Bioengineering

This course introduces students to the fundamental concepts of normal tissue development and how researchers have used this information to imitate nature in a lab setting, engineering cells and tissues that may be used to model diseases, treat disease, or develop drugs. Discussion topics include: societal challenges for tissue engineering, cell building blocks, normal tissue development and regeneration, adult stem cells, induced pluripotent stem cells, challenges in imitating nature, cell and tissue therapy, gene therapy, and drug development.

The course introduces the broad field of Health Technology Assessment (HTA). HTA is a collection of methodologies used to make evidenced-based assessment of the value added by new technologies to inform policy and decision making. The course introduces the full life-cycle of a new medical technology from the perspective of a device inventor and a government regulator, including safety regulations. It covers methodologies including systematic reviewing, decision theory, evidence synthesis, health economics, and the overall methodology used for HTA in practice.

This course addresses how modern techniques of structural and chemical biology are being used to solve biological problems. It draws on multiple aspects of macromolecular biochemistry including nucleic acid structure and interactions, signaling proteins, and membrane proteins.  The course demonstrates how this knowledge can be used in drug discovery and protein design in biotechnology. Topics include mechanisms of reversible and irreversible enzyme inhibitors, ligand binding, protein folding, the molecular basis for protein function, regulation of protein activity, cell signaling, and proteomics. Assessment: Tests count 40%; practicals, tutorials essays, and assignments count 10%; one 3-hour examination written in June counts 50%. A subminimum of 40% in the examination is required.

In this research course, students chose from a range of research topics in various academic fields and receive one-on-one training from an experienced mentor who helps them refine research ideas, formulate questions, define methods of data collection, execute a plan, and present findings. Students review background information for their project, summarize its key outcomes, write a clear and concise research paper or report, and present results orally.

The course has its starting point in yesterday's raw materials and describes the development of the petrochemical revolution to the chemical process industries of today. The course contains the following sections: historic development of the process industry, catalysis, common feedstocks in the process industry, refinery processes, production of organic and inorganic chemicals, specialty chemicals, biotechnical processes as well as paper and pulp production.

The course provides basic knowledge in the field of artificial intelligence and machine learning for applications in medicine and health. The course covers the chain from medical databases via algorithms to regulations and requirements for diagnostic software.

The course is practically oriented, and students work in project groups with the different concepts/change management methods before seminars and with a major project work together with a company (or other organization) during the course. A significant part of the course is made up of literature seminars, where the students actively discuss and analyze research articles in the field.

This course provides an introduction to medical robotics and its applications. It covers recent developments in robotics for medical applications, position and orientation (POSE) of a robotic system, the kinematics of arm-type and vehicle-type robots, the trajectory of an arm-type robot end effector, and the different biomedical system controls.

This course provides individual research training for students in the Junior Year Engineering Program through the experience of belonging to a specific laboratory at Tohoku University. Students are assigned to a laboratory with the consent of the faculty member in charge. They participate in various group activities, including seminars, for the purposes of training in research methods and developing teamwork skills. The specific topic studied depends on the instructor in charge of the laboratory to which each student is assigned. The methods of assessment vary with the student's project and laboratory instructor. Students submit an abstract concerning the results of their individual research each semester and present the results near the end of this program.

This course offers an introduction to data science for genomic data. It discusses basic algorithms for genome sequencing, compares DNA sequences or proteins, and analyzes databases with the genomic profiles of different patients for the extraction of information. Other topics include: computational methods for analyzing and performing DNA data sequencing; advanced statistical methods for the analysis of genomic data; statistical tests for the extraction of conclusions.