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Molecular Cell Biology AB_1053 Complete Summary

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  • Course
  • Molecular Cell Biology (AB_1053)
  • Institution
  • Vrije Universiteit Amsterdam (VU)

This is a very detailed summary of the content of the course Molecular Cell Biology AB_1053. This course is very difficult if the concepts of Metabolic Control Analysis and Flux Balance Analysis are new to you. This summary could help you go through the lectures more easily. The grades of the wri...

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Document information

  • October 6, 2021
  • 100
  • 2021/2022
  • Class notes
  • Rob van spanning
  • All classes

Subjects

  • molecular
  • cell
  • biology
  • fba
  • modelling
  • bio energetics
  • enzyme kinetics
  • rate equations

Written for

  • Vrije Universiteit Amsterdam (VU)
  • Bachelor Biomedical Sciences
  • Molecular Cell Biology (AB_1053)
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MOLECULAR CELL BIOLOGY
AB_1053




P2 2020

,Inhoud
Molecular Cell Biology: MCA-1 ................................................................................................................ 2
Molecular Cell Biology: MCA-2 .............................................................................................................. 12
Molecular Cell Biology: Enzyme Kinetics ............................................................................................... 24
Molecular Cell Biology: FBA-1 ............................................................................................................... 33
Molecular Cell Biology: FBA-2 ............................................................................................................... 42
Molecular Cell Biology: FBA-3 ............................................................................................................... 52
Molecular Cell Biology: Modelling The Cell 1 ........................................................................................ 65
Molecular Cell Biology: Bio-Energetics 1 ............................................................................................... 75
Molecular Cell Biology: Bio-Energetics 2 ............................................................................................... 83
Molecular Cell Biology: Bio-Energetics 3 ............................................................................................... 94




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,Molecular Cell Biology: MCA-1
Sendsteps questions and answers:

Q1: How complex is the simplest organism? (How many genes does its genome have?)

A. 1
B. 25,000
C. 400
D. 4,000
E. 6,000
F. 2

A1: C. Many mathematicians would assume 1, 2 and 3 to be good ones, but not necessary good guesses
for this question. It is due to the fact that they like working with such models. Unfortunately, in biology,
we have to work with models of 400 genes meaning a lot of variables. 400 genes is what the simplest
organism contains. Not counting viruses, because they are parasites. E. coli contains 4,000 genes and
S. cerevisiae contain 6,000. We contain 25,000. However, the 400 gene one is an intracellular parasite.
So, you could debate with the department whether they are at all experimenting with the simplest
‘organism’.

Q2: What is the evidence for this?

A. Genome sequencing: smallest genome
B. Sequencing plus knock outs
C. Thinking and arguing
D. Isolating all the proteins
E. Isolating all the genes
F. Determining all protein structures

A2: B. Genome sequencing would be good, isolating all the genes is too much work, but sequencing
plus knock outs is the best approach. The problem with genome sequencing is that, while you
determine the genes and the sequences, even in the simplest organism (with smallest genome) there
is redundancy. This means that you will sometimes sequence two same genes with the same function
or sequence parts that do not code. With the knock outs you can see whether certain sequence or a
gene has what or at all any function. Doing this method, a scientist found the simplest organism to
effectively have 350 genes.

Q3 (open question): How do we know that it is the networks that matter, not just the individual genes?

A3: We continue with the summary, in the next part it will be explained.

Networks matter. There are two parts of evidences for this: (i) In the smallest organism, all the 300
genes are needed together, in the same network: there is not an organism of 150 genes and another
one having the other 150 genes. Everything is together in a single cell. (ii) Fluxes of life require
collaboration of multiple enzymes, transcription factors and signals etc. So, all the genes work together
and affect each other, the entire network matters. For expression of a gene, the information from the
DNA is copied with RNA polymerase into an mRNA, the mRNA is translated with the help of ribosomes
and tRNA. There are a lot of molecules for mechanisms like that within a cell that are required to make
things work, thus meaning that there is an entire network of things. The first part of the evidence is
really just an observation, while the second part of the evidence is the theory.



2

, Not too many courses or studies really discuss the essence of networks, because we
used to look at single molecules historically. One of the most well-known and
important pathways in life is the glycolysis pathway, see picture. Glycolysis degrades
glucose to pyruvate and sometimes to lactate from there or catabolized further in
the citric acid cycle. As you can tell, it is not a single reaction, and therefore it will
also not be coded for by a single gene. For every reaction, there is an enzyme
required for catalysis and every enzyme is a protein and thus coded for by a gene or
a few genes (these are two laws of biochemistry). A process (an enzymatic reaction),
a component, is part of a system (metabolic pathway). So, the network works by a
number of proteins, enzymes and genes, regulated by transcription factors signalers
etc. An example of a system in a cell is glycolysis: every arrow is an enzyme-catalyzed
reaction, the kinetics of which can be studied in isolation.

Biology could be considered a better system than physics or chemistry etc. In
chemistry, if you have a set of chemical reactions, every individual reaction can not
be specifically modulated (pH, temperature, can be changed, but there can be a lot
of random things). Same goes for physics. But in biology, you can change the
expression of a gene (by targeting the promoter, gene editing techniques and so on).
You can change the property and activity of every component with mutations, knock-
ins and knock-outs as well. This is quite unique to biology, in particular molecular cell
biology.

Studying such an entire network and how components interact with each other in a network like that,
is called systems biology. In systems biology, the whole systems relates to its components, the
molecules constitute the basis of life and the emergence of function from networking is studied.

Another Sendsteps question and answer:

Q4: A biologist has only done biochemistry, what is she missing?

A. Structural biology
B. Network science (understanding what networking changes)
C. Mathematics
D. Data

A4: B. You require structural biology to understand the components much better. But at the end of
the day, you only need to understand the networking to understand the functioning. However, indeed,
for that you need to understand the components with structural biology. Mathematics can work as a
tool to help with the networking, and data is required to at all make anything work. However, answers
A, C and D are the tools, while answer B is the key thing to understand.

The department at VU works on networks. Despite the name of the lecture being ‘metabolic control
analysis’ (MCA), a historical terminology, the same analysis is valid for gene expression networks, signal
transduction networks, ecological networks and even for the corona network. Networking with
components lead to a function.




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