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RE: Systems Biology Task Force Kick-Off - tomorrow (Feb 22) at 11AM EDT

From: Michael Miller <mmiller@systemsbiology.org>
Date: Fri, 24 Feb 2012 12:28:44 -0800
Message-ID: <ac6ff6eca9cf99b829e0757479e2b479@mail.gmail.com>
To: Oliver Ruebenacker <curoli@gmail.com>, Bob Futrelle <bob.futrelle@gmail.com>
Cc: "Waard, Anita de A (ELS-NYC)" <A.dewaard@elsevier.com>, Helena Deus <helenadeus@gmail.com>, public-semweb-lifesci <public-semweb-lifesci@w3.org>
hi all,

i definitely echo your thoughts, oliver. and there is, in some cases, the
raw and processed data from gene expression (microarray and ngs),
proteomics (ms and ms/ms) and so on which the paper will reference as
deposited at some public repository.  the BioRDF group has shown how this
can be converted into RDF.

cheers,
michael

Michael Miller
Software Engineer
Institute for Systems Biology

> -----Original Message-----
> From: Oliver Ruebenacker [mailto:curoli@gmail.com]
> Sent: Friday, February 24, 2012 12:14 PM
> To: Bob Futrelle
> Cc: Waard, Anita de A (ELS-NYC); Helena Deus; public-semweb-lifesci
> Subject: Re: Systems Biology Task Force Kick-Off - tomorrow (Feb 22) at
> 11AM EDT
>
>      Hello Bob, Anita, All,
>
>   I'm not claiming that numbers determine the content of a paper
> entirely. But I do claim they play a central role. I guess it is not
> entirely impossible to publish a paper with no numbers, but it appears
> to be extremely rare. I would estimate the fraction of biology papers
> with no numbers to be less than one percent.
>
>   Images are numbers - radiation intensity measured as a function of
> space. Most biologists I know would use images to estimate the amounts
> or concentrations of the substances that cause the radiation.
>
>   I suppose it is possible to derive conclusions without numbers. But
> in my experience it is extremely rare. Who would report A grows faster
> than B without reporting, by how much? How can you even talk about
> chemical compositions without providing any numbers about what these
> compositions are?
>
>   If you have data plots, that means you have lots of numbers. Most
> biologists I know would try to fit the data to a mathematical model.
> Who would report that graph A has a bigger slope than graph B without
> reporting, what those slopes are?
>
>   Certainly, papers contain other things besides data. It is possible
> that two different authors, given the same data, arrive at different
> conclusions. But why should I care about things not derived from data?
> If it is about established knowledge, I don't need to get it from that
> paper - I can get it from somewhere else. And if some statement is
> neither derived from the data nor established knowledge, why would I
> believe it?
>
>      Take care
>      Oliver
>
> On Fri, Feb 24, 2012 at 2:24 PM, Bob Futrelle <bob.futrelle@gmail.com>
> wrote:
> > Statements are based on a wide variety of things.  Statements are
> produced
> > by author/experimenters who may describe and evaluate gels and other
> images
> > without ever reducing them to numbers. Statements often compare  (not
> > numerically) growth, morphology, chemical composition, to those
aspects
> > found in other portions of an experiment or reported in other papers.
 A
> > paper will often include data plots, but the authors may report
particular
> > characteristics of the data behavior or correlation in the plots by
looking
> > at the plots, not reducing them to numbers.
> >
> > You cannot discard the authors' evaluations and hope to reach the
> > conclusions that those humans have by some analysis of the numbers.
> >
> > Your system for doing that would have to include the world knowledge
of
> the
> > authors.  In other words, these problems are AI-Complete, as are so
many
> > important, complex, and interesting problems in the real (biological)
world.
> >
> > Anita's comments that followed mine are in the same spirit.
> >
> >   - Bob
> >
> >
> > On Fri, Feb 24, 2012 at 2:14 PM, Oliver Ruebenacker <curoli@gmail.com>
> > wrote:
> >>
> >>     Hello Bob,
> >>
> >>  There are many statements which contain no numbers. But isn't the
> >> evidence that they are true usually based on numbers?
> >>
> >>  My suggestion is to structure quantitative data based on the
> >> statements for which this data is used as evidence.
> >>
> >>     Take care
> >>     Oliver
> >>
> >> On Fri, Feb 24, 2012 at 1:59 PM, Bob Futrelle
<bob.futrelle@gmail.com>
> >> wrote:
> >> > A great deal of the content of papers is not about numbers, but
> >> > statements
> >> > intertwined with propositional attitudes. Good evidence for this is
in
> >> > the
> >> > use of hedging.  See,
> >> >
> >> > Hyland, K., Hedging in scientific research articles. Pragmatics &
> >> > beyond,1998, Amsterdam ; Philadelphia: John Benjamins Pub. Co. ix,
307
> >> > p.
> >> >
> >> >
> >> >  - Bob Futrelle
> >> >    BioNLP.org
> >> >
> >> > On Fri, Feb 24, 2012 at 1:46 PM, Oliver Ruebenacker
<curoli@gmail.com>
> >> > wrote:
> >> >>
> >> >>     Hello Anita, All,
> >> >>
> >> >>  I'm mostly interested in numbers. Presumably, these statements
have
> >> >> been derived from numbers that can be found in these papers. The
> >> >> challenge is to classify numbers and group them into systems and
> >> >> states. Could your epistemic model help do that?
> >> >>
> >> >>     Take care
> >> >>     Oliver
> >> >>
> >> >> On Fri, Feb 24, 2012 at 12:15 PM, Waard, Anita de A (ELS-NYC)
> >> >> <A.dewaard@elsevier.com> wrote:
> >> >> > All,
> >> >> >
> >> >> > I was struck by the phrase 'turning biological knowledge into
> >> >> > mathematical models' and wondering if anyone is interested to
> model
> >> >> > 'epistemic information' in biology articles, added to the
knowledge
> >> >> > of the
> >> >> > type that Oliver mentioned?
> >> >> >
> >> >> > In particular I am interested in modelling clauses such as 1 a.,
2
> >> >> > a., 3
> >> >> > a., and 3.b in the sentences below - I have a model to classify
these
> >> >> > and
> >> >> > would like to add this 'knowledge attribution' layer to existing
> >> >> > representations of triples in papers.
> >> >> >
> >> >> > 1 a. These studies have shown that
> >> >> > 1.b. the 5' untranslated region (5'UTR) can be complex
> >> >> >
> >> >> > vs.
> >> >> >
> >> >> > 2. a. It has been reported that
> >> >> > 2. b. 5' untranslated exons, and sometimes introns, can regulate
the
> >> >> > expression of genes in two different ways.
> >> >> >
> >> >> > vs.
> >> >> >
> >> >> > 3. a. Thus, our analysis revealed areas of active chromatin
> >> >> > remodeling
> >> >> > in the vicinity of exon 1
> >> >> > 3. b. suggesting that
> >> >> > 3. c. this area may be important for CCR3 transcription.
> >> >> >
> >> >> > Is this part of the remit of this group and/or is anyone
interested
> >> >> > in
> >> >> > collaborating on this topic?
> >> >> >
> >> >> > Thanks,
> >> >> > Best,
> >> >> >
> >> >> > - Anita.
> >> >> >
> >> >> > Anita de Waard
> >> >> > Disruptive Technologies Director, Elsevier Labs
> >> >> > http://elsatglabs.com/labs/anita/
> >> >> > a.dewaard@elsevier.com
> >> >> >
> >> >> >
> >> >> >
> >> >> > -----Original Message-----
> >> >> > From: Oliver Ruebenacker [mailto:curoli@gmail.com]
> >> >> > Sent: Fri 2/24/2012 11:55
> >> >> > To: Helena Deus; public-semweb-lifesci
> >> >> > Subject: Re: Systems Biology Task Force Kick-Off - tomorrow (Feb
22)
> >> >> > at
> >> >> > 11AM EDT
> >> >> >
> >> >> >
> >> >> >     Hello Helena, All,
> >> >> >
> >> >> >  I'm interested in joining the Systems Biology Taskforce. Sorry
I
> >> >> > could not make the initial call. My interest is turning
biological
> >> >> > knowledge into mathematical models, automatically. A brief
> >> >> > description
> >> >> > is below.
> >> >> >
> >> >> >  Thanks!
> >> >> >
> >> >> >     Take care
> >> >> >     Oliver
> >> >> >
> >> >> >  Living organisms are so enormously complex that we need
computer
> >> >> > simulations to understand the consequences of their vast
> biochemical
> >> >> > reaction networks. As we uncover an increasing part of these
> >> >> > networks,
> >> >> > our established knowledge is increasingly stored in free web
> >> >> > databases
> >> >> > and available for query and download in machine-readable
formats,
> >> >> > especially in the RDF/OWL-based community standard Biological
> >> >> > Pathways
> >> >> > Exchange (BioPAX) [1]. The available data is massive and
growing,
> >> >> > e.g.
> >> >> > Pathway Commons [2] stores 1,700 pathways, 414 organisms,
> 440,000
> >> >> > interactions and 86,000 substances. This data is fully linked
with
> >> >> > open controlled terminologies such as gene ontology (e.g.
> anatomical
> >> >> > features) [3] and other free online databases such as ChEBI
> >> >> > (chemicals) [4], KEGG (genes a.o.) [5], UniProt (proteins) [6]
and
> >> >> > PubMed (publications) [7].
> >> >> >
> >> >> >  Automatic use of this knowledge for computer simulations of
> >> >> > biological organisms has been an ongoing challenge [8,9,10].
Now,
> >> >> > Systems Biology Pathway Exchange (SBPAX) [11], a BioPAX
> extension,
> >> >> > allows the inclusion of quantitative data and systems biology
terms,
> >> >> > especially the Systems Biology Ontology (SBO) [12]. SBPAX
support
> has
> >> >> > been implemented by the Virtual Cell [13], Signaling Gateway
> Molecule
> >> >> > Pages [14] and System for the Analysis of Biochemical Pathways -
> >> >> > Reaction Kinetics (SABIO-RK) [15]. For the first time, a
mathematical
> >> >> > model can be automatically built and fully annotated from a
pathway
> >> >> > of
> >> >> > interest.
> >> >> >
> >> >> >  Citations:
> >> >> >
> >> >> >  [1] Biological Pathway Exchange (BioPAX), www.biopax.org
> >> >> >  [2] Pathway Commons, www.pathwaycommons.org
> >> >> >  [3] Gene Ontology (GO), www.geneontology.org/
> >> >> >  [4] Chemical Entities of Biological Interest (ChEBI),
> >> >> > www.ebi.ac.uk/chebi/
> >> >> >  [5] Kyoto Encyclopedia of Genes and Genomes (KEGG),
> >> >> > wwww.genome.jp/kegg/
> >> >> >  [6] UniProt, www.uniprot.org
> >> >> >  [7] PubMed, www.ncbi.nlm.nih.gov/pubmed/
> >> >> >  [8] Modeling without Borders: Creating and Annotating VCell
Models
> >> >> > Using the Web, Michael L. Blinov, Oliver Ruebenacker, James C.
> Schaff
> >> >> > and Ion I. Moraru,  Lecture Notes in Computer Science, 2010,
Volume
> >> >> > 6053 (2010).
> >> >> >  [9] Using views of Systems Biology Cloud: application for model
> >> >> > building, Oliver Ruebenacker, Michael Blinov, Theory in
Biosciences,
> >> >> > Volume 130, Number 1, 45-54 (2010).
> >> >> >  [10] Integrating BioPAX pathway knowledge with SBML models,
> Michael
> >> >> > L Blinov, Oliver Ruebenacker, Ion I Moraru, IET Syst. Biol.,
2009,
> >> >> > Vol. 3, Iss. 5, pp. 317-328 (2009).
> >> >> >  [11] Systems Biology Pathway Exchange (SBPAX), www.sbpax.org
> >> >> >  [12] Systems Biology Ontology, www.ebi.ac.uk/sbo/main/
> >> >> >  [13] Virtual Cell, http://vcell.org
> >> >> >  [14] Signaling Gateway Molecule Pages,
> >> >> > www.signaling-gateway.org/molecule/
> >> >> >  [15] System for the Analysis of Biological Pathways - Reaction
> >> >> > Kinetics (SABIO-RK), http://sabio.villa-bosch.de/
> >> >> >
> >> >> > On Tue, Feb 21, 2012 at 4:32 PM, Helena Deus
> <helenadeus@gmail.com>
> >> >> > wrote:
> >> >> >> Dear All,
> >> >> >>
> >> >> >> Please join me tomorrow for the kick-off telco of the Systems
> >> >> >> Biology
> >> >> >> Task
> >> >> >> Force. Systems Biology is about looking at biological systems
from
> >> >> >> an
> >> >> >> integrated perspective and to use that perspective to
understand
> >> >> >> disease. We
> >> >> >> will be discussing the general goals, strategy and structure of
the
> >> >> >> task
> >> >> >> force.
> >> >> >>
> >> >> >> Please see http://www.w3.org/wiki/HCLSIG/SysBio for an initial
> >> >> >> motivation,
> >> >> >> and description, of what this task will be focused on (with due
> >> >> >> flexibility
> >> >> >> according to participants input).
> >> >> >>
> >> >> >>
> >> >> >> * Date of Call: Tuesday February 21, 2012
> >> >> >> * Time of Call: 11:00am Eastern Daylight Time (EDT)
> >> >> >> * Dial-In #: +1.617.761.6200 (Cambridge, MA)
> >> >> >> * [Note: limited access to European dial in numbers below]
> >> >> >> * Dial-In #: +33.4.26.46.79.03 (Nice, France)
> >> >> >> * Dial-In #: +44.203.318.0479 (Bristol, UK)
> >> >> >> * Participant Access Code: 4257 ("HCLS").
> >> >> >> * IRC Channel: irc.w3.org port 6665 channel #HCLS
> >> >> >> For instant IRC access:
> >> >> >> see [http://www.w3.org/Project/IRC/ W3C IRC page] for details,
or
> >> >> >> see [http://cgi.w3.org/member-bin/irc/irc.cgi Web IRC]), Quick
> >> >> >> Start:
> >> >> >> Click
> >> >> >> on
> >> >> >>
> >> >> >>
> >> >> >>
> [http://www.mibbit.com/chat/?server=irc.w3.org:6665&channel=%23hcls mi
> bbit]
> >> >> >>
> >> >> >> * Duration: ~1h
> >> >> >> * Convener: Helena
> >> >> >> * Scribe: TBD
> >> >> >>
> >> >> >> Kind regards,
> >> >> >> Helena
> >> >> >
> >> >> >
> >> >> >
> >> >> > --
> >> >> > Oliver Ruebenacker, Computational Cell Biologist
> >> >> > Virtual Cell (http://vcell.org)
> >> >> > SBPAX: Turning Bio Knowledge into Math Models
> (http://www.sbpax.org)
> >> >> > http://www.oliver.curiousworld.org
> >> >> >
> >> >> >
> >> >> > Elsevier B.V. Registered Office: Radarweg 29, 1043 NX Amsterdam,
> The
> >> >> > Netherlands, Registration No. 33156677 (The Netherlands)
> >> >> >
> >> >>
> >> >>
> >> >>
> >> >> --
> >> >> Oliver Ruebenacker, Computational Cell Biologist
> >> >> Virtual Cell (http://vcell.org)
> >> >> SBPAX: Turning Bio Knowledge into Math Models
> (http://www.sbpax.org)
> >> >> http://www.oliver.curiousworld.org
> >> >>
> >> >
> >>
> >>
> >>
> >> --
> >> Oliver Ruebenacker, Computational Cell Biologist
> >> Virtual Cell (http://vcell.org)
> >> SBPAX: Turning Bio Knowledge into Math Models (http://www.sbpax.org)
> >> http://www.oliver.curiousworld.org
> >
> >
>
>
>
> --
> Oliver Ruebenacker, Computational Cell Biologist
> Virtual Cell (http://vcell.org)
> SBPAX: Turning Bio Knowledge into Math Models (http://www.sbpax.org)
> http://www.oliver.curiousworld.org
Received on Friday, 24 February 2012 20:29:10 UTC

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