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calmchessplayer
 3 years ago
Best ResponseYou've already chosen the best response.2well human growth rate is calculated by adding 1 year for ever 12 month calander so 1+1 <<<<<smartahole????

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0How about bacterial growth rate?

calmchessplayer
 3 years ago
Best ResponseYou've already chosen the best response.2well thats depends how quickly it incubates i guess

calmchessplayer
 3 years ago
Best ResponseYou've already chosen the best response.2look up bactierial incubation rate

calmchessplayer
 3 years ago
Best ResponseYou've already chosen the best response.2thats my best guess

blues
 3 years ago
Best ResponseYou've already chosen the best response.3First, I define growth as changes in cell number instead of cell size per unit time. To measure it, I'd pulse the growing organism with EdU or BrDU to label newly synthesised DNA. Then I'd measure the fluorescence intensity and compare it intensity of DAPI to calculate the mitotic status of each cell and build a differential model from that. Not a 9th grade answer.

calmchessplayer
 3 years ago
Best ResponseYou've already chosen the best response.2thats 9th grade?

calmchessplayer
 3 years ago
Best ResponseYou've already chosen the best response.2I only passed the 7th grade sorta

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0Good for you Calmchessplayer. I am impressed.

Schleifspur
 3 years ago
Best ResponseYou've already chosen the best response.0well, I AM impressed by blues. :D

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0Blues. That is an impressive answer. Thanks

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0So #cells (time t=t2) #cells (time t=t1)/(t2t1)

blues
 3 years ago
Best ResponseYou've already chosen the best response.3When did this, I built a differential model for cells cycling through multiple rounds of cell division. Then I extended the model put forward by Kimmel and Axelrood (1991) to get a discrete model. I found it a good read, worth it if you're doing work in the field. The purpose of doing this was to computationally identify a population of latent stem cells and to figure out what stimulated them. Is this for your real work  I assume you're a professional scientist  or general info?

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0Blues, this is for my own edification. So I figure you are an undergrad ? Grad student. This is impressive. Thanks for the info. Thanks for helping out on OpenStudy.

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0I am a scientist  a chemist. Now a days I try to get more peeps to stay in science!

blues
 3 years ago
Best ResponseYou've already chosen the best response.3I'm neither an undergraduate nor a graduate student. I am a university dropout, technically, but I am also good enough that I got a job as a "post doc." I specialize in NMR, protein structure determination and molecular dynamics, but I moonlight in other fields like signals processing, gene circuitry, computational ecology and anywhere I can use my Fourier skills. And on OpenStudy, which I think is the best thing since sliced bread.

blues
 3 years ago
Best ResponseYou've already chosen the best response.3What branch of chemistry are you in?

Preetha
 3 years ago
Best ResponseYou've already chosen the best response.0Blues, how cool. I trained as an NMR spectroscopist to do NMR of membrane glycolipods. Then did some protein NMR. Did some molecular dynamics. A lot of that was really in its infancy. So glad you like OpenStudy. I may call on you to give us a little publicity. Thanks for responding.

blues
 3 years ago
Best ResponseYou've already chosen the best response.3A while ago you asked about measuring cell growth in live organisms and I suggested labelling with EdU and DAPI. You thought the growth model would be something like [v(t1)  v(t2)] / delta(t). That it should be that simple! The problem is, to get the labels into cells you have to kill them so you can only obtain values for one time point. You have to set up a differential system for subpopulations of cells (based on mitotic status) and solve it as a boundary value problem. The BVP the straight forward part. The complicated part is quantifying the amount of fluorescent label in each nucleus. The tissue surrounding the cell and solution surrounding the sample absorb, refract and convolve the observed fluorescence intensities. Beer Lambert works only for cuvettes. Additionally, the EdU and the DAPI fluorescence affect each other and it's difficult to discern signals from spatially close nuclei. Quantification requires a Fourier system. After that, using the fluorescence subpopulations to define the mitotic subpopulations is not straight forward either as cells go through successive rounds of commitment and division. In my paper, I validated my model (as well as my computational procedure) by decomposing these "fast dividing" and "slow dividing" subpopulations and proving that they do actually correspond to different cell types in my model organism, which required another decomposition (deconvolution, in this case, not Fourier) technique. As a footnote, note that you have to simultaneously measure the apoptotic rate as well as the mitotic and commitment rates to make inferences about overall growth of the organism. That I might have left you with a wrong impression has been buggering me so here's the full answer. In a nut shell.
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