BabcockHall

Have you covered dilution calculations in class?

Could you be releasing oxygen just on the basis of its being less soluble at high temperature than low (as is typical for gases)? In other words, could this result be an artifact?

Do you think that this reaction will proceed via a SN2-type mechanism or another mechanism?

You must provide your thoughts before we can help you. I would start by defining or explaining what an autosomal dominant trait is.

Is this a homework problem, a research problem, or something else? Would you please provide your thoughts first? That is the general rule at this discussion board.

Do you know the pKa of the conjugate acid of a typical sulfide?

I would also post this at Chemical Forums. I do not recall seeing anything like this.

A good place to start is by giving us your thoughts. What sort of scenarios do you have in mind?

Is this homework?

As one raises pH, transphosphorylation becomes more of an issue. What happens at low pH, loss (or perhaps epimerization) of glycosidic bonds?

Did you store frozen, at refrigerator temperature, or at room temperature? From your initial post, it sounds as if you stored it frozen. My guess is that this is better than leaving it at room temperature, but I might have quick frozen the sample and stored it at -80 °C unless I had a better protocol. I cannot speak to the specific question of the best way to store RNA, but I can make some very general comments about freezing. One is that the pH of a buffer solution can change as the solution cools, and RNA is labile in basic conditions. Two is that there are shearing forces during the freezing process which can cause problems in some instances. Therefore, I would look into the use of cryoprotectants and fast freezing protocols for RNA. I do not have an explanation for the mass that you are seeing.

The equilibrium constant for aspartokinase is not particularly favorable, but it is heavily regulated in E. coli.

When delta-G is less than zero, a reaction is spontaneous, and when delta-G = 0, a reaction is at equilibrium. When delta-G° is negative, a chemical reaction has an equilibrium constant that is greater than one. This symbol, delta-G°, refers to standard state, meaning all reactants and products at 1 M in concentration. The two quantities do not provide the same information, and their values are often not the same number. That having been said, the in vivo values of delta-G are more difficult to obtain because one has to know the concentrations of reactants and products in vivo. If they are not available, one has no choice but to use delta-G° values in one's arguments. I think that "always" is a high bar, meaning that there could well be exceptions to any generalization. I would start by taking regulatory enzymes from glycolysis and gluconeogenesis (hexokinase, PFK-1, pyruvate kinase, pyruvate carboxylase, PEPCK, fructose 1,6-bisphosphatase, and glucose 6-phosphatase) and finding the values of Gibbs' free energy in a biochemistry textbook. Nelson and Cox or Metzer are my go-to sources, typically.

Is this a homework problem? It is up to you to provide your answer (or at least make an attempt) before we help you. A good place to start on this problem is to give a few examples. It might also help to differentiate between delta-G and delta-G°'.

What are your thoughts on these two questions? I'll give you a frame of reference for your first question: consider what would happen chemically to convert chymotrypsinogen into chymotrypsin. With respect to your second question, can you think of any reactions that require NADH as a reactant? EDT Think about reactions from glycolysis/gluconeogenesis that produce or consume NADH. Also there are at least three common fates of pyruvate that has been produced from glycolysis. Do any of these processes involve NADH?

I now have a copy of Seber and Wild's book from the library, but it is written at a level which is not easy for me to follow. In the section that covers confidence intervals. they do not use the nomenclature that the ProStat manual does. In other words I cannot find terms like "supporting plane" and "univariate" in this book.

@OP, This is just a guess, but were you asking because of an interest in mitochondrial depletion syndrome, which has been in the news recently? It seems that some forms of this disease are related to mutations in thymidine kinase 2 of mitochondria. This enzyme has a role in the salvage of deoxynucleotides, but I have not yet found a good review article.

What is the simplest chiral alcohol that you can think of? How can one imagine linking an alcohol to an amino acid (which functional group on the amino acid would you chose)/

Is this homework? What are your thoughts about how to do this?

Of course insulin also brings about homeostasis by down-regulating the pathways in the liver that produce glucose: gluconeogenesis and glycogenolysis. Insulin stimulates the production of acetyl CoA from glucose. There is also interplay among leptin, NPY, and insulin, but I am not very familiar with it.

Motulsky and Christopoulos Presented and compared three methods of generating confidence intervals: Asymptotic (Chapter 16), Monte Carlo (Chapter 17), and Model Comparison (F ratio or F test, Chapter 18). In chapter 19 they compare all three methods. Only the asymptotic method gives a symmetrical distribution.

Here are the books or articles that I have consulted or seen referenced in the ProStat manual: Seber GAF and Wild CJ, Nonlinear Regression J. Wiley and Sons (1989) Motulsky H and Christopoulos A, Fitting Models to Biological Data Using Linear and Nonlinear Regression, Oxford University Press, 2004 Motulsky H, Intuitive Biostatistics, 3rd ed., Oxford University Press (2014) Motulsky H and Ransnas , "Fitting curves to data using nonlinear regression: a practical and non mathematical review." FASEB J. (1987) (PMID: 3315805) At least some parts of Seber and Wild's book are online. Confidence intervals are discussed on p. 192 and p. 235, which is the chapter on Statistical Inference. BestFit - t*•SE) to BestFit + t*•SE (p. 103, Motulsky and Christopoulos).

Thank you; I am already understanding it a bit better. I am attaching a page from the ProStat Manual. I have not yet looked into whether or not the two books I mentioned are available on-line. However, I can type out a paragraph or so from each. I already wrote a Word document for myself to summarize some of this information, and I type out some quotes from the books into that (yeah, pretty old-school). ProStat_Manual65_p180.pdf

Hello Everyone, I regularly use linear and nonlinear regression. However, I realized recently that I don't know much about confidence intervals. After consulting several books and the ProStat manual, I found myself stuck on why ProStat calculates two values for the 95% confidence values. One is "Uninvariant," and the other is "supporting plane." The supporting plane limits are greater than the uninvariant ones in the examples from the manual. The manual says that when something called k = p (the number of parameters) we get the support plane CI's, and when k = 1, we get the uninvariant CI's. It is unclear from my reading of the manual what k is. The manual provides a formula and a citation to Seber (1989), and the formula has a function F that is mysterious. I have also consulted Motulsky and Christopoulos's book. Their calculation of the CI of a parameter is BestFit ± (t*)•SE, where SE is the standard error. I don't see how one could generate two values of t*•SE that might correspond to the two generated by ProStat. Does anyone have any thoughts?

It is easy to judge the progress of a protein purification by SDS PAGE, but I don't view mass spectrometry as a good tool for that application.