Core Required Courses. All trainees will be required to take two core courses: BE 605 or BE 755 and BE 700. Students in the BME and BI programs are required to take BE 605 as part of the core program curricula; MCBB students will typically join the program with a stronger molecular biology training and will instead use one of their electives to take BE 755.

BE 605 Molecular Bioengineering. This is a quantitative and engineering-based introduction to the building blocks of living cells and materials for biotechnology. Throughout the course, detailed structural and energetic properties of molecules are emphasized. Topics include: 1) biological pathways for synthesis of DNA, RNA, and proteins; 2) formal physical and mathematical treatment of transduction, transmission, storage and retrieval of biological information by macromolecules; 3) polymerase chain reaction, restriction enzymes, and DNA sequencing; 4) energetics of protein folding and trafficking; 5) energetic mechanisms of enzymatic catalysis and receptor-ligand binding; 6) cooperative proteins, multi-protein complexes and the control of metabolic pathways; 7) generation, storage, transmission, and release of biomolecular energy; and 8) physical bases of methods for study and manipulation of molecules. The laboratory portion of BE 605 is intended to provide a better understanding of theory and experimental skills for the most basic methods used in modern biotechnology. Laboratory exercises cover methods for DNA and protein secondary structure analysis, gel electrophoresis, methods of DNA amplification, and methods for studying DNA/protein interactions.

BE 755 Molecular Systems & Synthetic Biology Laboratory. This is an advanced laboratory course about the bioengineering of nucleic acids, genetic circuits, and the genome. The course is intended to provide exposure to the practice of modern biotechnology and underlying theory. It is a wet laboratory-based course that specifically covers advanced molecular and synthetic biology technology. The course is structured into two major themes that encompass multiple length scales of molecular biology: nucleic acid detection and genetic engineering and genome editing. In the nucleic acid detection portion, course material focuses on the theory and practice of DNA/RNA preparation, detection, and amplification for understanding changes to the genome or transcription state. In the genetic engineering and genome editing portion, students will learn some of latest DNA assembly technologies. Furthermore, students will also learn how to perform genome editing in mammalian cells (i.e., by using the CRISPR/Cas system).

BE 700 Methods and Logic in Quantitative Biology. Biology is in the midst of a transformation into a quantitative, theory-rich science. The advent of genomic techniques has presented opportunities to study biological processes on a genomic scale, achieve quantitative understanding not just of individual molecular mechanisms but also of their interactions at the systems level, and predictively engineer biological systems from genetic ‘parts’ (synthetic biology). The main focus of this course involves the close reading and critical discussion of original papers that lie at the interface of theory and experiment in quantitative and synthetic biology. Through reading, presentation, and discussion of these papers, students of diverse backgrounds (biology, engineering, physics, computational sciences, etc.) learn the conceptual underpinnings of synthetic biology, learn how to communicate in a common language, and engage directly with topics of robustness and rigor in research (experimental design, hypothesis generation, critical thinking, rigor and reproducibility, data analysis/interpretation). This course is considered essential education for students interested in pursuing research in synthetic biology, systems biology, and biophysics. Specific topics include cooperativity, robust adaptation, genetic circuits, synthetic biology, kinetic proofreading, pattern formation, sequence analysis, clustering, phylogenetics, and analysis of fluctuations. Finally, this course exposes graduate students to issues of ethics and governance in biotechnology.

Additional Required Course for MCBB students. Students from the MCBB program will take an additional quantitative course, BF 527, as one of their electives. Students in the BME and BI programs cover computation and quantitative methods as part of existing degree requirements.

BF 527 Applications in Bioinformatics. The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students’ research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics include the analysis of biological sequences, structures, and networks. Labs involve applying concepts learned during the lecture to practical bioinformatics problems. Students will learn to use the major bioinformatics databases as well as on- and off-line tools. Python programming will be taught during lab, leading to the creation of small but useful bioinformatics-oriented programs.

Elective Courses. BU offers a wealth of courses related to synthetic biology that can be used to enrich the academic synthetic biology training of our SB2 trainees. Below, we list representative examples of course offerings that students may elect to take to supplement programmatic requirements.

BE 500 Deep Learning for Biomedical Images and Signals. Deep learning networks have emerged as powerful tools for applications that are highly relevant to biomedical analysis such as computer vision and the processing of temporal sequence data. In this course, we will cover the mathematical basis for developing and training deep learning networks. The course will introduce artificial neural networks, convolutional neural networks, recurrent neural networks, and other architectures. We will apply these tools to the analysis of biomedical images and signals. In addition, the course will introduce PyTorch as a deep learning framework. Programming and mathematical analysis are integral parts of the class and homework assignments will feature programming exercises where students work with biomedical datasets.

BE500 A1 Nucleic Acid Engineering. This course is designed to provide graduate students and senior undergraduates with knowledge of the design principles required for engineering nucleic acid systems that can be used for biological control, therapeutics, and diagnostic applications. Students will gain an understanding of the thermodynamics and kinetics of nucleic acid interactions and familiarity with software tools used to analyze and design novel DNA and RNA systems. Students will also develop knowledge of the latest developments in nucleic acid engineering, in areas such as RNA biological circuits, mRNA therapeutics, and DNA computing and information-storage systems.

BE 562 Computational Biology: Genomes, Networks, Evolution. This course covers the algorithmic and machine-learning foundations of computational biology, including fundamental techniques such as dynamic programming, Gibbs sampling, expectation maximization, hidden Markov models, graph analysis, flux balance analysis, and Bayesian networks. It introduces important computational problems in sequence analysis, gene prediction, genome assembly, gene expression analysis, regulatory motif prediction, metabolic network modeling, phylogeny, and molecular evolution. Students gain hands-on experience analyzing large biological datasets, through a final project that requires significant computational effort, and they produce a short grant proposal in the NIH grant/fellowship format.

BE 567 Nonlinear Systems in Biomedical Engineering. Introduction to nonlinear dynamical systems in biomedical engineering. Qualitative, analytical, and computational techniques. Stability, bifurcations, oscillations, multistability, hysteresis, multiple timescales, chaos. Introduction to experimental data analysis and control techniques. Applications discussed include population dynamics, biochemical systems, genetic circuits, neural oscillators, etc.

BE 709 From Cells to Tissue: Engineering Structure and Function. This course is a primary literature-based course that will introduce students to engineering concepts in understanding and manipulating the behavior of biological cells. We will try to understand the interplay between cells, the extracellular environment, and intracellular signaling pathways in regulating cellular and multicellular structure and function. In particular, we will explore the use of modern experimental approaches to characterize and manipulate cells for bioengineering applications, and the concepts in scaling cellular engineering to functional issues. In this context, we will focus on several topics, including signal transduction and the molecular regulation of cell function, cellular microenvironment, cell adhesion and mechanics, stem cells, multicellularity, and experimental models of tissue development. We will introduce both classic approaches and those that are still in early development.

BF 571 Dynamics and Evolution of Biological Networks. This course focuses on mathematical models for exploring multiple levels of biological organization, from regulation and function of biochemical and genetic networks to evolution and ecology. Topics include stochastic simulations and deterministic modeling with differential equations, metabolic and genetic networks, biophysical properties and regulation of biochemical pathways, genome-scale models of metabolic reaction fluxes, models of regulatory networks, modular architecture of biological networks, coalescent theory and genetic inference, dynamics of adaptation and cancer, ecological interactions, models of microbial communities, and visualization and reproducibility of research.

BF 752 Legal and Ethical Issues of Science and Technology. This course addresses the ethical and legal aspects of 21st-Century biology resulting from the rise of new technologies. Students analyze cases by applying analytical tools used in modern bioethics, examine the legal system’s regulatory role, and discuss present and future challenges. Topics include gene therapy, DNA forensics, new reproductive techniques, biotechnology and patenting, transplantation, clinical research, and laboratory ethics. Students participate in weekly discussions, complete and present ethics case analyses, and write and peer review ethical/legal position papers.

BI / BF 510 Institutional Racism in Health and Science. This course traces the historical mischaracterization of race as a biological construct and the manifestations of racism on health outcomes. It focuses on the empirical process behind interrogating and dismantling disinformation in the specific context of racism. Through the study of primary sources, students develop competencies to distinguish between fact-based conclusions and unsupported pseudoscience, and gain skills needed for similar analyses applicable to issues of ableism, sexism, and gender discrimination.

BI 560 Systems Biology. Examines critical components of systems biology, including design principles of biological systems (e.g., feedback, synergy, cooperativity), and the generation and analysis of large-scale datasets (e.g., protein- protein interaction, mRNA expression).

BI 559: Quantitative Microbiology. The traditional molecular biology view of one gene/one phenotype has yielded vast insights into the biology of both microbes and multicellular organisms. However, we now might be hitting the limits of that paradigm. At the same time, new technologies for collecting enormous, “omics”-level datasets give us a lot of information, but maybe too much to extract clear lessons. This course seeks to find a middle path. Can we identify the key parameters of biological systems and create predictive models of microbial behaviors and phenomena? How do those models help us understand experimental data and suggest entirely new experiments? What do we learn from the spatial arrangements and temporal dynamics of molecules in a bacterial cell? We will tackle questions like these in this course by interpreting datasets from published papers in a critical, holistic way, and by learning some mathematical approaches to fit data into a more unified conceptual framework. By focusing on microbes, those “simplest” of all living things, perhaps we can get some insights into the most basic phenomena that separate the living from the non-living. Important bacterial behaviors that we will examine will include growth, motility, multispecies interactions, and cell differentiation. Every subject will be motivated directly by experimental data, with specific discussion of the techniques used to collect the data, their biases, and their limitations.

EC 552 / BE 552 Computational Synthetic Biology for Engineers. This course presents the field of computational synthetic biology through the lens of four distinct activities: Specification, Design, Assembly, and Test. Engineering students of all backgrounds are introduced to synthetic biology and then exposed to core challenges and approaches in each of the four areas. Homework assignments are provided that allow the students to use existing computational software to explore each of these themes. In addition, advanced concepts are presented around data management, design algorithms, standardization, and simulation challenges in the field. The course culminates in a group project in which the students apply computational design methods to an experimentally created system working with graduate students in the BDC and the DAMP Lab.