Mathematical Methods for Engineers

Covers applications to applied mechanics, thermofluids, and dynamics/control problems relevant to engineering. Topics include differential equations applied to modeling and characterization of processes, linear algebra used for multidimensional and complex system computations and modeling, and statistics and probability used for controls and signal analysis, among other applications. Introduces the foundational basis for approximate methods of engineering analysis, including its application to finite element analysis.

Principles of Bioengineering

Designed to introduce new graduate bioengineering students to the fundamentals of bioengineering research topics and methodology. Includes outside speakers to discuss general topics in bioengineering. Examples of course topics include the medical device qualification and regulatory environment, tissue engineering, cell engineering, mechanobiology, drug delivery, bioimaging, neuromotor control, effective design of experiments, writing research proposals for the National Institutes of Health (NIH) and how to evaluate and write a peer-reviewed journal article, etc. Expects students to read, critically evaluate, and present the research in a bioengineering journal article. Students are then expected to extend their article into a hypothesis-driven proposal in NIH format with an oral defense of the proposal.

Method and Logic in Systems Biology

This course is based on reading and discussing primary literature that exemplifies approaches that have revealed new concepts and principles governing biological systems. Special emphasis will be placed on difficulties that lie at the interface of theory and experiment in bioengineering and systems biology. Each week, the students will read and discuss two important papers, and the majority of the class time will be devoted to student discussions rather than to formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, network analysis, dynamical systems, and biological design principles.

Methods of Bioengineering: Mass spectrometry analysis

A module on mass-spectromery analysis from a survey course of advanced and emerging research methods in bioengineering. Broad range of topics include nucleic acid imaging, genetic engineering, mass spectrometry proteomics, non-linear systems and parameter estimation. Lectures are led weekly by different department faculty members, with particular emphasis on development of methods for quantitative analysis of experimental data. Students are expected to have some familiarity and proficiency with widely used analysis tools such as Matlab, Python or R.

Statistics for Proteomics

The course is a collection of short lectures. Each lecture introduces one topic in 5-10 min max. Topics include: Non-ignorable missing data, GSEA, Reproducibility versus accuracy, Batch effects, Data biases.
TouTube playlist: Statistics for Proteomics


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Mathematical Methods for Engineers Principles of Bioengineering Method and Logic in Systems Biology Methods of Bioengineering: Mass spec analysis Statistics for Proteomics Recorded Lectures Mathematical Methods for Engineers Covers applications to applied mechanics, thermofluids, and dynamics/control problems relevant to engineering. Topics include differential equations applied to modeling and characterization of processes, linear algebra used for multidimensional and complex system computations and modeling, and statistics and probability used for controls and signal analysis, among other applications. Introduces the foundational basis for approximate methods of engineering analysis, including its application to finite element analysis. Principles of Bioengineering Designed to introduce new graduate bioengineering students to the fundamentals of bioengineering research topics and methodology. Includes outside speakers to discuss general topics in bioengineering. Examples of course topics include the medical device qualification and regulatory environment, tissue engineering, cell engineering, mechanobiology, drug delivery, bioimaging, neuromotor control, effective design of experiments, writing research proposals for the National Institutes of Health (NIH) and how to evaluate and write a peer-reviewed journal article, etc. Expects students to read, critically evaluate, and present the research in a bioengineering journal article. Students are then expected to extend their article into a hypothesis-driven proposal in NIH format with an oral defense of the proposal. Method and Logic in Systems Biology This course is based on reading and discussing primary literature that exemplifies approaches that have revealed new concepts and principles governing biological systems. Special emphasis will be placed on difficulties that lie at the interface of theory and experiment in bioengineering and systems biology. Each week, the students will read and discuss two important papers, and the majority of the class time will be devoted to student discussions rather than to formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, network analysis, dynamical systems, and biological design principles. Catalog entry for Spring 2019 Papers for Spring 2019 Methods of Bioengineering: Mass spectrometry analysis A module on mass-spectromery analysis from a survey course of advanced and emerging research methods in bioengineering. Broad range of topics include nucleic acid imaging, genetic engineering, mass spectrometry proteomics, non-linear systems and parameter estimation. Lectures are led weekly by different department faculty members, with particular emphasis on development of methods for quantitative analysis of experimental data. Students are expected to have some familiarity and proficiency with widely used analysis tools such as Matlab, Python or R. Statistics for Proteomics The course is a collection of short lectures. Each lecture introduces one topic in 5-10 min max. Topics include: Non-ignorable missing data, GSEA, Reproducibility versus accuracy, Batch effects, Data biases. TouTube playlist: Statistics for Proteomics Recored lectures Prsentaions from the Slavov Lab Prsentaions from the Singe-cell Proteomics Conference Statistics for Proteomics

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