PROJECTS

Cells make decisions to move and form their own morphology by utilizing their force. This project aims to elucidate this decision-making process from experimental data. Applying principles found in previous research, such as bistability from intermolecular interactions, to everything is mathematical-driven rather than data-driven. At our lab, we conduct data-driven research on cell morphogenesis by carefully observing experimental data and considering physical quantities beyond molecules as signals. Our approach is to use experimental data to drive our research, instead of solely relying on mathematical models.

Cells can respond differently even under the same conditions, and multiple types of molecules and phenotypes cannot be observed simultaneously but are recorded as individually observed data. These "variations" and "individual observations" pose significant challenges for biological analysis, and are not easily solved by experimental researchers alone. Therefore, we aim to address these challenges through data-driven biology, developing methods to integrate varying data and individually observed data while accounting for system noise. Read more.

The technology for simultaneous measurement of multiple molecules, known as omics analysis, is constantly evolving. This has led to the discovery of many molecules involved in diseases. However, listing these molecules alone does not help us understand the mechanisms of biological phenomena. Our aim is to elucidate biological systems in a data-driven manner by using multiple omics data to construct a network consisting of many molecules (trans-omics analysis).

Anything can be deformed only by force, and cells are no exception. However, explaining cellular deformation using solely molecular information is impossible. To conduct a data-driven study on cellular deformation, we require data on cellular forces. Nevertheless, directly observing cellular forces is not feasible. Therefore, we are developing a method that accurately estimates cellular forces within the Bayesian statistics framework, using data observed by traction force microscopy.

The measurement of alcohol concentration in a driver's blood is commonly done through the breath, as the breath component extracted from the alveoli represents the blood component. By extension, it may be possible to diagnose diseases using breath tests instead of invasive tests like blood tests. In collaboration with doctors and sensor development engineers, we are working on this problem. Advancements in non-invasive, simple tests with high accuracy will lead to a significant development in preventive medicine.

We aim to understand the formation of biological tissues through a variety of physical quantities. By observing biological data, we have found that organisms are not just robust to external stimuli, but also resilient, able to recover from the effects of stimuli and achieve their goals. We are analyzing data and developing quantitative models for resilient somitogenesis, angiogenesis, and regulation of organ size.

Cells regulate various cellular functions, including migration, fate determination (proliferation and differentiation), and metabolism, by sensing environmental information such as growth factors and hormones and driving intracellular signaling and gene expression. To study analytical algorithms for artificial real-time control of cellular functions by designing environmental information optimally, we employ control theory and machine learning. Our objective is to develop a data-driven cellular control system that integrates measurement, analysis, and control.

The interaction between genes during development has been studied previously. In recent years, time series data on gene expression during developmental processes have become available, providing a wealth of information. We aim to estimate gene-gene interactions using the expression time series of various types of genes. Specifically, we are focusing on estimating gene networks during the development of the chick neural tube and mouse oocytes.

In human society, there are no perfect laws, but we have backup systems in place to deal with problems as they arise. Similarly, when quantifying features from biological images, there is no fully automated algorithm. An automated algorithm that is specific to particular images is known as an "overlearning algorithm" and is not generalizable. To address this issue, we are developing "semi-automatic" software that can be easily backed up by users, rather than being fully automated.

In managing a nation or a war, supply is considered the most important factor. Similarly, for cells to express their functions, the transport of materials is essential. Energy doesn't simply distribute itself evenly. By examining images of neurons, we can see that actin fibers play a crucial role not only in skeletal formation but also in the transport of materials through actin waves. We aim to build a quantitative mathematical model of the actin wave based on experimental data and elucidate its mathematical principles.