Doctor of Philosopy in Computational and Systems Biology
Computational and Systems Biology Program
Program Requirements
| Core Curriculum | ||
| CSB.100[J] | Topics in Computational and Systems Biology | 12 |
| Biology Requirement | 12 | |
| Select one of the following: | ||
| Principles of Biochemical Analysis | ||
| Genetics for Graduate Students | ||
| Molecular Biology | ||
| Eukaryotic Cell Biology: Principles and Practice | ||
| Immunology | ||
| Molecular and Cellular Neuroscience Core II | ||
| Computational Biology Requirement | 12 | |
| Select one of the following: | ||
| Modeling with Machine Learning: from Algorithms to Applications and Machine Learning for Molecular Engineering 1 | ||
| Advanced Computational Biology: Genomes, Networks, Evolution | ||
| Systems Biology | ||
| Computational Systems Biology: Deep Learning in the Life Sciences | ||
| Research Rotations | ||
| CSB.110 | Research Rotations in Computational and Systems Biology | 24 |
| Restricted Electives | ||
| Four subjects from the list below, including two subjects in the student's research area or department that are topically related, one engineering subject, and one biology subject 2 | 48 | |
| Teaching Experience | ||
| CSB.199 | Teaching Experience in Computational Systems Biology | 12 |
| Professional Development | ||
| Select one of the following: 3 | 2 | |
| Professional Development in Computational and Systems Biology | ||
| Research Experience in Biopharma | ||
| Leadership and Professional Strategies & Skills Training (LEAPS), Part I: Advancing Your Professional Strategies and Skills | ||
| Leadership and Professional Strategies & Skills Training (LEAPS), Part II: Developing Your Leadership Competencies | ||
| Thesis | ||
| CSB.THG | Graduate Thesis 4 | 252 |
| Total Units | 374 | |
| 1 | 6.C51 and 20.C51[J] must be taken concurrently for credit for a letter grade. |
| 2 | Other subjects may be approved on a case-by-case basis by the program directors. |
| 3 | With permission of their advisor, students may substitute another MIT subject of at least 2 units that supports career exploration or professional development. |
| 4 | The units listed here represent an average number taken during the doctoral program. |
| Restricted Electives 1 | ||
| Civil and Environmental Engineering | ||
| 1.545[J] | Atomistic Modeling and Simulation of Materials and Structures | 12 |
| 1.685[J] | Nonlinear Dynamics and Waves | 12 |
| 1.89 | Environmental Microbial Biogeochemistry | 12 |
| 1.873 | Mathematical Modeling of Ecological Systems | 12 |
| 1.881[J] | Genomics and Evolution of Infectious Disease | 12 |
| Mechanical Engineering | ||
| 2.18 | Biomolecular Feedback Systems | 12 |
| 2.717 | Optical Engineering | 12 |
| Chemistry | ||
| 5.64[J] | Advances in Interdisciplinary Science in Human Health and Disease | 12 |
| 5.70[J] | Statistical Thermodynamics | 12 |
| Electrical Engineering and Computer Science | ||
| 6.C51 & 20.C51[J] | Modeling with Machine Learning: from Algorithms to Applications and Machine Learning for Molecular Engineering 2 | 12 |
| 6.3702 | Introduction to Probability | 12 |
| 6.3722 | Introduction to Statistical Data Analysis | 12 |
| 6.5060 | Algorithm Engineering | 12 |
| 6.5150 | Large-scale Symbolic Systems | 12 |
| 6.5210[J] | Advanced Algorithms | 15 |
| 6.5420 | Randomness and Computation | 12 |
| 6.6220 | Power Electronics | 12 |
| 6.6500[J] | Integrated Microelectronic Devices | 12 |
| 6.7100[J] | Dynamic Systems and Control | 12 |
| 6.7300[J] | Introduction to Modeling and Simulation | 12 |
| 6.7310[J] | Introduction to Numerical Methods | 12 |
| 6.7330[J] | Numerical Methods for Partial Differential Equations | 12 |
| 6.7700[J] | Fundamentals of Probability | 12 |
| 6.7710 | Discrete Stochastic Processes | 12 |
| 6.7720[J] | Discrete Probability and Stochastic Processes | 12 |
| 6.7800 | Inference and Information | 12 |
| 6.7810 | Algorithms for Inference | 12 |
| 6.7830 | Bayesian Modeling and Inference | 12 |
| 6.7900 | Machine Learning | 12 |
| 6.7920[J] | Reinforcement Learning: Foundations and Methods | 12 |
| 6.7930[J] | Machine Learning for Healthcare | 12 |
| 6.7960 | Deep Learning | 12 |
| 6.8610 | Quantitative Methods for Natural Language Processing | 12 |
| 6.8700[J] | Advanced Computational Biology: Genomes, Networks, Evolution 3 | 12 |
| 6.8710[J] | Computational Systems Biology: Deep Learning in the Life Sciences | 12 |
| 6.S982 | Special Subject in Electrical Engineering and Computer Science | 12 |
| Biology | ||
| 7.493[J] | Microbial Genetics and Evolution | 12 |
| 7.51 | Principles of Biochemical Analysis 3 | 12 |
| 7.52 | Genetics for Graduate Students 4 | 12 |
| 7.58 | Molecular Biology 4 | 12 |
| 7.60 | Cell Biology: Structure and Functions of the Nucleus | 12 |
| 7.61[J] | Eukaryotic Cell Biology: Principles and Practice 4 | 12 |
| 7.63[J] | Immunology | 12 |
| 7.64 | Molecular Mechanisms, Pathology and Therapy of Human Neuromuscular Disorders | 12 |
| 7.66 | Molecular Basis of Infectious Disease | 12 |
| 7.69[J] | Developmental Neurobiology | 12 |
| 7.70 | Regulation of Gene Expression | 12 |
| 7.71 | Biophysical Technique | 12 |
| 7.72 | Stem Cells, Regeneration, and Development | 12 |
| 7.75 | Human Genetics and Genomics | 12 |
| 7.77 | Nucleic Acids, Structure, Function, Evolution, and Their Interactions with Proteins | 12 |
| 7.83 | Design Principles of Biological Systems | 12 |
| 7.84 | Advanced Concepts in Immunology | 12 |
| 7.85 | The Hallmarks of Cancer | 12 |
| 7.86 | Building with Cells | 12 |
| 7.91 | The CRISPR Revolution: Engineering the Genome for Basic Science and Clinical Medicine | 12 |
| 7.95 | Cancer Biology | 12 |
| Physics | ||
| 8.333 | Statistical Mechanics I | 12 |
| 8.334 | Statistical Mechanics II | 12 |
| 8.431[J] | Nonlinear Optics | 12 |
| 8.591[J] | Systems Biology 3 | 12 |
| 8.592[J] | Statistical Physics in Biology | 12 |
| 8.593[J] | Biological Physics | 12 |
| Brain and Cognitive Sciences | ||
| 9.013[J] | Molecular and Cellular Neuroscience Core II 4 | 12 |
| 9.015[J] | Molecular and Cellular Neuroscience Core I | 12 |
| 9.520[J] | Statistical Learning Theory and Applications | 12 |
| Chemical Engineering | ||
| 10.544 | Metabolic and Cell Engineering | 12 |
| 10.555[J] | Bioinformatics: Principles, Methods and Applications | 12 |
| 10.557 | Mixed-integer and Nonconvex Optimization | 12 |
| 10.637[J] | Computational Chemistry | 12 |
| Aeronautics and Astronautics | ||
| 16.391 | Statistics for Engineers and Scientists | 12 |
| 16.940 | Numerical Methods for Stochastic Modeling and Inference | 12 |
| Mathematics | ||
| 18.0651 | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | 12 |
| 18.0751 | Methods for Scientists and Engineers | 12 |
| 18.0851 | Computational Science and Engineering I | 12 |
| 18.0861 | Computational Science and Engineering II | 12 |
| 18.408 | Topics in Theoretical Computer Science | 12 |
| 18.417 | Introduction to Computational Molecular Biology | 12 |
| 18.418[J] | Topics in Computational Molecular Biology | 12 |
| 18.4531 | Combinatorial Optimization | 12 |
| 18.455 | Advanced Combinatorial Optimization | 12 |
| 18.6501 | Fundamentals of Statistics | 12 |
| 18.677 | Topics in Stochastic Processes | 12 |
| Biological Engineering | ||
| 20.201 | Fundamentals of Drug Development | 12 |
| 20.405[J] | Principles of Synthetic Biology | 12 |
| 20.409 | Instrumentation and Measurement for Biological Systems | 12 |
| 20.410[J] | Molecular, Cellular, and Tissue Biomechanics | 12 |
| 20.415 | Physical Biology | 12 |
| 20.420[J] | Principles of Molecular Bioengineering | 12 |
| 20.430[J] | Fields, Forces, and Flows in Biological Systems | 12 |
| 20.440 | Analysis of Biological Networks | 15 |
| 20.463[J] | Biomaterials Science and Engineering | 12 |
| 20.490 | Computational Systems Biology: Deep Learning in the Life Sciences 3 | 12 |
| 20.535[J] | Protein Engineering | 12 |
| Data, Systems, and Society | ||
| IDS.136[J] | Graphical Models: A Geometric, Algebraic, and Combinatorial Perspective | 12 |
| IDS.147[J] | Statistical Machine Learning and Data Science | 12 |
| Health Sciences and Technology | ||
| HST.176 | Cellular and Molecular Immunology | 12 |
| HST.508[J] | Evolutionary and Quantitative Genomics | 12 |
| 1 | Other subjects may be approved on a case-by-case basis by the program directors. |
| 2 | 6.C51 and 20.C51[J] must be taken concurrently for credit and a letter grade. |
| 3 | Can also fulfill the Computational Biology Requirement. |
| 4 | Can also fulfill the Biology Requirement. |

