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Course 6: Electrical Engineering and Computer Science |
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Artificial Intelligence6.4100 Artificial Intelligence
 ( )Not offered regularly; consult department Prereq: 6.100A Units: 4-3-5 
Introduces representations, methods, and architectures used to build applications and to account for human intelligence from a computational point of view. Covers applications of rule chaining, constraint propagation, constrained search, inheritance, statistical inference, and other problem-solving paradigms. Also addresses applications of identification trees, neural nets, genetic algorithms, support-vector machines, boosting, and other learning paradigms. Considers what separates human intelligence from that of other animals. Consult Department 6.4110 Representation, Inference, and Reasoning in AI
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Prereq: 6.1010, 6.1210, and (6.3700, 6.3800, 18.05, or 18.600) Units: 3-0-9
An introduction to representations and algorithms for artificial intelligence. Topics covered include: constraint satisfaction in discrete and continuous problems, logical representation and inference, Monte Carlo tree search, probabilistic graphical models and inference, planning in discrete and continuous deterministic and probabilistic models including Markov decision processes (MDPs) and partially observed Markov decision processes (POMDPs). Staff 6.4120[J] Computational Cognitive Science
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(Same subject as 9.66[J]) (Subject meets with 9.660) Prereq: 6.3700, 6.3800, 9.40, 18.05, 6.3900, or permission of instructor Units: 3-0-9  Lecture: TR1-2.30 (46-3002)
Introduction to computational theories of human cognition. Focus on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks covered include Bayesian and hierarchical Bayesian models; probabilistic graphical models; nonparametric statistical models and the Bayesian Occam's razor; sampling algorithms for approximate learning and inference; and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project. J. Tenenbaum No textbook information available 6.4130[J] Principles of Autonomy and Decision Making
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(Same subject as 16.410[J]) (Subject meets with 6.4132[J], 16.413[J]) Prereq: 6.100B, 6.1010, 6.9080, or permission of instructor Units: 4-0-8 Lecture: MW9.30-11 (4-163) Recitation: F10 (33-319) or F3 (33-319) +final
Surveys decision making methods used to create highly autonomous systems and decision aids. Applies models, principles and algorithms taken from artificial intelligence and operations research. Focuses on planning as state-space search, including uninformed, informed and stochastic search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, and Bayesian filtering. Also emphasizes planning with real-world constraints using constraint programming. Includes methods for satisfiability and optimization of logical, temporal and finite domain constraints, graphical models, and linear and integer programs, as well as methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments. A. Bobu Textbooks (Fall 2025) 6.4132[J] Principles of Autonomy and Decision Making
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(Same subject as 16.413[J]) (Subject meets with 6.4130[J], 16.410[J]) Prereq: 6.100B, 6.9080, or permission of instructor Units: 3-0-9 Lecture: MW9.30-11 (4-163) +final
Surveys decision making methods used to create highly autonomous systems and decision aids. Applies models, principles and algorithms taken from artificial intelligence and operations research. Focuses on planning as state-space search, including uninformed, informed and stochastic search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, and Bayesian filtering. Also emphasizes planning with real-world constraints using constraint programming. Includes methods for satisfiability and optimization of logical, temporal and finite domain constraints, graphical models, and linear and integer programs, as well as methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments. A. Bobu Textbooks (Fall 2025) 6.4150[J] Artificial Intelligence for Business
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(Same subject as 15.563[J]) Prereq: None Units: 3-0-6
Explores how to design and evaluate products and policy based on artificial intelligence. Provides a functional (as opposed to mechanistic) understanding of the emerging technologies underlying AI. Presents AI's opportunities and risks and how to create conditions under which its deployment can succeed. No technical background required. M. Raghavan 6.8110[J] Cognitive Robotics
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(Same subject as 16.412[J]) Prereq: (6.4100 or 16.413) and (6.1200, 6.3700, or 16.09) Units: 3-0-9
Highlights algorithms and paradigms for creating human-robot systems that act intelligently and robustly, by reasoning from models of themselves, their counterparts and their world. Examples include space and undersea explorers, cooperative vehicles, manufacturing robot teams and everyday embedded devices. Themes include architectures for goal-directed systems; decision-theoretic programming and robust execution; state-space programming, activity and path planning; risk-bounded programming and risk-bounded planners; self-monitoring and self-diagnosing systems, and human-robot collaboration. Student teams explore recent advances in cognitive robots through delivery of advanced lectures and final projects, in support of a class-wide grand challenge. Enrollment may be limited. B.C. Williams 6.8120 Tissues vs. Silicon in Machine Learning
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Prereq: 6.3900 Units: 3-0-9
Examines how brain neural circuits and function can affect the design of machine learning hardware and software, and vice versa. Builds an understanding of how similar and different the computational approaches of the two are, and what can be deduced from one area about the other. Studies the relationship between brain neural circuits and machine learning design, exploring how insights from one can inform the other. Compares biological concepts like neurons, connectomes, and non-backpropagation learning with artificial neural network hardware and software designs, scaling laws, and state-of-the-art optimization techniques. Staff Robotics6.4200[J] Robotics: Science and Systems
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(Same subject as 2.124[J], 16.405[J]) Prereq: ((1.00 or 6.100A) and (2.003, 6.1010, 6.1210, or 16.06)) or permission of instructor Units: 2-6-4
Presents concepts, principles, and algorithmic foundations for robots and autonomous vehicles operating in the physical world. Topics include sensing, kinematics and dynamics, state estimation, computer vision, perception, learning, control, motion planning, and embedded system development. Students design and implement advanced algorithms on complex robotic platforms capable of agile autonomous navigation and real-time interaction with the physical word. Students engage in extensive written and oral communication exercises. Enrollment limited. Staff 6.4210 Robotic Manipulation
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(Subject meets with 6.4212) Prereq: (6.100A and 6.3900) or permission of instructor Units: 4-2-9 URL: http://manipulation.mit.edu Lecture: MW9.30-11 (45-230) Lab: F1 (4-149) or F2 (4-149) or F12 (4-149)
Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based). Students taking graduate version complete additional assignments. Students engage in extensive written and oral communication exercises. T. Lozano-Perez No textbook information available 6.4212 Robotic Manipulation
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(Subject meets with 6.4210) Prereq: (6.100A and 6.3900) or permission of instructor Units: 3-0-9 URL: http://manipulation.mit.edu Lecture: MW9.30-11 (45-230) Lab: F1 (4-149) or F2 (4-149) or F12 (4-149)
Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based. Students taking graduate version complete additional assignments. T. P. Lozano-Perez No textbook information available 6.8200 Sensorimotor Learning
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Prereq: 6.3900 or 6.7900 Units: 3-0-9
Provides an in-depth view of the state-of-the-art learning methods for control and the know-how of applying these techniques. Topics span reinforcement learning, self-supervised learning, imitation learning, model-based learning, and advanced deep learning architectures, and specific machine learning challenges unique to building sensorimotor systems. Discusses how to identify if learning-based control can help solve a particular problem, how to formulate the problem in the learning framework, and what algorithm to use. Applications of algorithms in robotics, logistics, recommendation systems, playing games, and other control domains covered. Instruction involves two lectures a week, practical experience through exercises, discussion of current research directions, and a group project. Staff 6.8210 Underactuated Robotics
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Prereq: 18.03 and 18.06 Units: 3-0-9
Covers nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on computational methods. Topics include the nonlinear dynamics of robotic manipulators, applied optimal and robust control and motion planning. Discussions include examples from biology and applications to legged locomotion, compliant manipulation, underwater robots, and flying machines. R. Tedrake Graphics6.4400 Computer Graphics
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Prereq: 6.1010 and (18.06 or 18.C06) Units: 3-0-9 Lecture: WF11-12.30 (1-190)
Introduction to computer graphics algorithms, software and hardware. Topics include ray tracing, the graphics pipeline, transformations, texture mapping, shadows, sampling, global illumination, splines, animation and color. W. Matusik No textbook information available 6.4420[J] Computational Design and Fabrication
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(Same subject as 2.0911[J]) (Subject meets with 6.8420) Prereq: Calculus II (GIR) and (6.1010 or permission of instructor) Units: 3-0-9
Introduces computational aspects of computer-aided design and manufacturing. Explores relevant methods in the context of additive manufacturing (e.g., 3D printing). Topics include computer graphics (geometry modeling, solid modeling, procedural modeling), physically-based simulation (kinematics, finite element method), 3D scanning/geometry processing, and an overview of 3D fabrication methods. Exposes students to the latest research in computational fabrication. Students taking the graduate version complete additional assignments. W. Matusik 6.8410 Shape Analysis
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Prereq: Calculus II (GIR), 18.06, and (6.8300 or 6.4400) Units: 3-0-9
Introduces mathematical, algorithmic, and statistical tools needed to analyze geometric data and to apply geometric techniques to data analysis, with applications to fields such as computer graphics, machine learning, computer vision, medical imaging, and architecture. Potential topics include applied introduction to differential geometry, discrete notions of curvature, metric embedding, geometric PDE via the finite element method (FEM) and discrete exterior calculus (DEC),; computational spectral geometry and relationship to graph-based learning, correspondence and mapping, level set method, descriptor, shape collections, optimal transport, and vector field design. Staff 6.8420 Computational Design and Fabrication
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(Subject meets with 2.0911[J], 6.4420[J]) Prereq: Calculus II (GIR) and (6.1010 or permission of instructor) Units: 3-0-9
Introduces computational aspects of computer-aided design and manufacturing. Explores relevant methods in the context of additive manufacturing (e.g., 3D printing). Topics include computer graphics (geometry modeling, solid modeling, procedural modeling), physically-based simulation (kinematics, finite element method), 3D scanning/geometry processing, and an overview of 3D fabrication methods. Exposes students to the latest research in computational fabrication. Students taking graduate version complete additional assignments. W. Matusik Human-Computer Interaction & Society6.4500 Design for the Web: Languages and User Interfaces
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Prereq: None. Coreq: 6.1010 Units: 2-2-8
Instruction in the principles and technologies for designing usable user interfaces for Web applications. Focuses on the key principles and methods of user interface design, including learnability, efficiency, safety, prototyping, and user testing. Provides instruction in the core web languages of HTML, CSS, and Javascript, their different roles, and the rationales for the widely varying designs. These languages are used to create usable web interfaces and applications. Covers fundamentals of graphic design theory, as design and usability go hand in hand. Staff 6.4510 Engineering Interactive Technologies
()Not offered regularly; consult department Prereq: 6.1020, 6.2050, 6.2060, 6.9010, or permission of instructor Units: 1-5-6
Provides instruction in building cutting-edge interactive technologies, explains the underlying engineering concepts, and shows how those technologies evolved over time. Students use a studio format (i.e., extended periods of time) for constructing software and hardware prototypes. Topics include interactive technologies, such as multi-touch, augmented reality, haptics, wearables, and shape-changing interfaces. In a group project, students build their own interactive hardware/software prototypes and present them in a live demo at the end of term. Enrollment may be limited. Staff 6.4530[J] Principles and Practice of Assistive Technology
()Not offered regularly; consult department (Same subject as 2.78[J], HST.420[J]) Prereq: Permission of instructor Units: 2-4-6
Students work closely with people with disabilities to develop assistive and adaptive technologies that help them live more independently. Covers design methods and problem-solving strategies; human factors; human-machine interfaces; community perspectives; social and ethical aspects; and assistive technology for motor, cognitive, perceptual, and age-related impairments. Prior knowledge of one or more of the following areas useful: software; electronics; human-computer interaction; cognitive science; mechanical engineering; control; or MIT hobby shop, MIT PSC, or other relevant independent project experience. Enrollment may be limited. Staff 6.4550[J] Interactive Music Systems
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(Same subject as 21M.385[J]) (Subject meets with 21M.585) Prereq: 6.1010 and (21M.051, 21M.150, or 21M.151) Units: 3-0-9 URL: http://mta.mit.edu/music/class-schedule Lecture: MW11-12.30 (4-270)
Explores audio synthesis, musical structure, human computer interaction (HCI), and visual presentation for the creation of interactive musical experiences. Topics include audio synthesis; mixing and looping; MIDI sequencing; generative composition; motion sensors; music games; and graphics for UI, visualization, and aesthetics. Includes weekly programming assignments in python. Teams build an original, dynamic, and engaging interactive music system for their final project. Students taking graduate version complete different assignments. Limited to 36. E. Egozy No required or recommended textbooks 6.4570[J] Creating Video Games
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(Same subject as CMS.611[J]) Prereq: 6.100A or CMS.301 Units: 3-3-6 Lecture: TR1-4 (32-124)
Introduces students to the complexities of working in small, multidisciplinary teams to develop video games. Covers creative design and production methods, stressing design iteration and regular testing across all aspects of game development (design, visual arts, music, fiction, and programming). Assumes a familiarity with current video games, and the ability to discuss games critically. Previous experience in audio design, visual arts, or project management recommended. Limited to 36. P. Tan, R. Eberhardt No required or recommended textbooks 6.4590[J] Foundations of Information Policy
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(Same subject as STS.085[J]) Prereq: Permission of instructor Units: 3-0-9 Credit cannot also be received for STS.487
Studies the interaction of law, public policy, and technology in today's controversies over control of the Internet. Students use technical, legal, and rhetorical skills to analyze and participate in the evolution of global public policy frameworks. Explores lessons for the future of increasingly large-scale data analytics systems including AI-based technologies. Instruction on how to write persuasive technology policy pieces, refine oral policy presentation skills through role-playing simulations, and develop original responses to contemporary digital policy challenges provided. Topics include: history of Internet policy, the relationship between technical architecture and law, privacy, freedom of expression, platform regulation, privacy, intellectual property, digital surveillance, and international affairs. Students taking graduate version complete additional assignments. Enrollment limited. Staff 6.8510 Intelligent Multimodal User Interfaces
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Prereq: (6.1020 and 6.4110) or permission of instructor Units: 3-0-9
Implementation and evaluation of intelligent multi-modal user interfaces, taught from a combination of hands-on exercises and papers from the original literature. Topics include basic technologies for handling speech, vision, pen-based interaction, and other modalities, as well as various techniques for combining modalities. Substantial readings and a term project, where students build a program that illustrates one or more of the themes of the course. Staff 6.8530 Interactive Data Visualization
()Not offered regularly; consult department Prereq: 6.1020 Units: 3-0-9 Credit cannot also be received for 6.C35, 6.C85, 11.154, 11.454, 11.C35, 11.C85, CMS.C35, CMS.C85, IDS.C35, IDS.C85
Interactive visualization provides a means of making sense of a world awash in data. Covers the techniques and algorithms for creating effective visualizations, using principles from graphic design, perceptual psychology, and cognitive science. Short assignments build familiarity with the data analysis and visualization design process, and a final project provides experience designing, implementing, and deploying an explanatory narrative visualization or visual analysis tool to address a concrete challenge. A. Satyanarayan Computational Biology6.4710[J] Evolutionary Biology: Concepts, Models and Computation
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(Same subject as 7.33[J]) Prereq: (6.100A and 7.03) or permission of instructor Units: 3-0-9
Explores and illustrates how evolution explains biology, with an emphasis on computational model building for analyzing evolutionary data. Covers key concepts of biological evolution, including adaptive evolution, neutral evolution, evolution of sex, genomic conflict, speciation, phylogeny and comparative methods, life's history, coevolution, human evolution, and evolution of disease. R. Berwick, D. Bartel 6.8700[J] Advanced Computational Biology: Genomes, Networks, Evolution
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(Same subject as 20.488[J], HST.507[J]) (Subject meets with 6.8701[J], 20.387[J]) Prereq: (Biology (GIR), 6.1210, and 6.3700) or permission of instructor Units: 4-0-8 Lecture: TR1-2.30 (32-144) Recitation: F3 (32-144)
See description for 6.8701. Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication. E. Alm, M. Kellis No textbook information available 6.8701[J] Computational Biology: Genomes, Networks, Evolution
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(Same subject as 20.387[J]) (Subject meets with 6.8700[J], 20.488[J], HST.507[J]) Prereq: (Biology (GIR), 6.1210, and 6.3700) or permission of instructor Units: 3-0-9 Lecture: TR1-2.30 (32-144) Recitation: F3 (32-144)
Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks. E. Alm, M. Kellis No textbook information available 6.8710[J] Computational Systems Biology: Deep Learning in the Life Sciences
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(Same subject as HST.506[J]) (Subject meets with 6.8711[J], 20.390[J], 20.490) Prereq: Biology (GIR) and (6.3700 or 18.600) Units: 3-0-9
Presents innovative approaches to computational problems in the life sciences, focusing on deep learning-based approaches with comparisons to conventional methods. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design (the choice and interpretation of interventions). Focuses on machine learning model selection, robustness, and interpretation. Teams complete a multidisciplinary final research project using TensorFlow or other framework. Provides a comprehensive introduction to each life sciences problem, but relies upon students understanding probabilistic problem formulations. Students taking graduate version complete additional assignments. E. Alm, B. Berger 6.8711[J] Computational Systems Biology: Deep Learning in the Life Sciences
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(Same subject as 20.390[J]) (Subject meets with 6.8710[J], 20.490, HST.506[J]) Prereq: (6.100B and 7.05) or permission of instructor Units: 3-0-9
Presents innovative approaches to computational problems in the life sciences, focusing on deep learning-based approaches with comparisons to conventional methods. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design (the choice and interpretation of interventions). Focuses on machine learning model selection, robustness, and interpretation. Teams complete a multidisciplinary final research project using TensorFlow or other framework. Provides a comprehensive introduction to each life sciences problem, but relies upon students understanding probabilistic problem formulations. Students taking graduate version complete additional assignments. E. Alm, B. Berger 6.8720[J] Principles of Synthetic Biology
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(Same subject as 20.405[J]) (Subject meets with 6.8721[J], 20.305[J]) Prereq: None Units: 3-0-9 Lecture: TR2-3.30 (26-328) +final
Introduces the basics of synthetic biology, including quantitative cellular network characterization and modeling. Considers the discovery and genetic factoring of useful cellular activities into reusable functions for design. Emphasizes the principles of biomolecular system design and diagnosis of designed systems. Illustrates cutting-edge applications in synthetic biology and enhances skills in analysis and design of synthetic biological applications. Students taking graduate version complete additional assignments. R. Weiss No textbook information available 6.8721[J] Principles of Synthetic Biology
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(Same subject as 20.305[J]) (Subject meets with 6.8720[J], 20.405[J]) Prereq: None Units: 3-0-9 Lecture: TR2-3.30 (26-328) +final
Introduces the basics of synthetic biology, including quantitative cellular network characterization and modeling. Considers the discovery and genetic factoring of useful cellular activities into reusable functions for design. Emphasizes the principles of biomolecular system design and diagnosis of designed systems. Illustrates cutting-edge applications in synthetic biology and enhances skills in analysis and design of synthetic biological applications. Students taking graduate version complete additional assignments. R. Weiss No textbook information available Biomedical & Health6.4800[J] Biomedical Imaging with MRI: From Technology to Computation Applications
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(Same subject as 22.54[J]) Prereq: (18.C06 and (6.3000 or 16.002)) or permission of instructor Units: 2-3-7 Lecture: MW2 (45-102) Lab: F2-5 (38-530)
Presents medical imaging with MRI, motivated by examples of problems in human health that engage students in imaging hardware design, data acquisition and image reconstruction, and signal analysis and inference. Data from scientific and clinical applications in neuro- and cardiac MRI as applied in current practice are sourced for computational labs. Labs include kits for interactive and portable low-cost devices that can be assembled by the students to demonstrate fundamental building blocks of an MRI system. Students program lab MRI systems on their laptops for data collection and image reconstruction. Students apply concepts from lectures in labs for data collection for image reconstruction, image analysis, and inference by their own design, drawing on concepts in signal processing and machine learning. E. Adalsteinsson, T. Heldt, L. D. Lewis, C. M. Stultz, J. K. White No textbook information available 6.4810[J] Cellular Neurophysiology and Computing
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(Same subject as 2.791[J], 9.21[J], 20.370[J]) (Subject meets with 2.794[J], 6.4812[J], 9.021[J], 20.470[J], HST.541[J]) Prereq: (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor Units: 5-2-5
Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. Preference to juniors and seniors. Staff 6.4812[J] Cellular Neurophysiology and Computing
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(Same subject as 2.794[J], 9.021[J], 20.470[J], HST.541[J]) (Subject meets with 2.791[J], 6.4810[J], 9.21[J], 20.370[J]) Prereq: (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor Units: 5-2-5
Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. Staff 6.4820[J] Quantitative and Clinical Physiology
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(Same subject as 2.792[J], HST.542[J]) (Subject meets with 2.796[J], 6.4822[J], 16.426[J]) Prereq: Physics II (GIR), 18.03, or permission of instructor Units: 4-2-6 Lecture: TR9.30-11 (4-265) Recitation: W1 (34-301) or W2 (34-301)
Application of the principles of energy and mass flow to major human organ systems. Anatomical, physiological and clinical features of the cardiovascular, respiratory and renal systems. Mechanisms of regulation and homeostasis. Systems, features and devices that are most illuminated by the methods of physical sciences and engineering models. Required laboratory work includes animal studies. Students taking graduate version complete additional assignments. T. Heldt, R. G. Mark Textbooks (Fall 2025) 6.4822[J] Quantitative and Clinical Physiology
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(Same subject as 2.796[J], 16.426[J]) (Subject meets with 2.792[J], 6.4820[J], HST.542[J]) Prereq: 6.4810 and (2.006 or 6.2300) Units: 4-2-6 Lecture: TR9.30-11 (4-265) Recitation: W1 (34-301) or W2 (34-301)
Application of the principles of energy and mass flow to major human organ systems. Anatomical, physiological and clinical features of the cardiovascular, respiratory and renal systems. Mechanisms of regulation and homeostasis. Systems, features and devices that are most illuminated by the methods of physical sciences and engineering models. Required laboratory work includes animal studies. Students taking graduate version complete additional assignments. T. Heldt, R. G. Mark, L. G. Petersen No textbook information available 6.4830[J] Fields, Forces and Flows in Biological Systems
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(Same subject as 2.793[J], 20.330[J]) Prereq: Biology (GIR), Physics II (GIR), and 18.03 Units: 4-0-8
Introduction to electric fields, fluid flows, transport phenomena and their application to biological systems. Flux and continuity laws, Maxwell's equations, electro-quasistatics, electro-chemical-mechanical driving forces, conservation of mass and momentum, Navier-Stokes flows, and electrokinetics. Applications include biomolecular transport in tissues, electrophoresis, and microfluidics. J. Han, S. Manalis 6.4832[J] Fields, Forces, and Flows in Biological Systems
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(Same subject as 2.795[J], 10.539[J], 20.430[J]) Prereq: Permission of instructor Units: 3-0-9 Lecture: TR1-2.30 (4-231)
Molecular diffusion, diffusion-reaction, conduction, convection in biological systems; fields in heterogeneous media; electrical double layers; Maxwell stress tensor, electrical forces in physiological systems. Fluid and solid continua: equations of motion useful for porous, hydrated biological tissues. Case studies of membrane transport, electrode interfaces, electrical, mechanical, and chemical transduction in tissues, convective-diffusion/reaction, electrophoretic, electroosmotic flows in tissues/MEMs, and ECG. Electromechanical and physicochemical interactions in cells and biomaterials; musculoskeletal, cardiovascular, and other biological and clinical examples. Prior undergraduate coursework in transport recommended. C. Buie, A. Hansen No textbook information available 6.4840[J] Molecular, Cellular, and Tissue Biomechanics
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(Same subject as 2.797[J], 3.053[J], 20.310[J]) (Subject meets with 2.798[J], 3.971[J], 6.4842[J], 10.537[J], 20.410[J]) Prereq: Biology (GIR) and 18.03 Units: 4-0-8
Develops and applies scaling laws and the methods of continuum mechanics to biomechanical phenomena over a range of length scales. Topics include structure of tissues and the molecular basis for macroscopic properties; chemical and electrical effects on mechanical behavior; cell mechanics, motility and adhesion; biomembranes; biomolecular mechanics and molecular motors. Experimental methods for probing structures at the tissue, cellular, and molecular levels. Students taking graduate version complete additional assignments. M. Bathe, P. So, R. Raman 6.4842[J] Molecular, Cellular, and Tissue Biomechanics
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(Same subject as 2.798[J], 3.971[J], 10.537[J], 20.410[J]) (Subject meets with 2.797[J], 3.053[J], 6.4840[J], 20.310[J]) Prereq: Biology (GIR) and 18.03 Units: 3-0-9
Develops and applies scaling laws and the methods of continuum mechanics to biomechanical phenomena over a range of length scales. Topics include structure of tissues and the molecular basis for macroscopic properties; chemical and electrical effects on mechanical behavior; cell mechanics, motility and adhesion; biomembranes; biomolecular mechanics and molecular motors. Experimental methods for probing structures at the tissue, cellular, and molecular levels. Students taking graduate version complete additional assignments. M. Bathe, P. So, R. Raman 6.4850[J] Multiphysics Systems Modeling
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