Cellular and Systems Modeling Specialization Area

Cellular and Systems Modeling undertakes the ambitious task of studying the dynamics of biological and biomedical processes from a whole system point of view. The observed systems range over orders of magnitude, from tissue to cells to molecular assemblies! Engineering tools are used along with genome-scale information in mathematical and/or computational models that usually adopt a top-down approach. Modeling diseases, entire ‘virtual’ cells, or subcellular networks of interactions are among typical tasks. Major research topics include the modeling of complex signaling and regulatory networks, transport mechanisms, spatio-temporal evolution of microphysiological events, as well as establishing the links between the development of complex phenotypes and the seemingly unrelated molecular events.

Life Science Electives

Specialization Electives (3 credits/9 units)

Biology has been revolutionized by automated methods for generating large amounts of data on diverse biological processes. This, in addition to the finding that many more components are involved in each process than had earlier been thought, has led to a transition from a reductionist paradigm of biological research involving detailed study of single molecules or events to a systems biology paradigm involving comprehensive, systematic studies combined with computational data analysis. Integration of data from many types of experiments will be required to construct detailed, predictive models of cell, tissue or organism behaviors, and the complexity of the systems suggests the need for these models to be constructed automatically. This will require iterative cycles of acquisition, analysis, modeling, and experimental design, since it is not feasible to do all possible biological experiments. This course will cover a range of automated biological research methods, especially high-throughput screening and next generation sequencing, and a range of relevant computational methods, especially model structure learning and active learning. It assumes a basic knowledge of machine learning. Class sessions will consist of a combination of lectures and discussions of important research papers. Grading will be based on class participation, homeworks and a final project.

Computational Methods for Biological Modeling and Simulation

This course covers a variety of computational methods important for modeling and simulation of biological systems. It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems. The course will emphasize practical algorithms and algorithm design methods drawn from various disciplines of computer science and applied mathematics that are useful in biological applications. The general topics covered will be models for optimization problems, simulation and sampling, and parameter tuning. Course work will include problems sets with significant programming components and independent or group final projects.

Machine Learning from Neural Cortical Circuits


Computational Models of Neural Systems


Statistical Methods for Neuroscience


Cellular Biomechanics

This course discusses how mechanical quantities and processes such as force, motion, and deformation influence cell behavior and function, with a focus on the connection between mechanics and biochemistry. Specific topics include: (1) the role of stresses in the cytoskeleton dynamics as related to cell growth, spreading, motility, and adhesion; (2) the generation of force and motion by moot molecules; (3) stretch-activated ion channels; (4) protein and DNA deformation; (5) mechanochemical coupling in signal transduction; (6) protein trafficking and secretion; and (7) the effects of mechanical forces on gene expression. Emphasis is placed on the biomechanics issues at the cellular and molecular levels; their clinical and engineering implications are elucidated.

Computational Models in Neuroscience

Course covers computational and mathematical neuroscience. Class will do modeling and analysis of complex dynamics of single neurons and large-scale networks.

Computational Neuroscience Methods

This course offers an introduction to modeling methods in neuroscience. Topics range from modeling the firing patterns of single neurons to using computational methods to understand neural coding. Some systems level modeling is also done.

Computational Cell Biology

This course introduces a number of modeling methods for biological systems. We will examine a number of problems from cell biology, immunology, population biology, physiology and molecular genetics. The main tools will be techniques from ordinary and partial differential equations. Discrete and delay-differential equations will also be used however the background for these will not be assumed. We will take models from current and classic papers in the field.

Systems Approach to Inflammation

This course is focused on particular topics of great biologic complexity in critical illness, where modeling has the potential to translate in improved patient care. Lectures are provided by basic (biological and mathematical sciences) and clinical faculty, in conjunction with members of industry and speakers from outside institutions. This information will be communicated within the framework of defined themes that describe the complexity of inflammation in acute and chronic illnesses.

Drug Discovery

Drug discovery is an interdisciplinary science that seeks to identify small molecular and/or biologic entities for therapeutic intervention and to understand integrated biological systems and processes at the functional and molecular levels. This course will discuss various topics that are relevant to current approaches and principles in drug discovery including drug origins, drug target identification, and validation, biochemical and cell-based screening approaches, proteomic approaches to drug discovery, computational chemistry and biology, and quantitative systems pharmacology, as well as other topics in preclinical and clinical drug development, personalized medicine, Chinese herbal medicines and intellectual property. The course will include case studies intended to aid students in a full understanding of the drug discovery process.


Proteomics and metabolomics are the large scale study of proteins and metabolites, respectively. In contrast to genomes, proteomes and metabolomes vary with time and the specific stress or conditions an organism is under. Applications of proteomics and metabolomics include determination of protein and metabolite functions (including in immunology and neurobiology) and discovery of biomarkers for disease. These applications require advanced computational methods to analyze experimental measurements, create models from them, and integrate with information from diverse sources. This course specifically covers computational mass spectrometry, structural proteomics, proteogenomics, metabolomics, genome mining and metagenomics.

Computational Medicine

Modern medical research increasingly relies on the analysis of large patient datasets to enhance our understanding of, and our ability to treat human diseases. This course will focus on the computational problems that arise from studies of human diseases and the translation of research to the bedside to improve human health. The topics to be covered include computational strategies for advancing personalized medicine, pharmacogenomics for predicting individual drug responses, metagenomics for learning the role of the microbiome in human health, mining electronic medical records to identify disease phenotypes, and case studies in complex human diseases such as cancer and asthma. We will discuss how machine learning and other computational methods are being used by clinicians. Class sessions will consist of lectures, discussions of papers from the literature, and guest presentations by clinicians and other domain experts. Students enrolled in 02-518 will be graded based on homeworks, paper summaries, and a course project. Students enrolled in 02-718 will be graded based on in-class presentations, written summaries of papers, and a course project.