Course philosophy¶
The Computational Biology course explores how to gain insight into biological phenomena with computational methodologies. My teaching philosophy that guides this course is below.
Critical thinking is paramount¶
Critical thinking and problem-solving are essential in education, especially in computational biology. My teaching approach is centered around engaging students in real-world scenarios and challenges. This requires them to apply, analyze, and synthesize information and helps them understand the practical application of computational biology, moving away from rote memorization. This approach fosters a deep understanding of the subject matter, encouraging students to explore the complexities and interconnectedness of biological systems through computational methods. By encouraging inquiry, debate, and collaboration, I aim to equip students with the skills necessary to navigate and contribute to the ever-evolving landscape of computational biology. The goal is not just to impart knowledge but to cultivate innovative thinkers and problem solvers who are prepared to tackle the challenges of the future.
Unlike traditional biology courses such as foundations of biology or biochemistry, this computational biology class stands out with its unique learning approach, resembling a computer science course. While the problems and concepts explored may have roots in similar biological foundations, the course's computational nature demands a distinct set of skills and mindsets. As part of this course, students will be expected to take the initiative in their learning and problem-solving. They will encounter open-ended challenges that transcend the boundaries of a single discipline, fostering their ability to think critically and independently. This course aims to foster a spirit of independent inquiry and adaptability, preparing students to thrive in the multidisciplinary and rapidly evolving field of computational biology.
Info
In traditional biology courses, such as foundations of biology or biochemistry, students often learn through rote memorization of facts, concepts, and processes. For example, students might be asked to memorize the steps of DNA replication or the names and functions of various enzymes involved in cellular metabolism. While this knowledge is essential, it only sometimes challenges students to apply their understanding to novel situations or develop problem-solving skills.
In contrast, a computational biology course focuses on problem-based and open-ended learning, which is more akin to the approach in computer science courses. Students are presented with complex, real-world biological problems that require them to apply their knowledge innovatively and develop their own computational solutions. For instance, students might be asked to analyze large genomic datasets to identify patterns or anomalies, develop algorithms to predict protein structures, or create models to simulate the spread of infectious diseases.
These open-ended problems necessitate a deep understanding of biological concepts and the ability to translate that knowledge into computational frameworks. Students must think critically, break complex problems into manageable components, and develop creative solutions. They often need to engage in self-driven learning, exploring new computational tools and techniques to tackle the challenges.
A computational biology course emphasizes problem-based and open-ended learning, cultivating essential critical thinking, creativity, and adaptability skills. These skills prepare students for careers in computational biology and equip them with the tools to tackle the novel challenges they will face in any scientific field.
Learning happens outside your comfort zone¶
This computational biology course embraces the idea that authentic learning and growth often occur when one steps outside one's comfort zone. This approach is particularly relevant in a field that combines biology's complexities with computational methods' challenges. As you navigate this course, you will encounter concepts, problems, and methodologies that may initially seem daunting or unfamiliar. This is intentional. By pushing the boundaries of your knowledge and skills, you develop resilience, adaptability, and a deeper understanding of the subject matter.
Rest assured that your willingness to step outside your comfort zone will not negatively affect your grade. We have meticulously structured the course to support your growth while ensuring fair and transparent assessment methods.
- Most assignments employ a scaffolded or tiered approach, where more straightforward questions that test foundational knowledge are worth more points than the more challenging, advanced questions.
- Optional "stretch" questions or projects for those seeking extra challenges, with bonus points that can't negatively affect your grade.
These strategies are designed to create a nurturing and supportive learning environment where you can push your boundaries, take intellectual risks, and grow your skills in computational biology without fear of academic consequences.
Real-world scenarios enhance learning¶
As we delve into computational biology, I want to emphasize the practical relevance of each module. Instead of a traditional approach, we'll use motivating real-world scenarios that underscore the importance of the concepts you'll be learning. Consider yourself a problem solver in a scientific expedition, applying computational tools to tackle actual challenges faced in the field. Our focus will be on tangible applications, from predicting the impact of genetic variations on disease susceptibility to simulating the dynamics of biological systems under different conditions. This approach ensures that what you learn in this course is not confined to theoretical frameworks but has direct implications for understanding and addressing complex biological phenomena.
Connecting you to opportunities¶
Embarking on a journey in computational biology is most effectively achieved by actively participating in a research lab at Pitt. This hands-on experience enriches your understanding and provides a practical immersion; computational biology research is becoming an expectation to remain competitive in today's job market. The modules in this course integrate seamlessly with this approach, offering direct links to labs that leverage tools relevant to the specific content covered. You should contact these labs to see if they have paid or for-credit research positions available.
Single source of truth¶
Each course should have a single source of truth (SSOT), which is having a single authoritative source of data and information across the course. Having an SSOT can help you access all the information related to the course in one place. SSOT can help reduce confusion and ensure everyone is on the same page. For this course, the SSOT is pitt-biosc1540-2024f.oasci.org.
Another benefit of having an SSOT website is that it can help keep the course content up-to-date and accurate. Your instructor can change the website as needed; you will always have access to the most current information. This can help ensure you learn the most relevant and up-to-date material. Finally, having an SSOT website can help improve communication between your instructor and the students. Your instructor can share important announcements, assignments, and other course-related information on the website. This can help ensure everyone is informed and up-to-date on what's happening in the course.
Everything is a draft¶
Open science and education are my core values and extend to my teaching. You will likely see me working on things I assign in an hour or next week. Teaching new courses or making significant changes always works like this. Thus, looking ahead is acceptable, and all drafts are marked with the following admonition. If you start working ahead and things change, your time is lost.
DRAFT
This page is a work in progress and is subject to change at any moment.
I will do my best to avoid mistakes, but they can happen. If you see grammar or spelling issues or need clarification on passages that don't have the DRAFT admonition, feel free to tell me so I can correct them.