welcome to lecture twenty this is the nptel online certification course on bioreactors
in the previous two lectures we had looked at bioreactions documentary as one of the
means of getting some insights into what is happening at the cell level in the bioreactors
the cells in the bioreactor are the actual factories we worked out of problem in the
previous lecture now let us go forward with the module itselfthe module five we are going
to begin talking about windows to the cell now we said we will first consider it is a
black box the cell and then we will open up windows and look through the windows to see
what is happening inside the cell so the windows to the cell are the indicators of cellular
status of the cell or indicators of metabolic status of the cell these terms cellular status
metabolic status energy status and so on so forth redox status and so on these are kind
of soft terms you knowneed to you should not take them um too strictly these are kinds
of indications or what could be happening and these are kind of common terms that are
used they not off course fully understood but they are a useful the cellular redox
status is a well known indicator and cellular energy status is another well known accepted
indicator of the cellular or a metabolic status of the cells the cellular redox status is
typically derivedfrom n a d h you know n a d h make it in a mean adenine dinucleotide
and the hydrogenated form of that this conversion takes place in various metabolic reactions
or accompanied with various metabolic reactions in the cell n a d plus plus h plus giving
you n a d h so this has a reducing power which is taken forward for so many different important
functions in the cell so the redox ratio one of the ways you looking at at least at n a
d the ratio of n a d h concentration to the total concentration of n a d plus and n a
d h we were already seen that n a d h florescence can be used to monitor the cell concentration
because n a d [vocalized-noise] the the intensity of n a d h florescence can be directly
link to cell concentration here it is redox ratio the n a d h divided by the total n a
d h whole which is [vocalized-noise] consist of n a d plus and n a d h ok this is a redox
ratio similarly or in in some situations the ratio of n a d h to flavin adenine dinucleotide
and other important such molecule is also take into be the redox ratiofor energy status
is somewhat a little more straight forward we all know the energy is made energy
currency of the cell a t p adenosine triphosphate is made by the phosphoral relation of adenosine
diphosphate so the ratio of a t p to the pool a t p plus ama d p sometimes a m p is also
added here but let us just look at these two a t p divided by a t p plus a d p is taken
to indicate the cellular energy status so lot of measurements are made on these correlated
to the happenings inside the cells correlations with respect to what is important from a bioreactor
context and efforts are made so that uh these indicators are varied in a particular
fashion or kept constant at a particular known value to achieve a desired performances by
the cells in the bioreactor so that is one more step towards understanding what is happening
at a cellular status cellular level sometimes even a t p by a d p is considered the cellular
energy status there are various ways of defining this at at this given you couple of them what
one needs to understand is that very many metabolic processes contribute the above ok
so it is not a one to one kind of a relationships so many things contribute to redox status
so many things contribute to energy status so one needs to have uh some level of uh
you know interpretation one is interpret this in that kind of fuzzy fashion its more
of an indicator you may want to look at this paper which is
also been given in your notes has one of the papers that you would want to look at let
me direct that you this is number thirty n a d h culture florescence uh the author of
scheper gebauer and schuell this is the paper for n a d h culture florescence the
title of the paper itself is monitoring n a d h dependent culture florescence during
cultivation on e coli it was published in chemical engineering a journal in nineteen
eighty seven this is something that gives you some relationship to the culture energy
status let me do have a paper here yes let me briefly tell you how this is done with
energy with n a d h status to very briefly read the abstract n a d p h dependent culture
florescence was monitored during variousecoli cultivation processes batch and continuous
cultivation in stirred tank reactors or bubble column reactors various strings of e coli
including genetically modified e coli were used measurement of florescence signal allows
biomass estimation under certain conditions this we have already seen the monitoring of
culture florescence is useful for obtaining information on the metabolic status of cells
the effect of cells of different on different substrateglucose and oxygen was studied the
measurement were performed in synthetic and complex media and the influence of the composition
on of each medium was investigated a microflory meter was used as a routine senserforcultivation
control and provided insight into the status of cells ok the what i wouldlike to point
out is this you see the this is biomass was sustain the points here are the individual
points lets increase the size are little bit so that you can see it a little better right
yah the points are experimental points and the line is given by culture florescence you
see a very nice relationship this is what we talked about as a measure of the cell concentration
itself this is biomass or cell concentration in grams per litre verses time by two methods
one is by the traditional gram per the o d method and the other one is by the culture
florescence method it agrees in many situation situations it doesn't agree in one such situation
this is this is a bubble column conditions if it is stirred reactor there is absolutely
no problem whereas in the bubble column the seems to be interference which lead to
a difference between these two so one should be careful while using culture florescence
in bubble column it make sense because bubbles will interfere
then one more thing i will point out on this paper here you see this figure let me glow
it up a little bit if you wantyes this is culture florescence versus time the florescence
was as a certain level when an oxygen was used when oxygen and air was employed let
us read the this is ok [vocalized-noise] that doesn't matter the you have culture florescence
at a certain level when nitrogen is introduced within few seconds the culture florescence
goes up the anaerobic status goes up you know when nitrogen is an employed the uh cells
become anaerobic because oxygen is no longer available as final electronic acceptors right
so the culture florescence at an anaerobic state is much higher than the culture florescence
aerobic state because you will find any d h forming more so this very clearly indicates
the aerobic state of cells when oxygen is turn back on or air is turn back on it comes
back is towards its original level so this is the nice indication of what is
happening inside the cell just by measuring the culture florescence that's that's what
i had to talked about earlier you can read this paper here interested i for the purpose
of this lecture i think this is good enough ok in the case of
so that is using culture florescence now let me talk about some of r work which
is done quite while ago but it also is on the same lines as this this is measurement
of intracellular p h you know this is p h inside the cells this is distinct from the
p h of the medium p h of the medium we know goes up and down because of acid base additions
and we said that the p h needs to be controlled at an optimum value for pest bioreactor performance
so on and so forth we are talking of intracellular p h p h inside
the cells now the p h inside the cells can somewhat be related to the energy status of
the cells because they something called a mitchells hypothesis in fact peter mitchell
won the nobel prize for this hypothesis this essentially is says that a hydrogen ion gradient
across membrane intact membrane is what provides the energy for a t p formation
from a d b eight eight d b so you in some sense we can link the energy
status of the cell to the intercellular p h level [vocalized-noise] there are some florescent
dyes that indicate the p h through their florescence by getting inside the cell and so on but we
couldn't really use them because those dyes leaked out b c e c e f a m is one such dye
is of course some detail uh it might be of interest to some so thats why i am mentioning
it briefly these dyes could leak out so we used to dye called nine [vocalized-noise]
weak base distribution depends on the difference between the extracellular and the intracellular
p h you know there are there are also these uh these processes takes place [vocalized-noise]
plasma membrane in uh lower organisms such as bacteria and so on so that is what we are
using so what we did was we developed this method
of intracellular p h measurement online continuously in the bioreactor and then we use intracellular
p h to grow cells at a particular intracellular p h which is indicator of energy status after
studying that different intracellular p h levels indicate different metabolic conditions
in the cell so what we did we um measure intracellular p h through florescence and we found that
glucose addition the addition of the substrate can modify intracellular p h so the glucose
additions were done so that the intracellular p h was maintain at a certain higher level
compare to the base level so higher energy level and when we did that we could reduce
the anaerobic product formation uh by about four fold ok so if it is anaerobic growth
then the outcomes are much less in the aerobic growth in terms of cell healed and so on so
we were able to do that much better and we could reduce the anaerobic product yieldso
these things could also be done there are couple of papers on intracellular p h which
are given here i would not discuss them further because of the introductory course but if
you are interested you can you can these two papers given under thirty one and no more
about the intracellular p h its ok let us move forward from here so the control
this is what i mentioned earlier i would given this as a diagram here intracellular
p h was controlled using florescence is not a p h probe p h probe will measure only the
p h of the medium then transmitted and the controller was the one that determine the
glucose feed whether it needs to be fed or the feed rate of glucose so that became the
manipulate variable to maintain the intracellular p h at a certain set point to reduce the anaerobic
product formation so this is the control schematic of the whole thing ok
so now lets starts looking inside the cell through various you know windows doors
and so on and so for i will tell you one example of such things uh it will looking inside the
cell and that is called metabolic flux analysis ok we will spends some time on this metabolic
flux analysis its interesting it is it became popular in the early nineteen nineties it
is keep the method was known from the seventies onwards nineteen seventies onwards it became
popular uh when vallino and stephenapublished their paper on using metabolic flux analysis
to provide them with directions to improve lysine production lysine as a is a amino
acid that animals its cannot make and therefore and that amino acid is required for
a protein so we need to supplemented through diet or are the means and therefore lysine
is a huge product you know large amounts of lysine are produced annually and therefore
its a its commercially a very important product so if you improve the lysine production a
little bit then the profits could be high or the price could be further bebroad down
and all these made the use of metabolic flux analysis a popular one in the early nineties
and a lot of work went on for the next twenty years or about fifteen years on metabolic
flux analysis we will look at the principles of metabolic flux analysis next
so as an as an introduction cells make products as a result of hundreds of reactions that
take place inside them you already know this to improve product yields the chemical reactions
of the metabolic pathways can be targeted and let us look at a method of analysis of
those pathways toward improving product yields ok this is are main aim we are going to look
at a method of analysis toward improving product yields and that method is called metabolic
flux analysis or m f a for short to know what metabolic flux analysisall about let us start
with this let us represent all these cells in culture
by a single large cell to begin with ok so the volume of the cell equals the volume of
all the cells in the bioreactor remaining volume is the broth volume this could probably
be less than ten percent of the eveneven in theconcentrated situations it would be about
ten to twenty percent alright of the total volume and let us consider these set of reactions
that are occurring in the cell hypothetical set of reactions but these are good to understand
the principles of metabolic flux analysis s o is a substrate that outside that goes
inside and gets converted to s throughprobably the reaction r zero then s could get converted
to a or b it gets converted to a at a rate of r one this r one is moles per time and
it gets converted to b at the r two moles per time b gets converted to d at the rate
of four which comes outside a gets converted to c at the rate of r three which also comes
outside ok so this is the situation here let us say that this is uh some important metabolic
pathway that is happening in the cell this is the case just put a note that s a b are
inside the cell and therefore r intracellular metabolites whereas s o c and d are extra
cellular metabolites and metabolite flux is common term that is used the term flux is
very different from the engineering term flux engineering term flux is amount transferred
per unit area perpendicular to the direction of transfer per unit time that's of flux so
right in the case of metabolic flux historically moles per time another its a rate has been
used an amount rate has been used to indicate flux we will continue using that because its
widely accepted that the literature it is a rate a not flux as in engineering terms
this we need to keep in mind so the typically units of r r r zero r one
r two r the mille mole per gram cell per time note third does normalize with respect to
mass and not volume right rather extras in terms of mass and it isnot been normalized
with volume now let us consider this large cell as a system
let us write a meta balance on into cellular metabolites first i like you to note that
if we are writing balances on into cellular metabolites we considered the cell as a assistant
this is our system and we write the balances on the metabolites inside the cell lets begin
with this s here so rate of input of s minus rate of output
of s and this is a system that's what we taking aboutplus the rate of generation of s minus
the rate of consumption of s equals the rate of accumulation of s here you need to if s
zero as come an as s zero then we consider at s transport across the system boundary
these two terms input and output have relevance as you know only when there is transport across
the boundary of the system so if s naught as become s naught here then we can consider
it as the input term otherwise we considered it as a reaction term which could be appointed
for in generation or consumption but we need to make sure that we counted only one we cannot
counted as uh input as well as generation even if you want follow a different strategy
for doing this if you follow this strategy that if s naught becomes s naught then we
consider it has transport if s naught becomes something else inside then it becomes uh a
reaction similarly a going to c this ahwe are going to consider it has a reaction because
a has becomes c a is not become a outside then it can be looked at a transport the main
thing is they should not be any double counting so there is if you look at that there is no
input of s into the cell there is no output of s into the cell thats not going anywhere
[vocalized-noise] whereas there is definitely a rate of generation and the rate of consumption
the rate of generation is r zero the rate of consumption as through these two reactions
right therefore minus of r one plusr two that becomes minus r one minus r two and that equals
d s d p similarly for a and b a going a is formed
at the rate of r one it gets converted at the rate of r two so generation is r one consumption
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
at r four therefore r two minus r four is d b d t now there is a trick here ok want
you to pay some attention we are going to consider the surrounding of
the cell as isas a system ok when we focus on extra cellular metabolites we will consider
the surrounding as cell as a system and write a balances thats what turns out to be consistent
with r approach here [vocalized-noise] so if you write a balance on the extracellular
metabolites so if you write a balance on the extracellular metabolites on first one s naught
s naught is getting out of the system here this is our system now ok getting out of the
system therefore there is no input where there is an output and therefore minus r zero which
comes from here it is not been generated its not being consume minus r zero is d s zero
d t similarly for d it is being produced here you know the generation term alone exist
there is no input there is no output there is no i am sorry this is uh there is no
in the case of the earlier one there is no input there is no output there is no generation
of s zero there is consumption of s zero through the reaction r zero it takes to the other
side and therefore r zero is the consumption term here i would just lined up r zero r one
r two thats the reason why r zero is here and it is actually the consumption term therefore
minus r zero is d is not d t you need to be a little careful with these things
then the consumption term for c is r three therefore r three it is the consumption term
the other terms as zero is d c d t and r four again a consumption or the the sorry i'm
i am getting a little confuse here ok this is um for r three this is our system right
yeah i got confused between the system that is the reason i made that the error here we
need to be little careful this is our system here c is a extracellular
metabolite inside it system now this is being generated at the rate of r three and therefore
this is the generation term so that is what is r three here r i r o and r c at zero and
therefore it is plus r g equals d c d t similarly for d this is here it is being
generated at the rate of r four by this reaction input output and consumption term as zero
therefore plus r four equals d b d t ok so this is the way you go about it once you get
used to at a simpler but you need to be a little carefully you cannot make errors like
i in made by switching the system in a mind be a little careful
so now the reason for me writing at this way is that can you see a matrix evolving here
now i just lined up r zeros r ones t twos r threes and so on so the set of these equations
can be written in the compact form as this in are first equation we will write it
in this order s zero s a b c d are equation you know the way we do matrices multiplication
right this term into this term plus this term into this term plus this term into this
term plus this term into this term plus this term into this term equals this term so minus
r zero is you know the other terms are all zero for the first term minus r zero is d
d t of s zero which is what this one says minus r zero i will show you one more thing
r zero minus r one minus r two the rest of the terms are zero equals d d t of s if
you remember that the first equation r zero minus r one minus r two is d s d t one more
equation this were completeness and then we will go forward this first term is zero
r one r two is zero r one minus r three is d d t of a
r one minus r three is d d t of a right thats the way the representation happens in a matrix
form therefore this matrix represents the set of equations that we got by doing material
balances on the various metabolites on the cell extracellular and intracellular
the number of equations would equal the number of metabolites as we have seen and that would
equal the number of rows that we have here right here we have one two three four five
six and we had six metabolites s naught s a b c d so six and therefore that equals the
number of rows and number of rates would equal the number of columns here there five rates
and five columns r zero r one r two r three r four and there are five columns here in
this matrix so this is what is called a stoichiometric
matrix is with a hat above stoichiometric matrix multiplied with the rate vector or
the rate matrix are s d d t of the state variable as it is called state variable matrices x
hat so this is the compact representation of the entire set of equations and this kind
of a representation becomes much easier even if you have hundreds or thousands of equations
so first to convert them into a compute a program but once you have converted them into
compute the program you can do all sort of multiplications if you look at it in this
form so thats the reason why we got it into this form reaction rate vector and x is a
state vector vector of state variables [vocalized-noise] in other words metabolites of state variables
ok this is an important concept now we had looked at something called pseudo steady state
approximation earlier let us revisit that we talked about the rated which the bolt
is made [vocalized-noise] bolt made every one every five seconds and an engine a car
engine made one every hour we said since this is made every five seconds in this is
made every hour even if you takes seven seconds or three seconds or four seconds to make
a bolt that variation and time the unsteady stateunsteady nature of this process will
not affect the nature of engine making ok the because this process is very fast this
process is very slow if you focus on the slower process then the much faster process can be
assume to be a steady state whether it is actually a steady state or not that is the
pseudo steady state of approximation thats what is given here if are interest is in engine
making about an hour characteristic time characteristic time the unsteady aspects of bolt making a
few seconds are irrelevant thus bolt making can be consider to be at steady state compared
to the time scale of our interest irrespective to of whether this is be a steady state or
not so you need comparison so in comparison with the time scale of interest much faster
processes can be assume to be a steady state this is called pseudo steady state approximation
we are revisiting this is an important concept little difficult appreciate in the beginning
but once you get the hang of it this becomes very powerful similarly if you look at the
intracellular metabolite concentration change it happens over seconds where as growth typically
happens
over lets say of few lets say over characteristic times of hours so this is the slow process
growth compared to the the process of intracellular metabolite concentration changes so and comparison
to the growth process if we are interested in growth related processes the intracellular
metabolites can be assume to be at steady state ok so this is the pseudo steady state
of approximation um now see how its simplifies are approach all the rate derivatives the
time derivatives of intracellular metabolite can be set to zero by pseudo steady state
approximation if we are interested in growth related processes which we are growth and
product information the case of a bioreactor definitely we are related only in definitely
we are interested only in such processes and in such a case we can directly take the intracellular
metabolite variations with time to be zero or the this process to be act steady state
if we do that then the intracellular metabolite sorry abc they can be directly put to zero
ok the other rates are intracellular metabolite let them be there if this is the case then
we could spilt up this matrix as related to extracellular and intracellular matrixes
because intracellular matrixes intracellular metabolite related matrixes will back ok i
just take in r zero r three and r four and returns the stoichiometric matrix from this
part with this is relevant to that to give us d d t of s naught c and d and this is for
the extracellular part the intracellular part of the extracellular part as this minus one
zero zero zero one zero and zero zero one corresponding to
r naught r three and r four and they correspond correspond on this side to d d t of s naught
c and d where as intercellular which corresponds to a b and c which are actually zero here
can be taken from this particular from the a b c part of the matrix here this particular
part of the stoichiometric matrix so i represented this as a matrix that represent extracellular
metabolite and a matrix that represent intracellular metabolite you can take a look at the details
convince yourself with time let us move forward now so the metabolite flux analysis this is
something like this war what all can be done with [vocalized-noise] metabolite flux analysis
right this this stoichiometric matrix gives us the various rates the stoichiometric
matrix sorry this representation gives us stoichiometric matrix various rates and the
rates of accumulation so let us see what all can be done or what are the common things
that i would done with with such a representation which would be useful for us from a bioreactor
point of view so estimation of intracellular metabolite flux from extracellular metabolite
rates extracellular metabolite can be measured by some of the methods that we talked about
in in earlier module module three so with by measuring those can we can get an estimate
of the intracellular metabolite flux right the rates things are going on inside the cell
identification of branch point control nodal rigidity in cell pathways this can done identification
of alternate pathways can be done analysis of fluxome metabolome and any omes can be
done ome is nothing but a complete collection of everything that happens a genome for example
is a complete representation of collection of all the genes in the cell fluxome is the
complete representation of all the fluxes in the cell and so on so far ok you can analyze
that using this approach we are going to look at on the first two as examples of what can
we done with metabolite flux analysis so let us look at the thing first estimation of intracellular
metabolite flux from extracellular metabolite rates this is the same figure that we saw
earlier is not going to s which further goes to a and b a goes c and b goes to d s naught
c and b an extracellular metabolites s a and b intracellular metabolites so we are going
to measure s naught c and d and then get the rated that are involved here how do we do
that we no that they extracellular the formulation corresponding extracellular
metabolites is this we are already seen that earlier minus one zero zero zero one zero
zero zero one r naught r three r four these are the the rates at corresponded extracellular
metabolites the d d t of s naught c and d the equations corresponding to extracellular
metabolites have been written in this matrix and this is the intracellular one if s naught
c and d can be measured you can calculate this right you take points at reasonably closing
phase time intervals then you can actually get the rates of variation of x naught c and
d in the extracellular space knowing this knowing rates of x naught c and d let us sayour
interest is estimating r one and r two the thats are interest that is what we said
here estimation of intracellular metabolite flux or rates from extracellular metabolite
x r zero the substrate of substrate uptake rate can be estimated from s naught measurements
thats ok directly gives you r naught r three r four the metabolite of the product formation
rates can be estimated from c and d measurements respectively the intracellular matrix equation
has three equations and five unknowns right number of equations number of unknowns you
know the degree of freedom concept the degree of freedom degrees of freedom is an number
of independent variables that are need to be known to solve this set of equation completely
we have three equations five unknowns so five minus three is two so we need two of extracellular
fluxes to determine r one and r two let us say that those two are r three and r four
which are known which can be known from c and d measurements we take measurements of
c and d concentrations at reasonable close these space times and we have these concentrations
we have these rates as derivatives c two minus c one divided by t two minus t one and so
on so for so this is the matrix here we intracellular matrix which we are taken to
be zero which we had represented as which can be represented as this part alone corresponding
to r naught r one r two and this part alone corresponding to r three r four rate that
will essentially be the same as this you can tested out so minus one minus one minus one
zero one zero zero zero one this corresponds to r zero r one r two so we were written that
separately and zero zero minus one zero zero one this corresponds to r three and r four
and so we written them separately is zero zero zero
now if we transpose this matrix equation ok this is an equation consistingof matrixes
we transpose these and pre multiplied both side by the inverse of the first matrix you
should be little comfortable of the linear algebra do it so little do difficult to get
into those were principles in this lecture so if you followed it its fine otherwise you
need to take it on belief and then go and check the matrix algebra and then understand
these things better so if we pre multiply this matrix were this inverse this will drop
out and pre multiple this with this same inverse matrix this termremain and anyway this is
zero so this is going to drop out ok so we have transpose these equation and therefore
all the minus ones have becomes ones these zeros will remain the same so this equals
this thats whats is the first step and then we have free multiplied with these matrix
this [vocalized-noise] with the matrix such the inverse of this matrix which is this so
i i inverse is a unity matrix we are not going to represent that this matrix times this
into r the r four will directly give us r naught r one and r four or r three r four
we are interested in by measuring r naught r one and r two no r two r four are ones set
of being measured here calculated here from the measurements of c and d with that
we can get r not r one and r two so r not and r one and r two if you work things out
it will convert in this three multiplication this matrix would result in if you multiply
and so on so far r three plus r four r three and r four therefore r not equals r three
and r four so just by doing this kind of a manipulation
we directly get the intracellular rates based on the extracellular rates that can be measured
thats now let us look at nodal rigidity identification ok this is what content to the lysine improvement
that we talked about [vocalized-noise] value vallinos and stephena early nineties to understand
this let us take an example let us consider the example that is that popularized metabolic
flux analysis the lysine introduction this is the reference here it is been also given
and your list of references you can go on take a located Stephanopoulos and vallino
nineteen ninety one network rigidity and metabolic engineering in metabolite over production
this is publishing science and then there is on other paper
that was published in biotechnology and bioengineering in nineteen ninety three which gives some
of details as mentioned earlier lysine minus lysine is produce an annual quantities
of hundred of thousands of tons because this amino acid is not made by ani by animal cells
but is required forproteins and so on therefore it needs to provided for that itself causes
or gives you the reason for the large production of minus lysine corynebacterium glutamicum
which is an organism this produces a lysine the yield when this was being produce was
only about fifteen percent and this fifteen percent was increased more than three four
togreater than fifty percent through metabolic flux analysis you see the powerhere fifteen
percent is gone to fifteen fifty percent through metabolic flux analysis and subsequent genetic
manipulations see metabolic flux analysis gives you directions
based on that you need to modify the organisms genetically and then in this case it produced
more than three fold yield so that some details this is the primary path way that we should
be interested in for lysine production this is a part of glycolysis glucose glucose going
to glucose six phosphate to fructose six phosphate to fructose six phosphate to phosphate pyruvateto
pyruvate and then there is branch point which is important here glucose s phosphate through
ri bullose five phosphate comes back to fructose six phosphatephosphor pyruvate here goes tooxaloacetic
acidand then to lysine pyruvate can also go through lysine and there is an another branch
point here from pyruvate to acetyl co a people would recognize that this is the t c a cycle
which is indicated by the dotted circle here dash circlelet us now define a node which
is the metabolite where more than one reactionoccursso there are three principles nodes or branch
points in this network g six p where there is branch branching more than one reaction
occurs one reaction two reac[tion]- second reaction and then p e p this goes here into
oxaloacetic acid and pyruvate here it is going to oxaloacetic acid and lysine so these are
the three branch points here these are the ones that one focuses on to do node of
rigidity rigidity analysis nodes provides us the possibility of manipulation to preferentially
increase the flux through one of the desired branches suppose we want to flux the go here
the lysine and this is one of one of the places where
a possibility exists to channel the flux more here by some means compare to the flux here
that is what is going to increase the lysine production right so that is why we look at
these nodes the branch points so in in formal terms if m metabolite under goes three reactions
with rates j one j two and j three or fluxes j one j two and j three in this terminology
to give you x y and z and let us say y is the desired product here j two needs to be
increased selectively over j one and j three right so the split ratio as it is called this
is flux through the desired branch divided by the sum of fluxes through all branches
that is the split ratio it can be written as j two divided by the sum of all js j one
j two j three in this case so j two by j one j two plus j three this is the split ratio
this is fine but the main reason why we need to by this is not so easy is it the flux cannot
be changed at will due to the nature of node ok all nodes in all nodes you just cannot
say wewill take more carbon through this branch and stop by probably the cutting of
the enzyme that is responsible for this branch and so on so forth it will not happen ok let
me briefly tell you what it is there are two kinds of nodes that are possible it could
be rigid node or flexible node to do that let us consider this example s going a a goes
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
a to c is fifty and c to d is twenty five c to e is twenty five and some units mille
mole per time whateverrate so this is the branching
when nothing has been done to this particular network like some genetic means if we can
cut off the enzymes may be for this branch then this branch can be cut off and this branch
also cut off let us say then this c to d conversion cannot takes place if it is if the observed
fluxes need not really observed it let us say that the fluxes on doing these two
cuts stands out to be something like this ok on cutting of these the hundreds becomes
zero here but it is hundred here whereasby cutting of this both the fluxes have become
zero ok let us say that this happened its not easy to observe this but let us say this
is happened if this is happened at the node a
it is possible to reroute the entire flux through one branch ok lets say this is the
desired branch by cutting off this branch we are able to reroute all the flux of this
branch and therefore this is a flexible node ok whereas at node c because we cut this off
this is also got cut off ok we didn't intend to cut this off but just because we cut off
this path this also got cut off such a node c is called a rigid node we cannot expect
to increase the flux preferentially through one of the branch point if the other branch
point is cut off that's a rigid node so we need to look for flexible nodes to manipulate
things rigid nodes will not help us so rigidity could arise due to many reasons for example
d could be necessary for the branch that is producing e in some way some metabolic
way some genetic way it could be responsible may be through a feedback loop or something
like that therefore if you cut this off also gets cut off so these three branch points
are shown g six p p e p pyruvate these three nodes through experiments and flux analysis
it was found that these two nodes g six p pyruvate these are flexible it is possible
to reroute the flux through a desire branch by cutting off the other branch whereas p
e p is rigid and rigidity needs to be overcome for better lysine production you can or in
other words p e p is going to o a this is the desired branch this is the p e p carboxylase
enzyme that is doing this p e p carboxylase from the regular organism is leads to rigidity
node whereas p e p carboxylase is a enzyme from pseudomonas is helpful ok is helpful
to break the rigidity therefore the approach was take pseudomonas
p e p carboxylase express in corynebacterium glutamicum and that has helped to decrease
the rigidity of this node and thereby increase the production of lysine by about three fold
okso in the last part actually taken the gene from some other organism expressed it in the
hose organism this is the recombinent d n a technology that i was talking about and
this is a way of modifying this cell to produce more ok so i have shown you an example where
the two major aspects one is looking at the cell in detail and then modify the cell are
have both been shown to improve the yield of the product in this case lysine and r d
n a has been used heavily for insulin production
insulin is made by using bacteriumthey have taken the insulin gene they took the insulin
gene and express it in bacterium and produceinsulin that is the way by which they could produce
large amount of insulin and bring down the cost and availability and modernity by significant
significant and one more step further this is changing putting r d n a r d n a technology
is taking d n a and putting it to another organism ones the further is changing the
entire cell ok an example for that is hybridoma you take a cell that is cancerous that can
grow forever you take a cell that can produce a particular type of antibody put them together
you get a cell that produces only that kind of a antibody and can live forever ok that
is changing the cell completely that can also be done to make bioreact and if you use
them in bioreactorsyou can get production of the required product the monoclonal antibodies
from bioreactors so in summary if you look at the look at module five will summarize
the entire course in the next lecture the hm in module five we began by considering
the cells themselves the actual factories that produce the product we said that we could
get some input into what is happening by considering the cell as ablack box and one of the method
of doing that is by stoichiometry bioreactions stoichiometry and the degree of reluctance
concept is an important concept there then we said that we will open a wind a window
look at it some windows are the cell status indicators the metabolic status indicators
energy status indicators such as redox ratio the energy ratio and
the intercellularp h these three are examples they could be others and then we said we will
go and change we will go and look inside the cell that is what we did by metabolic
flux analysis through the methods that we have described in detail here some detail
here not all detail and then based on the input provided by the metabolic flux analysis
we could do directed change through r d n a technology of the genetic machinery and
improve the productivity for example lysine by more than three fold and then we said that
you could actually change the cell itself by bringing two types of cells together to
produce monoclonal antibodies i or any other product of interest
ok with this we finish the five modules in the next lecture let me summarize in brief
it will be brief lecture what all we did in the course that will provide you the nice
summary for the course see you in the next lecture