WEBVTT
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welcome to lecture twenty this is the nptel
online certification course on bioreactors
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in the previous two lectures we had looked
at bioreactions documentary as one of the
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means of getting some insights into what is
happening at the cell level in the bioreactors
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the cells in the bioreactor are the actual
factories we worked out of problem in the
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previous lecture now let us go forward with
the module itselfthe module five we are going
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to begin talking about windows to the cell
now we said we will first consider it is a
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black box the cell and then we will open up
windows and look through the windows to see
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what is happening inside the cell so the windows
to the cell are the indicators of cellular
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status of the cell or indicators of metabolic
status of the cell these terms cellular status
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metabolic status energy status and so on so
forth redox status and so on these are kind
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of soft terms you knowneed to you should not
take them um too strictly these are kinds
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of indications or what could be happening
and these are kind of common terms that are
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used they not off course fully understood
but they are a useful the cellular redox
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status is a well known indicator and cellular
energy status is another well known accepted
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indicator of the cellular or a metabolic status
of the cells the cellular redox status is
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typically derivedfrom n a d h you know n a
d h make it in a mean adenine dinucleotide
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and the hydrogenated form of that this conversion
takes place in various metabolic reactions
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or accompanied with various metabolic reactions
in the cell n a d plus plus h plus giving
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you n a d h so this has a reducing power which
is taken forward for so many different important
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functions in the cell so the redox ratio one
of the ways you looking at at least at n a
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d the ratio of n a d h concentration to the
total concentration of n a d plus and n a
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d h we were already seen that n a d h florescence
can be used to monitor the cell concentration
02:46.400 --> 02:51.260
because n a d [vocalized-noise] the the
intensity of n a d h florescence can be directly
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link to cell concentration here it is redox
ratio the n a d h divided by the total n a
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d h whole which is [vocalized-noise] consist
of n a d plus and n a d h ok this is a redox
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ratio similarly or in in some situations
the ratio of n a d h to flavin adenine dinucleotide
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and other important such molecule is also
take into be the redox ratiofor energy status
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is somewhat a little more straight forward
we all know the energy is made energy
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currency of the cell a t p adenosine triphosphate
is made by the phosphoral relation of adenosine
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diphosphate so the ratio of a t p to the pool
a t p plus ama d p sometimes a m p is also
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added here but let us just look at these two
a t p divided by a t p plus a d p is taken
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to indicate the cellular energy status so
lot of measurements are made on these correlated
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to the happenings inside the cells correlations
with respect to what is important from a bioreactor
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context and efforts are made so that uh these
indicators are varied in a particular
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fashion or kept constant at a particular known
value to achieve a desired performances by
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the cells in the bioreactor so that is one
more step towards understanding what is happening
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at a cellular status cellular level sometimes
even a t p by a d p is considered the cellular
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energy status there are various ways of defining
this at at this given you couple of them what
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one needs to understand is that very many
metabolic processes contribute the above ok
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so it is not a one to one kind of a relationships
so many things contribute to redox status
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so many things contribute to energy status
so one needs to have uh some level of uh
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you know interpretation one is interpret
this in that kind of fuzzy fashion its more
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of an indicator
you may want to look at this paper which is
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also been given in your notes has one of the
papers that you would want to look at let
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me direct that you this is number thirty n
a d h culture florescence uh the author of
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scheper gebauer and schuell this is the
paper for n a d h culture florescence the
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title of the paper itself is monitoring n
a d h dependent culture florescence during
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cultivation on e coli it was published in
chemical engineering a journal in nineteen
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eighty seven this is something that gives
you some relationship to the culture energy
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status let me do have a paper here yes
let me briefly tell you how this is done with
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energy with n a d h status to very briefly
read the abstract n a d p h dependent culture
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florescence was monitored during variousecoli
cultivation processes batch and continuous
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cultivation in stirred tank reactors or bubble
column reactors various strings of e coli
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including genetically modified e coli were
used measurement of florescence signal allows
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biomass estimation under certain conditions
this we have already seen the monitoring of
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culture florescence is useful for obtaining
information on the metabolic status of cells
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the effect of cells of different on different
substrateglucose and oxygen was studied the
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measurement were performed in synthetic and
complex media and the influence of the composition
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on of each medium was investigated a microflory
meter was used as a routine senserforcultivation
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control and provided insight into the status
of cells ok the what i wouldlike to point
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out is this you see the this is biomass was
sustain the points here are the individual
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points lets increase the size are little bit
so that you can see it a little better right
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yah the points are experimental points and
the line is given by culture florescence you
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see a very nice relationship this is what
we talked about as a measure of the cell concentration
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itself this is biomass or cell concentration
in grams per litre verses time by two methods
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one is by the traditional gram per the
o d method and the other one is by the culture
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florescence method it agrees in many situation
situations it doesn't agree in one such situation
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this is this is a bubble column conditions
if it is stirred reactor there is absolutely
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no problem whereas in the bubble column the
seems to be interference which lead to
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a difference between these two so one should
be careful while using culture florescence
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in bubble column it make sense because bubbles
will interfere
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then one more thing i will point out on this
paper here you see this figure let me glow
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it up a little bit if you wantyes this is
culture florescence versus time the florescence
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was as a certain level when an oxygen was
used when oxygen and air was employed let
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us read the this is ok [vocalized-noise] that
doesn't matter the you have culture florescence
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at a certain level when nitrogen is introduced
within few seconds the culture florescence
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goes up the anaerobic status goes up you know
when nitrogen is an employed the uh cells
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become anaerobic because oxygen is no longer
available as final electronic acceptors right
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so the culture florescence at an anaerobic
state is much higher than the culture florescence
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aerobic state because you will find any d
h forming more so this very clearly indicates
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the aerobic state of cells when oxygen is
turn back on or air is turn back on it comes
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back is towards its original level
so this is the nice indication of what is
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happening inside the cell just by measuring
the culture florescence that's that's what
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i had to talked about earlier you can read
this paper here interested i for the purpose
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of this lecture i think this is good enough
ok in the case of
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so that is using culture florescence
now let me talk about some of r work which
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is done quite while ago but it also is on
the same lines as this this is measurement
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of intracellular p h you know this is p h
inside the cells this is distinct from the
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p h of the medium p h of the medium we know
goes up and down because of acid base additions
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and we said that the p h needs to be controlled
at an optimum value for pest bioreactor performance
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so on and so forth
we are talking of intracellular p h p h inside
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the cells now the p h inside the cells can
somewhat be related to the energy status of
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the cells because they something called a
mitchells hypothesis in fact peter mitchell
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won the nobel prize for this hypothesis this
essentially is says that a hydrogen ion gradient
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across membrane intact membrane is
what provides the energy for a t p formation
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from a d b eight eight d b
so you in some sense we can link the energy
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status of the cell to the intercellular p
h level [vocalized-noise] there are some florescent
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dyes that indicate the p h through their florescence
by getting inside the cell and so on but we
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couldn't really use them because those dyes
leaked out b c e c e f a m is one such dye
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is of course some detail uh it might be
of interest to some so thats why i am mentioning
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it briefly these dyes could leak out so
we used to dye called nine [vocalized-noise]
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weak base distribution depends on the difference
between the extracellular and the intracellular
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p h you know there are there are also these
uh these processes takes place [vocalized-noise]
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plasma membrane in uh lower organisms such
as bacteria and so on so that is what we are
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using
so what we did was we developed this method
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of intracellular p h measurement online continuously
in the bioreactor and then we use intracellular
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p h to grow cells at a particular intracellular
p h which is indicator of energy status after
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studying that different intracellular p h
levels indicate different metabolic conditions
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in the cell so what we did we um measure intracellular
p h through florescence and we found that
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glucose addition the addition of the substrate
can modify intracellular p h so the glucose
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additions were done so that the intracellular
p h was maintain at a certain higher level
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compare to the base level so higher energy
level and when we did that we could reduce
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the anaerobic product formation uh by about
four fold ok so if it is anaerobic growth
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then the outcomes are much less in the aerobic
growth in terms of cell healed and so on so
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we were able to do that much better and we
could reduce the anaerobic product yieldso
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these things could also be done there are
couple of papers on intracellular p h which
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are given here i would not discuss them further
because of the introductory course but if
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you are interested you can you can these two
papers given under thirty one and no more
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about the intracellular p h its
ok let us move forward from here so the control
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this is what i mentioned earlier i would
given this as a diagram here intracellular
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p h was controlled using florescence is not
a p h probe p h probe will measure only the
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p h of the medium then transmitted and the
controller was the one that determine the
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glucose feed whether it needs to be fed or
the feed rate of glucose so that became the
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manipulate variable to maintain the intracellular
p h at a certain set point to reduce the anaerobic
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product formation so this is the control schematic
of the whole thing ok
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so now lets starts looking inside the cell
through various you know windows doors
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and so on and so for i will tell you one example
of such things uh it will looking inside the
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cell and that is called metabolic flux analysis
ok we will spends some time on this metabolic
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flux analysis its interesting it is it became
popular in the early nineteen nineties it
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is keep the method was known from the seventies
onwards nineteen seventies onwards it became
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popular uh when vallino and stephenapublished
their paper on using metabolic flux analysis
15:36.950 --> 15:44.160
to provide them with directions to improve
lysine production lysine as a is a amino
15:44.160 --> 15:49.760
acid that animals its cannot make and therefore
and that amino acid is required for
15:49.760 --> 15:55.230
a protein so we need to supplemented through
diet or are the means and therefore lysine
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is a huge product you know large amounts
of lysine are produced annually and therefore
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its a its commercially a very important product
so if you improve the lysine production a
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little bit then the profits could be high
or the price could be further bebroad down
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and all these made the use of metabolic flux
analysis a popular one in the early nineties
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and a lot of work went on for the next twenty
years or about fifteen years on metabolic
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flux analysis we will look at the principles
of metabolic flux analysis next
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so as an as an introduction cells make products
as a result of hundreds of reactions that
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take place inside them you already know this
to improve product yields the chemical reactions
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of the metabolic pathways can be targeted
and let us look at a method of analysis of
16:49.190 --> 16:55.660
those pathways toward improving product yields
ok this is are main aim we are going to look
16:55.660 --> 17:02.280
at a method of analysis toward improving product
yields and that method is called metabolic
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flux analysis or m f a for short to know what
metabolic flux analysisall about let us start
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with this
let us represent all these cells in culture
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by a single large cell to begin with ok so
the volume of the cell equals the volume of
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all the cells in the bioreactor remaining
volume is the broth volume this could probably
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be less than ten percent of the eveneven in
theconcentrated situations it would be about
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ten to twenty percent alright of the total
volume and let us consider these set of reactions
17:41.300 --> 17:46.790
that are occurring in the cell hypothetical
set of reactions but these are good to understand
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the principles of metabolic flux analysis
s o is a substrate that outside that goes
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inside and gets converted to s throughprobably
the reaction r zero then s could get converted
18:04.730 --> 18:12.370
to a or b it gets converted to a at a rate
of r one this r one is moles per time and
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it gets converted to b at the r two moles
per time b gets converted to d at the rate
18:19.830 --> 18:25.930
of four which comes outside a gets converted
to c at the rate of r three which also comes
18:25.930 --> 18:32.530
outside ok so this is the situation here let
us say that this is uh some important metabolic
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pathway that is happening in the cell this
is the case just put a note that s a b are
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inside the cell and therefore r intracellular
metabolites whereas s o c and d are extra
18:52.750 --> 19:00.130
cellular metabolites and metabolite flux is
common term that is used the term flux is
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very different from the engineering term flux
engineering term flux is amount transferred
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per unit area perpendicular to the direction
of transfer per unit time that's of flux so
19:11.650 --> 19:17.200
right in the case of metabolic flux historically
moles per time another its a rate has been
19:17.200 --> 19:23.070
used an amount rate has been used to indicate
flux we will continue using that because its
19:23.070 --> 19:28.100
widely accepted that the literature it is
a rate a not flux as in engineering terms
19:28.100 --> 19:34.790
this we need to keep in mind
so the typically units of r r r zero r one
19:34.790 --> 19:41.570
r two r the mille mole per gram cell per time
note third does normalize with respect to
19:41.570 --> 19:47.770
mass and not volume right rather extras
in terms of mass and it isnot been normalized
19:47.770 --> 19:54.680
with volume
now let us consider this large cell as a system
19:54.680 --> 20:01.490
let us write a meta balance on into cellular
metabolites first i like you to note that
20:01.490 --> 20:07.020
if we are writing balances on into cellular
metabolites we considered the cell as a assistant
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this is our system and we write the balances
on the metabolites inside the cell lets begin
20:12.500 --> 20:17.930
with this s here
so rate of input of s minus rate of output
20:17.930 --> 20:23.280
of s and this is a system that's what we taking
aboutplus the rate of generation of s minus
20:23.280 --> 20:36.390
the rate of consumption of s equals the rate
of accumulation of s here you need to if s
20:36.390 --> 20:44.930
zero as come an as s zero then we consider
at s transport across the system boundary
20:44.930 --> 20:50.140
these two terms input and output have relevance
as you know only when there is transport across
20:50.140 --> 20:55.090
the boundary of the system so if s naught
as become s naught here then we can consider
20:55.090 --> 21:01.740
it as the input term otherwise we considered
it as a reaction term which could be appointed
21:01.740 --> 21:08.750
for in generation or consumption but we need
to make sure that we counted only one we cannot
21:08.750 --> 21:14.860
counted as uh input as well as generation
even if you want follow a different strategy
21:14.860 --> 21:18.810
for doing this if you follow this strategy
that if s naught becomes s naught then we
21:18.810 --> 21:23.830
consider it has transport if s naught becomes
something else inside then it becomes uh a
21:23.830 --> 21:30.890
reaction similarly a going to c this ahwe
are going to consider it has a reaction because
21:30.890 --> 21:35.970
a has becomes c a is not become a outside
then it can be looked at a transport the main
21:35.970 --> 21:42.420
thing is they should not be any double counting
so there is if you look at that there is no
21:42.420 --> 21:49.780
input of s into the cell there is no output
of s into the cell thats not going anywhere
21:49.780 --> 21:54.870
[vocalized-noise] whereas there is definitely
a rate of generation and the rate of consumption
21:54.870 --> 22:00.870
the rate of generation is r zero the rate
of consumption as through these two reactions
22:00.870 --> 22:08.220
right therefore minus of r one plusr two that
becomes minus r one minus r two and that equals
22:08.220 --> 22:20.500
d s d p
similarly for a and b a going a is formed
22:20.500 --> 22:26.140
at the rate of r one it gets converted at
the rate of r two so generation is r one consumption
22:26.140 --> 22:38.560
is r two r one minus r two is d a d t similarly
r two for b formed at r two and getting consumed
22:38.560 --> 22:48.610
at r four therefore r two minus r four is
d b d t now there is a trick here ok want
22:48.610 --> 22:53.530
you to pay some attention
we are going to consider the surrounding of
22:53.530 --> 23:00.220
the cell as isas a system ok when we focus
on extra cellular metabolites we will consider
23:00.220 --> 23:05.890
the surrounding as cell as a system and write
a balances thats what turns out to be consistent
23:05.890 --> 23:11.100
with r approach here [vocalized-noise] so
if you write a balance on the extracellular
23:11.100 --> 23:15.080
metabolites so if you write a balance on the
extracellular metabolites on first one s naught
23:15.080 --> 23:20.900
s naught is getting out of the system here
this is our system now ok getting out of the
23:20.900 --> 23:28.020
system therefore there is no input where there
is an output and therefore minus r zero which
23:28.020 --> 23:35.630
comes from here it is not been generated its
not being consume minus r zero is d s zero
23:35.630 --> 23:48.060
d t similarly for d it is being produced here
you know the generation term alone exist
23:48.060 --> 23:55.809
there is no input there is no output there
is no i am sorry this is uh there is no
23:55.809 --> 24:02.890
in the case of the earlier one there is no
input there is no output there is no generation
24:02.890 --> 24:07.780
of s zero there is consumption of s zero through
the reaction r zero it takes to the other
24:07.780 --> 24:13.720
side and therefore r zero is the consumption
term here i would just lined up r zero r one
24:13.720 --> 24:19.520
r two thats the reason why r zero is here
and it is actually the consumption term therefore
24:19.520 --> 24:25.170
minus r zero is d is not d t you need to
be a little careful with these things
24:25.170 --> 24:35.000
then the consumption term for c is r three
therefore r three it is the consumption term
24:35.000 --> 24:42.090
the other terms as zero is d c d t and r four
again a consumption or the the sorry i'm
24:42.090 --> 24:50.700
i am getting a little confuse here ok this
is um for r three this is our system right
24:50.700 --> 24:54.100
yeah i got confused between the system that
is the reason i made that the error here we
24:54.100 --> 24:59.440
need to be little careful
this is our system here c is a extracellular
24:59.440 --> 25:05.200
metabolite inside it system now this is being
generated at the rate of r three and therefore
25:05.200 --> 25:13.309
this is the generation term so that is what
is r three here r i r o and r c at zero and
25:13.309 --> 25:20.390
therefore it is plus r g equals d c d t
similarly for d this is here it is being
25:20.390 --> 25:29.200
generated at the rate of r four by this reaction
input output and consumption term as zero
25:29.200 --> 25:35.890
therefore plus r four equals d b d t ok so
this is the way you go about it once you get
25:35.890 --> 25:40.120
used to at a simpler but you need to be a
little carefully you cannot make errors like
25:40.120 --> 25:48.900
i in made by switching the system in a mind
be a little careful
25:48.900 --> 25:56.120
so now the reason for me writing at this way
is that can you see a matrix evolving here
25:56.120 --> 26:03.490
now i just lined up r zeros r ones t twos
r threes and so on so the set of these equations
26:03.490 --> 26:11.960
can be written in the compact form as this
in are first equation we will write it
26:11.960 --> 26:19.370
in this order s zero s a b c d are equation
you know the way we do matrices multiplication
26:19.370 --> 26:24.510
right this term into this term plus this
term into this term plus this term into this
26:24.510 --> 26:29.670
term plus this term into this term plus this
term into this term equals this term so minus
26:29.670 --> 26:36.450
r zero is you know the other terms are all
zero for the first term minus r zero is d
26:36.450 --> 26:45.840
d t of s zero which is what this one says
minus r zero i will show you one more thing
26:45.840 --> 26:55.510
r zero minus r one minus r two the rest
of the terms are zero equals d d t of s if
26:55.510 --> 27:02.400
you remember that the first equation r zero
minus r one minus r two is d s d t one more
27:02.400 --> 27:08.670
equation this were completeness and then we
will go forward this first term is zero
27:08.670 --> 27:18.010
r one r two is zero r one minus r three is
d d t of a
27:18.010 --> 27:26.240
r one minus r three is d d t of a right thats
the way the representation happens in a matrix
27:26.240 --> 27:31.590
form therefore this matrix represents the
set of equations that we got by doing material
27:31.590 --> 27:39.160
balances on the various metabolites on the
cell extracellular and intracellular
27:39.160 --> 27:43.640
the number of equations would equal the number
of metabolites as we have seen and that would
27:43.640 --> 27:49.590
equal the number of rows that we have here
right here we have one two three four five
27:49.590 --> 27:55.670
six and we had six metabolites s naught s
a b c d so six and therefore that equals the
27:55.670 --> 28:02.600
number of rows and number of rates would equal
the number of columns here there five rates
28:02.600 --> 28:07.680
and five columns r zero r one r two r three
r four and there are five columns here in
28:07.680 --> 28:14.650
this matrix
so this is what is called a stoichiometric
28:14.650 --> 28:22.290
matrix is with a hat above stoichiometric
matrix multiplied with the rate vector or
28:22.290 --> 28:31.890
the rate matrix are s d d t of the state variable
as it is called state variable matrices x
28:31.890 --> 28:37.710
hat so this is the compact representation
of the entire set of equations and this kind
28:37.710 --> 28:42.570
of a representation becomes much easier even
if you have hundreds or thousands of equations
28:42.570 --> 28:47.940
so first to convert them into a compute a
program but once you have converted them into
28:47.940 --> 28:53.110
compute the program you can do all sort of
multiplications if you look at it in this
28:53.110 --> 29:00.960
form so thats the reason why we got it into
this form reaction rate vector and x is a
29:00.960 --> 29:07.930
state vector vector of state variables [vocalized-noise]
in other words metabolites of state variables
29:07.930 --> 29:15.620
ok this is an important concept now we had
looked at something called pseudo steady state
29:15.620 --> 29:22.950
approximation earlier let us revisit that
we talked about the rated which the bolt
29:22.950 --> 29:29.780
is made [vocalized-noise] bolt made every
one every five seconds and an engine a car
29:29.780 --> 29:36.090
engine made one every hour we said since
this is made every five seconds in this is
29:36.090 --> 29:41.720
made every hour even if you takes seven seconds
or three seconds or four seconds to make
29:41.720 --> 29:48.800
a bolt that variation and time the unsteady
stateunsteady nature of this process will
29:48.800 --> 29:56.710
not affect the nature of engine making
ok the because this process is very fast this
29:56.710 --> 30:02.040
process is very slow if you focus on the slower
process then the much faster process can be
30:02.040 --> 30:07.060
assume to be a steady state whether it is
actually a steady state or not that is the
30:07.060 --> 30:13.920
pseudo steady state of approximation thats
what is given here if are interest is in engine
30:13.920 --> 30:20.020
making about an hour characteristic time characteristic
time the unsteady aspects of bolt making a
30:20.020 --> 30:25.309
few seconds are irrelevant thus bolt making
can be consider to be at steady state compared
30:25.309 --> 30:29.490
to the time scale of our interest irrespective
to of whether this is be a steady state or
30:29.490 --> 30:35.160
not so you need comparison so in comparison
with the time scale of interest much faster
30:35.160 --> 30:40.240
processes can be assume to be a steady state
this is called pseudo steady state approximation
30:40.240 --> 30:44.770
we are revisiting this is an important concept
little difficult appreciate in the beginning
30:44.770 --> 30:52.980
but once you get the hang of it this becomes
very powerful similarly if you look at the
30:52.980 --> 31:00.240
intracellular metabolite concentration change
it happens over seconds where as growth typically
31:00.240 --> 31:01.240
happens
31:01.240 --> 31:08.990
over lets say of few lets say over characteristic
times of hours so this is the slow process
31:08.990 --> 31:16.190
growth compared to the the process of intracellular
metabolite concentration changes so and comparison
31:16.190 --> 31:22.040
to the growth process if we are interested
in growth related processes the intracellular
31:22.040 --> 31:26.410
metabolites can be assume to be at steady
state ok so this is the pseudo steady state
31:26.410 --> 31:34.350
of approximation um now see how its simplifies
are approach all the rate derivatives the
31:34.350 --> 31:41.040
time derivatives of intracellular metabolite
can be set to zero by pseudo steady state
31:41.040 --> 31:45.900
approximation if we are interested in growth
related processes which we are growth and
31:45.900 --> 31:51.059
product information the case of a bioreactor
definitely we are related only in definitely
31:51.059 --> 31:57.910
we are interested only in such processes and
in such a case we can directly take the intracellular
31:57.910 --> 32:04.809
metabolite variations with time to be zero
or the this process to be act steady state
32:04.809 --> 32:12.610
if we do that then the intracellular metabolite
sorry abc they can be directly put to zero
32:12.610 --> 32:18.900
ok the other rates are intracellular metabolite
let them be there if this is the case then
32:18.900 --> 32:28.330
we could spilt up this matrix as related to
extracellular and intracellular matrixes
32:28.330 --> 32:34.770
because intracellular matrixes intracellular
metabolite related matrixes will back ok i
32:34.770 --> 32:42.420
just take in r zero r three and r four and
returns the stoichiometric matrix from this
32:42.420 --> 32:49.130
part with this is relevant to that to give
us d d t of s naught c and d and this is for
32:49.130 --> 32:53.881
the extracellular part the intracellular part
of the extracellular part as this minus one
32:53.881 --> 33:01.110
zero zero zero one zero and zero zero one
corresponding to
33:01.110 --> 33:07.799
r naught r three and r four and they correspond
correspond on this side to d d t of s naught
33:07.799 --> 33:14.440
c and d where as intercellular which corresponds
to a b and c which are actually zero here
33:14.440 --> 33:21.720
can be taken from this particular from
the a b c part of the matrix here this particular
33:21.720 --> 33:29.590
part of the stoichiometric matrix so i represented
this as a matrix that represent extracellular
33:29.590 --> 33:33.870
metabolite and a matrix that represent intracellular
metabolite you can take a look at the details
33:33.870 --> 33:40.320
convince yourself with time let us move forward
now so the metabolite flux analysis this is
33:40.320 --> 33:47.080
something like this war what all can be done
with [vocalized-noise] metabolite flux analysis
33:47.080 --> 33:54.530
right this this stoichiometric matrix gives
us the various rates the stoichiometric
33:54.530 --> 33:59.890
matrix sorry this representation gives us
stoichiometric matrix various rates and the
33:59.890 --> 34:05.630
rates of accumulation so let us see what all
can be done or what are the common things
34:05.630 --> 34:12.050
that i would done with with such a representation
which would be useful for us from a bioreactor
34:12.050 --> 34:20.600
point of view so estimation of intracellular
metabolite flux from extracellular metabolite
34:20.600 --> 34:25.080
rates extracellular metabolite can be measured
by some of the methods that we talked about
34:25.080 --> 34:32.190
in in earlier module module three so with
by measuring those can we can get an estimate
34:32.190 --> 34:37.830
of the intracellular metabolite flux right
the rates things are going on inside the cell
34:37.830 --> 34:46.970
identification of branch point control nodal
rigidity in cell pathways this can done identification
34:46.970 --> 34:53.260
of alternate pathways can be done analysis
of fluxome metabolome and any omes can be
34:53.260 --> 34:57.930
done ome is nothing but a complete collection
of everything that happens a genome for example
34:57.930 --> 35:03.011
is a complete representation of collection
of all the genes in the cell fluxome is the
35:03.011 --> 35:07.870
complete representation of all the fluxes
in the cell and so on so far ok you can analyze
35:07.870 --> 35:14.050
that using this approach we are going to look
at on the first two as examples of what can
35:14.050 --> 35:20.180
we done with metabolite flux analysis so let
us look at the thing first estimation of intracellular
35:20.180 --> 35:28.150
metabolite flux from extracellular metabolite
rates this is the same figure that we saw
35:28.150 --> 35:35.119
earlier is not going to s which further goes
to a and b a goes c and b goes to d s naught
35:35.119 --> 35:40.260
c and b an extracellular metabolites s a and
b intracellular metabolites so we are going
35:40.260 --> 35:47.300
to measure s naught c and d and then get the
rated that are involved here how do we do
35:47.300 --> 35:55.180
that we no that they extracellular the
formulation corresponding extracellular
35:55.180 --> 36:00.740
metabolites is this we are already seen that
earlier minus one zero zero zero one zero
36:00.740 --> 36:06.490
zero zero one r naught r three r four these
are the the rates at corresponded extracellular
36:06.490 --> 36:12.170
metabolites the d d t of s naught c and d
the equations corresponding to extracellular
36:12.170 --> 36:19.200
metabolites have been written in this matrix
and this is the intracellular one if s naught
36:19.200 --> 36:26.330
c and d can be measured you can calculate
this right you take points at reasonably closing
36:26.330 --> 36:31.800
phase time intervals then you can actually
get the rates of variation of x naught c and
36:31.800 --> 36:40.670
d in the extracellular space knowing this
knowing rates of x naught c and d let us sayour
36:40.670 --> 36:47.480
interest is estimating r one and r two the
thats are interest that is what we said
36:47.480 --> 36:52.990
here estimation of intracellular metabolite
flux or rates from extracellular metabolite
36:52.990 --> 37:02.040
x r zero the substrate of substrate uptake
rate can be estimated from s naught measurements
37:02.040 --> 37:09.490
thats ok directly gives you r naught r three
r four the metabolite of the product formation
37:09.490 --> 37:17.300
rates can be estimated from c and d measurements
respectively the intracellular matrix equation
37:17.300 --> 37:22.350
has three equations and five unknowns right
number of equations number of unknowns you
37:22.350 --> 37:27.780
know the degree of freedom concept the degree
of freedom degrees of freedom is an number
37:27.780 --> 37:33.080
of independent variables that are need to
be known to solve this set of equation completely
37:33.080 --> 37:40.310
we have three equations five unknowns so five
minus three is two so we need two of extracellular
37:40.310 --> 37:49.730
fluxes to determine r one and r two let us
say that those two are r three and r four
37:49.730 --> 37:54.730
which are known which can be known from c
and d measurements we take measurements of
37:54.730 --> 38:01.369
c and d concentrations at reasonable close
these space times and we have these concentrations
38:01.369 --> 38:07.030
we have these rates as derivatives c two minus
c one divided by t two minus t one and so
38:07.030 --> 38:18.810
on so for so this is the matrix here we
intracellular matrix which we are taken to
38:18.810 --> 38:27.369
be zero which we had represented as which
can be represented as this part alone corresponding
38:27.369 --> 38:34.320
to r naught r one r two and this part alone
corresponding to r three r four rate that
38:34.320 --> 38:40.130
will essentially be the same as this you can
tested out so minus one minus one minus one
38:40.130 --> 38:46.760
zero one zero zero zero one this corresponds
to r zero r one r two so we were written that
38:46.760 --> 38:55.300
separately and zero zero minus one zero zero
one this corresponds to r three and r four
38:55.300 --> 39:00.370
and so we written them separately is zero
zero zero
39:00.370 --> 39:06.510
now if we transpose this matrix equation ok
this is an equation consistingof matrixes
39:06.510 --> 39:11.530
we transpose these and pre multiplied both
side by the inverse of the first matrix you
39:11.530 --> 39:16.060
should be little comfortable of the linear
algebra do it so little do difficult to get
39:16.060 --> 39:21.760
into those were principles in this lecture
so if you followed it its fine otherwise you
39:21.760 --> 39:29.940
need to take it on belief and then go and
check the matrix algebra and then understand
39:29.940 --> 39:40.680
these things better so if we pre multiply
this matrix were this inverse this will drop
39:40.680 --> 39:50.020
out and pre multiple this with this same inverse
matrix this termremain and anyway this is
39:50.020 --> 39:58.130
zero so this is going to drop out ok so we
have transpose these equation and therefore
39:58.130 --> 40:04.990
all the minus ones have becomes ones
these zeros will remain the same so this equals
40:04.990 --> 40:10.290
this thats whats is the first step and then
we have free multiplied with these matrix
40:10.290 --> 40:15.200
this [vocalized-noise] with the matrix such
the inverse of this matrix which is this so
40:15.200 --> 40:21.930
i i inverse is a unity matrix we are not going
to represent that this matrix times this
40:21.930 --> 40:28.700
into r the r four will directly give us r
naught r one and r four or r three r four
40:28.700 --> 40:33.660
we are interested in by measuring r naught
r one and r two no r two r four are ones set
40:33.660 --> 40:39.340
of being measured here calculated here
from the measurements of c and d with that
40:39.340 --> 40:46.640
we can get r not r one and r two so r not
and r one and r two if you work things out
40:46.640 --> 40:54.661
it will convert in this three multiplication
this matrix would result in if you multiply
40:54.661 --> 40:59.869
and so on so far r three plus r four r three
and r four therefore r not equals r three
40:59.869 --> 41:07.180
and r four
so just by doing this kind of a manipulation
41:07.180 --> 41:15.960
we directly get the intracellular rates based
on the extracellular rates that can be measured
41:15.960 --> 41:25.940
thats now let us look at nodal rigidity identification
ok this is what content to the lysine improvement
41:25.940 --> 41:32.330
that we talked about [vocalized-noise] value
vallinos and stephena early nineties to understand
41:32.330 --> 41:37.240
this let us take an example let us consider
the example that is that popularized metabolic
41:37.240 --> 41:43.620
flux analysis the lysine introduction this
is the reference here it is been also given
41:43.620 --> 41:49.300
and your list of references you can go on
take a located Stephanopoulos and vallino
41:49.300 --> 41:54.300
nineteen ninety one network rigidity and metabolic
engineering in metabolite over production
41:54.300 --> 41:58.869
this is publishing science and then there
is on other paper
41:58.869 --> 42:02.990
that was published in biotechnology and bioengineering
in nineteen ninety three which gives some
42:02.990 --> 42:11.150
of details as mentioned earlier lysine
minus lysine is produce an annual quantities
42:11.150 --> 42:17.859
of hundred of thousands of tons because this
amino acid is not made by ani by animal cells
42:17.859 --> 42:24.040
but is required forproteins and so on therefore
it needs to provided for that itself causes
42:24.040 --> 42:33.369
or gives you the reason for the large production
of minus lysine corynebacterium glutamicum
42:33.369 --> 42:42.190
which is an organism this produces a lysine
the yield when this was being produce was
42:42.190 --> 42:47.490
only about fifteen percent and this fifteen
percent was increased more than three four
42:47.490 --> 42:53.010
togreater than fifty percent through metabolic
flux analysis you see the powerhere fifteen
42:53.010 --> 42:59.320
percent is gone to fifteen fifty percent through
metabolic flux analysis and subsequent genetic
42:59.320 --> 43:02.880
manipulations
see metabolic flux analysis gives you directions
43:02.880 --> 43:09.510
based on that you need to modify the organisms
genetically and then in this case it produced
43:09.510 --> 43:19.030
more than three fold yield so that some details
this is the primary path way that we should
43:19.030 --> 43:25.109
be interested in for lysine production this
is a part of glycolysis glucose glucose going
43:25.109 --> 43:29.550
to glucose six phosphate to fructose six phosphate
to fructose six phosphate to phosphate pyruvateto
43:29.550 --> 43:34.960
pyruvate and then there is branch point which
is important here glucose s phosphate through
43:34.960 --> 43:42.640
ri bullose five phosphate comes back to fructose
six phosphatephosphor pyruvate here goes tooxaloacetic
43:42.640 --> 43:49.930
acidand then to lysine pyruvate can also go
through lysine and there is an another branch
43:49.930 --> 43:56.760
point here from pyruvate to acetyl co a people
would recognize that this is the t c a cycle
43:56.760 --> 44:06.510
which is indicated by the dotted circle here
dash circlelet us now define a node which
44:06.510 --> 44:15.200
is the metabolite where more than one reactionoccursso
there are three principles nodes or branch
44:15.200 --> 44:22.791
points in this network g six p where there
is branch branching more than one reaction
44:22.791 --> 44:29.960
occurs one reaction two reac[tion]- second
reaction and then p e p this goes here into
44:29.960 --> 44:36.620
oxaloacetic acid and pyruvate here it is going
to oxaloacetic acid and lysine so these are
44:36.620 --> 44:45.260
the three branch points here these are the
ones that one focuses on to do node of
44:45.260 --> 44:51.250
rigidity rigidity analysis nodes provides
us the possibility of manipulation to preferentially
44:51.250 --> 44:56.500
increase the flux through one of the desired
branches suppose we want to flux the go here
44:56.500 --> 45:00.770
the lysine and this is one of one of the places
where
45:00.770 --> 45:06.740
a possibility exists to channel the flux more
here by some means compare to the flux here
45:06.740 --> 45:13.520
that is what is going to increase the lysine
production right so that is why we look at
45:13.520 --> 45:24.099
these nodes the branch points so in in formal
terms if m metabolite under goes three reactions
45:24.099 --> 45:29.280
with rates j one j two and j three or fluxes
j one j two and j three in this terminology
45:29.280 --> 45:38.720
to give you x y and z and let us say y is
the desired product here j two needs to be
45:38.720 --> 45:45.430
increased selectively over j one and j three
right so the split ratio as it is called this
45:45.430 --> 45:50.180
is flux through the desired branch divided
by the sum of fluxes through all branches
45:50.180 --> 45:57.660
that is the split ratio it can be written
as j two divided by the sum of all js j one
45:57.660 --> 46:06.720
j two j three in this case so j two by j one
j two plus j three this is the split ratio
46:06.720 --> 46:15.020
this is fine but the main reason why we need
to by this is not so easy is it the flux cannot
46:15.020 --> 46:20.970
be changed at will due to the nature of node
ok all nodes in all nodes you just cannot
46:20.970 --> 46:28.300
say wewill take more carbon through this
branch and stop by probably the cutting of
46:28.300 --> 46:33.590
the enzyme that is responsible for this branch
and so on so forth it will not happen ok let
46:33.590 --> 46:38.359
me briefly tell you what it is there are two
kinds of nodes that are possible it could
46:38.359 --> 46:45.510
be rigid node or flexible node to do that
let us consider this example s going a a goes
46:45.510 --> 46:53.920
to b and c c goes to d and e these are normalized
fluxes s to a is hundred a to b is fifty and
46:53.920 --> 46:59.800
a to c is fifty and c to d is twenty five
c to e is twenty five and some units mille
46:59.800 --> 47:05.130
mole per
time whateverrate so this is the branching
47:05.130 --> 47:11.990
when nothing has been done to this particular
network like some genetic means if we can
47:11.990 --> 47:18.150
cut off the enzymes may be for this branch
then this branch can be cut off and this branch
47:18.150 --> 47:29.849
also cut off let us say then this c to d conversion
cannot takes place if it is if the observed
47:29.849 --> 47:35.240
fluxes need not really observed it let
us say that the fluxes on doing these two
47:35.240 --> 47:42.740
cuts stands out to be something like this
ok on cutting of these the hundreds becomes
47:42.740 --> 47:51.070
zero here but it is hundred here whereasby
cutting of this both the fluxes have become
47:51.070 --> 47:56.700
zero ok let us say that this happened its
not easy to observe this but let us say this
47:56.700 --> 48:00.052
is happened if this is happened at the node
a
48:00.052 --> 48:06.550
it is possible to reroute the entire flux
through one branch ok lets say this is the
48:06.550 --> 48:13.170
desired branch by cutting off this branch
we are able to reroute all the flux of this
48:13.170 --> 48:21.050
branch and therefore this is a flexible node
ok whereas at node c because we cut this off
48:21.050 --> 48:25.890
this is also got cut off ok we didn't intend
to cut this off but just because we cut off
48:25.890 --> 48:33.680
this path this also got cut off such a node
c is called a rigid node we cannot expect
48:33.680 --> 48:39.190
to increase the flux preferentially through
one of the branch point if the other branch
48:39.190 --> 48:44.640
point is cut off that's a rigid node so we
need to look for flexible nodes to manipulate
48:44.640 --> 48:51.210
things rigid nodes will not help us so rigidity
could arise due to many reasons for example
48:51.210 --> 48:57.660
d could be necessary for the branch that
is producing e in some way some metabolic
48:57.660 --> 49:02.410
way some genetic way it could be responsible
may be through a feedback loop or something
49:02.410 --> 49:10.290
like that therefore if you cut this off also
gets cut off so these three branch points
49:10.290 --> 49:17.500
are shown g six p p e p pyruvate these three
nodes through experiments and flux analysis
49:17.500 --> 49:22.980
it was found that these two nodes g six p
pyruvate these are flexible it is possible
49:22.980 --> 49:28.741
to reroute the flux through a desire branch
by cutting off the other branch whereas p
49:28.741 --> 49:37.040
e p is rigid and rigidity needs to be overcome
for better lysine production you can or in
49:37.040 --> 49:43.730
other words p e p is going to o a this is
the desired branch this is the p e p carboxylase
49:43.730 --> 49:52.090
enzyme that is doing this p e p carboxylase
from the regular organism is leads to rigidity
49:52.090 --> 49:58.359
node whereas p e p carboxylase is a enzyme
from pseudomonas is helpful ok is helpful
49:58.359 --> 50:03.190
to break the rigidity
therefore the approach was take pseudomonas
50:03.190 --> 50:13.510
p e p carboxylase express in corynebacterium
glutamicum and that has helped to decrease
50:13.510 --> 50:20.260
the rigidity of this node and thereby increase
the production of lysine by about three fold
50:20.260 --> 50:28.370
okso in the last part actually taken the gene
from some other organism expressed it in the
50:28.370 --> 50:33.640
hose organism this is the recombinent d n
a technology that i was talking about and
50:33.640 --> 50:38.970
this is a way of modifying this cell to produce
more ok so i have shown you an example where
50:38.970 --> 50:45.980
the two major aspects one is looking at the
cell in detail and then modify the cell are
50:45.980 --> 50:55.070
have both been shown to improve the yield
of the product in this case lysine and r d
50:55.070 --> 51:00.100
n a has been used heavily for insulin production
51:00.100 --> 51:06.910
insulin is made by using bacteriumthey have
taken the insulin gene they took the insulin
51:06.910 --> 51:12.700
gene and express it in bacterium and produceinsulin
that is the way by which they could produce
51:12.700 --> 51:18.360
large amount of insulin and bring down the
cost and availability and modernity by significant
51:18.360 --> 51:28.080
significant and one more step further this
is changing putting r d n a r d n a technology
51:28.080 --> 51:33.430
is taking d n a and putting it to another
organism ones the further is changing the
51:33.430 --> 51:40.230
entire cell ok an example for that is hybridoma
you take a cell that is cancerous that can
51:40.230 --> 51:46.030
grow forever you take a cell that can produce
a particular type of antibody put them together
51:46.030 --> 51:49.599
you get a cell that produces only that kind
of a antibody and can live forever ok that
51:49.599 --> 51:57.099
is changing the cell completely that can also
be done to make bioreact and if you use
51:57.099 --> 52:03.210
them in bioreactorsyou can get production
of the required product the monoclonal antibodies
52:03.210 --> 52:18.770
from bioreactors so in summary if you look
at the look at module five will summarize
52:18.770 --> 52:28.640
the entire course in the next lecture the
hm in module five we began by considering
52:28.640 --> 52:33.800
the cells themselves the actual factories
that produce the product we said that we could
52:33.800 --> 52:40.070
get some input into what is happening by considering
the cell as ablack box and one of the method
52:40.070 --> 52:46.420
of doing that is by stoichiometry bioreactions
stoichiometry and the degree of reluctance
52:46.420 --> 52:52.430
concept is an important concept there then
we said that we will open a wind a window
52:52.430 --> 52:59.260
look at it some windows are the cell status
indicators the metabolic status indicators
52:59.260 --> 53:07.200
energy status indicators
such as redox ratio the energy ratio and
53:07.200 --> 53:14.099
the intercellularp h these three are examples
they could be others and then we said we will
53:14.099 --> 53:19.950
go and change we will go and look inside
the cell that is what we did by metabolic
53:19.950 --> 53:24.770
flux analysis through the methods that we
have described in detail here some detail
53:24.770 --> 53:30.589
here not all detail and then based on the
input provided by the metabolic flux analysis
53:30.589 --> 53:37.970
we could do directed change through r d n
a technology of the genetic machinery and
53:37.970 --> 53:43.630
improve the productivity for example lysine
by more than three fold and then we said that
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you could actually change the cell itself
by bringing two types of cells together to
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produce monoclonal antibodies i or any other
product of interest
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ok with this we finish the five modules in
the next lecture let me summarize in brief
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it will be brief lecture what all we did in
the course that will provide you the nice
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summary for the course see you in the next
lecture