1. Research Background

Aging organisms are complex systems. They involve a large number of interacting components and processes, and fail in a variety of modes. One way to understand a complex system is to first answer two questions: How can we quantify it, and how can we perturb it?

The most accurate approaches to quantifying aging at the molecular level are aging clocks, which useomics-scale measurements to predict chronological age. However, chronological age alone does not accurately determine the degree of progression along the aging trajectory, also known as the "biologicalage". In many cases, we are interested in the extent to which biological age can decouple from chronological age. The success of aging clocks in quantifying chronological age raises the question: Is there a molecular clock for biological age?

Another way to quantify a complex system is to engineer a probe for its performance. Software stress tests of computer hardware are a well-known example: the failure of even a small number of components, like single bits of memory, can be detected by repeatedly writing patterns to memory, reading them back, and checking the results. The analogy to aging biology is that cellular components, such as individual proteins, can be damaged over time. If synthetic genetic circuits can be designed that are sensitive to the fidelity of the cellular components, then their performance would serve as non-destructive sensors of cellular damage.

The most robust ways to slow the rate of aging are by reducing dietary intake and increasing physical activity. These treatments both alter energy constraints: dietary restriction reduces the energy input, while increased physical activity demands more energy output. However, neither intervention has a strong effect on total energy expenditure. This surprising result suggests that energy constraints slow the rate of aging by altering the allocation of energy to different processes. In recent years, a kit of tools has beendeveloped that can be used to quantify energy allocation during the aging process.

2. Research questions addressed by the group:

• What is biological age and how can we measure it at the molecular level?
• How does the rate at which an organism ages correlate with the state of its microbiome? Is there a causal relationship between the microbiome state and the rate of aging?
• How does the performance of a genetic circuit change as the cellular components age?
• Does total energy expenditure correlate with lifespan and healthspan?
• What fraction of an organism’s energy budget is allocated to the processes of growth, reproduction, maintenance, and repair?
• How does energy allocation change with age, and is it affected by perturbations like dietary restriction, exercise, or genetic and pharmaceutical interventions?

3. Possible project(s):

4. Applied Methods and model organisms:

We are a multidisciplinary group using experimental methods of molecular biology, biochemistry, cellbiology, biophysics, bioengineering, and microscopy. We foster an atmosphere of collaboration in which all members learn from and teach each other. We study model systems including fish, worms, microbes, and cultured cells.

5. Desirable skills and qualifications:

We are seeking enthusiastic, ambitious, and intellectually curious scientists to join our group. Previous coursework in systems biology, biochemistry, thermodynamics or other branches of physics, or engineering is beneficial but not required.

6. References and key publications:

• Pleška, Maroš, David Jordan, Zak Frentz, BingKan Xue, and Stanislas Leibler. "Nongenetic individuality, changeability, and inheritance in bacterial behavior." Proceedings of the National Academy of Sciences 118,no. 13 (2021): e2023322118.
• Frentz, Zak, and Jonathan Dworkin. "Bioluminescence dynamics in single germinating bacterial sporesreveal metabolic heterogeneity." Journal of the Royal Society Interface 17, no. 170 (2020): 20200350.
• Chuang, John S., Zak Frentz, and Stanislas Leibler. "Homeorhesis and ecological succession quantified in synthetic microbial ecosystems." Proceedings of the National Academy of Sciences 116, no. 30 (2019): 14852-14861.
• Frentz, Zak, Seppe Kuehn, and Stanislas Leibler. "Strongly deterministic population dynamics in closed microbial communities." Physical Review X 5, no. 4 (2015): 041014.