Reviewer’s Precis of the manuscript
Reviewer 1
“In silico experiments on a faulty ubiquitin-proteasome system in the pathogenesis of
Parkinson’s disease” .
This paper formulates and analyses mathematical models of the pathogenic processes
that are implicated in Parkinson’s disease. The models are formulated in the framework
of the stochastic pi-calculus, in combination with the framework of the chemical master
equation. The models are simulated computationally using the Stochastic PI-Machine,
which incorporates a modified Gillespie Stochastic Simulation Algorithm.
Overall this is a worthwhile computational study of a set of biochemical reactions that
are significant to our understanding of PD. This material would be of general interest to
the readership of the Online Journal of Bioinformatics.
Reviewer 2
The above mentioned manuscript describes 3 in silico models of potentially faulty
mechanisms of protein re-folding and malfunctions in the degradation of resulting
misfolded proteins.
Even though interesting, the article is difficult to read and the original contribution of
this work is somewhat unclear. The paper reads rather like a review article than original
research. Therefore, I recommend rejecting this paper in its current form. I suggest and
encourage the authors to resubmit after rigorous revision of the manuscript.
Details below:
Review of the manuscript
“In silico experiments on a faulty ubiquitin-proteasome system in the pathogenesis of
Parkinson’s disease” by Paola Lecca.
Summary
This paper formulates and analyses mathematical models of the pathogenic processes that
are implicated in Parkinson’s disease. The models are formulated in the framework of the
stochastic pi-calculus, in combination with the framework of the chemical master
equation. The models are simulated computationally using the Stochastic PI-Machine,
which incorporates a modified Gillespie Stochastic Simulation Algorithm.
Overall this is a worthwhile computational study of a set of biochemical reactions that are
significant to our understanding of PD. This material would be of general interest to the
readership of the Online Journal of Bioinformatics.
Accept with suggested minor changes (Editors note)
General comments
There are many spelling and grammar errors. The following are some examples.
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p2, (the introduction section), para 3, ‘mitocondrial’ should be replaced with
mitochondrial.
p3, first sentence of section 2, “… are thought to cause…”
p9, para 1, “We refer the reader… ”
p9, para 3, “midolfed” should be misfolded.
p11, last para, “… these coefficients and the …”
p16, middle para, “… the curve has a wide peak …”
p18, (the conclusions section) para 2, “…collecting more accurate data fpr
estimating…” should be for
p18, para 2, “…as it employees kinetic constants…” should be “…as it employs kinetic
constants…”.
p18 last sentence: “that are probabilistically enabled to occur via the Gillespie
algorithm”
The last two sentences of the conclusion need to be revised so that they make
sense. For example, the second last sentence could be replaced with
“The pi-calculus is well-suited to modeling the parallel nature of protein processing.”
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No title is given for references 13 and 27.
Reference 11 is missing a bracket.
Specific comments
1) The Gillespie SSA is a temporal modeling framework but it is known that cells are not
homogeneous environments. The spatial distribution of chaperones in the cell may not be
uniform, for example. Can the author include a brief discussion of the significance of
spatial effects for the processes they are modeling?
2) Section 3 should be revised to explain the formalism of the stochastic pi calculus in
simple terms. Using a simple example for this purpose is a very good idea. The author
does this on p7-8 but it still needs work so that it can be easily understood. Particular care
must be taken with the caption for Table 1 and the explanation of Table 2.
3) The author cites reference 19 for an explanation of the stochastic pi calculus. One of
the authors of this reference is Luca Cardelli, who has another paper entitled “A Process
Algebra Master Equation”. This paper states that the framework of the chemical master
equation is equivalent to the framework of the process algebra (Theorem 4.1–4). Is that
correct? If so, are the results of using the SpiM the same as what we would get by using
the Gillespie SSA (reference 9)? If not, can just one simulation be performed with the
Gillespie SSA, for say, Figure 4A, so that the reader may compare the two modeling
frameworks?
4) Within section 3 it is noted that the SpiM can incorporate delays. Are the delays
associated with translation incorporated into the models? If so, are the time delays
stochastic or deterministic and what are the approximate magnitudes of these delays?
Note that the waiting time until the next reaction occurs, which is usually denoted by τ in
the Gillespie SSA, is different to including a time delay associated with a reaction such as
translation. See, e.g., variations of the Gillespie SSA that have been developed to take
delays into account, such as:
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Bratsun D, Volfson D, Tsimring LS, Hasty J., Proc Natl Acad Sci U S A. 2005
Oct 11;102(41):14593-8
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Barrio M, Burrage K, Leier A, Tian T., PLoS Comput Biol. 2006 Sep 8;2(9):e117.
Is an algorithm of this type incorporated into the SpiM?
5) The label on the y-axis of all figures showing simulations is “number of processes.”
Can this label be changed so that it has an easy interpretation in terms of the chemical
species being modeled? For example, would it be correct to change the label to “number
of molecules”?
If not, then a clear explanation of what is meant by “processes” must be given, and the
Figures need to be redrawn with scales that are proportional to the copy numbers per cell
of the species being studied.
If so, then Figure 4 shows fractional numbers of molecules in some cases. Figure 4 also
shows that misfolded proteins occur in copy numbers of the order of 10 per cell. Other
figures show the copy numbers as high as 100. Is this sufficient to induce cell death?
Related to this point, are the numbers in Table 5 physiologically reasonable? For
example, is it reasonable to only have 100 copies of Ubiquitin per cell? I have seen
estimates that are several orders of magnitude larger than this.
6) Figure 4A shows one simulation of a stochastic process for a particular set of
parameters. When studying stochastic models it is conventional to perform many
simulations. How many simulations were performed for the parameters used in Figure
4A? Is the simulation shown typical of these? Can the mean and variance also be
reported? For example, it is common to include a two-standard deviation envelope
around the simulation results. This would not take much extra work.
7) One of the stated aims of the paper is to suggest new hypotheses and ways of testing
them. In the Conclusions section, can the author clearly state (1) what the new hypotheses
are and (2) how to test them? (The last sentence of p17 does not provide enough detail for
this purpose.) For example, can the author describe how to test the “threshold
phenomena” experimentally? Presumably this would relate to the 0.01u/s rate from
section 3.1.
8) Additional Discussion points the author may consider including
- The time scales for the simulation results are in microseconds but the onset of
PD is much slower. Can a very brief discussion be given of how the fast accumulation of
misfolded proteins leads to such a gradual effect? Some background to this is already
provided in the introduction but at the end of the paper the author could add another
perspective based on the results of their models.
- On a related note, the paper models the accumulation of misfolded proteins in a
single cell, but does not explicitly model how this leads to cell death and how this in turn
affects dopamine levels. Could a brief discussion be added describing how the model
may be extended in future work to quantitatively address this? Are their estimates of
about how many misfolded proteins are needed to trigger cell death? Or how many cells
must perish to have a significant effect on dopamine levels?
- One of the recent advances in neurosurgical treatments for PD and for tremorlike symptoms is deep brain stimulation. Can the author discuss briefly how their models
may be extended in future work to understand the effect of such electrical stimulation?
REVIEW 2
Accept with major changes (Editors note)
Re: In silico experiments on a faulty ubiquitin-proteasome system in the pathogenesis of
Parkinson's disease, by Paola Lecca
The abovementioned manuscript describes 3 in silico models of potentially faulty
mechanisms of protein re-folding and malfunctions in the degradation of resulting
misfolded proteins.
Even though interesting, the article is difficult to read and the original contribution of this
work is somewhat unclear. The paper reads rather like a review article than original
research. Therefore, I recommend rejecting this paper in its current form. I suggest (and
encourage!) the author(s) (??? see below) to resubmit after rigorous revision of the
manuscript. In detail:
a) The manuscript is far too long.
b) The article is currently organised in chapters that read like the excerpt of a thesis.
Chapters 1&2, and part of 3 are together some sort of introduction. The subheadings of
chapter 3 explain the models which, I suppose, comprise the original research. Chapter 4
discusses some of the results.
-> A research article should contain (i) introduction (such as chapter 1 in the current
manuscript plus aim of the research), (ii) results, (iii) a more detailed description of the
methods/algorithms (such as mat&met in a "wet-lab" paper), and (iv) a discussion which
summarises the results and puts them in context with the literature.
c) (minor) The term "wet-lab" is certainly jargon and should not be used in a publication.
d) The article lists only one author but throughout the manuscript the term "our models")
is used. Should there be other contributors mentioned?
e) (minor) Figure legend titles should start with a capital letter after "Fig. X:".
I am looking forward to re-assess this manuscript after rigorous rewriting.
Dear Editor,
enclosed is a paper, entitled In silico experiments on a faulty ubiquitin-proteaosome
system in the pathogenesis of Parkinson's disease. Please accept it as a candidate for
publication in the Online Journal of Bioinformatics.
The paper presents a computational quantitative model of a faulty chaperone and
ubiquitin-proteasome system. At the best of my knowledge this work is the _rst
attempt to formalize in a mathematical model the larger and larger amount of qualitative data, that recent wet-lab experiments are storing up about the molecular
mechanisms of protein folding and ubiquitination.
As consequence of a faulty functioning of the chaperones, ubiquitination and proteolysis, misfolded proteins accumulate in the cell and cause its death. Currently a
growing body of evidence is suggesting that the accumulation of misfolded proteins
in brain tissues is a crucial event in the Parkinson's disease neurodegeneration. Both
pathogenic genetic mutations and the exposure to environmental toxins may induce
abnormal protein conformations or compromise the ability of the cellular machinery
(mainly the chaperones and ubiquitin-proteasome systems) to detect and degrade
misfolded proteins. Although the recent explosion in the rate of discovery of genetic
defects and environmental factoes linked to Parkinson's disease have provided tangible clues to the neurobiology of the disorder, they have provided neither direct
explanation for the disease process or its key biochemical mechanism. The aim of
the paper is to provide computational models for in silico experiments, that can
enable the medical researchers to formulate new hypotheses for elucidating some
important and still elusive aspects of the Parkinson's disease and for designing new wet
experiments to test them.
The study proposes three stochastic models of a faulty mechanism of protein refolding and degradation of misfolded proteins: (i) a model describing the e_ects of
environmental stress factors on the processing of misfolded proteins, and (ii) two
models of genetic Parkinson due to the mutations in the _-synuclein gene and recessive mutations in parkin gene. The study presents also the numerical simulations of
the models in order to experiment in silico the time evolution of the chaperone and
ubiquitine-proteasome system and investigate its responses to the variations of the
kinetic parameters governing the dynamics of the biochemical interactions. The simulation results of all the models have suggested also a further hypothetical model
that
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relates the onset of the Parkinson's disease to an insu_cient quantity of chaperones,
as very recent experiments are starting to con_rm.
The models are speci_ed in the formalism of biochemical stochastic _-calculus and
are
based on what is currently known about the genetic mutations and environmental
stress causing Parkinson's disease. The remarkable expressive capabilities of this
formalism in the description of parallel and heterogeneous biochemical interactions
make it particularly suitable for modeling the intricate mechanism of proteins folding,
re-folding and degradation. Furthermore, the simulation results point out those
kinetic quantitative parameters (e. g. reaction rate coe_cients and the number of
available chaperons), whose variations lead to signi_cant changes in the capability
of the system to react to the accumulation of dangerous proteins.
I declare that the submitted paper contains original, unpublished results, and it is
not currently under consideration elsewhere. Given the novelty of the models and of
the approaches used to formalize them, I believe that the contribution of this study
can warrant its publication in the Online Journal of Bioinformatics.
Yours sincerely,
Paola Lecca.
Paola Lecca
The Microsoft Research - University of Trento
Centre for Computational and System Biology
piazza Manci 17, 38100 Povo (Trento), Italy
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E-mail:lecca@cosbi.eu
Phone: 0039 0461 882819
Fax: 0039 0461 882814
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