Great news: our group has been awarded a 2-year grant from NSF-Simons National Institute for Theory and Mathematics in Biology (NITMB) to support our project on mathematical modeling of microbial communities (Sept 2025–Aug 2027). This should be enough to fund the rest of Zihan Wang until his defense and then fund someone else after his graduation!
Our experimental collaborator on this project is Seppe Kuehn from U Chicago.
What if biochemical systems could learn similar to neural networks? Our new study explores how dimerization networks “evolve” the ability to classify inputs—chemical style.
Collaborators: Alexei Tkachenko (Brookhaven National Laboratory), Bartolo Mognetti (Université libre de Bruxelles) and yours truly.
The ideas about dimerization networks recently developed by Jacob Parres-Gold, Michael Elowitz and collaborators go back to my papers on mass action equilibrium in protein interaction networks:
S. Maslov, I. Ispolatov
Propagation of large concentration changes in reversible protein-binding networks
PNAS 104(34): 13655-13660 (2007).
PNAS link
Reveals how concentration perturbations transmit (or not) through networks via mass action.
S. Maslov, I. Ispolatov
Spreading out of perturbations in reversible reaction networks
New Journal of Physics 9, 273 (2007).
NJP link
Introduces a mathematical framework for fluctuation propagation in mass-action-governed networks and maps them to current flow in resistor networks.
K.K. Yan, D. Walker, S. Maslov
Fluctuations in Mass-Action Equilibrium of Protein Binding Networks
Physical Review Letters 101, 268102 (2008).
PRL link
Focuses on fluctuations in mass-action equilibria.
J. Zhang, S. Maslov, E.I. Shakhnovich
Constraints imposed by non-functional protein-protein interactions on gene expression and proteome size
Molecular Systems Biology 4:210 (2008).
MSB link
Non-specific protein interactions restrict gene expression and proteome size.
J. Zhang, S. Maslov, E.I. Shakhnovich
Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions
PNAS 107(52): 22599-22604 (2010).
PNAS link
Network topology sets protein abundances and governs functional vs. nonspecific interactions.
Big congrats to lab alum Tong Wang—he’s now officially a professor at Purdue (and our neighbor)! Check out his new faculty profile:
https://www.bio.purdue.edu/People/profile/wang7403.html
Way to go, Tong!
And for a little nostalgia, here’s a photo from my 9/2021 Cambridge, MA reunion with the lab’s Boston diaspora: Tong, Akshit, and Ashish.
Hey everyone,
Ananthan Nambiar will be defending his Ph.D. thesis on May 7, 2025, at 1 pm-3pm CT in Grainger Commons (Room 233/235 at Grainger Engineering Library). Let’s come together to cheer Ananthan!
After graduation, he’s heading to Seattle to join Bill Noble’s lab at the University of Washington for his postdoc—super exciting news! If you’re curious about the group, here’s the Noble Lab website: https://noble.gs.washington.edu/
Here is Ananthan, me, Veronika and Simon in October 2021
We have been awarded a UIUC Strategic Research Initiative (SRI) program with the proposal “Multi-scale learning for analysis of spatial transcriptomics data” .
Our team will start working on April 2, 2024:
Sergei and Zihan attended the APS March meeting in Minneapolis and gave talks on recent work on microbial communities. It was also a chance to meet with lab alumni: Tong Wang and Akshit Goyal. We also made contacts and discussed potential collaborations with several experimental groups. Griffin Chure from Jonas Cremer’s group at Stanford will give a talk at our group meeting on 3/29 at 9:30am CT.
Sergei + Alexei Tkachenko’s paper on origin of life has been accepted at eLife:
https://elifesciences.org/reviewed-preprints/91397/reviews
We gave an interview for IGB press release, which will be coming up shortly.
Tarun +Sergei submitted a paper on a model of a complex synthetic human gut microbiome: Coarse-Grained Model of Serial Dilution Dynamics in Synthetic Human Gut Microbiome. The model is necessarily coarse-grained because the community has >60 species! Data for serial dilution experiments are from a paper co-authored by a former group member, Veronika Dubinkina.
See UIUC Physics Department press release and The Atlantic magazine article. In (eLife 2021) we developed a new epidemiological model that encompasses randomness and dynamic variability of individual social interactions and used it to explain COVID-19 waves in US between July 2020 and March 2021.
Our eLife research editor was Mark Lipsitch, Harvard epidemiologist and the director of science in the newly formed Disease Forecasting Center at CDC. In his editorial statement, he wrote: “This is an excellent and elegant example of what theory can do at its best in epidemiology: it takes a widely observed phenomenon that is an ‘”embarrassment’” (my word) to current theories; proposes a parsimonious explanation that is plausible for the phenomenon by extending the existing theories in a specific way; and makes a plausible case for the importance of the mechanism in explaining key features of the data. In this case, the embarrassing phenomenon is long periods of very slowly changing incidence/prevalence, and the modification to theory is incorporation of dynamic social heterogeneity. This should stimulate much further work in the field. Congratulations to the authors.”
See IGB press release.
We studied population dynamics (Nat Comm 2021 Nov) in a community of microbes sequentially utilizing resources (diauxie) in the order determined by each species regulatory network. Such communities realized both in serial dilution experiments, as well as in naturally occurring boom-and-bust cycles (e.g., in the upper ocean microbiome of temperate regions).
