Upload
michael-stumpf
View
2.167
Download
0
Embed Size (px)
DESCRIPTION
Talk given in the "Integrative Genomics" session at the 18th Congress of the International Society for Human and Animal Mycology
Citation preview
Time-Variable Gene Regulation Networks in Candida Glabrata
Michael P.H. Stumpf & Thomas Thorne
Theoretical Systems Biology Group, Division of MolecularBiosciences, Imperial College London
12th June 2011
Networks: Mapping Processes and Understanding
����������
�� ���
��� ����
����� �� ����
����
�������
��������
���
����������������
�����������
��������� ����
��������� ����
��� ���� ����
����������������������������
���
���� �� � ��
��
���������������� ������ ������������ ��� ��� ���� ����� �����
���� �� �������
���
��� ���
���������������
��������
�������
��
��
� �
��
�
��
�
�
�
��
��
��
��
��
��
���
���
���
���
���
���
���
���
��
����
����
����
��
��
��
�
��
������
���
���
�������
���
��������������
���
������
������
�� ���������
��
��
��
��
��
��
��
��
���
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
����
��
��
��
��
��
��
��
��
��
����
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
�� ��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
����
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
��
����
��
��
��
����
��
��
��
���� !
�"#$%��&�'�
(%�'�)*
+�'(#$�)�'��%'�+�(%�'�)*
,')--�������
�./�'�)*
����'��/��0&)'��'�� '
1�)2%&�$�$ 3
� ����0/ '32��%*
4$���/(%'�
5��(&')#��'���%*
�)�/�(2��%*
�)$��&��+�(%��/
���������� ��� �
(� �'��%'�
6�()�/&')����
������������
������ ������������ �
�� ������ �� �
���7��/�'2��%*
���� ������� ����� � ������
�!������ �� �
���� ��� �� �
��������� �����
"�� ������ ��� �
# ����� �� �$�
# ����� �� %����
& ��������� ��� �
� ������� �%�����!�'������ �%����
(�����)���� ���
*������� �
�� �� �����
+� ���������������� �
��� ���� ��� �
����!����� �
������� � �%����
# ��� �!��������,��
��� �����������(��������
(��������
������������ ��-�
������ �
�'���� �����
# ��� �!�������
$�� �� �
�2"�/0��'(3��/
�'�2% !�'���'����������
��.��� ����
,�� �� ��
/� ���� ��
�#$8��!�"/�
� ����-�
& ��������
�� !���� �%����
�����!�������
��� �������������!�'�������
%��� ����
����!��������
0������������ 1�������
/'�����!�������
�����������
��%09�'"��/ '3
� ���������
(����������'���
/� �+� )�� �
����'���
/���� ��%� ������ �
2��������� �
����������
�!������!����$�
��� �!������� �
/�������� �$�
�!�'������� �
� ������� �
��������� ��
&' ����� ���������!����$�
+� ��������/�!��� �
%��$��������
���3� ��������
1�'-���(%'�
+�� �� ����
%������� ��-�����
&���������� � �-���� �
�������� �
������� �
�������� ����� -!��
�� ����� �
$��������
+'!�� �� -!��
# �������
+� ��������
4� !��� �
�!�'����
� ����� ��
��������
#��� ��!��� �
0��������� � -!��
"�����!�� �� �
/������� �& �5 ' �!����� �4� !��� �
+�� ���� �
# ���'���!����� �
/� �- ������ �
&����� ���!�
%������ ����
�-����� �
����������
�!���!�� �+� ��
+. ���!���+�����
/����� �� �
# ��� �!��� ���������
���������
�����3��
+������������ �����6�� �
�������� ������
����: � �"(#$�'�6�'%��
$�� � ���" ���(����
�����7����!��,��
(�!�� ��� �� �����
����� !��� �
��3� �6�����
�������� �����
���!�� ��� ���������� ���������������
�������� �
�� ���� ��� �
��6� ��!�� ������
&-������� �
%� ��� � �� �
*���� ������������
(�������!�'����
/� �- ��������
��%$�)(��%���"%*
������ �!� ���� �����
,��� ��������
������� � ������(-�������� ������
� �������!������
���������������
7�����7� �
$���������
,�����$����-���
# ��� �!�������������
�������!�� �&� ���
�!���5�� �
1�� ���������������
& �������
$����� ���������
+������-�
+������� �8911�� �� �
��������� �����
����� ��� �� ��� �
%������ �����
�������
������ �����������(�� ����������� ��� ������� ��:��������
%���������������
,�6�����������
��!������
�� ����
0����/���� �����0�� �� ������
�� ����� �
%� ��� ��� �
������������������ �
+�� ���� �������
������
������1�� ����������
,�6�����������
����������
/���� ����
/�������� ��� �
#�� ������� ������
$� ��� �
�!��� ��������� �
(����!����� �� ��� � �����������
*������ �
# �������������(��������� �
�� �!��������
������� �%����
�� ���� ��� �
%���!�.�� ��� ��������� �
����(��������� �����
�������������� �� �
, ������� �
�� ����� �����
������
�� ��������
, ������� � �$�
%� ������!����� �
�!�� ����� �� �,����� �
(���� �
����� �
/� ��!����!�� �����
,���������
0��������� -!��
9�((� :�5;;
��)&�/"��%��4�%�'&'�#$)������ ����(� �����3� ��� �+���(����<�;$�'� %%��&)'��2���� �� �����2������ 3� ��� (";<� ("=<� (�>;� ������ ������' ��!��
�� ��������������� ������
��
� �����������
�
�
��������
���������
�� �����������������������
��
� �!����
� "�� ���
�
�� #�������� ��������
�������������������������
�
$�����
�
%� ���� � �
�
����������&#�'�%� ����()���*���
�������
�
��+� ��)����
�,���� �
���
$� �����,����
�
�� � �%���
�
���� ����
�
���
��-����������
�������������)������
� �
� ������
�
������������
�
��
�
��
=������/� �� �������� ���� �*� ��� ���� �����?�*&@
� �
� �� �
�� ��
��������.��-��������������������.��������%� ���� � �.�������������������.��-� ����/������������������� ������������������������� ���
�
��
��
� �
������������������������.���!����/&.�������,� /*#��������� ���������.����� ��#��������� ���������.���!����/�,��������.�����)���� �'�,��������.������������� �������������.���,��������������.��+� ��)����� �
��
��
����� �������.����� ����/��� �������.��0��� ������&"�� ����.�*� �!����.������������� �!����.��+������&.�������������*& �!����.�*������������.�����)������ &����1����*��������������������������������������.��+�����������������������������.��%��, �����+���
� �� �
� �
������������ ��2��.��3���� ��2�+������.��$�������������,� /�.��#��))��0�����������,� /�.��4�����������+� /������.����5����,� /
� �� �� �� �
�������������%���.�����, �������� �%����.��� �1��������$� ������,�����.��$����� �����.�����)���� ��2�-������ ��2��.��$� ������ ��� /�� �
�
������������ ������������6�����'�7897�:897�3����6������;�������� �������'�7897�<877�3��
� � ���������� ��� ������������������������������������� ����!�"���������!�� �������������#������
�
�� ;
��'�"���"�"����%*������� �����/��� ��
�A2��������������2����� ��' ��!������������������� ����� ������ ����������������
����������(� �����3� ��� ��� ����� ���������
�� �!������������������������ ����� �������������
���%������� �����#�� ���������� ��� ����������
#� ������������������� �������������
:��� ��� �,���������������������������
:�
�� �� �� ��� �:� �� 8%���� ������������������� ����� ���� ��������� �������
:� �� �������������-�� �(����������� ������ ������� �������
��''"�'�3'�"�/)'#$���'�"����� ���� �! ���
��
��
���������B�;;��7������ �>C;;
���� ���� �*� ��� ���� �����?�*&@�����*&����*&�1����1���� B�CDC�;E�FF�E
����������� ������������/�������������CDC�>E�=F�DD�DD
����
*� ��� �3� ������� ���� ������� *�����������*���9���!���� B�CDC�>G�F;�F;�F;
��'>"#�������
�� �� �� ��� �:� �� 8%���� ��� ��������� � �������*� ��� ��������������� ����� ���� "��������� ����� � ������������������������
:� �� �-�� �(������� ��������� � �������*� ��� ��������������� ������ ������� ����� � ������������������������
$� ���� ��!���� ���#�� �$��
��� ����
��
������
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 1 of 11
Biology is Dynamic — Networks Change with Time
A
AP
AP
B
• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus toinfluence regulation; or complexes formed by signalling cascades may be required toactivate transcription.
• Many factors that are not a part of a traditional regulatory network model can also influenceregulatory interactions.
• These relationships may change depending on external signals or other factors.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
Biology is Dynamic — Networks Change with Time
A AP
AP
B
• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus toinfluence regulation; or complexes formed by signalling cascades may be required toactivate transcription.
• Many factors that are not a part of a traditional regulatory network model can also influenceregulatory interactions.
• These relationships may change depending on external signals or other factors.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
Biology is Dynamic — Networks Change with Time
A
AP
AP
B
• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus toinfluence regulation; or complexes formed by signalling cascades may be required toactivate transcription.
• Many factors that are not a part of a traditional regulatory network model can also influenceregulatory interactions.
• These relationships may change depending on external signals or other factors.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 2 of 11
Biology is Dynamic — Networks Change with Time
A
AP
AP
B
• Inferred regulatory network structures represent correlations rather than direct interactions.
• Gene products may require activation and need to be transported into the nucleus toinfluence regulation; or complexes formed by signalling cascades may be required toactivate transcription.
• Many factors that are not a part of a traditional regulatory network model can also influenceregulatory 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 forNetworks
• We can include hidden factors that my change the regulatory interactions taking place in our model byallowing 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 differentnetwork 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 SystemsBiology, 4:130, 2010.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 3 of 11
Capturing Biological Dynamics — Changepoint Models forNetworks
• We can include hidden factors that my change the regulatory interactions taking place in our model byallowing 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 differentnetwork 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 SystemsBiology, 4:130, 2010.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 3 of 11
The Chinese Restaurant Process
. . .
θ1 θ2 θ3 θ4
H
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
H
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
H
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
H
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
H
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
What We Want to Know is Often Not Measured: Hidden MarkovModels
• Here we measure transcriptomic data, whereas the action is all due to proteins andtheir interactions among themselves and with DNA/RNA.
• We measure mRNA expression (yi ) which is influenced by a network (si ) that is notor cannot be observed directly.
• We allow the network to change and learn this change from the observed data.
s1
y1
θs1
πs1
s2
y2
θs2
s3
y3
θs3
. . . sT
. . . yT
θsT
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 5 of 11
What We Want to Know is Often Not Measured: Hidden MarkovModels
• Here we measure transcriptomic data, whereas the action is all due to proteins andtheir interactions among themselves and with DNA/RNA.
• We measure mRNA expression (yi ) which is influenced by a network (si ) that is notor cannot be observed directly.
• We allow the network to change and learn this change from the observed data.
s1
y1
θs1
πs1
s2
y2
θs2
s3
y3
θs3
. . . sT
. . . yT
θsT
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 5 of 11
What We Want to Know is Often Not Measured: Hidden MarkovModels
• Here we measure transcriptomic data, whereas the action is all due to proteins andtheir interactions among themselves and with DNA/RNA.
• We measure mRNA expression (yi ) which is influenced by a network (si ) that is notor cannot be observed directly.
• We allow the network to change and learn this change from the observed data.
s1
y1
θs1
πs1
s2
y2
θs2
s3
y3
θs3
. . . sT
. . . yT
θsT
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 5 of 11
Systems at Different Times are Related: The Chinese RestaurantFranchise
α
θ2 θ1 θ1 θ3 θ2 θ2
θ1 θ2 θ3 θ ′ ∼ H
γ
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
Systems at Different Times are Related: The Chinese RestaurantFranchise
α
θ2 θ1 θ1 θ3 θ2 θ2
θ1 θ2 θ3 θ ′ ∼ H
γ
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
Systems at Different Times are Related: The Chinese RestaurantFranchise
α
θ2 θ1 θ1 θ3 θ2 θ2
θ1 θ2 θ3 θ ′ ∼ H
γ
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
Systems at Different Times are Related: The Chinese RestaurantFranchise
H
γ β
α πi,·
s0 s1 s2 sn
y1 y2 yn
∞
• Base measure H• Shared state distribution β• Transition distributions πi,·
• State sequence s0, . . . , sn
• Observations y1, . . . , yn
Chinese restaurant franchise analogyStates correspond to restaurants, dishes served correspond to transitions to one of the shared set ofstates and customers to observations
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 6 of 11
Biological Systems do Not Change Wildly (Assumption!): HiddenStates 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 9Time-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 2
3 4
Fre
quen
cy
0 10 20 30 40 50 60 70 80 90 105 120
0.0
0.2
0.4
0.6
0.8
1.0
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 inthe 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
FPS1
EMC6
SMX3
ISD11
MKS1
CAGL0K04235g
FPS1
VMA22 SRB8
SMX3
CAGL0H00704g
ISD11
BUD31 CAGL0K06127g
YJR085C
CUE22
1
Time point (mins)
Fre
quen
cy
0.0
0.2
0.4
0.6
0.8
1.0
15 30 60 90 120 150 180 240
Two distinct regulatoryarchitectures appear tocontrol the expression of thegenes involved in osmoticstress response in C.glabrata.
Temporal DependenciesT<30min:
ISD11→ SMX3
T>30min:
ISD11→ BUD31
SMX3→ BUD31
Interactions change with timeand may be contingent onpast interactions.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 9 of 11
Candida glabrata osmotic stress response (0.5M NaCl)
SPT16
FPS1
EMC6
SMX3
ISD11
MKS1
CAGL0K04235g
FPS1
VMA22 SRB8
SMX3
CAGL0H00704g
ISD11
BUD31 CAGL0K06127g
YJR085C
CUE22
1
Time point (mins)
Fre
quen
cy
0.0
0.2
0.4
0.6
0.8
1.0
15 30 60 90 120 150 180 240
Two distinct regulatoryarchitectures appear tocontrol the expression of thegenes involved in osmoticstress response in C.glabrata.
Temporal DependenciesT<30min:
ISD11→ SMX3
T>30min:
ISD11→ BUD31
SMX3→ BUD31
Interactions change with timeand may be contingent onpast interactions.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 9 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights
All science iseither physics orstamp collecting.
Ernest Rutherford.
• Temporally resolved data sheds light on transient dynamics. The transientdynamics 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 additionaltime-points over generating replicate data: we can use statistical methods to assessand cope with noise but have no way of “guessing” transient behaviour.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights
All science iseither physics orstamp collecting.
Ernest Rutherford.
• Temporally resolved data sheds light on transient dynamics. The transientdynamics 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 additionaltime-points over generating replicate data: we can use statistical methods to assessand cope with noise but have no way of “guessing” transient behaviour.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights
All science iseither physics orstamp collecting.
Ernest Rutherford.
• Temporally resolved data sheds light on transient dynamics. The transientdynamics 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 additionaltime-points over generating replicate data: we can use statistical methods to assessand cope with noise but have no way of “guessing” transient behaviour.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights
All science iseither physics orstamp collecting.
Ernest Rutherford.
• Temporally resolved data sheds light on transient dynamics. The transientdynamics 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 additionaltime-points over generating replicate data: we can use statistical methods to assessand cope with noise but have no way of “guessing” transient behaviour.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights
All science iseither physics orstamp collecting.
Ernest Rutherford.
• Temporally resolved data sheds light on transient dynamics. The transientdynamics 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 additionaltime-points over generating replicate data: we can use statistical methods to assessand cope with noise but have no way of “guessing” transient behaviour.
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 10 of 11
Capturing Biological Dynamics
From Stamp-Collecting to Dynamics to Insights
All science iseither physics orstamp collecting.
Ernest Rutherford.
• Temporally resolved data sheds light on transient dynamics. The transientdynamics 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 additionaltime-points over generating replicate data: we can use statistical methods to assessand 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
• Thomas Thorne
• Justina Zurauskine• Paul Kirk• Daniel Silk
Exter University• Andrew McDonagh• Melanie Puttnam• Lauren Ames• Ken Haynes
Time-Variable Networks in Candida Glabrata Stumpf&Thorne 11 of 11