Noah Olsman

Postdoctoral fellow in the lab of Johan Paulsson
studying biomolecular feedback circuits in single cells

My research broadly focuses on systems and synthetic biology, where I use the mathematical tools and concepts from engineering, such as sensitivity, robustness, and efficiency, to analyze biological processes. Much of my work centers on studying how cells use feedback control to guarantee that their behavior is robust to the inevitable uncertainties that pervade the living world. Currently I am working to develop an experimental platform to design and study biomolecular circuits in single cells. I hope to combine this with my background in theory to test quantitative predictions about the fundamental limits on biomolecular feedback control.

I received my undergraduate education at the University of Southern California, where I majored in electrical engineering and minored in mathematics. Though my initial interest was in robotics, over the course of my degree I became increasingly interested in biology. I knew almost nothing about the field, but it seemed like the place where interesting things were happening. After graduating in 2012, I spent a little over a year working in the lab of Thierry Emonet at Yale, studying theoretical aspects of the bacterial chemotaxis pathway in Escherichia coli, which governs the cell's ability to direct its motion towards or away from chemical gradients in the environment.

In 2013 I started graduate school at the California Institute of Technology in the Department of Computing and Mathematical Sciences, where I was a part of the Control and Dynamical Systems (CDS) option. I was co-advised by professors Lea Goentoro in the Systems Biology department and John Doyle in CDS. The common theme of my graduate work with both Lea and John was the development of theoretical tools to understand how low-level molecular mechanisms give rise to high-level functional behavior in biomolecular circuits. After defending my thesis, I began a postdoctoral fellowship with Johan Paulsson in the Department of Systems Biology at Harvard Medical School. In the Paulsson Lab I am focused on developing an experimental platform to rigorously test theoretical predictions about circuit behavior using a microfluidic device called the mother machine, which allows us to track hundreds of thousands of single cells for days at a time.

Outside of my academic work, I am an avid guitar and intermittent mandolin player. I love American folk and blues music, and to go to as many as live shows as possible. During graduate school I served as the chief analytics officers for the nonprofit Seed Consulting Group, where I helped provide pro bono consulting services to environmental non-profits through the state of California. In 2018 I taught a Summer course on communication and information theory for advanced middle school students through Math Academy, and am currently teaching extracurricular classes for elementary school students through Cambridge Math Circle.

Curriculum Vitae


Here are a few projects that I think are representative of my overall research interests, though I have been fortunate to be involved in many fun side projects listed in the Publications section.

Homeostasis is one of the central processes that pervades all of life. Each organism must regulate its internal state, be it a warm-blooded vertebrate regulating its temperature or a single bacterium balancing its osmotic pressure. To do this, they generally rely on feedback control. While it has long been known that feedback control exists in biology all the way down to the molecular level, it has so far been difficult to engineer reliable feedback regulation into cells to perform synthetic functions.

This has changed in recent years, with the development of a simple mechanism for implementing precise adaption in cells known as Antithetic Integral Feedback (AIF). This mechanism has the fortunate properties of both being simple to design using known biological parts and having certain theoretical guarantees of its performance. Our work uses tools from control theory to describe a set of mathematical relationships that impose strict performance tradeoffs and hard limits on such a circuits behavior. These can be thought of a system of guidelines that can help to inform the design of synthetic biological feedback systems.

This work was published in two papers, one in Cell Systems and one in iScience. The work was done in collaboration with Ania Baetica, Fangzhou Xiao, and Yoke Peng Leong, with advisement from John Doyle and Richard Murray at Caltech.

Sensory systems in biology are faced with two goals: they must be able to sensitively respond to changes in stimuli, while also being able to sense a broad range of possible intensities. One way that signaling systems can achieve both of these goal simultaneously is with a phenomenon known as fold-change detection, where the internal response of the cell is a function not of the absolute change in intensity, but the ratio of stimulus to background. This means that a cell can respond to a change from 1 to 3 just as well as it can respond to a change from 100 to 300.

We used mathematical models of protein dynamics to understand the molecular mechanisms that facilitate fold-change detection in real biological systems. This work was primarily with my Ph.D. adviser Lea Goentoro, and was presented at the 2015 Winter q-bio Meeting. It has since been published in the Proceedings of the National Academy of Sciences.

While much progress has been made in the construction of synthetic gene-regulatory networks, it is still difficult to precisely and efficiently tune the underlying parameters of these pathways. To tackle this problem, we are developing a platform based on a microfluidic device known as a mother machine (shown to the left) to design, measure, and test synthetic biomolecular circuits. We hope that this platform will make it dramatically easier to optimize the behavior of engineered pathways, and make it possible to carefully probe the behavior of single cells.

As a first case study, we are focusing on the design and optimization of several classes of synthetic feedback circuit architecture that yield a range of diverse dynamic behavior. Our goal is to make it possible to start from a prototype architecture to produce a tuned and optimized design by systematically varying molecular parameters of the circuit. If successful, this will make it possible to understand not only specific circuit architectures, but also the design space of a given synthetic network.

This work is in progress in collaboration with Jacob Quinn Shenker and Madison Adamthwaite, under the advisement of Johan Paulsson at Harvard Medical School.


  1. Olsman, N. and Forni, F., 2019. Antithetic integral feedback for the robust control of monostable and oscillatory biomolecular circuits. arXiv preprint arXiv:1911.05732. [Paper] [PDF]

Research Publications

  1. Olsman, N., Baetica, A.A., Xiao, F., Leong, Y.P., Murray, R.M. and Doyle, J.C., 2019. Hard limits and performance tradeoffs in a class of antithetic integral feedback networks. Cell Systems, 9(1), pp.49-63. [Paper] [PDF]
  2. Olsman, N., Xiao, F. and Doyle, J.C., 2019. Architectural principles for characterizing the performance of antithetic integral feedback networks. iScience, 14, pp.277-291. [Paper] [PDF]
  3. Olsman, N., Alonso, C.A. and Doyle, J.C., 2018, December. Architecture and Trade-offs in the Heat Shock Response System. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 1096-1103). IEEE. [Paper] [PDF]
  4. Al-Anzi, B., Gerges, S., Olsman, N., Ormerod, C., Piliouras, G., Ormerod, J. and Zinn, K., 2017. Modeling and analysis of modular structure in diverse biological networks. Journal of theoretical biology, 422, pp.18-30. [Paper] [PDF]
  5. Mossel, E., Olsman, N., and Tamuz, O., 2016, September. Efficient Bayesian learning in social networks with gaussian estimators. In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 425-432). IEEE. [Paper] [PDF]
  6. Olsman, N. and Goentoro, L., 2016. Allosteric proteins as logarithmic sensors. Proceedings of the National Academy of Sciences, 113(30), pp.E4423-E4430. [Paper] [PDF]
  7. Al-Anzi, B., Arpp, P., Gerges, S., Ormerod, C., Olsman, N. and Zinn, K., 2015. Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network. PLoS computational biology, 11(5). [Paper] [PDF]

Invited Publications

  1. Olsman, N. and Paulsson, J., 2019. Universal control in biochemical circuits. Nature, 570(7762), pp.452-453. [Paper] [PDF]
  2. Olsman, N. Xiao, F. and Doyle, J., 2018. Evaluation of Hansen et al.: Nuance Is Crucial in Comparisons of Noise. Cell Systems, 7(4), pp.352-355. [Paper] [PDF]
  3. Olsman, N. and Goentoro, L., 2018. There's (still) plenty of room at the bottom. Current opinion in biotechnology, 54, pp.72-79. [Paper] [PDF]

Ph.D. Thesis

  1. Olsman, N. 2019. Architecture, Design, and Tradeoffs in Biomolecular Feedback Systems (Doctoral dissertation, California Institute of Technology). [Thesis]