BMS5021 · Introduction to Bioinformatics
Multi-Omics & Systems Biology
Week 11 of Monash University BMS5021 (a guest lecture by A/Prof Lan Nguyen) puts the layers together: integrating genomic, transcriptomic and proteomic data, biological networks and protein-protein-interaction maps, and a systems view of the cell. The message is that no single omic layer gives the full picture, and that integrating evidence raises confidence and reveals complete regulatory models. This reasoning underpins how you interpret and contextualise the Topic 3 proteomics results in your report.
What this chapter covers
- 01Cancer can be characterised at multiple omic levels: genome, epigenome, transcriptome, proteome, metabolome - no single layer gives the full picture
- 02Systems biology (Leroy Hood, 1999): studying complex processes as integrated systems - 'the whole is more than the sum of its parts'
- 03Detecting each layer: epigenome (ChIP-seq, WGBS), transcriptome (RNA-seq), proteome (MS-based proteomics + PTM/phospho states)
- 04Benefits of integration: (1) compensate for missing/unreliable single-omic data, (2) converging evidence lowers false positives, (3) reveal the complete model
- 05Integration timing: early (concatenation) / middle / late integration; methods = matrix factorisation, Bayesian, network-based, correlation
- 06Integration tools named in the unit: mixOmics (PCA/PLS/DIABLO/MINT), iCluster, MOFA, SNF
- 07Protein-protein-interaction networks: genes as nodes, interactions as edges (interactomes)
- 08Applications: cancer subtyping, biomarker discovery, drug-response prediction, precision medicine
Why integrate multiple omics layers? (Week 11 reasoning)
- +1State the benefits. Integrating layers (i) compensates for missing or unreliable data in any single omic - if a signal is weak or absent in the transcriptome it may be clear in the proteome; and (ii) provides CONVERGING evidence, so a change supported by both RNA and protein is far less likely to be a false positive, raising confidence. (A third benefit is revealing the complete biological model across regulation levels.)
- +1Interpret the systems-biology principle. 'The whole is more than the sum of its parts' (systems biology, Leroy Hood, 1999) means a cell's behaviour emerges from the INTERACTIONS between its components, not from any one molecule in isolation. Studying genes, transcripts and proteins together as an interacting system reveals regulation - e.g. where transcript and protein disagree because of post-transcriptional control - that no single layer shows.
- +1Distinguish integration timing. EARLY integration concatenates the different omic datasets into one combined matrix and analyses them together from the start; LATE integration analyses each omic layer separately and then combines the results at the end. (Middle integration transforms each layer into a shared representation before combining.)
Key terms
- Multi-omics
- Combining data from more than one molecular layer - genome, epigenome, transcriptome, proteome, metabolome - to characterise a biological system more completely than any single layer allows.
- Systems biology
- The study of complex biological processes as integrated systems of many interacting components (Leroy Hood, 1999); its guiding idea is that 'the whole is more than the sum of its parts'.
- Early / middle / late integration
- Points at which omic layers are combined: early concatenates raw datasets into one matrix, middle transforms each into a shared representation first, and late analyses each layer separately then merges the results.
- Protein-protein-interaction (PPI) network / interactome
- A network in which genes/proteins are nodes and physical interactions are edges; mapping the interactome shows how proteins work together and is central to systems biology.
- Benefits of integration
- The three taught advantages: compensating for missing/unreliable single-omic data, lowering false positives through converging evidence, and revealing the complete regulatory model across levels.
- Integration tools
- Software named in the unit for combining omics: mixOmics (PCA/PLS/DIABLO/MINT), iCluster, MOFA and SNF, using matrix-factorisation, Bayesian or network-based approaches.
Multi-Omics & Systems Biology FAQ
How is Week 11 assessed?
Week 11 is a guest lecture whose reasoning underpins how you interpret and contextualise the Topic 3 proteomics report (35%). You are not usually asked to run a full multi-omics integration, but you should be able to explain WHY integration matters and situate your proteomic findings in a systems-biology frame. Confirm the exact expectations for the Topic 3 report on Moodle.
Why isn't one omic layer enough?
Because each layer captures only part of the regulation. The genome is fixed, the transcriptome shows what is being transcribed, and the proteome shows the functional molecules - and these often disagree because of post-transcriptional and post-translational control. No single layer gives the full picture, so integrating them reveals regulation you would otherwise miss and gives converging evidence that reduces false positives.
What does integration actually improve?
Three things the lecture stresses: it fills gaps when one omic layer is missing or unreliable; it raises confidence because a finding supported by several layers is much less likely to be a false positive; and it builds a more complete model of the biology across regulation levels. In cancer this feeds subtyping, biomarker discovery and drug-response prediction. The trade-off is added complexity and the need for appropriate integration methods.
Can AI help me with systems biology and multi-omics?
Yes. Sia can explain the benefits of integration, the early/middle/late distinction, and what a protein-protein-interaction network represents, and it can help you frame your Topic 3 proteomics results in a systems-biology context for the report. It explains and checks your reasoning; it does not write your graded report, and Monash University academic-integrity rules apply.
Exam move
Treat Week 11 as conceptual scaffolding for your Topic 3 interpretation rather than another calculation to learn. Be able to state, crisply, the three benefits of integration (fill missing data, lower false positives via converging evidence, complete model) and the systems-biology principle that the whole exceeds the sum of its parts. Keep the early/middle/late integration distinction and the PPI-network idea (nodes = genes, edges = interactions) ready. When you write the Topic 3 report, use this frame to contextualise your proteomic findings - what would other layers add, and how confident is a single-layer result? Ask Sia to quiz you on the integration concepts and to check how you situate your results. Confirm report expectations on Moodle.
Working through Multi-Omics & Systems Biology in BMS5021? Sia is AskSia’s AI Biology tutor — ask any BMS5021 Multi-Omics & Systems Biology question and get a clear, step-by-step explanation grounded in how BMS5021 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
