Last call for postdoctoral applications to join my group at University of Toronto. Feel free to reach out if you want to learn more about the position.

Postdoctoral Job Opportunity! I am hiring a postdoc to join my group next fall. Many possible research directions in theoretical biophysics and nonequilibrium statistical mechanics. See the posting linked below for details — apply by Jan 15th for full consideration.

Finally, thanks to Yale's Department of Physics and Quantitative Biology Institute, and the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting our work!

The non-Markovian dynamics of biological systems arise from coarse-graining the underlying fundamental physics to produce simplified descriptions. Going forward, our work paves the way to study how non-Markovian dynamics emerge through coarse-graining across scales. Stay tuned!

This is roughly the timescale biologists have identified as the length of the fly’s working memory for both odor sensing and navigation, which we’ve detected using only recorded behavior data! This hints that memory is the main driving factor for the fly’s non-Markovian behavior.

We then turn to biological data, 400,000 minutes of recorded fruit fly behavior with 10ms time resolution. Our most intriguing result: we discover a unique timescale that maximizes how much information the fly’s past behavior holds about its future behavior, about 7 seconds.

We first explore analytically-tractable minimal models for non-Markovian dynamics. This lets us build intuition for the highly counterintuitive behavior of non-Markovian dynamics with long-range history dependence. E.g.: autocorrelations can fail to reflect true dependencies!

We develop new information-theoretic tools to 

1. Quantify how strongly the dynamics of biological systems depend on their past.
2. Decompose this dependence into contributions from different parts of the past, quantifying the information you gain by learning each past state.

arxiv.org/abs/2512.13933
arxiv.org/abs/2512.13936

The fundamental laws of physics are Markovian: the next state of a physical system depends only on its current state. Biology, however, is often non-Markovian: the next state can depend on states arbitrarily far back into the past.

Excited to share two final preprints for the year:

“Decomposing Non-Markovian History Dependence”,
and
“Tractable Model for Tunable Non-Markovian Dynamics”.

Both with chriswlynn.bsky.social.

This is roughly the timescale biologists have identified as the length of the fly’s working memory for both odor sensing and navigation, which we’ve detected using only recorded behavior data! This hints that memory is the main driving factor for the fly’s non-Markovian behavior.

We then turn to biological data, 400,000 minutes of recorded fruit fly behavior with 10ms time resolution. Our most intriguing result: we discover a unique timescale that maximizes how much information the fly’s past behavior holds about its future behavior, about 7 seconds.

We first explore analytically-tractable minimal models for non-Markovian dynamics. This lets us build intuition for the highly counterintuitive behavior of non-Markovian dynamics with long-range history dependence. E.g.: autocorrelations can fail to reflect true dependencies!

We develop new information-theoretic tools to 
1. Quantify how strongly the dynamics of biological systems depend on their past.
2. Decompose this dependence into contributions from different parts of the past, quantifying the information you gain by learning each past state.

arxiv.org/abs/2512.13933
arxiv.org/abs/2512.13936

The fundamental laws of physics are Markovian: the next state of a physical system depends only on its current state. Biology, however, is often non-Markovian: the next state can depend on states arbitrarily far back into the past.

Johann du Buisson, Jannik Ehrich, mleighton.bsky.social, davidasivak.bsky.social, and John Bechhoefer introduce a one-coordinate test that infers heat flow to flag “demonic” operation. In kinesin simulations tuned to experiments, the motor grows more demon-like as active fluctuations rise.

Our recent work (elifesciences.org/articles/104...) combines theory and experiments (by Alex Papagiannakis and Christine Jacobs-Wagner) to understand how chromosome segregation is coupled to growth in E coli. We demonstrate that the nonequilibrium dynamics of polysomes may play a key role.

Thanks to NSERC, the Canada Research Chairs program, and @sfuphysics.bsky.social for supporting our research! Special thanks to @mitacscanada.bsky.social for funding Julian’s time as a visiting researcher in the Sivak Group last Summer.

Over the voltage range typical of a neuronal action potential, at low voltages the pump exhibits Maxwell-demon behavior and high efficiency, while at high voltages the pump instead operates as a conventional engine and achieves higher turnover at the cost of lower efficiency.

Remarkably, we find that sodium-potassium pumps can exhibit Maxwell-demon behavior, supporting internal information flow that enables the ion-transporting subsystem to leverage thermal fluctuations to produce useful electrochemical work.

We study sodium potassium pumps through the lens of bipartite stochastic thermodynamics, identifying and computing energy and information flows between the ATP-consuming and ion-transporting parts of these machines.

These pumps are nanoscale molecular machines that consume chemical energy to transport ions across membranes into, out of, and within cells. They are essential both for maintaining cellular homeostasis, and for propagating electrical signals in neurons.

New preprint out today: “Information Thermodynamics of Cellular Ion Pumps”. Led by talented undergraduate student Julián Jiménez-Paz, and working with @davidasivak.bsky.social, we explore the thermodynamics of cellular ion pumps like the sodium-potassium pump.

arxiv.org/abs/2506.11248

Canada Excellence Research Chairs offer $4-8M over eight years to build a world-class research focus.

Interested in coarse-graining, irreversibility, or neural activity in the hippocampus?

If so, check out our new preprint exploring how maximizing the irreversibility preserved from microscopic dynamics leads to interpretable coarse-grained descriptions of biological systems!

Postdoc opportunity!
Join us in heavenly Vancouver (Canada) to develop fundamental nonequilibrium stat mech, thermo, and info theory applied to biomolecular machines and in close collaboration with experiment.
Details: www.sfu.ca/physics/siva...

Check it out here: doi.org/10.1146/annu... or on arXiv: arxiv.org/abs/2406.10355.

Thanks to NSERC, the Canada Research Chairs program, and @sfuphysics.bsky.social for supporting our research!

We explore the free energy transduction strategies used by various evolved biological molecular machines in different contexts, and suggest possible design principles we might learn from them to help guide the engineering of synthetic nanomachines.

We focus on flows of energy and information between the components of biological machines, and highlight several possible engine configurations. One intriguing example: an “information engine” mode where one subsystem uses information to harvest heat energy from its environment.