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.
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.
Computational Perception and Scene Analysis
Formal Methods in Systems Biology
This is a seminar-style course on the use of formal methods in modeling biological systems. Topics will include applications of model checking, algebraic methods, rule-based modeling, and type theory.
Special Topics: Computational Neuroscience
Computational Models of Neural Systems
Introduction to Mathematical Modeling of Biological Systems
Statistical Methods for Neuroscience
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.
Quantitative Elements of Cell Form and Movement
This course covers the basic as well as certain selected topics pertaining to the physicochemical origins of architecture and motility of biological cells. It is aimed at graduate students pursuing degrees in various fields of biology (and also in mathematics, physics, chemistry, or engineering), who have taken university-level courses in mathematics, physics, and chemistry. This course material draws upon the variety of quantitaive disciplines but maintains a biological perspective. Physical properties and chemical kinetics that determine the structure and function of the cytoskeleton (the assembly of non-covalent polymers at the base of the cellular architecture) will be covered, as will the physicochemical mechanisms of motility driven by biological force-generating macromolecules. The final grade will be based on homework problems and on a closed-book exam. The didactic material will be presented from the perspective of a practical researcher, and the problem sets will emphasize developing a sense of what makes for a good research strategy.
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 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.