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Hi Robert,

This is the kind of trollish comments that are not really worth an answer.
They are easy to made and people feel smart when doing them while they just
look shallow. We have various variants, such as "the human genome did not
cure any disease" etc.

First, as you know, the sentence "SBML model has led to anything of any
practical use" is devoid of meaning. There is no such thing as "an SBML
model". SBML is of just a format to encode models. Think English language
and philosophy. More people read Kant in English than in German. Does that
make Kant's philosophy "English philosophy"?

So, the real comment this person makes, thinking s/he is very smart, is "no
quantitative model has led to anything of any practical use". And of course
there one can dismiss the comment with Turing, Hodgkin-Huxley (action
potential), Noble (heart pacemaker), Goldbeter and Koshland (covalent
cascades) etc. [replace by the most famous models in any domain of life
science], all discovering new emerging properties, that were unsuspected
before.

As for the legitimacy of models in the recent years, see the following list
that I compiled to answer this kind of question. All published in journals
that are arguably of "good quality". Some are in BioModels Db

Metabolic networks
==================
Fung et al. A synthetic gene-metabolic oscillator. Nature 2005;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000067
Herrgård et al. A consensus yeast metabolic network reconstruction obtained
from a community approach to systems biology. Nat Biotechnol 2008
http://www.ebi.ac.uk/biomodels-main/MODEL0072364382

Signalling pathways
===================
Bray et al. Receptor clustering as a cellular mechanism to control
sensitivity. Nature 1998;
Bhalla ad Iyengar. Emergent properties of signaling pathways. Science 1999;
http://www.ebi.ac.uk/biomodels-main/MODEL9071122126
Schoeberl et al. Computational modeling of the dynamics of the MAP kinase
cascade activated by surface and internalized EGF receptors. Nat Biotechnol
2002;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000019
Hoffmann et. The IkB-NF-kB signaling module: temporal control and selective
gene activation. Science 2002;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000139
Smith et al. Systems analysis of Ran transport. Science 2002;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000164
Bhalla et al. MAP kinase phosphatase as a locus of flexibility in a
mitogen-activated protein kinase signaling network. Science 2002;
http://www.ebi.ac.uk/biomodels-main/MODEL9070467164
http://www.ebi.ac.uk/biomodels-main/MODEL9079179924
Nelson et al. Oscillations in NF-kB Signaling Control the Dynamics of Gene
Expression. Science 2004;
Werner et al. Stimulus specificity of gene expression programs determined
by temporal control of IKK activity. Science 2005;
http://www.ebi.ac.uk/biomodels-main/MODEL1008110000
Sasagawa et al. Prediction and validation of the distinct dynamics of
transient and sustained ERK activation. Nat Cell Biol 2005;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000049
Basak et al. A fourth IkappaB protein within the NF-kB signaling module.
Cell 2007;
http://www.ebi.ac.uk/biomodels-main/MODEL8478881246
McLean et al. Cross-talk and decision making in MAP kinase pathways. Nat
Genet 2007;
Ashall et al. Pulsatile Stimulation Determines Timing and Specificity of
NF-kB-Dependent Transcription. Science 2009;
Becker et al. Covering a broad dynamic range: information processing at the
erythropoietin receptor. Science 2010

Gene regulatory networks
========================
McAdams and Shapiro. Circuit simulation of genetic networks. Science 1995;
Yue et al. Genomic cis-regulatory logic: Experimental and computational
analysis of a sea urchin gene. Science 1998;
Von Dassow et al. The segment polarity network is a robust developmental
module. Nature 2000;
Elowitz and Leibler. A synthetic oscillatory network of transcriptional
regulators. Nature 2000;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000012
Shen-Orr et al, Network motifs in the transcriptional regulation network of
Escherichia coli. Nat Genet 2002;
Yao et al. A bistable Rb-E2F switch underlies the restriction point. Nat
Cell Biol 2008;
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000318
Friedland. Synthetic gene networks that count. Science 2009
http://www.ebi.ac.uk/biomodels-main/BIOMD0000000301

Pharmacometrics models
======================
Labrijn et al. Therapeutic IgG4 antibodies engage in Fab-arm exchange with
endogenous human IgG4 in vivo. Nat Biotechnol 2009

Physiological models
====================
Noble. Modeling the heart from genes to cells to the whole organ. Science
2002;
Izhikevich and Edelman. Large-scale model of mammalian thalamocortical
systems. PNAS 2008

Infectious diseases
===================
Perelson et al. HIV-1 dynamics in vivo: Virion clearance rate, infected
cell life-span, and viral generation time. Science 1996;
Nowak. Population dynamics of immune responses to persistent viruses.
Science 1996;
http://www.ebi.ac.uk/biomodels-main/MODEL1006230050
Neumann et al. Hepatitis C viral dynamics in vivo and the antiviral
efficacy of interferon-alpha therapy. Science 1998

On 20/04/11 18:41, Robert Muetzelfeldt wrote:
> Hi,
>
> I'm at a Royal Society meeting on systems modelling in ecology
> (http://royalsociety.org/events/ecological-forecasting/). I held up
> SBML as a terrific example of what we should be aspiring to in
> ecological modelling, which currently is a mish-mash of a whole variety
> of modelling tools, Fortran programs, etc. One of the participants
> stated (rather outrageously) that no " (in terms of medical advances etc).
>
> I assume he is wrong, but would appreciate a couple of really good
> examples to prove it. Can any one provide a couple of links to these,
> and ideally point to the relevant BioModels entries?
>
> Many thanks,
> Robert Muetzelfeldt
>
>


--
Nicolas LE NOVERE, Computational Systems Neurobiology, EMBL-EBI, WTGC,
Hinxton CB101SD UK, Mob:+447833147074, Tel:+441223494521 Fax:468,
Skype:n.lenovere, AIM:nlenovere, twitter:@lenovere
http://www.ebi.ac.uk/~lenov/, http://www.ebi.ac.uk/compneur/

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