Computational Biology (BIOSC 1540)

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Welcome to my computational biology course. This page gives you a quick overview: what the course covers, why I teach it this way, and where to find more details. You can find all the rules and their reasons in the syllabus. If you have any questions, comments, concerns, or feedback, let me know. You can email me at alex.maldonado@pitt.edu.

What the course covers

Modern biologists rely on tools that align genomes, model proteins, and screen drug candidates. This course won’t teach you to run those tools. It will teach you why they work and when to trust them.

We move through four modules, each capped by an exam: search and similarity (sequence alignment, phylogenetics, read mapping), signal detection (gene finding, variant calling, differential expression), physical modeling (molecular dynamics, protein structure prediction), and cheminformatics (molecular descriptors, docking, drug discovery).

This is an introductory course, so we trade depth for breadth: you’ll see the shape of the whole field instead of mastering one corner of it. There’s no programming prerequisite, so we stay at the level of concepts rather than code. If a piece of this grabs you, Computational Genomics (BIOSC 1542) and Simulation and Modeling (BIOSC 1544) go deeper into specific domains, and Computational Biology Research (BIOSC 1640) picks up the programming this course skips.

How I teach it

I approach the material like a physicist or an engineer: first principles, systems, and high expectations. My job is to help you turn a messy biological problem into a solvable one, not to hand you a set of tools to run on faith. I hold the bar high because the field does, but I don’t hold it high and walk away. I ask for quick, anonymous feedback during the term, and if most of you are lost, I adjust how I teach. The material stays just as demanding either way. Expect dry humor and a straightforward lecture style. I’ll say when I don’t know something, and I’ll say when I got something wrong. A mistake, yours or mine, is information to work with, not a verdict.

How the course is built

I see you as a junior colleague, not just a name on a list. I run the course the way I run a workplace: with high expectations, steady support, and clear rules. Every exam follows a blueprint that I share with you in advance. This way, you know exactly how each part is weighted, from simple recall to open-ended reasoning, before you take the test. Homework assignments are graded based on completion, since their purpose is to help you practice. The exam is where accuracy matters most. When it comes to generative AI, I’ve shared my thoughts instead of making a strict rule, since no one really knows yet how these tools affect learning.

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