Curriculum 2016 - 2018 | NYU Tandon School of Engineering

Curriculum 2016 - 2018

View curriculum and suggested courses. Students who were admitted prior to Fall 2018 between academic year 2016-2018 are encouraged to follow the outlined course order under suggested courses section.


Suggested Courses

Degree Requirements: 30 Credits


Curriculum

Required Core Courses (9 Credits)

3 Credits Algorithms and Data Structures for Bioinformatics BI-GY7453
The online course is aimed at introducing the foundational ideas from computer science in designing and implementing bioinformatics algorithms. The goal of the underlying algorithms and data structures is to accurately abstract and model the biological problems and to devise provably correct procedures with efficient computational complexity bounds. The algorithms will be described in pseudo-codes in order to simplify the correctness and complexity analysis, but with sufficient details to enable the students implement them in any suitable software pipelines and hardware architectures.
Prerequisites: MA-UY 2314
3 Credits Proteomics for Bioinformatics BI-GY7543
The online proteomics course contributes an application focused specialty class to the bioinformatics curriculum. It will be a tour-de-force of modern proteomics methods and analysis in the context of practical research and clinical applications. The course will teach fundamentals, applications, experiments and predictions in parallel. Thus, each week will include a mix of interactive approaches from background learning, to understanding experimental methodology pro and con, to software usage and sophisticated bioinformatics approaches to prediction. Limitations and complementary of prediction methods will be emphasized. It is desirable (but not required) for students to complete a Biochemistry course before taking this course.
Prerequisites: Bioinformatics I.
3 Credits Bioinformatics Iii: Functional Prediction BI-GY7553
The course covers functional classifications of proteins; prediction of function from sequence and structure; Orthologs and Paralogs; representations of biological pathways; available systems for the analysis of whole genomes and for human-assisted and automatic functional prediction.
Prerequisites: Bioinformatics II

Electives - Select Five (15 Credits)

3 Credits Biological Foundation for Bioinformatics BI-GY7523
This course intensively reviews the aspects of biochemistry, molecular biology and cell biology necessary to begin research in bioinformatics and to enter graduate courses in biology. The areas covered include cell structure, intracellular sorting, cellular signaling (i.e., receptors), Cytoskelton, cell cycle, DNA replication, transcription and translation. This course extensively uses computer approaches to convey the essential computational and visual nature of the material to be covered.
Prerequisites: General Chemistry, General Physics, Organic Chemistry, Calculus or permission of instructor.
3 Credits Special Topics in “informatics in Chemical and Biological Sciences” BI-GY7573
This course covers special topics on various advanced or specialized topics in chemo- or bioinformatics that are presented at intervals.
3 Credits Introduction to Systems Biology BI-GY7613
This course explains the functioning of basic circuit elements in transcription regulation, signal transduction and developmental networks of living cells, using simplified mathematical models. The course focuses on design principles and information processing in biological circuits. It discusses network motifs, modularity, robustness, evolutional optimization and error minimization by kinetic proofreading in specific applications to bacterial chemotaxis, developmental patterning, neuronal circuits and immune recognition in several well-studied biological systems.
Prerequisites: Bioinformatics II
3 Credits Systems Biology: -omes and -omics BI-GY7623
This course summarizes knowledge in genomics, proteomics, transcriptomics, metabolomics and relative molecular technologies. Topics include an overview of technologies in functional genomics (DNA chip arrays); whole genome expression analysis (EST, MPSS, SAGE, arrays); proteome analysis technology (2D-electrophoresis, protein in situ digestion for mass spectrometric analysis, yeast 2-hybrid analysis. 2-D PAGE, MALDI-TOF spectroscopy); the principles of Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry technologies for metabolomics, including general principles, the strengths and weaknesses of each technique, the requirements for sample preparation and the options for the management of output data. This course explains how to exploit different -ome database resources for investigations via special practical tasks to lectures. Special attention is focused on nutrigenomics, a multidisciplinary science that uses genomics, transcriptomics and proteomics to study metabolic health. This relatively new area of metabolomics has the potential to contribute significantly to advances in nutrition and health.
Prerequisites: Bioinformatics II, Bioinformatics III
3 Credits Transcriptomics BI-GY7633
Screening of differential expression of genes using microarray technology builds the opportunities for personalized medicine converging soon to medical informatics and to our health care system. The course will start with a discussion of gene expression biology, presenting microarray platforms, design of experiments, and Affymetrix file structures and data storage. R programming is introduced for the preprocessing Affymetrix data for Image analysis, quality control and array normalization, log transformation and putting the data together. Bioconductor software will be dealt with data importing, filtering, annotation and analysis. Machine learning concepts and tools for statistical genomics will be addressed along with distance concept, cluster analysis, heat map and class discovery. Case studies link the methodology to biomolecular pathways, gene ontology, genome browsing and drug signatures.
3 Credits Next Generation Sequence Analysis for Bioinformatics BI-GY7653
The online course is aimed at developing practical bioinformatics skills of next generation sequencing analysis. Students will be introduced to current best practices and in high-throughput sequence data analysis and they will have the opportunity to analyze real data in a high-performance Unix-based computing environment. Special attention will be given to understand the advantages, limitations, and assumptions of most widely bioinformatics methods and the challenges involved in the analysis of large scale datasets. Some of the topics that will be covered include, current sequencing platforms, data formats (FASTA, SAM, BAM, VCF), sequence alignment, sequence assembly, variant calling, RNA-seq analysis, and their biological applications. Students enroll into this course should have knowledge of Basic of programming, unix tools, and shell scripting.
3 Credits Problem Solving for Bioinformatics BI-GY7663
This course will introduce students to programming in Bioinformatics. The focus will be on object oriented techniques of scripting. Cancer data will be used as examples throughout the course.
3 Credits Biology and Biotechnology for Bioinformatics BI-GY7683
The online course is aimed at introducing the key ideas from biology and biochemistry and how they are used in modern biotechnology. The goal of this course is to develop students’ critical thinking and analytical reasoning skills in the specific context of biotechnology and its modern applications. This course will explore a plethora of technologies used in the fields of genetic engineering, forensics, agriculture, bioremediation and medicine in order to give the students a basic but fundamental experimental skill set which can be applied in conjunction with computational skills to solve biological problems in a scalable manner. Students enroll into this course should have knowledge of basic Sciences (Biology, Physics and Chemistry).
3 Credits Statistics and Mathematics for Bioinformatics BI-GY7723
The online course is aimed at introducing the fundamental concepts from mathematics, probability and statistics, as relevant to bioinformatics and computational biology. Students enroll into this course should have knowledge of Calculus and Discrete Mathematics.

Capstone

To satisfy the Capstone students may choose to take either the Guided Studies or Thesis courses.

Guided Studies (maximum 6 Credits)

3 Credits Guided Studies in Bioinformatics I BI-GY7583
This research/case course can be handled in different ways at the faculty adviser’s discretion. The course may involve a series of cases that are dissected and analyzed, or it may involve teaming students with industry personnel for proprietary or non-proprietary research projects. Generally, the student works under faculty supervision, but the course is intended to be largely self-directed within the guidelines established by the supervising faculty member. Master’s degree candidates must submit an unbound copy of their report to adviser/s one week before the last day of classes.
Prerequisite: degree status.
3 Credits Guided Studies in Bioinformatics II BI-GY7593
This research/case course can be handled in different ways at the faculty adviser’s discretion. The course may involve a series of cases that are dissected and analyzed, or it may involve teaming students with industry personnel for proprietary or non-proprietary research projects. Generally, the student works under faculty supervision, but the course is intended to be largely self-directed within the guidelines established by the supervising faculty member. Master’s degree candidates must submit an unbound copy of their report to adviser/s one week before the last day of classes.
Prerequisite: degree status.

(OR)

Thesis Course (maximum 9 Credits)

You can register for the Thesis course each semester up to a maximum of three times equivalent to 9 Credits maximum.

MS Thesis in Bioinformatics BI-GY997X
Original research, which serves as basis for master’s degree. Minimum research registration requirements for the master’s thesis: 12 units. Registration for research required each semester consecutively until students have completed adequate research projects and acceptable theses and have passed required oral examinations. Research credits registered for each semester realistically reflect time devoted to research.
Prerequisites for MS candidates: Degree status and consent of graduate adviser and thesis director.


Suggested Courses

Either thesis, 9 credits cumulatively over three semesters, or Guided Studies, six credits over two semesters, is a Capstone requirement for completion of the MS in Bioinformatics.

All courses are subject to change.

Semester I

3 Credits Algorithms and Data Structures for Bioinformatics BI-GY7453
The online course is aimed at introducing the foundational ideas from computer science in designing and implementing bioinformatics algorithms. The goal of the underlying algorithms and data structures is to accurately abstract and model the biological problems and to devise provably correct procedures with efficient computational complexity bounds. The algorithms will be described in pseudo-codes in order to simplify the correctness and complexity analysis, but with sufficient details to enable the students implement them in any suitable software pipelines and hardware architectures.
Prerequisites: MA-UY 2314
3 Credits Problem Solving for Bioinformatics BI-GY7663
This course will introduce students to programming in Bioinformatics. The focus will be on object oriented techniques of scripting. Cancer data will be used as examples throughout the course.
3 Credits Biology and Biotechnology for Bioinformatics BI-GY7683
The online course is aimed at introducing the key ideas from biology and biochemistry and how they are used in modern biotechnology. The goal of this course is to develop students’ critical thinking and analytical reasoning skills in the specific context of biotechnology and its modern applications. This course will explore a plethora of technologies used in the fields of genetic engineering, forensics, agriculture, bioremediation and medicine in order to give the students a basic but fundamental experimental skill set which can be applied in conjunction with computational skills to solve biological problems in a scalable manner. Students enroll into this course should have knowledge of basic Sciences (Biology, Physics and Chemistry).

Semester II

3 Credits Bioinformatics Iii: Functional Prediction BI-GY7553
The course covers functional classifications of proteins; prediction of function from sequence and structure; Orthologs and Paralogs; representations of biological pathways; available systems for the analysis of whole genomes and for human-assisted and automatic functional prediction.
Prerequisites: Bioinformatics II
3 Credits Next Generation Sequence Analysis for Bioinformatics BI-GY7653
The online course is aimed at developing practical bioinformatics skills of next generation sequencing analysis. Students will be introduced to current best practices and in high-throughput sequence data analysis and they will have the opportunity to analyze real data in a high-performance Unix-based computing environment. Special attention will be given to understand the advantages, limitations, and assumptions of most widely bioinformatics methods and the challenges involved in the analysis of large scale datasets. Some of the topics that will be covered include, current sequencing platforms, data formats (FASTA, SAM, BAM, VCF), sequence alignment, sequence assembly, variant calling, RNA-seq analysis, and their biological applications. Students enroll into this course should have knowledge of Basic of programming, unix tools, and shell scripting.
3 Credits Applied Biostatistics for Bioinformatics BI-GY7673
This online course will introduce the basics of statistics and its applications in various fields of biology. It will lean towards practical applications, allowing for an intuitive understanding of concepts and some rigor in the application of statistics. It will use R for all the programming exercises. The course will not be requiring a lot of programming, and the requisite skills will be introduced in the lectures. The problems, exercises and assignments will be drawn from real-life problems in research papers and books. The student should be able the initiate and solve problems in the field at the end of the course. Students enroll into this course should have knowledge of basics of programming, probability and statistics.
3 Credits Statistics and Mathematics for Bioinformatics BI-GY7723
The online course is aimed at introducing the fundamental concepts from mathematics, probability and statistics, as relevant to bioinformatics and computational biology. Students enroll into this course should have knowledge of Calculus and Discrete Mathematics.

Semester III

3 Credits Proteomics for Bioinformatics BI-GY7543
The online proteomics course contributes an application focused specialty class to the bioinformatics curriculum. It will be a tour-de-force of modern proteomics methods and analysis in the context of practical research and clinical applications. The course will teach fundamentals, applications, experiments and predictions in parallel. Thus, each week will include a mix of interactive approaches from background learning, to understanding experimental methodology pro and con, to software usage and sophisticated bioinformatics approaches to prediction. Limitations and complementary of prediction methods will be emphasized. It is desirable (but not required) for students to complete a Biochemistry course before taking this course.
Prerequisites: Bioinformatics I.
3 Credits Guided Studies in Bioinformatics I BI-GY7583
This research/case course can be handled in different ways at the faculty adviser’s discretion. The course may involve a series of cases that are dissected and analyzed, or it may involve teaming students with industry personnel for proprietary or non-proprietary research projects. Generally, the student works under faculty supervision, but the course is intended to be largely self-directed within the guidelines established by the supervising faculty member. Master’s degree candidates must submit an unbound copy of their report to adviser/s one week before the last day of classes.
Prerequisite: degree status.
3 Credits Transcriptomics BI-GY7633
Screening of differential expression of genes using microarray technology builds the opportunities for personalized medicine converging soon to medical informatics and to our health care system. The course will start with a discussion of gene expression biology, presenting microarray platforms, design of experiments, and Affymetrix file structures and data storage. R programming is introduced for the preprocessing Affymetrix data for Image analysis, quality control and array normalization, log transformation and putting the data together. Bioconductor software will be dealt with data importing, filtering, annotation and analysis. Machine learning concepts and tools for statistical genomics will be addressed along with distance concept, cluster analysis, heat map and class discovery. Case studies link the methodology to biomolecular pathways, gene ontology, genome browsing and drug signatures.

Semester IV

3 Credits Special Topics in “informatics in Chemical and Biological Sciences” BI-GY7573
This course covers special topics on various advanced or specialized topics in chemo- or bioinformatics that are presented at intervals.
3 Credits Guided Studies in Bioinformatics II BI-GY7593
This research/case course can be handled in different ways at the faculty adviser’s discretion. The course may involve a series of cases that are dissected and analyzed, or it may involve teaming students with industry personnel for proprietary or non-proprietary research projects. Generally, the student works under faculty supervision, but the course is intended to be largely self-directed within the guidelines established by the supervising faculty member. Master’s degree candidates must submit an unbound copy of their report to adviser/s one week before the last day of classes.
Prerequisite: degree status.
3 Credits Population Genetics and Evolutionary Biology for Bioinformatics BI-GY7693
The online course is aimed at introducing the key ideas from population genetics and how they are used to understand the interaction of basic evolutionary processes (e.g., including mutation, natural selection, genetic drift, inbreeding, recombination and gene flow) that determine the genetic composition and evolutionary trajectories of natural populations. The goal of this course is to develop students’ critical thinking and analytical reasoning skills in the specific context of many mechanisms shaping genetic variations and within and between populations. This course will equip the students with mathematical and experimental skills to address public health issues.
3 Credits Translational Genomics and Computational Biology BI-GY7733
This online course will introduce will expose the students to different aspects of the data analysis and modeling activities that are expected of a Bioinformatician or a Computational Biologist. This course will offer a wide spectrum of examples of applications roughly divided in two broad parts: (a) data analysis in a "translational" settings and (b) more "computational" approaches to Biology pertaining the simulation of biological systems. This course will explore a different set of online resources that contain complex data models of data (e.g., cancer data from TCGA and ICGC); the data thus collected will be used to expose novel model reconstruction tools. Other online resources and related exchange formats will be explored in order to show how simulation of biological systems models (and the related problem of their parameter tuning) in its different forms has become more and more usable and an important tool for biomedicine. Students enroll into this course should have knowledge of basics of programming, undergraduate calculus, probability and statistics, introductory cell biology.
Pre-requisites: BI-GY 7673