How to Integrate Biology and Computational Knowledge

Most genomics pipelines move in a straight line. FASTQ → QC → Alignment → DEGs → Plot → Done. But biology doesn't work like that. One of the most underused tools in bioinformatics? Systems Thinking. Instead of just analyzing mutations or differentially expressed genes in isolation, ask: 1)-> How does this change affect the larger biological system? 2) -> What signaling pathway does this gene influence? 3) -> How does that impact immune response or cell cycle regulation? 4) -> What expression ripple effects happen elsewhere in the genome? Because cancer isn't caused by one mutation. And resistance doesn't emerge from a single gene. It's not a list of hits..... it's a network of effects. What's actually in a systems network? >> Core Elements: Genes, transcripts, and regulatory sequences • Proteins (enzymes, transcription factors, signaling molecules) • Metabolites and small molecules >> Relationship Types: • Regulatory interactions (gene → protein, TF → target) • Physical interactions (protein binding, enzyme reactions) • Functional relationships (pathway membership, co-expression) >> Biological Context: • Metabolic pathways (glycolysis, TCA cycle) • Signaling cascades (p53, Wnt, MAPK) • Cellular processes (cell cycle, apoptosis) • Spatial organization (nucleus, mitochondria, membrane) • Temporal dynamics (expression timing, feedback loops) >> Data Integration: • Genomics + Transcriptomics + Proteomics + Metabolomics + Epigenomics 💡 Systems thinking helps you: ✅ See beyond the "top 20" DEGs ✅ Connect molecular data to clinical outcomes ✅ Build biological insight, not just computational output #Bioinformatics #SystemsThinking #ComputationalBiology #CancerGenomics #NGS #PathwayAnalysis #Omics #PrecisionMedicine #ScientificThinking #ResearchMindset #NetworkBiology #SystemsBiology

Computational Biology is an interdisciplinary field comprising expertise in Computer Science, Biology and Math. I often see folks discount the importance of biological domain expertise, particularly in the advent of LLMs and Gen AI. Truthfully, I think the bio know-how is the most integral part. Say you want to build a model to predict drug targets (neoepitopes) from tumor scRNA-seq data. Here’s a short list of things you need to consider before writing a single line of code: 1. How many cells can my machine reliably handle? 2. How would using only a subset of the data impact cellular representation? 3. Am I concerned with predicting neoepitopes, or neoepitope immunogenicity? These are different goals, requiring different cell type representations. 4. How are neoepitopes formed? In other words, what biological phenomena in my data will I leverage to identify these cell surface proteins? 5. Back to #3, does my data have the requisite information (ex. transcript-level annotation vs gene-level annotation) to derive accurate predictions from? And many more. It’s easy to build models and run stats. Harder to discern whether the outputs are biologically meaningful. ________ #compbio #drugdiscovery #ml #singlecell #rna

the NIH recently announced a $50M program for funding multi-omic research. the goal is to support the aggregation and analysis of multi-modal biological data, such as genomics, epigenomics, transcriptomics, and proteomics. while this will be an impactful initiative, there remains an under-investigation more broadly of theoretical frameworks for multi-omic biology. specifically, how do different modalities of biological data interrelate? how do interactions across omic levels relate to function and disease? and, most critically, what is the optimal level of analysis for a given biological question? computational neuroscientist david marr famously posited that information processing systems, like the brain, ought to be analyzed at 3 distinct levels: the computational level (defining the problem the system addresses), the algorithmic level (outlining high-level processes to solve that problem), and the implementation level (the physical mechanisms, such as neural circuits, that carry out these processes). marr's framework is a highly effective approach for understanding complex systems, and analogous theories should be adapted more generally across biology. disease results from dysfunction across multiple levels of biology, from genetic sequences to gene expression to cell, tissue, and organ morphology. by incorporating the overall functional goals of the system and characterizing how changes at each of these levels propagate across the system, we will a develop a more comprehensive understanding of biological function. this will result in a more structured approach to multi-omic research. it will help scientists determine the appropriate scale and modality of data to focus on for a particular biological question, and will maximize the potential of the field.