Time-Variable Gene Regulation Networks in Candida Glabrata




                             Michael P.H. Stumpf & Thomas Thorne

                           Theoretical Systems Biology Group, Division of Molecular
                                    Biosciences, Imperial College London


                                            12th June 2011
Networks: Mapping Processes and Understanding
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    Time-Variable Networks in Candida Glabrata                             Stumpf&Thorne                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          1 of 11
Biology is Dynamic — Networks Change with Time


                                                  A                          B




• Inferred regulatory network structures represent correlations rather than direct interactions.




     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                                    2 of 11
Biology is Dynamic — Networks Change with Time

                                                                      P
                                                  A               A




• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus to
  influence regulation; or complexes formed by signalling cascades may be required to
  activate transcription.




     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                                    2 of 11
Biology is Dynamic — Networks Change with Time

                                                                      P
                                                  A               A




• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus to
  influence regulation; or complexes formed by signalling cascades may be required to
  activate transcription.
• Many factors that are not a part of a traditional regulatory network model can also influence
  regulatory interactions.


     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                                    2 of 11
Biology is Dynamic — Networks Change with Time

                                                                      P
                                                  A               A          B




• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus to
  influence regulation; or complexes formed by signalling cascades may be required to
  activate transcription.
• Many factors that are not a part of a traditional regulatory network model can also influence
  regulatory interactions.
• These relationships may change depending on external signals or other factors.

     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                                    2 of 11
Capturing Biological Dynamics — Changepoint Models for
Networks

  • We can include hidden factors that my change the regulatory interactions taking place in our model by
      allowing the regulatory network structure to vary between timepoints and/or conditions.
  • In changepoint models the time series is divided into a number of segments, allowing a different
      network structure in each.
  • Using Bayesian inference it is possible to infer the posterior distribution of changepoint positions.




    `
S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks. BMC Systems
Biology, 4:130, 2010.

           Time-Variable Networks in Candida Glabrata      Stumpf&Thorne                                                                                    3 of 11
Capturing Biological Dynamics — Changepoint Models for
Networks

  • We can include hidden factors that my change the regulatory interactions taking place in our model by
      allowing the regulatory network structure to vary between timepoints and/or conditions.
  • In changepoint models the time series is divided into a number of segments, allowing a different
      network structure in each.
  • Using Bayesian inference it is possible to infer the posterior distribution of changepoint positions.




                   Time point     1          2           3          4          5          6           7          8          9         10
    `
S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks. BMC Systems
Biology, 4:130, 2010.

           Time-Variable Networks in Candida Glabrata        Stumpf&Thorne                                                                                  3 of 11
The Chinese Restaurant Process




    Time-Variable Networks in Candida Glabrata   Stumpf&Thorne   4 of 11
The Chinese Restaurant Process




                                                                               θ1          θ2         θ3         θ4


Analogy for the Dirichlet process due to Pitman and Dubins
                                                    ´       ´ ´              ´
D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983




           Time-Variable Networks in Candida Glabrata       Stumpf&Thorne                                                 4 of 11
The Chinese Restaurant Process




                                                                               θ1          θ2         θ3         θ4

                                                                                                                          H
Analogy for the Dirichlet process due to Pitman and Dubins
                                                    ´       ´ ´              ´
D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983




           Time-Variable Networks in Candida Glabrata       Stumpf&Thorne                                                     4 of 11
The Chinese Restaurant Process




                                                                               θ1          θ2         θ3         θ4       θ5


Analogy for the Dirichlet process due to Pitman and Dubins
                                                    ´       ´ ´              ´
D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983




           Time-Variable Networks in Candida Glabrata       Stumpf&Thorne                                                      4 of 11
The Chinese Restaurant Process




                                                                                                                                   ...

                                                                               θ1          θ2         θ3         θ4       θ5

                                                                                                                               H
Analogy for the Dirichlet process due to Pitman and Dubins
                                                    ´       ´ ´              ´
D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983




           Time-Variable Networks in Candida Glabrata       Stumpf&Thorne                                                            4 of 11
What We Want to Know is Often Not Measured: Hidden Markov
Models
• Here we measure transcriptomic data, whereas the action is all due to proteins and
  their interactions among themselves and with DNA/RNA.
• We measure mRNA expression (yi ) which is influenced by a network (si ) that is not
  or cannot be observed directly.
• We allow the network to change and learn this change from the observed data.
                                   π s1

                         s1

                    θs1

                         y1

    Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                         5 of 11
What We Want to Know is Often Not Measured: Hidden Markov
Models
• Here we measure transcriptomic data, whereas the action is all due to proteins and
  their interactions among themselves and with DNA/RNA.
• We measure mRNA expression (yi ) which is influenced by a network (si ) that is not
  or cannot be observed directly.
• We allow the network to change and learn this change from the observed data.



                         s1                      s2

                    θs1                  θs2

                         y1                      y2

    Time-Variable Networks in Candida Glabrata    Stumpf&Thorne                        5 of 11
What We Want to Know is Often Not Measured: Hidden Markov
Models
• Here we measure transcriptomic data, whereas the action is all due to proteins and
  their interactions among themselves and with DNA/RNA.
• We measure mRNA expression (yi ) which is influenced by a network (si ) that is not
  or cannot be observed directly.
• We allow the network to change and learn this change from the observed data.



                         s1                      s2               s3   ...     sT

                    θs1                  θs2                  θs3            θsT

                         y1                      y2               y3   ...     yT

    Time-Variable Networks in Candida Glabrata    Stumpf&Thorne                        5 of 11
Systems at Different Times are Related: The Chinese Restaurant
Franchise




                  θ2         θ1                            θ1    θ3   θ2   θ2




    Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                  6 of 11
Systems at Different Times are Related: The Chinese Restaurant
Franchise




                  θ2         θ1                            θ1    θ3   θ2   θ2




    Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                  6 of 11
Systems at Different Times are Related: The Chinese Restaurant
Franchise



                                                                      α




                  θ2         θ1                            θ1    θ3            θ2   θ2
                                                                           γ


                                        θ1                θ2          θ3   θ ∼H

    Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                           6 of 11
Systems at Different Times are Related: The Chinese Restaurant
Franchise

                                        H




                         γ              β
                                                                    • Base measure H
                                                                    • Shared state distribution β
                         α             πi ,·                        • Transition distributions πi ,·
                                               ∞                    • State sequence s0 , . . . , sn
                                                                    • Observations y1 , . . . , yn
          s0             s1             s2            sn


                         y1             y2            yn



Chinese restaurant franchise analogy
States correspond to restaurants, dishes served correspond to transitions to one of the shared set of
states and customers to observations
       Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                                       6 of 11
Biological Systems do Not Change Wildly (Assumption!): Hidden
States are Correlated




                               s1         s2        s3       s4      s5   s6   s7   s8   s9




              Observations     y1         y2        y3       y4      y5   y6   y7   y8   y9

               Time point       1          2        3            4   5    6    7    8    9
    Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                                7 of 11
Regulatory Interactions During the S. cerevisae Cell Cycle
Expression data for S. cerevisae over two cell cycles, at 25 time points.




                                                                                             1.0
                                                                                             0.8
                                                                                             0.6
                                                                                 Frequency
       3                                4




                                                                                             0.4
                                                                                             0.2
                                                                                             0.0
       1                                2

                                                                                                   0   10 20 30 40 50 60 70 80 90                   105     120
T. Pramila, W. Wu, S. Miles, W.S. Noble et al., The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in
the transcriptional circuitry of the cell cycle. Genes Dev Aug 15;20(16):2266-78, 2006.

           Time-Variable Networks in Candida Glabrata     Stumpf&Thorne                                                                                     8 of 11
Candida glabrata osmotic stress response (0.5M NaCl)

                                SPT16




                                                                  1.0
                   EMC6




                                                                  0.8
                                      SMX3




                                                                  0.6
                                                      Frequency
             MKS1
                             FPS1




                                                                  0.4
                                                                  0.2
                        ISD11
1   CAGL0K04235g




                                                                  0.0
                       BUD31                                            15   30   60   90   120 150 180 240
                                    CAGL0K06127g
                                                                                   Time point (mins)


                   ISD11       SMX3

                                                      Two distinct regulatory
                   YJR085C            FPS1
                                                      architectures appear to
                           VMA22               SRB8   control the expression of the
    CAGL0H00704g
                                                      genes involved in osmotic
                                                      stress response in C.
2
                              CUE2
                                                      glabrata.
            Time-Variable Networks in Candida Glabrata            Stumpf&Thorne                               9 of 11
Candida glabrata osmotic stress response (0.5M NaCl)

                                SPT16




                                                                  1.0
                   EMC6




                                                                  0.8
                                                                                                              Temporal Dependencies
                                      SMX3




                                                                  0.6
                                                      Frequency
             MKS1
                             FPS1
                                                                                                                T<30min:




                                                                  0.4
                                                                                                                     ISD11 → SMX 3




                                                                  0.2
                        ISD11
1   CAGL0K04235g                                                                                                T>30min:




                                                                  0.0
                       BUD31                                            15   30   60   90   120 150 180 240
                                    CAGL0K06127g
                                                                                   Time point (mins)
                                                                                                                      ISD11 → BUD31
                   ISD11       SMX3

                                                      Two distinct regulatory                                         SMX 3 → BUD31
                   YJR085C            FPS1
                                                      architectures appear to
                                                                                                              Interactions change with time
                           VMA22               SRB8   control the expression of the
    CAGL0H00704g
                                                      genes involved in osmotic                               and may be contingent on
                                                      stress response in C.                                   past interactions.
2
                              CUE2
                                                      glabrata.
            Time-Variable Networks in Candida Glabrata            Stumpf&Thorne                                                           9 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights

      All science is
      either physics or
      stamp collecting.

   Ernest Rutherford.




     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne   10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights

      All science is
      either physics or
      stamp collecting.

   Ernest Rutherford.




     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne   10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights

      All science is
      either physics or
      stamp collecting.

   Ernest Rutherford.




     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne   10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights

       All science is
       either physics or
       stamp collecting.

   Ernest Rutherford.

 • Temporally resolved data sheds light on transient dynamics. The transient
   dynamics in turn determine the ultimate outcome.




     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights

       All science is
       either physics or
       stamp collecting.

   Ernest Rutherford.

 • Temporally resolved data sheds light on transient dynamics. The transient
   dynamics in turn determine the ultimate outcome.
 • It is hard to see what can be learned from data that is not temporally resolved.
   Many of the results have only anecdotal value compared to time-course data.




      Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                      10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights

       All science is
       either physics or
       stamp collecting.

   Ernest Rutherford.

 • Temporally resolved data sheds light on transient dynamics. The transient
   dynamics in turn determine the ultimate outcome.
 • It is hard to see what can be learned from data that is not temporally resolved.
   Many of the results have only anecdotal value compared to time-course data.
 • If resources are limited then we would suggest generating data at additional
   time-points over generating replicate data: we can use statistical methods to assess
   and cope with noise but have no way of “guessing” transient behaviour.
     Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                           10 of 11
Acknowledgements

Imperial College London                                             Exter University
                                                                     • Andrew McDonagh
                                                                     • Melanie Puttnam
                                                                     • Lauren Ames
                                                                     • Ken Haynes




                                            • Justina Zurauskine
 • Thomas Thorne                            • Paul Kirk
                                            • Daniel Silk
       Time-Variable Networks in Candida Glabrata   Stumpf&Thorne                        11 of 11

Time-Variable Networks in Candida Glabrata

  • 1.
    Time-Variable Gene RegulationNetworks in Candida Glabrata Michael P.H. Stumpf & Thomas Thorne Theoretical Systems Biology Group, Division of Molecular Biosciences, Imperial College London 12th June 2011
  • 2.
    Networks: Mapping Processesand Understanding Berlin Liniennetz Routemap Legende Legend z z z Barrierefreier Zugang/Aufzug nur zu den 444 angegebenen Verkehrsmitteln 76 S+U-Bahn-Linie mit Umsteigemöglichkeit Entrance barrier-free/Lift to the staded Hennigsdorf x wT Urban Rail and Metro line, means of transportation only Waidmannslust x Bernau 2 change of trains optional Zugang über Rampe nur zu den 6 Alt-Tegel x x Birkenwerder 8 RE1 RB22 RB22 Linie des Regionalverkehrs angegebenen Verkehrsmitteln Wittenau 8 x Oranienburg x 1 Line of regional train Entrance via ramp to the staded means of transportation only > > > > > > > Strecke im Bau Transportation lines under construction A B C Tarifbereich Berlin 4: Otisstr. 4: Lindauer Allee 4: Pankow-Heinersdorf Fare zone 0A 0b Bus-Anbindung zum Flughafen 27 4: Scharnweberstr. 4: Paracelsus-Bad Bus service to airport E .N 82 Service service E5 Fernbahnhof Kurt-Schumacher-Platz Residenzstr. .R 9 2 4: Pankow Long-distance railway station AB 3 128 RE 0A Tegel TXL Franz-Neumann-Pl. 99 4: Wollankstr. 4: ZOB Zentraler Omnibusbahnhof Main bus station TXL X9 109 128 Afrikanische Str. Am Schäfersee Osloer Str. Barrierefrei durch Berlin Berliner Verkehrsbetriebe (BVG) www.BVG.de Barrier-free Service BVG Call Center: 030 19 44 9 4: Rehberge 8 12 4: Barrierefreier Zugang/Aufzug zum Bahnhof Entrance barrier-free lift to the station TXL 128 TXL Seestr. Nauener Platz Vinetastr. 4: Zugang zum Bahnhof über Rampe www.s-bahn-berlin.de Pankstr. Entrance via ramp to the station S-Bahn Kundentelefon 030 29 74 33 33 X9 Leopoldplatz 4: Bornholmer 10 4: Amrumer Str. Verkehrsverbund Berlin-Brandenburg 9 Str. 4: VBBonline.de Siemens- 4: TXL RE6 RE7 NE27 Schönhauser Allee 4: Prenzlauer Allee 4: Stand: 11. Dezember 2011 VBB Infocenter: 030 25 41 41 41 Zitadelle Haselhorst Paulsternstr. Rohrdamm damm Halemweg Beusselstr. 4: Westhafen 4: Wedding 4: Gesundbrunnen 4: Rathaus Jakob-Kaiser-Platz RE4.RE6.RB10.RB21 RE3.RE5.RE6.RE7 Voltastr. 4: Greifswalder Str. Spandau 4: Reinicken- > Altstadt Spandau X9 109 Jungfernheide 4: 7 x 4: Humboldthain RE dorfer Str. Eberswalder Str. 4: < 3R Bernauer Str. 4: <S X9 109 Wartenberg x . RE uT x Ahrensfelde 7 Birkenstr. 1 42 Schwartz- S4 4: Nordbahnhof 4 . RE > > S4 42 4: kopffstr. Senefelderplatz 4: < 5 1 . RE <S 4: Oranien- 7R . B RE1.RE2.RE4.RE6.RB10.RB14.RB21 RE4.RE6.RB10.RB21 Naturkunde- burger Rosenthaler Poel- 10 4: museum . RB Spandau > Str. 4: Platz Rosa-Luxemburg-Pl. chaustr. Stresow 4: RE1.RE2.RB14 Mierendorffplatz 21 x 5 4: Turmstr. eE RB10 RB21 4: Oranien- Weinmeisterstr. Ruhleben RB12.OE25.OE60 dF 4: 4: Hauptbahnhof burger Tor Hackescher Markt 4: Landsberger Allee 4: Spring- 22 4: Westend 55 4: Alexanderplatz pfuhl4: Pichels- 4: Olympia- RE1.RE2.RE7.RB14 4: berg Richard-Wagner-Platz 4: Bundestag Fried- Schillingstr. Stadion richstr.4: Neu-Westend Bellevue 4: Strausberger Platz 4: 4: Friedrichs- Sophie- 4: Branden- Weberwiese 4: Storkower Str. 4: felde Ost Strausberg Olympiastadion Jannowitz- NE26 Nord 4: Kaiserdamm Charlotte-Platz Bismarckstr. burger Tor eE 4: > x 5 4: 4: Tiergarten Klosterstr. brücke 4: Frankfurter Tor 4: Französische Str. 4: Märkisches Magdalenen- 4: Biesdorf Theodor-Heuss-Platz Messe Nord/ Deutsche Ernst-Reuter- Hansaplatz 4: Samariterstr. 4: 4: Heerstr. str. Friedrichsfelde Messe ZOB ICC 4: Oper Platz 4: Potsdamer Platz Mohrenstr. Museum > 4: Wilmers- 4: Ostbahnhof 5 x ICC Stadtmitte Frankfurter Lichtenberg Hönow Messe Süd 4: dorfer Str. Savignyplatz Mendelssohn-Bartholdy- Hausvogtei- Spittel- Heinrich- Nöldner- RE3.RE4.RE5.RE7 RE7 4: 4: 11 4: 4: Allee gE RB12 NE26 OE25 OE36 4: Park platz markt Heine-Str. Warschauer Str. platz Zoologischer Garten 4: OE60 4: Bies- Westkreuz RE1.RE2.RB14 Anhalter Bhf 4: 4: Tierpark RE2 Kochstr. 4: Moritzplatz dorf-Süd gE 4: Charlotten- 4: Wittenberg- Kurfürsten- Rummelsburg 4: burg 4: 11Uhlandstr. platz 4: str. Prinzenstr. 4: < Schlesisches Tor Ostkreuz RE1.RE2. Betriebsbahnhof OE36 33 RE 7R 4: Halensee . B Adenauer- Kurfürsten- Gleis- Hallesches Tor 4: Kottbusser Tor Görlitzer Bhf Rummelsburg 4: Möckern- 14 damm dreieck platz Nollen- Bülow- 4: brücke 4: Grunewald Augsburger dorfplatz str. 4: Schönleinstr. 4: Karlshorst Spichernstr. Str. 3434: Mehringdamm 4: Viktoria-Luise- Hermannplatz 4: Treptower Park 9 4: RE1.RE2 > x 3 Konstanzer Str. Hohenzollern- Wuhl- Erkner A B platz Platz Gneisenaustr. Südstern 4: Rathaus heide Güntzelstr. Yorckstr. 4: Großgörschenstr. Neukölln 4: RE2.RE7.RB14.OE36 Yorckstr. Platz der Boddinstr. 4: Fehrbelliner Platz Berliner Str. 4: 4: Kleistpark < RE3.RE4.RE5.RE7 Luftbrücke Plänterwald 4: 2 4: Hohenzollerndamm S4 Blissestr. Bayerischer Platz Eisenacher Karl-Marx- 41 Paradestr. 4: Leinestr. <S Str. Str. <S Rathaus Julius- 4: Sonnenallee 4: 41 Baubedingte Unterbrechung < Schöneberg S Leber-Brücke Tempelhof 4: 4: Neukölln 42 des Bahn-Regionalverkehrs Wannsee <> Charlottenburg. Köllnische Heide 4: Umfahrung mit Umleiterverkehr 4: Heidelberger Platz RE1, RE7, RB21 sowie S-Bahn Rüdesheimer Platz 4: dE Bundesplatz Innsbrucker Schöneberg 4: Südkreuz Hermannstr. 4: Baumschulenweg dG dG möglich. 4: 4Platz Alt-Tempelhof Spind- 4: Breitenbachplatz dE dF 4: dG 88 4: Grenzallee Friedrich-Wilhelm-Platz Oberspree lersfeld Kaiserin-Augusta-Str. 4: dG 4: Schöneweide Podbielskiallee Friedenau 4: Blaschkoallee 4: Dahlem-Dorf 4: Walther-Schreiber-Platz Ullsteinstr. Betriebsbahnhof Schöneweide Thielplatz 4: Priesterweg Westphalweg Parchimer Allee Feuerbachstr. 4: Schloßstr. 4: Adlershof Oskar-Helene-Heim Alt-Mariendorf 66 4: Attilastr. Britz-Süd 4: >> 4: Südende x 8 Grünau Onkel Toms Hütte 99 4: Rathaus Steglitz x Zeuthen 8 >> Johannisthaler x Königs rZ Krumme Lanke Marienfelde Chaussee 4: Wusterhausen 33 4: 4: Botanischer Garten Lankwitz x Flughafen Berlin-Schönefeld 9 > > > > > x Flughafen Berlin-Schönefeld rT Potsdam Hbf x 7 Wannsee x 1 Teltow Stadt wT x Blankenfelde (Kr. Teltow-Fläming) 2 7 Rudow x rT Flughafen Berlin-Schönefeld <> Südkreuz uT Wartenberg <> Westkreuz (<> Bundesplatz) uT Wartenberg <> Lichtenberg 1 1 Wannsee <> Oranienburg rZ Königs Wusterhausen <> Westend 8 (Zeuthen <>) Grünau <> Birkenwerder 2 2 Blankenfelde <> Bernau rZ Königs Wusterhausen <> Südkreuz 8 Grünau <> Pankow (<> Birkenwerder) 1 1 Warschauer Straße <> Uhlandstraße tT Brandenburger Tor <> Hauptbahnhof wT wT Teltow Stadt <> Hennigsdorf rU Spindlersfeld <> Hermannstr. (Grünau <>) Schöneweide <> Waidmannslust 2 2 Pankow <> Ruhleben 6 6 Alt-Tegel <> Alt-Mariendorf 3 3 Erkner <> Ostkreuz rU Spindlersfeld <> Schöneweide (nur Mo-Fr) (only Mon-Fri) 3 3 Nollendorfplatz <> Krumme Lanke 7 7 Rathaus Spandau <> Rudow S+U-Bahn-Nachtverkehr 2 7 S+U-Bahn nighttime traffic 2 7 rQ rQ Ring > im Uhrzeigersinn 5 5 Strausberg Nord <> Spandau 9 Flughafen Berlin-Schönefeld <> Pankow 4 Nollendorfplatz <> Innsbrucker Platz 8 8 Wittenau <> Hermannstraße nur Fr/Sa ca. 0:30-5:30 Uhr Fri/Sat ca. 0:30 am-5:30 am rW rW Ring > gegen Uhrzeigersinn 7 7 Ahrensfelde <> Potsdam Hbf 9 Flughafen Berlin-Schönefeld <> Treptower Park 5 5 Hönow <> Alexanderplatz 9 9 Osloer Straße <> Rathaus Steglitz Sa/So und vor Feiertagen ca. 0:30-7:00 Uhr Sat/Sun and prior to holidays ca. 0:30 am-7:00 am © 2011 Kartographie Berliner Verkehrsbetriebe (BVG) Time-Variable Networks in Candida Glabrata Stumpf&Thorne 1 of 11
  • 3.
    Biology is Dynamic— Networks Change with Time A B • Inferred regulatory network structures represent correlations rather than direct interactions. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
  • 4.
    Biology is Dynamic— Networks Change with Time P A A • Inferred regulatory network structures represent correlations rather than direct interactions. • Gene products may require activation and need to be transported into the nucleus to influence regulation; or complexes formed by signalling cascades may be required to activate transcription. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
  • 5.
    Biology is Dynamic— Networks Change with Time P A A • Inferred regulatory network structures represent correlations rather than direct interactions. • Gene products may require activation and need to be transported into the nucleus to influence regulation; or complexes formed by signalling cascades may be required to activate transcription. • Many factors that are not a part of a traditional regulatory network model can also influence regulatory interactions. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
  • 6.
    Biology is Dynamic— Networks Change with Time P A A B • Inferred regulatory network structures represent correlations rather than direct interactions. • Gene products may require activation and need to be transported into the nucleus to influence regulation; or complexes formed by signalling cascades may be required to activate transcription. • Many factors that are not a part of a traditional regulatory network model can also influence regulatory interactions. • These relationships may change depending on external signals or other factors. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
  • 7.
    Capturing Biological Dynamics— Changepoint Models for Networks • We can include hidden factors that my change the regulatory interactions taking place in our model by allowing the regulatory network structure to vary between timepoints and/or conditions. • In changepoint models the time series is divided into a number of segments, allowing a different network structure in each. • Using Bayesian inference it is possible to infer the posterior distribution of changepoint positions. ` S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks. BMC Systems Biology, 4:130, 2010. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 3 of 11
  • 8.
    Capturing Biological Dynamics— Changepoint Models for Networks • We can include hidden factors that my change the regulatory interactions taking place in our model by allowing the regulatory network structure to vary between timepoints and/or conditions. • In changepoint models the time series is divided into a number of segments, allowing a different network structure in each. • Using Bayesian inference it is possible to infer the posterior distribution of changepoint positions. Time point 1 2 3 4 5 6 7 8 9 10 ` S. Lebre, J. Becq, F. Devaux, M. P. H. Stumpf, G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks. BMC Systems Biology, 4:130, 2010. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 3 of 11
  • 9.
    The Chinese RestaurantProcess Time-Variable Networks in Candida Glabrata Stumpf&Thorne 4 of 11
  • 10.
    The Chinese RestaurantProcess θ1 θ2 θ3 θ4 Analogy for the Dirichlet process due to Pitman and Dubins ´ ´ ´ ´ D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 4 of 11
  • 11.
    The Chinese RestaurantProcess θ1 θ2 θ3 θ4 H Analogy for the Dirichlet process due to Pitman and Dubins ´ ´ ´ ´ D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 4 of 11
  • 12.
    The Chinese RestaurantProcess θ1 θ2 θ3 θ4 θ5 Analogy for the Dirichlet process due to Pitman and Dubins ´ ´ ´ ´ D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 4 of 11
  • 13.
    The Chinese RestaurantProcess ... θ1 θ2 θ3 θ4 θ5 H Analogy for the Dirichlet process due to Pitman and Dubins ´ ´ ´ ´ D. Aldous, Exchangeability and Related Topics. In l’Ecole d’ete de probabilites de Saint-Flour, XIII, pages 1-198. 1983 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 4 of 11
  • 14.
    What We Wantto Know is Often Not Measured: Hidden Markov Models • Here we measure transcriptomic data, whereas the action is all due to proteins and their interactions among themselves and with DNA/RNA. • We measure mRNA expression (yi ) which is influenced by a network (si ) that is not or cannot be observed directly. • We allow the network to change and learn this change from the observed data. π s1 s1 θs1 y1 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 5 of 11
  • 15.
    What We Wantto Know is Often Not Measured: Hidden Markov Models • Here we measure transcriptomic data, whereas the action is all due to proteins and their interactions among themselves and with DNA/RNA. • We measure mRNA expression (yi ) which is influenced by a network (si ) that is not or cannot be observed directly. • We allow the network to change and learn this change from the observed data. s1 s2 θs1 θs2 y1 y2 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 5 of 11
  • 16.
    What We Wantto Know is Often Not Measured: Hidden Markov Models • Here we measure transcriptomic data, whereas the action is all due to proteins and their interactions among themselves and with DNA/RNA. • We measure mRNA expression (yi ) which is influenced by a network (si ) that is not or cannot be observed directly. • We allow the network to change and learn this change from the observed data. s1 s2 s3 ... sT θs1 θs2 θs3 θsT y1 y2 y3 ... yT Time-Variable Networks in Candida Glabrata Stumpf&Thorne 5 of 11
  • 17.
    Systems at DifferentTimes are Related: The Chinese Restaurant Franchise θ2 θ1 θ1 θ3 θ2 θ2 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
  • 18.
    Systems at DifferentTimes are Related: The Chinese Restaurant Franchise θ2 θ1 θ1 θ3 θ2 θ2 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
  • 19.
    Systems at DifferentTimes are Related: The Chinese Restaurant Franchise α θ2 θ1 θ1 θ3 θ2 θ2 γ θ1 θ2 θ3 θ ∼H Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
  • 20.
    Systems at DifferentTimes are Related: The Chinese Restaurant Franchise H γ β • Base measure H • Shared state distribution β α πi ,· • Transition distributions πi ,· ∞ • State sequence s0 , . . . , sn • Observations y1 , . . . , yn s0 s1 s2 sn y1 y2 yn Chinese restaurant franchise analogy States correspond to restaurants, dishes served correspond to transitions to one of the shared set of states and customers to observations Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
  • 21.
    Biological Systems doNot Change Wildly (Assumption!): Hidden States are Correlated s1 s2 s3 s4 s5 s6 s7 s8 s9 Observations y1 y2 y3 y4 y5 y6 y7 y8 y9 Time point 1 2 3 4 5 6 7 8 9 Time-Variable Networks in Candida Glabrata Stumpf&Thorne 7 of 11
  • 22.
    Regulatory Interactions Duringthe S. cerevisae Cell Cycle Expression data for S. cerevisae over two cell cycles, at 25 time points. 1.0 0.8 0.6 Frequency 3 4 0.4 0.2 0.0 1 2 0 10 20 30 40 50 60 70 80 90 105 120 T. Pramila, W. Wu, S. Miles, W.S. Noble et al., The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle. Genes Dev Aug 15;20(16):2266-78, 2006. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 8 of 11
  • 23.
    Candida glabrata osmoticstress response (0.5M NaCl) SPT16 1.0 EMC6 0.8 SMX3 0.6 Frequency MKS1 FPS1 0.4 0.2 ISD11 1 CAGL0K04235g 0.0 BUD31 15 30 60 90 120 150 180 240 CAGL0K06127g Time point (mins) ISD11 SMX3 Two distinct regulatory YJR085C FPS1 architectures appear to VMA22 SRB8 control the expression of the CAGL0H00704g genes involved in osmotic stress response in C. 2 CUE2 glabrata. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 9 of 11
  • 24.
    Candida glabrata osmoticstress response (0.5M NaCl) SPT16 1.0 EMC6 0.8 Temporal Dependencies SMX3 0.6 Frequency MKS1 FPS1 T<30min: 0.4 ISD11 → SMX 3 0.2 ISD11 1 CAGL0K04235g T>30min: 0.0 BUD31 15 30 60 90 120 150 180 240 CAGL0K06127g Time point (mins) ISD11 → BUD31 ISD11 SMX3 Two distinct regulatory SMX 3 → BUD31 YJR085C FPS1 architectures appear to Interactions change with time VMA22 SRB8 control the expression of the CAGL0H00704g genes involved in osmotic and may be contingent on stress response in C. past interactions. 2 CUE2 glabrata. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 9 of 11
  • 25.
    Capturing Biological Dynamics FromStamp-Collecting to Dynamics to Insights All science is either physics or stamp collecting. Ernest Rutherford. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
  • 26.
    Capturing Biological Dynamics FromStamp-Collecting to Dynamics to Insights All science is either physics or stamp collecting. Ernest Rutherford. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
  • 27.
    Capturing Biological Dynamics FromStamp-Collecting to Dynamics to Insights All science is either physics or stamp collecting. Ernest Rutherford. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
  • 28.
    Capturing Biological Dynamics FromStamp-Collecting to Dynamics to Insights All science is either physics or stamp collecting. Ernest Rutherford. • Temporally resolved data sheds light on transient dynamics. The transient dynamics in turn determine the ultimate outcome. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
  • 29.
    Capturing Biological Dynamics FromStamp-Collecting to Dynamics to Insights All science is either physics or stamp collecting. Ernest Rutherford. • Temporally resolved data sheds light on transient dynamics. The transient dynamics in turn determine the ultimate outcome. • It is hard to see what can be learned from data that is not temporally resolved. Many of the results have only anecdotal value compared to time-course data. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
  • 30.
    Capturing Biological Dynamics FromStamp-Collecting to Dynamics to Insights All science is either physics or stamp collecting. Ernest Rutherford. • Temporally resolved data sheds light on transient dynamics. The transient dynamics in turn determine the ultimate outcome. • It is hard to see what can be learned from data that is not temporally resolved. Many of the results have only anecdotal value compared to time-course data. • If resources are limited then we would suggest generating data at additional time-points over generating replicate data: we can use statistical methods to assess and cope with noise but have no way of “guessing” transient behaviour. Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
  • 31.
    Acknowledgements Imperial College London Exter University • Andrew McDonagh • Melanie Puttnam • Lauren Ames • Ken Haynes • Justina Zurauskine • Thomas Thorne • Paul Kirk • Daniel Silk Time-Variable Networks in Candida Glabrata Stumpf&Thorne 11 of 11