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Reichl: 2/2 Opening Remarks

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Reichl: 2/2 Opening Remarks
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2015
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English

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Glucose milk sugars transition states biotechnology chromatographic growth metabolic batch Ammonium Bernsteinsäure pharmakokinetischen Vaccine cell Suspension flux Glutaminstoffwechsel Steadystate <Physiologie> power phase surface oil concentration dynamics batch slides media Zeitverschiebung Chemotherapie enzymes cell carriers calculations
Glucose growth metabolic media batch flow pharmakokinetischen cell old localization Suspension amino acids flux Glutaminstoffwechsel Steadystate <Physiologie> power Glucose phase growth concentration firm reduction processes conditions cultivation substrates Suspension Sauce ammonia
Steadystate <Physiologie> German Glucose rates Prolin batch batch chemical media Chemotherapie conditions flow cell cultivation old cell flux
Glucose Glutaminstoffwechsel German metabolic batch LCT man Hypobromite firm Ham Muraminsäure Zearalenone pharmakokinetischen cell old flux storage Glutaminstoffwechsel Acc Glucose growth batch chemical media glutamic acid conditions Tau protein function cultivation cell Streptomycin water waves Sauce orange
Glucose Glutaminstoffwechsel factors growth metabolic chemical specific firm model flux storage Glutaminstoffwechsel standards Glucose rates synthetic biology batch glutamic acid systems conditions Simulation Transdermales therapeutisches System cultivation cell Streptomycin Glutamin
so they come again for the afternoon session as I totally have a bit to reschedule this session I will give you a few slides to address the problem we have actually discussed on Monday and also in some of the discussions on about biology reproducibility in biology and a few thoughts about assumptions we make just to to stimulate some discussions so this is from our work I've mainly work with mammalian cells and it's just one to say very often if we do flux analysis we do steady-state assumptions right all of us do we people working with prokaryotes but also people to working with oil cards are doing this so you look considered gross with a lag faced and you consider the exponential phase and there is a transition phase in a stationary grow space and usually for metabolic flux analysis this stage here or this phase is considered stationary okay this is for a Terran cells which takes some time to attach to a surface and then grow if you're lucky you're working in mammalian cells with suspension cells here you have the option to go in through creamer start cultures so you can achieve a true steady state and to calculations then I would like to talk a bit on batch to batch variation because this was also discussed on Monday and let me start with these quasi-steady state assumption mdck cells is a dog cells doggedness else we use for influenza vaccine production if you look at gross pattern you have an increase in the cell numbers here and then is the stationary grows as they are confluent on a surface here in a micro carrier or energy flask so if it comes to this phase you this exponential phase in these cells there's an early exponential phase with true exponential growth but then soon the surface gets limited and they cannot grow exponentially anymore there's a transition here write a long tradition face in an intermediate phase than the stationary phase and a lot of people including myself we have to define quasi steady States if we want to do flux analysis and then usually we go into this face here now you see here this is the glucose concentration this is the glute glutamine ammonium and so on however this looks nice and in a lot of publications you reconsidered only the extracellular metabolite but if you look into the self the situation not as simple here in this phase there is a steep decrease in the glucose 6-phosphate concentration look at the succinate for instance here is an increase in its deep decrease this is dropping this is increasing and tropic again here the complex dynamic on my late so the steady state you can define based on X facilitate data and usually you take a rather large interval here you have chance to be a very in the cell the situation can be far really far from stationary yeah if you make assumption you're risking that if you take the wrong time window you're facing non steady states in the cell ok and then
torbert suspension sauce you can do continuous cultivations you grow the cells as exponential growth you switch here to the supply of medium to continuous cultivation conditions they readjust and here we did a glucose pass don't don't worry about this so you have an exponential phase and you have a steady state phase here and same here for the glucose and for the lactate concentration here first of all the locals is taken up the limiting substrate steady-state lactate accumulates and is washed out and also here steady state and now you can say I that's ideal so I take all of information here from the steady state about for metabolic flux analysis however if you do this you have to be
aware the research you get here for the continuous cultivation versus the exponential growth phase is not the same yeah in the continuous cultivation this a switch from high closed glucose to low glucose metabolism so it's a switch in metabolism reduction glutamine uptake ammonia release I can show you that drastic changes in amino acid metabolism so even if you achieve steady
state conditions and continuous cultivations and you do flux analysis be aware that an experiment in batch is not necessarily giving the same results as an experiment in continuous cultivation significant differences here and then we
have the initial problem if we want to reproduce experiments in mammalian cells for instance this is an experiment this is the second experiment the illusion rates almost the same and if you for
instance now analyze lactate uptake or glutamine glucose uptake you see what these cells achieve here is multiple steady States so it's very easy if you do my Meghan seller cultivations that even if you try to achieve a very very high reproducibility every single experiment you do you achieve multiple steady States so two different groups is a good chance that you get different results they can discuss forever this is known from our group its was done also in other groups also punished if I nopales for instance so in biology there's a good chance you get multiple steady States and the these multiple steady States don't make analysis easier
and then we have this problem in the reproducibility this is a very experiments we wanted to see is there a difference whether we cultivate sorrows in a stirred tank director or in a wave by director we did quite a few cultivations in still tank and also in wave by rector's this is all the accumulated data here and you see there is some variation actually is quite a lot of variation and we analyze the data and we we described a metabolic flux analysis approach to differentiate between these cultivation and then the reviewers we are not happy to say okay you have no clue how to handle mammalian cells and your experiments at just grab and then I say okay what can I do but then luckily Genentech publish this data here this is data for Megan sauce growing under I would say the best standardized conditions we have and the GMP conditions and here you see the same story has a large variation between the batches so this is a post all the same yeah the red ones are ones where you have a different steady-state so this these cells also they are supposed to behave as these cells that have a different metabolism and you see the large variation if you analyze biology and this indicates also the range you have to to cope with and then if you
look for batch to batch variations and we consider the essay errors errors we do in calculating these metabolic flux rates biological application and so on the oval errors here on the rates in our experiments go from 7.45 for here to glutamate 80.2 percent now so there's a significant error you do in calculating these flux rates and if you now compare
these patches we've done all in a similar growth rate here specific gross weight range we see their significant variations here the glucose in this experiment the rate is 203 this 290 in the wave but if you look for lactate for instance here it's 300 vs 600 this is supposed to be the same experiment in the same director and the same holds for glutamine is a factor of 2 between biological applications so you have to be extremely careful if you interpret metabolic flux analysis results so my summary here it would be this inter experimental variance for experiments performed under seemingly identical conditions however with different pre cultures is a major major contributor to variance the differences we see between these bioreactor systems are statically statistically not significant and insufficient to confirm differences between cultivation system even if they look large and finally if you have a limited set of experiments you how many experiments would you need to confirm a difference between both systems and if you do some statistics we can say for the data I've shown you we would need at least seven biologically independent experiments to separate the performance of this align in a stirred tank versus a bioreactor and tell me who is willing to do these number of experiments probably nobody the maximum number i see is three if I see is three at all so it's just the morning biology has a lot of variation it has multiple steady States and unless you do a lot of experiments probably you will not be able to see or to validate differences between cultivations okay now we switch to the first speaker which is eric whitacre know she will talk on synthetic biology for the production of high-value chemicals
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