Institute of Molecular Biology, Academia Sinica

Quantitative biology of adaptive cellular behavior

The big picture

Cells are highly sophisticated information-processing systems that operate in an ever-changing environment. They sense environmental signals using sensor molecules that typically reside on the cell membrane, process these signals through chemical reaction networks within the membrane, and then use this processed information to modulate their behavior, enhancing their survival in dynamic surroundings. Our research goal is to understand the principles underlying such adaptive cellular behaviors, using microbes such as bacteria as model systems and combining various quantitative approaches.

A means to control and predict cellular behaviors is currently lacking, which underlies various challenges facing our society, including preventing biofilm formation and treating infectious diseases and cancer. In engineering contexts, it remains difficult to develop autonomous and adaptive systems capable of operating in real-world settings. The relative simplicity of microbial behaviors provides unique opportunities to extract the design principles of adaptive systems in a quantitative manner, which our lab believes can offer key insights into solutions for these challenges.

Our questions

The cellular 'computers' that sense and process environmental information comprise a web of chemical reactions. However, unlike human-engineered digital computing systems, the nature of cellular information processing is probabilistic, rendering it difficult to characterize. The central questions we are currently pursuing include: What kinds of information processing do cells undertake?; How 'good' are cellular computers, or more precisely, how can we characterize their proficiency?; How do functional dynamics emerge from noisy and apparently unreliable chemical reactions?; and Why (and how) has a specific chemical reaction network evolved to perform a particular biological task?

Approaches

We are addressing all of these fundamental questions by combining several approaches, ranging from experimental to theoretical. In addition to standard microbiology and cell biology methods, our lab particularly focuses on utilizing and extending the following techniques:

FRET measurement: To understand how cellular 'computers' work, it is necessary to observe the dynamics of chemical reactions inside cells. We use a single-cell Fluorescence Resonance Energy Transfer (FRET) measurement system for this purpose. FRET is a quantum-mechanical phenomenon that allows us to convert invisible molecular interactions inside living cells into detectable fluorescence signals. We conduct measurements at the single-cell level because the performance of cellular information processing critically depends on cell-to-cell and temporal variations in the dynamics, which are lost in population-averaged data.

Microfluidics: The key to successful quantitative live-cell experiments is precise control of the environment surrounding the cells, as cells respond differently to various environments. Microfluidics, which are versatile techniques used across many scientific fields, allow for the precise manipulation of tiny amounts of liquid. For example, we develop microfluidic devices specifically designed for our single-cell FRET measurements to control the dynamics of signals to which cells are exposed.

Data modeling: We utilize various theoretical frameworks to analyze and interpret our data. We adopt Bayesian statistics and machine-learning techniques extensively to process raw data obtained by microscopy. The processed data is then further analyzed by exploiting frameworks of dynamical systems theory and information theory.

Training

Our research approach is usually highly interdisciplinary, but nobody can be an expert in all those relevant fields simultaneously, especially when they are new to the field. Don't worry! We develop tailored training programs in the lab, and all of our lab members with diverse academic backgrounds help each other to acquire new skills.

Principal Investigator, 2022-present, PRESTO, Japan Science and Technology Agency
Associate Research Scientist, 2019-2022, Yale University
PDF, 2017-2019, Yale University
PDF, 2013-2017, AMOLF Institute, Amsterdam, Netherlands
Ph.D., 2013, Dept. Basic Science, Graduate School of Arts and Sciences, University of Tokyo
MS, 2010, Dept. Basic Science, Graduate School of Arts and Sciences, University of Tokyo
BS, 2008, Dept. Physics, University of Tokyo

Mattingly, H.H.*, Kamino*, K., Ong, J., Kottou, R., Emonet, T., & Machta, B.B. (2024) E. coli do not count single molecules. arXiv (*Equal contribution).
Moore*, J.P., Kamino*, K., Kottou, R., Shimizu, T.S., & Emonet, T.(2024). Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis. Cell Systems (*Equal contribution).
Kamino*, K., Kadakia, N., Aoki, K., Shimizu, T. S., & Emonet*, T. (2023). Optimal inference of molecular interactions in live FRET imaging. PNAS (*Corresponding authors).
Mattingly*, H.H., Kamino*, K., Machta, B.B., & Emonet, T. (2021). Escherichia coli chemotaxis is information limited. Nature Physics (*Equal contribution).
Kamino, K., Keegstra, J.M., Long, J., Emonet, T., & Shimizu, T.S. (2020). Adaptive tuning of cell sensory diversity without changes in gene expression. Science Advances.
Kamino*, K., Kondo, Y., Nakajima, A., Honda-Kitahara, M., Kaneko, K., & Sawai*, S. (2017). Fold-change detection and scale invariance of cell-cell singling in social amoeba. PNAS (*Corresponding authors).

We are currently looking for postdocs, graduate students, and research assistants with expertise in biology, physics, computer science, mathematics, or engineering. Opportunities span experimental, computational, and theoretical projects centered around quantitative microbiology and cell biology. Although the research topics are adaptable, potential areas of exploration include live-cell imaging of individual and collective cellular behaviors (potentially involving strain construction), information-theoretic analyses of cell signaling, image and time-series analyses using advanced statistical techniques such as machine learning, and the development of pioneering measurement tools in microscopy and microfluidics. These positions are supported by stable core funding available at IMB, alongside grants from other international and Taiwanese bodies. Multiple fellowship opportunities are available. Applications will be evaluated on an ongoing basis until all positions are filled and start dates are flexible.

Our team is highly diverse and international. We actively seek applicants who can contribute to an inclusive group culture. An interdisciplinary ethos is at the heart of our lab, facilitating close interactions between individuals from varied backgrounds and fostering the generation of innovative ideas. Although past experience in a related field helps, it is not necessary. We will provide in-lab training programs on an individual basis.

Interested candidates are encouraged to email Keita, attaching their CV, a concise outline of their scientific interests, and contact information for two references.

For those currently outside Taiwan and considering applying to the graduate program in Taiwan, we recommend checking out the Taiwan International Graduate Program (TIGP), which is an all-English program. Keita is affiliated with the Molecular & Cell Biology (MCB) and the Nanoscience & Technology (Nano) programs of TIGP.