Questions & explanations
1. What is the main goal of metabolic control analysis?
Metabolic control analysis (MCA) studies how each enzyme in a metabolic pathway controls the overall flux, which is the rate of material flow through the pathway. MCA uses control coefficients to measure how much a small change in enzyme activity affects the flux. For example, if one enzyme has a high control coefficient, changing its activity will greatly change the flux. This helps identify which enzyme is the best target to speed up or slow down a pathway. In contrast, flux balance analysis (FBA) is a computational method that predicts flux distribution without needing kinetic details. FBA uses stoichiometry, which is the balance of chemical reactions, and an objective function like maximizing growth. MCA is more detailed but requires more data, while FBA is simpler and used for genome-scale models.
2. Compare the information provided by TRRUST and ENCODE for studying human gene regulation.
TRRUST provides curated data on transcription factors and their target genes in humans and mice, focusing on direct regulatory relationships. ENCODE, on the other hand, is a large project that maps all functional elements in the human genome, including binding sites for many proteins. TRRUST is easier to use for finding known regulatory pairs, while ENCODE gives raw data like ChIP-seq peaks for many factors. For a specific transcription factor, TRRUST lists its validated targets, whereas ENCODE shows where that factor binds across the genome. Both are useful: TRRUST for confirmed interactions, ENCODE for discovering new potential targets. Researchers often combine both to build comprehensive networks.
3. Give an example where metabolic control analysis is more useful than flux balance analysis.
Metabolic control analysis (MCA) is more useful when you want to understand how a specific enzyme controls the flux in a small pathway. For example, in the production of a valuable chemical like lycopene, MCA can identify which enzyme step limits the overall production rate. By measuring control coefficients, you can decide to overexpress that enzyme to increase yield. Flux balance analysis (FBA) would not capture this because it assumes the cell optimizes growth, not product formation. MCA also helps in drug development by finding the enzyme with the highest control over a disease pathway. In contrast, FBA is better for genome-scale predictions of growth under different nutrient conditions.
4. How does the trp operon differ from the lac operon in its regulatory mechanism?
The trp operon controls genes for making tryptophan, an amino acid. Unlike the lac operon, which is induced by lactose, the trp operon is repressed by tryptophan. When tryptophan is plentiful, it binds to the repressor, which then binds to the operator and blocks transcription. This is called negative feedback. Also, the trp operon has an attenuation mechanism that fine-tunes expression based on tryptophan levels. In contrast, the lac operon uses an inducer to remove the repressor. Both operons are examples of bacteria adjusting gene expression to save energy. The trp operon is turned off when the product is abundant, while the lac operon is turned on when the substrate is available.
5. Compare the data requirements of MCA and FBA.
Metabolic control analysis (MCA) needs detailed kinetic data for each enzyme, such as Michaelis-Menten constants and enzyme concentrations. It also requires knowledge of the pathway structure and metabolite concentrations. This makes MCA data-intensive and only feasible for small, well-studied pathways. Flux balance analysis (FBA) only needs the stoichiometry of reactions, which is the balanced chemical equation, and constraints like uptake rates. It does not require kinetic parameters or enzyme amounts. FBA can be applied to genome-scale models with thousands of reactions. Therefore, FBA is much easier to use for large networks, but MCA gives more mechanistic insight into control.
6. How does flux balance analysis predict metabolic fluxes?
Flux balance analysis (FBA) uses a mathematical model of all reactions in a metabolic network. It assumes the cell is in a steady state, meaning the amount of each metabolite inside the cell does not change. FBA applies constraints like nutrient uptake rates and reaction stoichiometry, which is the ratio of reactants and products. Then it solves an optimization problem, for example maximizing biomass production, to find a set of fluxes that satisfy all constraints. This gives a predicted distribution of reaction rates. FBA is useful for studying metabolism in bacteria like E. coli without needing enzyme kinetic data. However, it does not account for regulation or dynamic changes.
7. Compare the use of transcriptomics and metabolomics in understanding diabetes complications.
Transcriptomics measures RNA changes in tissues affected by diabetes, like kidney or retina. For diabetic kidney disease, it might show increased RNA for inflammatory proteins. Metabolomics measures small molecules in blood or urine, such as advanced glycation end-products (AGEs) that form from high sugar. Transcriptomics reveals which genes are turned on to cause damage, while metabolomics shows the actual harmful compounds. For example, high glucose leads to AGEs (metabolomics), which then trigger inflammatory gene expression (transcriptomics). Both layers together explain how high sugar causes tissue damage, helping identify early markers and targets to prevent complications.
8. What is collateral cleavage and why is it important for detection?
Collateral cleavage is the non-specific cutting of nearby nucleic acids by an activated CRISPR nuclease. For example, after Cas12a binds to its target DNA, it cuts any single-stranded DNA in the vicinity, not just the target. This property is important for detection because you can add a reporter that is a single-stranded nucleic acid with a fluorescent dye and quencher. When the reporter is cut, fluorescence increases. This amplifies the signal because one target molecule activates many Cas12a proteins, each cutting many reporters. This leads to high sensitivity, allowing detection of very few target molecules. Collateral cleavage is the basis of many CRISPR diagnostic methods.
9. How does sensitivity differ from specificity in biosensor calibration?
Sensitivity is how much the sensor's output changes when the analyte concentration changes by a small amount. A highly sensitive sensor gives a large signal change for a tiny concentration change. Specificity is the ability to detect only the target analyte and not other similar molecules. For example, a glucose sensor should not respond to fructose. A sensor can be very sensitive but not specific if it responds to many substances. Calibration involves testing the sensor with known concentrations to determine its sensitivity (slope of the calibration curve) and checking for cross-reactivity with interferents to ensure specificity. Both are important for accurate measurements.
10. Give an example of how you would calibrate a biosensor for glucose in blood.
To calibrate a glucose biosensor, first prepare several standard solutions with known glucose concentrations, like 0, 1, 5, 10, and 20 mM. Then measure the sensor's output, such as current or fluorescence, for each standard. Plot the output against glucose concentration to get a calibration curve. The slope of this curve is the sensitivity. Also test the sensor with solutions containing similar sugars like fructose or lactose to check specificity. If the sensor responds to them, it lacks specificity. The dynamic range is the concentration range where the curve is linear. For a blood glucose sensor, the dynamic range should cover 2-20 mM. Regular calibration ensures accuracy.
11. Compare how cis-regulatory modules integrate signals from activators and repressors.
Cis-regulatory modules contain binding sites for both activators and repressors. The final gene expression depends on the balance of these factors. For example, if an activator binds, it can recruit RNA polymerase to start transcription. But if a repressor binds nearby, it can block that recruitment. Some modules have multiple activator sites that work together to increase expression. Others have repressor sites that can override activators. The integration happens through protein-protein interactions and competition for DNA binding. This allows the cell to combine multiple signals into a single output. It is a fundamental mechanism for making decisions during development.
12. Compare toehold switches and RNA aptamers for sensing.
Both toehold switches and RNA aptamers are RNA-based sensors, but they work differently. Toehold switches detect RNA triggers by changing the structure of mRNA to control translation. They are used to detect specific RNA sequences and can be easily designed for new targets. RNA aptamers bind to a wide range of targets, including small molecules, proteins, and ions, and they produce a signal through a conformational change. Aptamers require a separate reporter system, while toehold switches directly control gene expression. Toehold switches are typically used in cell-free systems, whereas aptamers can be used in cells or in vitro. Both are highly specific and programmable.