Statement of Teaching Philosophy: Andre R. O. Cavalcanti

Since I began college I have wanted to teach in higher education. For some, teaching is an inconvenient aspect of doing research in academia; for me, it is one of the primary reasons why I became a researcher. During my Ph.D. studies at the Universidade Federal de Pernambuco ( Recife, Brazil), I had the opportunity to be an adjunct professor for a year and a half in the Fundamental Chemistry Department, in which I taught four different courses.

Below I discuss my experience in and opinions about what it takes to be a great teacher.

My Objectives in Teaching

With the rapid advances in biology, it is useless just to memorize content. Students have to understand concepts and be able to expand their knowledge by themselves.

Students should have a globalunderstanding of why they are learning a given subject. It is very important that they associate the subject with applications for it. In my area of concentration, Bioinformatics, it is common to teach the theory and methods but to forget to provide a reason for why one should learn it, or even how one should apply the methodologies to real problems.

One of the most important things for students is to be able to develop their own opinions and to argue persuasively in favor of them. Students should also learn to be critical and not to automatically accept an idea as common knowledge without careful analysis.

Some methodologies I have already used or would like to use to achieve these goals include:

Bringing Down the Ivory Tower

In 1837, the French literary critic Charles-Augustin Sainte-Beuve said that the poet Alfred de Vigny lived in an Ivory Tower because of his solitary studies and isolation from the real world. It is easy for academics to make the same mistake by concentrating exclusively on their own fields of interest, distancing themselves from their students. I believe that professors should interact with students. Not only should discussions be encouraged outside the classroom, but professors should welcome students' scientific interests even if outside their own.

Encouraging Participation

In my experience, I received strong student participation in experimental classes by doing the experiments along with the students, instead of waiting for them to raise questions about a step in the experiment. I walked around the lab, going from group to group, and asked questions about why they were conducting the experiment and how it could be improved. This made the students feel more comfortable asking questions and participating actively in the class.

Developing Critical Thinking Skills

As a student, one of the best courses I attended was a Darwinian Medicine course at The University of Chicago. In this course the professor started each class/discussion by proposing an outstanding controversy in the field and encouraging students to suggest experiments that might prove or disprove the underlying hypotheses. The professor then introduced and guided us through literature on the topic by presenting papers in which our proposed experiments had been applied already and discussing the results. When applicable, she also mentioned published criticisms of the original papers, in an effort to recreate the controversy in the field and continually challenge us to present new ideas and suggestions.

Even though we usually started with naïve experimental designs, by the end of the lesson the entire class was engaged in a hot debate and the proposed experiments, now with several controls, reached a much higher level. By withholding information about what had already been done in the field, the professor was able to lead the class through a series of improvements toward a sophisticated experimental design.

I want to implement this teaching strategy in my classroom. Besides encouraging participation from the students, it teaches an important concept of learning through mistakes. The students learn that even papers in important journals can be challenged. This eases the students' fears of being wrong, thus building self-confidence, and teaching them to critically assess what they read.

Teaching for the Real World

Some of the classes I taught in Brazil were for students majoring in Chemistry Education, future high school teachers. High schools in Brazil usually do not have access to sophisticated labs, so whenever possible I introduced experiments that used a very simple list of reagents and equipment that would be available to everyone.

The possibility of using the experiments in their future classrooms increased the participation of the students and led to very good and unexpected feedback. During the semester my students started to propose new experiments that they could implement in their classrooms, and when possible these new experiments were discussed in class.

One of my objectives is to adapt my classes to the practical requirements of the students, whether they intend to stay in academia as a researcher or to compete in the job market outside academia.

Teaching Professional Skills

Another fundamental component to teaching in a higher education institution is to guide young scientists in their first steps toward developing their own research abilities. A capacity to improvise, to think independently, to work hard, to speak publicly, and to problem-solve efficiently, among others, are components of scientific research that are also some of the valuable talents most employers seek out.

In my career, I have had the opportunity to co-supervise several undergraduate students. Upon the conclusion of their research, three of these students published papers (as authors or co-authors) which gave them the feeling of a job well done and the satisfaction of seeing their work in print.

Teaching for Minorities

Science is an area that is still very restricted for minority students. I come from a city in one of the poorest areas of Brazil, a country not very advanced in biological research. However, through hard work I am now a post-doctoral fellow at Princeton University, one of the most respected research centers in the world.

I would like for my personal experiences and teaching to serve as an example for minority students, and to show that even when you start with some disadvantages if you work hard you can achieve your goals.

Example Syllabus for a Bioinformatics Course and Other Proposed Activities

Below is an example syllabus for an introductory bioinformatics course I would like to teach. Besides this introductory level course, I would like to offer other advanced courses with emphasis on large scale (genomic/proteomic) sequence analysis and the development of bioinformatics tools using the Perl programming language.

As a further means to encourage and interact with students, I would also like to lead a non-credit book club; students would read some popular science books, like The Selfish Gene, Wonderful Life, Why We Get Sick: The New Science of Darwinian Medicine, and we would discuss them over lunch either on a weekly or bi-weekly basis.

More details about the courses I taught and the students I co-supervised can be found in my Curriculum vitae.

Proposed Syllabus for a Bioinformatics Course

Introduction to Bioinformatics

Course Description In this course we will address several methods available to analyze biological sequence data. We will discuss the methodology and algorithms implemented in many freely available software tools, but the emphasis will be on understanding their applications to solve specific biological problems. Each class will be followed by a practical computer lab session in which we will be able to use the software discussed in the theoretical session.

Course Objective – By the end of the semester the students should be able to analyze their own sequences by determining what questions they want to ask, deciding which software to use to perform the analyses, installing and using the necessary software, and interpreting the results.


Proposed Textbook: Bioinformatics Sequence and Genome Analysis, David W. Mount.

The names in parenthesis following topics are the software programs that will be used in the laboratory.

I. Introduction:

  • About the course; what to expect or not; why bioinformatics; historical Introduction.
  • Biological Databases: Sequence Databases and Special Databases. Nucleic Acids Research Database Issue and Molecular Biology Database on-line compilation.
  • Sequence file formats. Depositing sequences in GenBank (Sequin).
  • Picking up the right method: The ROC (Receiver Operating Characteristic) curve.

II. Sequence Analysis:

  • Why compare biological sequences?
  • Nucleic Acid Sequence Analysis: Open reading frames and statistical methods for detecting coding regions (ORFER and the Staden Package).
  • Sequence Alignment: Dot matrix methods, Global and Local Alignments (Jdotter, BioEdit, Smith-Waterman Implementations).
  • Sequence Alignment: Multiple sequence alignments (Clustal, tcoffee).
  • Substitution Matrices: PAM, BLOSUM, and beyond

III. Finding a needle in a haystack: Database Searches:

  • Why sequences databases are so important and what can we learn from them?
  • Sequence Homology (BLAST, FASTA)
  • Motifs and profiles: Hidden Markov Models
  • Pattern Search (Psi-BLAST, HMMER, PFAM)

IV. Phylogenetic Reconstruction:

  • Why construct trees? Introduction to phylogenetic trees.
  • Tree building methods (Phylip, Mega3, TreeView, Clustal)
  • Trees X Networks (SplitsTree, Spectronet)
  • Calculating distances between nucleotide sequences (PAML)

V. Gene Prediction:

  • What to do if your sequence has no known homologues?
  • Protein coding region prediction (Glimmer)
  • tRNA prediction (tRNA scan)
  • Promoter analysis (MEME)

VI. Structural Methods:

  • Why study structures?
  • Protein Secondary Structure Prediction
  • Protein Threading (DeepView, CN3D)
  • RNA secondary structure prediction (RNA Structure)
  • Structural Alignment of Proteins and Nucleic Acids

VII. Advanced Topics:

  • How to analyze large scale datasets?
  • Genomics and Proteomics
  • Comparative and Functional Genomics
  • Gene Expression Analysis: SAGE, Microarrays
  • Microarray Analysis (Cluster, Treeview)
  • Gene Ontology: The GO Database Project
  • "Perl: the Programming Language of Bioinformatics", or "Shameless Advertisement for the Advanced Bioinformatics Course" ( Reading: "How Perl Saved the Human Genome Project", by Lincoln Stein)