Physiology, Biophysics and Systems Biology

The objective of this one semester course is to prepare students for thesis research in fields that involve concepts of bioengineering, including tissue engineering and regenerative medicine. The course will be team taught by Weill Cornell and Ithaca faculty using video conferencing facilities. Examples will be chosen from musculoskeletal and cardiovascular fields.

This course begins with a discussion of the history, techniques and statistical analyses used in bioinformatics today. Students will begin to analyze how these tools can be used to predict RNA, gene and protein structure. The final two weeks of the course will be focused on systems biology including current techniques used to model of protein-protein interactions, protein networks and cell signaling.

The rapid advancement of Next-Generation Sequencing (NGS) has opened a wealth of opportunities for research in many fields: cancer biology, epigenetics, tumor evolution, microbiome and infectious disease dynamics, neuro-degeneration, personalized medicine, and improved diagnosis and risk assessment for patients. Moreover, there are emerging, faster NGS technologies that promise comprehensive molecular portraits of disease and actionable clinical results for doctors within a single day. Scientists and physicians will be better equipped to design studies and help patients if they possess an intricate knowledge of these molecular-profiling methods, their biological context and their applicability to specific cases and diseases. Finally, a rich understanding of the complexity of the human genome is essential for the proper annotation of characterization of any new mutations/modifications found, since large-scale efforts at tumor and normal genome sequencing have dramatically altered our view of the “normal” genome and epigenome.

Thus, in this 10-week course, students will build a strong foundation of knowledge of NGS technologies (both existing and emerging), learn the applications of these technologies for basic and clinical research, and finally learn the essential tools for the analysis, integration, and application of these data relative to other public databases and phenotype repositories. We have a broad range of expertise being contributed from many leaders in the field.

Students prepare for 21st century research in the function, analysis, modeling and understanding of living systems at each of several scales, from the molecular through the cellular to the organ system and organism. Multiscale and translational examples develop conceptual skills necessary to design meaningful experiments, derive insight from journal reports, work within the group structure now essential for contemporary research, and communicate new developments and related findings to today’s peers and future students. Structural and developmental concepts are covered as they illuminate function. The course is modular, presented in six independent but coordinated modules. All first-year students in the PBSB Program take all six modules. The entire course and individual modules are open to students of other programs with the permission of the course director. NOTE: all students must register separately for each module taken. Each module consists of multiple weeks. Typical weeks for modules 1-5 include two in-depth lecture-conferences that combine careful presentation of core material with student participation, and conclude with either a computational analysis and/or model, or a relevant illuminating article from the literature. The final module, CPBSB6, introduces new instructional modalities and perspectives designed to instill skills essential for researchers.

This course is required for all first and second year PBSB graduate students, but is open to all WCGS students. It is designed to train students in scientific presentation and critique. The structure is a formalized "journal club." Each student will choose a paper, which is subject to approval by the course directors. Each session will consist of a student formally presenting their selected paper to the class, which is expected to serve as a critical audience. The presentation should consist of an introduction of the relevant background literature, an objective presentation of the study, a subjective analysis/critique of the work and suggestions for future work. Presentations by 2nd year students will be scheduled first, giving the 1st year students the opportunity to learn from their more senior colleagues. Grading will be based on presentation quality and contribution to constructive feedback.

This course is required for all first year PBSB graduate students, but is open to all WCGS students. Come for lunch and listen to program faculty describe their research.

The objective of this one semester course is to prepare students for thesis research in fields that encompass bioengineering. The course will be team taught by Weill Cornell and Ithaca faculty using video conferencing facilities. Examples will be chosen from musculoskeletal and cardiovascular fields.

This course introduces the tools of computational and theoretical neuroscience, with a focus on principles and mathematical foundations. Students should be familiar with complex numbers, matrices, and univariate differential and integral calculus. The specific topics addressed in the course vary depending on interests of the students.

This course focuses on the biophysics, properties, and physiological roles of ion channels and transporter. We discuss the contributions to cell function in physiology and pathology of the principal ion channel and transporter families. We emphasize the mechanistic insights that have emerged from the recent explosion of structural information and how this has drastically changed our understanding of gating and selectivity of these proteins.

This course is the seminar series in the Physiology, Biophysics & Systems Biology Program. Most lectures are given by speakers invited from outside the Weill Cornell community, but speakers are also drawn from within the Program and scientists at the WCGS with related interests.

After a brief overview of all major medical modalities, including x-ray, CT, MRI, SPECT/PET and US, this course will focus on the formulations of spatial encoding and image contrasts as exemplified in MRI. The inverse problem between detected signal and image source will be discussed for biomedical applications. The concepts of image resolution, image contrast, SNR and scan time will be illustrated quantitatively from an engineering point of view.

This survey course will cover the basic physical, biochemical, computational and engineering principles underlying current medical imaging techniques including: magnetic resonance imaging, positron emission tomography, radionuclide production and radiochemistry, optical imaging, X-ray computed tomography and ultrasound. The goal of the course will be to provide students with a broad knowledge of the concepts and implementation strategies of various imaging methods relevant in current research and clinical practice. Practical applications will be used to illustrate the main themes of the course. Tours of the Biomedical Imaging Core Facility and other imaging laboratories will augment the formal course material. At the end of the course students will be able to identify appropriate imaging strategies for clinical research and have a working knowledge of the major techniques available to the investigator.

A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: BTRY 3080 and introductory statistics or equivalent

Outcomes

• Students will learn a statistical modeling strategy that is both basic and general, as well as how to apply this strategy to learn information about biological systems when analyzing genome-wide data. More specifically, students will learn the mathematics and interpretation of linear statistical models.
• Students will learn what these models can be used to infer when applied to genome-wide genetic and related data.
• Students will learn how to effectively and efficiently analyze large-scale genomic data and how to program in R for this purpose.
• Students will learn the limits of interpretation when applying these statistical models to genomic data when inferring information about a biological system.

To learn more, please visit: https://classes.cornell.edu/browse/roster/SP23/class/BTRY/4830.

This course will prepare students to apply quantitative techniques to the analysis of experimental data and the modeling of biological systems. To emphasize both practical and theoretical skills, the material will be presented whenever possible in a hands-on workshop style, and the completion of several projects by the students will be required. Topics include: practical aspects of data formatting and management; communication of quantitative concepts (verbal, graphical and mathematical); a review of statistics, with emphasis on the selection of appropriate statistical tests; the use of modern software packages; the interpretation of results; the formulation, evaluation and analysis of mathematical models of biological function, with an emphasis on linear and non-linear regression, determination of model parameters; and the critical comparison of alternative models with regard to over-parameterization. The formal components will introduce (and demystify) ordinary and partial differential equations and basic principles of non-linear dynamics, in order to enable quantitative modeling in biological arenas such as neural function, enzyme kinetics, cardiac dynamics and signaling pathways. Additional special topics will also be presented (e.g., control theory, machine learning, information theory, and image analysis) and their application will be illustrated with ongoing research in the laboratories of participating faculty.

2021 Quantitative Biology (Qbio) Course Registration

Non-PBS students may now satisfy the Quantitative Biology course requirement by taking IDPT 9001 01, Applied Quantitative Techniques for Biological Sciences.  PBS students should register for PBSB 5005 01, Quantitative Understanding in Bio 1.  

If any non-PBS student including students in the Tri-Institutional programs wishes to enroll, please email Dr. Lucy Skrabanek to request permission to enroll.  

Applied Quantitative Techniques for the Biological Sciences

There is a new course this Fall substituting the traditional Quant Bio course. The new course is entitled Applied Quantitative Techniques for the Biological Sciences. This course will be delivered to students of all programs including Neuro, Pharmacology, IMP, and BCMB. 

Dates: August 23, 2021- October 22, 2021

Day(s)/Time: Tuesday and Thursday, 5:30pm – 7:00pm

Syllabus:  2021_applied_quantitative_techniques_for_biologicial_sciences_syllabus.pdf 

If you are interested in enrolling in the course please contact: Chun-Jun Guo 

The RCR course is open to all members of the Tri-Institutional (Tri-I) and WCMC Clinical and Translational Science Center (CTSC) communities. Successful completion of the course is required for all trainees, fellows, participants, and scholars receiving support through NIH or NSF Institutional Research Training Grants, Individual Fellowship Awards, Career Development Awards (Institutional and Individual), Research Education Grants, Dissertation Research Grants, or other grant programs with a training component that requires instruction in responsible conduct of research as noted in the Funding Opportunity Announcement. The responsible conduct of research is the practice of scientific investigation with integrity. Training in this area is an essential component of research training; awareness and application of established professional norms and ethical principles is required in the performance of all activities related to scientific research. Weill Cornell Medical College is committed to fostering an environment that promotes the practice of scientific investigation with integrity. This course is intended to help fulfill that commitment. 

The RCR course is open to all members of the Tri-Institutional (Tri-I) and WCMC Clinical and Translational Science Center (CTSC) communities. Successful completion of the course is required for all trainees, fellows, participants, and scholars receiving support through NIH or NSF Institutional Research Training Grants, Individual Fellowship Awards, Career Development Awards (Institutional and Individual), Research Education Grants, Dissertation Research Grants, or other grant programs with a training component that requires instruction in responsible conduct of research as noted in the Funding Opportunity Announcement. The responsible conduct of research is the practice of scientific investigation with integrity. Training in this area is an essential component of research training; awareness and application of established professional norms and ethical principles is required in the performance of all activities related to scientific research. Weill Cornell Medical College is committed to fostering an environment that promotes the practice of scientific investigation with integrity. This course is intended to help fulfill that commitment. 

This course will teach students the fundamental skills and knowledge required for scientific computing in the biomedical sciences. Topics include: scripting, working with large datasets, data and software management, and effective use of high-performance computing resources. Students will learn relevant theory as well as develop practical application skills using contemporary tools and technologies including R for data analysis and presentation, SQL databases for structured data management, the Ruby scripting language for practical programming tasks, git for software and data revision control, Sun Grid Engine for batch job management on large clusters, and Maestro from the Schrödinger Suite for molecular modeling and visualization.