A unique curriculum has been designed to provide training to span the disciplines of translational environmental health sciences. This curriculum takes the place of the Graduate Group electives and is designed not to delay the time to attain the PhD degree. Upon completion of the didactic curriculum, trainees will have completed the requirements for the Certificate in Environmental Health Sciences. Upon completion of their thesis, trainees will receive the Certificate in Environmental Health Sciences and a PhD from their graduate group.
Curriculum for Certificate Program in Environmental Health Sciences
Cell Biology – BIOM 6000 (1.0 c.u.)
Experimental Genome Science – GCB 5340 (1.0 c.u.)
Required Graduate Group Elective or Rotation (2.0 c.u.)
Molecular Toxicology – PHRM 5900 (1.0 c.u.)
Foundations in Statistics – BIOM 6100 (1.0 c.u.)
Required Graduate Group Elective or Rotation (2.0 c.u.)
Community Environmental Health Rotation (1.0 c.u.)
Laboratory Rotation (1.0 c.u.)
Data Science for Biomedical Informatics – BMIN 5030 (1.0 c.u.)
Environmental Epidemiology – EPID 7110 (1.0 c.u.)
Graduate Group Electives (2.0 c.u.)
Thesis Proposal – Independent Study (2.0 c.u.)
Candidacy Examination (2.0 c.u.)
Enter Thesis Research Laboratory
Students are required to do three rotations. One rotation can be taken in the summer before matriculation. One rotation must involve a mentored community-based experience or epidemiology/population based study. Rotations must be done in the lab of a CEET investigator.
Cell Biology: BIOM 6000 (Mandatory)
BIOM 6000 is a beginning-to-intermediate-level graduate school course designed to introduce students to the molecular components and physiological mechanisms that underlie the structure and function of eukaryotic cells. The course emphasizes core cell biology concepts by describing both landmark experiments and methods as well as current scientific research questions and technical approaches.
Experimental Genome Science: GCB 5340 (Mandatory)
This course will survey methods and questions in experimental genomics, including next generation sequencing methods, genomic sequencing in humans and model organisms, functional genomics, proteomics, and applications of genomics methods. Students will be expected to review and discuss current literature and to propose new experiments based on material learned in the course.
Molecular Toxicology: PHRM 5900 (Mandatory)
Exposures to foreign compounds (drugs, carcinogens, pollutants) can disrupt normal cellular processes leading to toxicity. This course will focus on the molecular mechanisms by which environmental exposures lead to end-organ injury and to diseases of environmental etiology (neurodegenerative and lung diseases, reproduction disruption and cardiovascular injury). Students will learn the difficulties in modeling response to low-dose chronic exposures, how these exposures are influenced by metabolism and disposition, and how biological reactive intermediates alter the function of biomolecules. Mechanisms responsible for cellular damage, aberrant repair, and end-organ injury will be discussed. Students will learn about modern predictive molecular toxicology to classify toxicants, predict individual susceptibility and response to environmental triggers, and how to develop and validate biomarkers for diseases of environmental etiology. Students are expected to write a term paper on risk assessment on an environmental exposure using available TOXNET information.
Foundations in Statistics: BIOM 6100 (Mandatory)
Technological advances have transformed biomedical research, making the generation of complex and high-dimensional datasets routine, and underscoring the importance of robust statistical methods for data analyses. In this course, students will learn to use the open-source R programming language to explore foundational topics in statistics, including regression, hypothesis testing, survival analysis, inference, and handling missing data. The course will be virtual and asynchronous (self-paced) and will leverage online resources, including DataCamp.com.
Data Science for Biomedical Informatics: BMIN 5030 (Mandatory)
This course will use R and other freely available software to learn fundamental data science applied to a range of biomedical informatics topics, including those making use of health and genomic data. After completing this course, students will be able to retrieve and clean data, perform exploratory analyses, build models to answer scientific questions, and present visually appealing results to accompany data analyses; be familiar with various biomedical data types and resources related to them; and know how to create reproducible and easily shareable results with R and github.
Environmental Epidemiology: EPID 7110 (Mandatory)
Environmental Epidemiology is an advanced epidemiology course that addresses epidemiological research methods used to study environmental exposures from air pollution to heavy metals, and from industrial pollutants to consumer product chemicals. The course will provide an overview of major study designs in environmental epidemiology, including cohort studies, panel studies, natural experiments, randomized controlled trials, time-series, and case-crossover studies.
The following electives are highly recommended:
Introduction to Superfund Sites and Health Effects of Hazardous Waste: PHRM 6570/ENVS 6570 Superfund hazardous waste sites are prevalent in our nation and the exposures to toxicants from these sites raises, immediate public health concerns. The aims of this course are to educate students about these sites and provide a scientific basis for hazard identification, hazard characterization, risk communication and risk management. The course will describe the effect of these hazardous chemicals on the ecosystem and vice-versa and remediation and mitigation approaches. These environmental science issues will lead into the environmental health aspects of exposures including: bio-monitoring (external and internal dose, biomarkers and the exposome), toxicological properties of contaminants and mode-of-action. The course will be complemented with visits to two Superfund sites in the region: Ambler (Asbestos) and Palmerton (Heavy Metals).
Methods for Statistical Genetics in Complex Human Disease: BSTA 7870
This is an advanced elective course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics, Computational Biology, and other BGS disciplines. This course will cover statistical methods for the analysis of genetics and genomics data. Topics covered will include genetic linkage and association analysis, analysis of next-generation sequencing data, including those generated from DNA sequencing and RNA sequencing experiments. Students will be exposed to the latest statistical methodology and computer tools on genetic and genomic data analysis.
Molecular Basis of Disease: BIOM 5020
BIOM 502 introduces students to basic mechanisms of disease and examines a different disease each week. The focus of the course will be on understanding the pathophysiology of the diseases and how research has enhanced not only our knowledge of disease mechanisms but has also led to improved therapy for patients with these diseases.
Neurotransmitter Signaling & Neuropsychopharmacology: PHRM 5100
The goals of this course are three-fold: 1) Provide an overview of major psychiatric disorders. 2) Provide in-depth information on neurotransmitters, emphasizing the wealth of new molecular information on how neurons function and communicate, as well as the basis for psychotherapeutics. 3) Develop skills to appreciate, present and critically evaluate the the current literature in neurotransmitter signaling and neuropsychopharmacology.
Introduction to Bioinformatics: GCB 5350
This course provides overview of bioinformatics and computational biology as applied to biomedical research. A primary objective of the course is to enable students to integrate modern bioinformatics tools into their research activities. Course material is aimed to address biological questions using computational approaches and the analysis of data. A basic primer in programming and operating in a UNIX environment will be presented, and students will also be introduced to Python R, and tools for reproducible research. This course emphasizes direct, hands-on experience with applications to current biological research problems. Areas include DNA sequence alignment, genetic variation and analysis, motif discovery, study design for high-throughput sequencing RNA, and gene expression, single gene and whole-genome analysis, machine learning, and topics in systems biology. The relevant principles underlying methods used for analysis in these areas will be introduced and discussed at a level appropriate for biologists without a background in computer science.