological systems can generalize past experiences, i.e., learn, and respond differently to future inputs. 

Current work includes:
1.  Biological learning and adaptation in changing environments
     (in circadian clocks with the Rust lab, in gene regulation with the Tay lab,
      in molecular evolution with the Wang and Ranganathan labs, in neural networks)
2. Learned behaviors in materials
     (elastic materials, DNA systems, active materials).


Openings: If you are interested in working on such themes as a postdoc, graduate student or undergrad, contact  amurugan@uchicago.edu. 

Funding: Our work is primarily supported by the NSF and a Simons Foundation Investigator award. Lab members have been supported by the James S. McDonnell Foundation and NSF fellowships.

Some recent publications            (List + summaries here)

Non-equilibrium statistical mechanics of continuous attractors
W. Zhong, Z. Lu, D.J.Schwab+, A. Murugan+
Neural Computation (in press, 2020)

Supervised learning in a mechanical system
with: M. Stern, C. Arinze, L. Perez, S. Palmer
arxiv (Oct 2019)

Physical constraints on epistasis
with: K. Husain
arxiv (Oct 2019)

Tuning environmental timescales to evolve and maintain generalists
with: V. Sachdeva*, K. Husain*, J. Sheng, S. Wang+
arXiv (June 2019)

Kalman-like Self-Tuned Sensitivity in Biophysical Sensing 
with: K Husain, W Pittayakanchit, G Pattanayak, M J Rust
Cell Systems 2019, 459–465.e6

Learned multi-stability in mechanical networks
 with: M. Stern, M. Pinson
arXiv (Feb 2019)

Temporal pattern recognition through analog molecular computation
 with: J O'Brien
ACS Synthetic Biology (March 2019)
Popular summary by MIT Tech Review

Bioinspired nonequilibrium search for novel materials
 with: H. Jaeger 
MRS Bulletin 44(2):96-105       pdf here

Information content of downwelling skylight for non-imaging visual systems
 with: R. Thiermann, A. Sweeney
bioRxiv (Sep 2018) 
​
Shaping the topology of folding pathways in mechanical systems
with: M. Stern, V. Jayaram,
Nature Communications 9:4303 (2018)

​Biophysical clocks face a trade-off between internal and external noise resistance
 with: W. Pittayakanchit*, Z. Lu*, J. Chew, M. Rust
eLife 2018;7:e37624

High Protein Copy Number Is Required to Suppress Stochasticity in the Cyanobacterial Circadian Clock 
 with: J. Chew, E. Leypunskiy, J. Lin, M. Rust
Nature Communications 9:3004 (2018)

Shaping dynamical pathways in mechanical systems
with: M. Stern, V. Jayaram
Nature Communications 9:4303 (2018)

 The difficulty of folding self-folding origami
 with: M. Stern, M. Pinson
Physical Review X,  arXiv link (2017) 

Self-folding origami at any energy scale
 with: M. Pinson*, M. Stern*, A Ferrero, T.Witten, E. Chen
Nature Communications 8:15477 (2017) 

 Associative pattern recognition through macro-molecular self-assembly
 with: W. Zhong, D.J. Schwab
Journal of Statistical Physics,  Volume 167, Issue 3–4, May 2017

Topologically protected modes in non-equilibrium stochastic systems
with: S. Vaikuntanathan
Nature Communications (Jan 2017) 

The Information Capacity of Specific Interactions
 with: M. Huntley, M. Brenner
Proceedings of the National Academy of Sciences (May 2016)

Receptor arrays optimized for natural odor statistics,
with: D. Zwicker, M. Brenner
Proceedings of the National Academy of Sciences (Apr 2016)

Biological implications of dynamical phases in non-equilibrium reaction networks,
with: S. Vaikuntanathan
invited contribution, Journal of Statistical Physics (2016, 162 (5))
 
Undesired usage and the robust self-assembly of heterogeneous structures,   
with: J. Zou, and M. Brenner 
Nature Communications 6, 6203 (Jan 2015) 

Multifarious Assembly Mixtures: Systems Allowing Retrieval of Diverse Stored Structures,           
with: Z. Zeravcic, S. Leibler and M. Brenner 
Proceedings of the National Academy of Sciences 112(1) 54-59 (Dec 2014)