Curriculum

Green and blue butterfly logo onlyCoursework

Trainees will take two required courses:

  • Introduction to Biological Feedback Control. Uses case studies to teach the basic concepts of feedback control from different perspectives in natural sciences and engineering. Case studies will be drawn from diverse scientific application areas, corresponding to the research areas of the NRT faculty, in order to expose students to the multiple ways feedback is used in biological systems and via examples of engineered systems that tap into biological control mechanisms. Examples include heat shock response, wound closure, mechanical homeostasis in cells and tissues, microbial population growth and evolution, and bioinspired autonomous vehicles. Offered in the Spring semester.
  • Feedback Control Theory for Biological Systems. This course will introduce feedback control theory from a biological systems perspective, including classical approaches of control theory for linear systems, while simultaneously presenting basic approaches for analyzing nonlinear systems alongside realistic biological models. Students will be introduced to methods for analyzing nonlinear systems, such as identifying equilibrium points and stability, and they will learn how to approximate these systems using linearized models. These efforts will explicitly discuss when linearized methods are appropriate and what other tools and techniques can be applied in nonlinear contexts. Throughout, this material will be integrated with specific examples from the literature, so that students gain the ability to translate ideas from the modern biological control literature into skills that allow them to identify and analyze feedback systems within natural biological systems, and design novel feedback mechanisms using biological parts and components. The course will also include a final team project where students work in small, providing a structure for addressing open-ended questions and further reinforcing team building. Offered in the Fall semester.

Trainees will also choose two electives from the following list:

  • BIO 594: Introduction to Quantitative Microbiology. Introductory course on identifying the key parameters of biological systems; creating predictive models of biological behaviors and phenomena. This elective is recommended for students who need more mathematics preparation than is provided in the Year 1 Bootcamp.
  • BF 550: Foundations of Programming, Data Analytics, and Machine Learning in Python. Develops practical skills and theoretical foundations in handling datasets and developing simple computational solutions to problems arising in biological research. Introductory programming, statistics, and data analysis methods are covered.
  • BME 700: Methods and Logic in Quantitative Biology. Covers topics at the interface of theory and experiment in biology, with an emphasis on quantitative approaches. Includes rigorous discussions of primary literature.
  • BME/ME 549: Structure and Function of the Extracellular Matrix. Introductory course on the role of the environment in tissue mechanobiology. Covers structure of the extracellular matrix at different scales (molecules, fibrils, organs) and the role of physical forces.
  • PHYS 571: Introduction to Biological Physics. Introduction to biomolecular forces, energy flow, information, and thermodynamics in biological systems. Emphasis on physical principles underlying biological structure and function.
  • BME 567: Nonlinear Systems in Biomedical Engineering. Introduction to nonlinear dynamical systems in biology, as well as to experimental data analysis and control techniques. Qualitative, analytical, and computational techniques including stability, bifurcations, and multiple timescales.

NB: These courses are appropriate for students coming from a variety of backgrounds and cover biology concepts as well as quantitative methods and programming. Students may also petition to have other coursework counted provided it covers biology concepts as well as quantitative methods and/or programming. Core departmental requirements cannot count for this, but elective courses for the degree can. There will likely be no increase in the time to degree; all course requirements for the NRT should fit into the trainees’ PhD programs.

Bootcamp

Starting with the summer between their first and second year of the PhD program, trainees will convene each summer for a bootcamp with content that is tailored to the trainees’ progression through the program:

  • Year 1: New NRT Trainees and Affiliates will receive focused instruction in the technical content required for the “Feedback Control Theory for Biological Systems” course. Students with the necessary background in biology who lack prior training in physics or engineering will receive instruction in the basics of ordinary differential equations, linear vs. nonlinear systems of equations, and modeling strategies. Students with the necessary engineering or mathematical background who lack prior training in biology will receive instruction in the basics of gene expression and protein synthesis, cell structure, and biomaterial properties. Trainees will also complete a strengths-finder assessment and receive an introduction to Professional Development & Postdoctoral Affairs at BU, which will be the anchor for their professional development activities throughout the traineeship.
  • Year 2: Trainees will focus on continuing to build teamwork, receive training in peer mentoring, and will create an individual development plan (IDP).
  • Year 3: Trainees will participate in workshops on management & leadership, building professional networks, ongoing peer mentoring activities, writing skills, and the kickoff of the “Biocontrol Challenge” (see directly below).
  • Year 4: Trainees will receive job search training (if they haven’t already), continue with peer mentoring, and assist with running the Year 1 Bootcamp.

Biocontrol Challenge

The Year 3 bootcamp will kick off a semester-long competition in which teams of trainees identify and work on a problem in biological control (e.g., forming a given spatially/temporally heterogeneous pattern of gene expression in cells; maximize task efficiency of a soft robotic system with distributed control, etc.). Faculty and peer mentors (4th- and 5th-year trainees) will mentor and advise these teams, which will meet throughout the bootcamp and have check-ins twice per month in the Fall with their assigned peer mentors. Projects will be presented during the annual symposium in December.

 

With support from:

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NSF DGE #2244366

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