Bimolecular Biochemical analysis involves the characterization of bimolecular within a sample using appropriate laboratory techniques. There are two principal approaches: 1 . Qualitative analysis – where a sample is analyzed to determine whether a biomedical is present or absent. As an example, a blood sample might be analyzed for a specific antibody or a bacterial cell might be probed for a nucleic acid sequence. 2. Quantitative analysis – where the quantity of a particular biomedical in a sample is determined, either as an amount (e. . As g, or mol) or in terms of a concentration in he sample (e. G. As VI, or mol/l). For example, a blood sample might be analyzed to determine its pH (-eggs [H+]), alcohol concentration in MGM/ml, or glucose concentration in mol/l. Your choice of approach will be determined by the purpose of the investigation and by the level of accuracy required. Major types of bimolecular that are assayed include amino acids, peptides, proteins, carbohydrates, sugars, and nucleic acids.

Using a Standard Curve in Quantitative Analysis There are many instances in biological research where it is necessary to accurately measure the quantity of an analyze (test substance) in a sample. The analyze is detected and quantified by measuring a ‘signal’ (measured response). In some instances, you can measure a signal due to an inherent property of the analyze, e. G. The absorption of UP light by nucleic acids, whereas in other cases you will need to react it with another substance to measure a signal. The signal is usually color, fluorescence, or radioactivity, which can be easily detected using the appropriate procedure and equipment.

In order to quantify the amount or concentration of an analyze in a sample, a standard curve (calibration curve) is usually generated. The standard curve is a ARPA that established the relationship between signal intensity and amount or concentration of the analyze. A standard curve is generated by preparing a set of solutions (termed standards), each containing either (I) a known amount or (it) a specific concentration of the analyze, and then measuring the signal of each standard solution. There are various types of standard curve.

Some different relationships are illustrated in Fig. 1. In the simplest cases, the relationship between signal and analyze will be linear, or nearly so, and the standard curve will be represented best by a straight-line graph (Fig. A. ). In some instances, you will need to transform either the x or y values in order to produce a linear graph (e. G. Square of the values, log of the values, etc. ). In other instances, the straight-line relationship may only hold up to a certain value (the linear dynamic range) and beyond this point the graph may curve (Fig. B. ). Some curves are sigmoid (Fig. 1 c. ). Finally, the relationship might be linear, curvilinear, or sigmoid but the signal decreases in response to an increase in the analyze giving rise to an inverse curve. An inverse linear curve is shown in Fig. 1 d. Typically, no matter what type of standard curve, the linear region of the curve is used to determine the relationship and considered useful to determine the amount or concentration of an analyze in an unknown sample.

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For example, when the signal is color (colorimetric assay) and a spectrophotometer is used to measure the signal as an absorbency value (quantitative spectrophotometer), the standard curve produced is curvilinear as shown in Fig. 1 b. However, the relationship between analyze and absorbency is only valid in the linear range. At high amounts or concentrations of an analyze, the graph curves and the relationship becomes invalid. The mathematical relationship of the linear region of the curve (y = a + box) is used to measure analyze amount or concentration in your test sample from an absorbency value.

Some Considerations when Using a Standard Curve Biochemical assays have a minimum amount or concentration of an analyze that can be detected at a particular confidence level. This minimum concentration is referred to as the detection limit. For a spectrophotometers assay, the smallest concentration in your standard curve that gives you a reliable absorbency value is your detection limit. An absorbency value of 0. 010 and above is reliable. An absorbency value below this threshold is not reliable. There is also an upper concentration of analyze where the assay becomes imprecise.

The range from the detection limit to the upper limit is the concentration range of the assay. The concentration range is determined by the linear range of the curve. Samples that contain the analyze under the detection limited must be concentrated (if possible) to detect the analyze or another technique must be used. Samples that contain the analyze above the concentration range must be diluted to get an accurate concentration value. The amount or concentration obtained for this diluted sample sing the standard curve is then multiplied by the magnitude of the dilution to obtain the actual amount or concentration in the original sample.

Normally, if the concentration of an analyze in a sample is unknown (I. E. No approximation), a standard curve is created with a large range of concentrations (large step interval) and the sample is assayed. If a high degree of accuracy is required, a second assay can be conducted Witt a standard curve using a smaller step interval centered around the concentration of the analyze obtained in the first assay. In addition, substances (other than the analyze) in your test sample might interfere tit the signal (e. G. Protein in blood serum samples). The interference can artificially increase or decrease the signal in your sample.

Proper ‘control’ samples can be employed to determine the extent of this artificial signal (background). In addition, a suspected interfering substance can be included in the standards used to generate your standard curve to cancel out the background. Dilutions A. Making a single dilution In many instances, it is necessary in biological research to prepare a dilution of a stock solution to give a particular mass or molar concentration or to dilute a test ample with an unknown concentration of analyze to use with a particular standard curve. A dilution factor represents the magnitude of a dilution.

For example, a dilution of a test sample with a factor of 4 represents will make a sample 4 times less concentrated. In other words, your diluted preparation contains 1 part sample and 3 parts diluents. A dilution factor of 4 could be represented by any of the following: a XX (times) dilution, a 4-fold dilution, a 1 dilution, or a 0. 25 dilution. Suppose you want to make 100 ml of a solution that was XX diluted compared to a stock solution: 1 . What volume of the concentrated stock do I add? Divide the total volume of your desired diluted solution by the dilution factor. 00 ran/4=25 ml This is the volume of the concentrated stock solution to add. 2. What volume of diluents (solvent used to dilute) do I use? Subtract the volume calculated in 1. From the total volume of the diluted solution. 100 ml – 25 ml = 75 ml This is the volume of the diluents to add. 3. Mix well to ensure a homogeneous solution. B. Preparing a dilution series Dilution series are used in a wide range of procedures, including the preparation of standards to generate a standard curve for quantifying bimolecular. We will create dilution series of a glucose solution to prepare our standard curve in Lab 1 .

Figure 2 illustrates a dilution series created by serial dilution where each sample is sequentially diluted by the same proportion (the step interval). In this case the step interval is 102 or 100. Three diluted preparations of the test sample are created: 1 :OHIO, and dilutions. A serial dilution allows you to create a series tot dilutions wit another procedure. EXERCISES n a wide range tot concentrations tort your sat naiad curve or These exercises are based on Lab 1. In this lab, we will use a biochemical assay to assure glucose in serum samples taken from an individual during an oral glucose tolerance test (GOAT).

The test determines how quickly glucose is cleared from the blood and can be used to test for diabetes mellitus and other conditions. The glucose standards or test serums are treated with GOD-POD reagent to develop a red color (signal) that directly correlates with the level of glucose in the sample. The signal is then measured by quantitative spectrophotometer (510 NM). Hand in a printed copy of your graphs and answers to the questions. N. B. Include the question with your answers and answer the questions using complete sentences. . Generate the standard curve in Microsoft Excel for the following data.

What type of standard curve did the data generate? Hint: Not sigmoid. Do not include the concentrations below the detection limit of the assay when estimating the shape of the standard curve. Answer: Curvilinear 3. Play around with the graph by taking some of the extreme values out until you get the best linear relationship between glucose concentration and absorbency. However, you must try to include as many data points as possible! The more data points you include, the more useful your standard curve will be. You can gage linear it by getting Excel to carry out linear regression to produce a linear trend line and determine the RE value.

The closer the value is to 1. 00, the better the linear fit. Include the equation (y = a + box) of the linear relationship and RE value in your graph. Use the following figure legend for the graph: Figure 1 b. Standard Curve. Relationship between absorbency values and glucose concentration for glucose standards treated with GOD-POD reagent. The graph represents the linear range of the standard curve. 4. What is the detection limit for glucose in the GOD-POD reagent method? (Hint: What is the threshold for a valid absorbency value? 5.


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