Courses 2023-24

Undergraduate research with a written report at the end of each term; supervised by a Caltech faculty member, or co-advised by a Caltech faculty member and an external researcher. Graded pass/fail. May not be taken after BE 99.

Research in Bioengineering, supervised by a Caltech faculty member, culminating in a thesis. The topic is determined by the research adviser and the student and is subject to approval by the Bioengineering faculty. The first and second terms are taken pass/fail and require a written report at the end of each term. The third term is taken on grades and requires completion of a thesis and final presentation. The last two terms must be completed in the final year of study. Total units arranged with the advising faculty member.

This course combines three parts. First, it will cover fundamental aspects of kinetics, mass-transport, and fluid physics that are relevant to microfluidic systems. Second, it will provide an understanding of how new technologies are invented and reduced to practice. Finally, students in the course will work together to design microfluidic systems that address challenges in Global Health, with an emphasis on students' inventive contributions and creativity. Students will be encouraged and helped, but not required, to develop their inventions further by working with OTT and entrepreneurial resources on campus. Participants in this course benefit from enrollment of students with diverse backgrounds and interests. For chemical engineers, suggested but not required courses are ChE 101 (Chemical Reaction Engineering) and ChE 103 abc (Transport Phenomena). Students are encouraged to contact the instructor to discuss enrollment.

Learn about viruses as fascinating biological machines, focusing on naturally-occurring and evolved variants, in silico viral vector engineering, and computational methods that include structure visualization and machine learning. This course will introduce the fundamentals in the chemistry and biology of viruses, emphasizing their engineerable properties for use in basic research and translational applications. Topics include: viruses by the numbers, mammalian and non-mammalian (plant, bacteria) viruses, enveloped vs. non-enveloped viruses, host-virus interactions, viral life cycles (replication vs. dormancy), immune responses to viruses, zoonosis, diverse mechanisms of entry and replication, the application of viruses as gene-delivery vehicles (with a focus on adeno-associated viruses or AAVs, lentiviruses, and rabies), and how to engineer viral properties for applications in basic research and gene therapy. The lectures will be complemented by short lab exercises in AAV preparation, bioinformatics and machine learning, and structure visualization. Given in alternate years; offered 2023-24.

The first part of the course will concern the basic biology of cancer, covering oncogenes, tumor suppressors, tumor cell biology, metastasis, tumor angiogenesis, and other topics. The second part will concern newer information on cancer genetics and other topics, taught from the primary research literature. The last part of the course will concern treatments, including chemotherapy, anti-angiogenic therapy, and immunotherapy. Textbook: The Biology of Cancer, 2nd edition, by Robert Weinberg. Given in alternate years; offered 2023-24.

Ecosystems are defined by dynamical interactions between groups of organisms, the communities they constitute, and the physical and chemical conditions and processes occurring in the environment. These dynamics are complex and multiscale across both length and time. This course will explore quantitative approaches that observe, measure, model, and monitor ecosystems and the services that they provide society-and the emerging opportunities that could employ these approaches to improve and strengthen global sustainability when it comes to our own ecology. This course will feature lectures each week from different members of the Caltech faculty working on ecological problems from different angles in order to illustrate how fresh insights can emerge by drawing on diverse ways-of-knowing. Given in alternate years; not offered 2022-23.

Quantitative studies of cellular and developmental systems in biology, including the architecture of specific circuits controlling microbial behaviors and multicellular development in model organisms. Specific topics include chemotaxis, multistability and differentiation, biological oscillations, stochastic effects in circuit operation, as well as higher-level circuit properties, such as robustness. The course will also consider the organization of transcriptional and protein-protein interaction networks at the genomic scale. Topics are approached from experimental, theoretical, and computational perspectives.

This course will explore the process of creating and validating theoretical models in systems biology and physiology. It will examine several macroscopic physiological systems in detail, including examples from immunology, endocrinology, cardiovascular physiology, and others. Emphasis will be placed on understanding how macroscopic behavior emerges from the interaction of individual components.

Physical models applied to the analysis of biological structures ranging from individual proteins and DNA to entire cells. Topics include the force response of proteins and DNA, models of molecular motors, DNA packing in viruses and eukaryotes, mechanics of membranes, and membrane proteins and cell motility.

The course introduces rational design and evolutionary methods for engineering functional protein and nucleic acid systems. Rational design topics include molecular modeling, positive and negative design paradigms, simulation and optimization of equilibrium and kinetic properties, design of catalysts, sensors, motors, and circuits. Evolutionary design topics include evolutionary mechanisms and tradeoffs, fitness landscapes and directed evolution of proteins. Some assignments require programming (Python is the language of instruction).

Biology is becoming an increasingly data-intensive science. Many of the data challenges in the biological sciences are distinct from other scientific disciplines because of the complexity involved. This course will introduce key computational, probabilistic, and statistical methods that are common in computational biology and bioinformatics. We will integrate these theoretical aspects to discuss solutions to common challenges that reoccur throughout bioinformatics including algorithms and heuristics for tackling DNA sequence alignments, phylogenetic reconstructions, evolutionary analysis, and population and human genetics. We will discuss these topics in conjunction with common applications including the analysis of high throughput DNA sequencing data sets and analysis of gene expression from RNA-Seq data sets.

Micro/nano fabrication technologies are useful to make advanced devices such as electronics, optics, sensors and medicine. This course will emphasize the sciences, theories and fundamentals of selected micro/nanofabrication technologies. For example, technologies include wet chemical etching, plasma process, RIE/deep RIE, micro/nano molding and advanced packaging. This course will also cover devices used for sensors and medicine such as pressure sensors, accelerometers/gyros, microfluidics, micro total-analysis system, neuromodulation devices, biomedical implants, etc.

Students will learn to use an Arduino microcontroller to interface sensing and actuation hardware with the computer. Students will learn and practice engineering design principles through a set of projects. In part a, students will design and implement biosensing systems; examples include a pulse monitor, a pulse oximeter, and a real-time polymerase-chain-reaction incubator. Part b is a student-initiated design project requiring instructor's permission for enrollment. Enrollment is limited based on laboratory capacity.

This course investigates computation by molecular systems, emphasizing models of computation based on the underlying physics, chemistry, and organization of biological cells. We will explore programmability, complexity, simulation of, and reasoning about abstract models of chemical reaction networks, molecular folding, molecular self-assembly, and molecular motors, with an emphasis on universal architectures for computation, control, and construction within molecular systems. If time permits, we will also discuss biological example systems such as signal transduction, genetic regulatory networks, and the cytoskeleton; physical limits of computation, reversibility, reliability, and the role of noise, DNA-based computers and DNA nanotechnology. Part a develops fundamental results; part b is a reading and research course: classic and current papers will be discussed, and students will do projects on current research topics.

In consultation with, and with the approval of, a faculty advisor (usually the student’s academic advisor) and the Caltech Center for Teaching, Learning, and Outreach. Students may obtain credit for engaging in volunteer efforts to promote public understanding of science; to mentor and tutor young people and underserved populations; or to otherwise contribute to the diversity, equity, and inclusiveness of the scientific enterprise. Students will be required to fill out short pre- and post-outreach activity forms to describe their proposal and to report on the results. Students may petition their option representative (graduate students) or academic advisor (undergraduate students) if they seek credits beyond the 12-unit limit. Offered pass/fail.

By arrangement with members of the staff, properly qualified graduate students are directed in bioengineering research.

Participants will read, discuss, and critique papers on diverse topics within the bioengineering literature. Offered only for Bioengineering graduate students.

This course provides an intensive, hands-on, pragmatic introduction to computer programming aimed at biologists and bioengineers. No previous programming experience is assumed. Python is the language of instruction. Students will learn basic concepts such as data types, control structures, string processing, functions, input/output, etc., while writing code applied to biological problems. At the end of the course, students will be able to perform simple simulations, write scripts to run software packages and parse output, and analyze and plot data. This class is offered as a week-long summer "boot camp" the week after Commencement, in which students spend all day working on the course. Students who do not have a strong need for the condensed boot camp schedule are encouraged to take BE/Bi 103 a instead. Graded pass/fail.

This course is a practical introduction to machine learning methods for biological data, focusing on three common data types in biology-images, sequences, and structures. This course will cover how to represent biological data in a manner amenable to machine learning approaches, survey tasks that can be solved with modern deep learning algorithms (e.g. image segmentation, object tracking, sequence classification, protein folding, etc.), explore architectures of deep learning models for each data type, and provide practical guidance for model development. Students will have the opportunity to apply these methods to their own datasets. Not offered 2023-24.

The cytosol, and fluid spaces within the nucleus, were once envisioned as a concentrated soup of proteins, RNA, and small molecules, all diffusing, mixing freely, and interacting randomly. We now know that proteins in the cytosol frequently undergo only restricted diffusion and become concentrated in specialized portions of the cytosol to carry out particular cellular functions. This course consists of lectures, reading, student presentations, and discussion about newly recognized biochemical mechanisms that confer local structure and reaction specificity within the cytosol, including protein scaffolds and "liquid-liquid phase separations" that form "membraneless compartments".

Discussion and laboratory-based course covering the practical use of the confocal microscope, with special attention to the dynamic analysis of living cells and embryos. Course topics include: basic optics, microscope design, Kohler illumination, confocal microscopy, light sheet microscopy, spectral unmixing and fluorescence correlation spectroscopy, three-dimensional reconstruction of fixed cells and tissues. Students will construct a light sheet microscope based on the openSPIM design, and perform time-lapse confocal analysis of living cells and embryos. Enrollment is limited. Given in alternate years; offered 2023-24.

Topics relevant to the general educational goals of the bioengineering option. Graded pass/fail.

Maximum enrollment: 12. Applications of spatial genomics technology to various biological samples. Projects will be selected to represent problems in neurobiology, developmental biology and translational medicine. Emphasis will be placed on generating experimental data and analysis of data with machine learning algorithms for segmentation and clustering cells with single cell genomics tools, and preparation for publication.

Internal flows: steady and pulsatile blood flow in compliant vessels, internal flows in organisms. Fluid dynamics of the human circulatory system: heart, veins, and arteries (microcirculation). Mass and momentum transport across membranes and endothelial layers. Fluid mechanics of the respiratory system. Renal circulation and circulatory system. Biological pumps. Low and High Reynolds number locomotion.

This course provides an intensive introduction to thinking like a quantitative biologist. Every student will build a microscope from scratch, use a confocal microscope to measure transcription in living fly embryos and perform a quantitative dissection of gene expression in bacteria. Students will then use Python to write computer code to analyze the results of all of these experiments. No previous experience in coding is presumed, though for those with previous coding experience, advanced projects will be available. In addition to the experimental thrusts, students will use "street fighting mathematics" to perform order of magnitude estimates on problems ranging from how many photons it takes to make a cyanobacterium to the forces that can be applied by cytoskeletal filaments. These modeling efforts will be complemented by the development of physical models of phenomena such as gene expression, phase separation in nuclei, and cytoskeletal polymerization. Graded pass/fail.

Introduction to current research topics in Caltech bioengineering labs. Graded pass/fail.