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Daehee Hwang
Department of New Biology, DGIST
Systems approaches in
biology and medicine
2040 Visions of Process Systems Engineering
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Daehee@MIT
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Daehee@MIT
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Systems Biology in Early Times
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• Elements (genes, proteins, etc.)- nodes
• Interactions between the elements- edges
• Elements and their interactions are affected
by the Context of other systems within-
cells and organisms
• Interactions between/among elements give
rise to the system’s Emergent properties
Systems biology: dynamic network
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Non-Diseased Diseased
Disease Arises from Disease-Perturbed Networks
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System Perturbation
Generation of comprehensive global data
Identification of key molecules
Network modeling
Generation of hypotheses
Validation of hypotheses
Current Systems Biology
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Resources and tools for systems approaches
Choi et al., Molecular Plant (2014)
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System Perturbation
Generation of comprehensive global data
Identification of key molecules
Network modeling
Generation of hypotheses
Validation of hypotheses
Current Systems Biology
Analysis of genomic, proteomic & metabolomics data & data integration.
Network modeling and analysis for ID of key regulators and pathways.
Identification of differentially expressed molecules.
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Computational and systems
biological tools
Pointillist
MS-BID/ Prequips
Mathematical modeling
tools
Principal network analysis
iNID
TEMPI
Integration of multi-omics data
PNAS, 2005a & b
Integration,
visualization, &
analysis of LC-
MS/MS
proteomics data
Bioinformatics,
2008a & b
Functions of key
temporal & spatial
regulatory motifs
Science, 2009;
Developmental
Cell, 2013
ID of subnetworks
representing major dynamics
Bioinformatics, 2011
Network analysis
for understanding
interplays among
signaling pathways
Molecular Plant,
2014
Probabilistic
modeling of
dynamic networks
with multi-
information
Bioinformatics,
2014
Development of tools for systems biology
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iNID database: transcriptome & interactome
1,171,417 interactions for 27,103 molecules
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Developmental signaling pathways
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Interplays among developmental pathways
Interplays of hormone and pathogen pathways
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Interplays among developmental pathways
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Network models showing Interplays among
developmental pathways
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Computational and systems
biological tools
Pointillist
MS-BID/ Prequips
Mathematical modeling
tools
Principal network analysis
iNID
TEMPI
Integration of multi-omics data
PNAS, 2005a & b
Integration,
visualization, &
analysis of LC-
MS/MS
proteomics data
Bioinformatics,
2008a & b
Functions of key
temporal & spatial
regulatory motifs
Science, 2009;
Developmental
Cell, 2013
ID of subnetworks
representing major dynamics
Bioinformatics, 2011
Network analysis
for understanding
interplays among
signaling pathways
Molecular Plant,
2014
Probabilistic
modeling of
dynamic networks
with multi-
information
Bioinformatics,
2014
Development of tools for systems biology
![Page 17: Systems approaches in biology and medicine Daehee Hwang · 6/19/2017 · The utility of these tools has been demonstrated in various systems biology studies. Dynamic disease-perturbed](https://reader033.vdocument.in/reader033/viewer/2022050418/5f8dce0237f86b4162585db0/html5/thumbnails/17.jpg)
GI(GIGANTEA), an Important Modulator of the Core Oscillator of Arabidopsis
Harmer, Annu.Rev.Plant.Biol.(2009)
Kim et al., Nature (2007)
A model of the plant circadian clock
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Mathematical modeling predicts network structure
Input 1Input 2
GI_cGI_n
LHY
expre
ssio
n
time
Difference?
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Log10(probability)
Structure 3 yielded the highest likelihood of generating the wild type oscillatory expression of LHY
wild type
Mathematical modeling predicts network structure
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Experimental confirmation by transgenic plants
Nuclear and cytosolic GI form incoherent feedforward loop to regulate LHY
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Application of systems approaches
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Application of these tools for various studies
The utility of these tools has been demonstrated in various systems biology studies.
Dynamic disease-perturbed networks during the course of prion disease
progression Molecular Systems Biology, 2009 & age-related networks of
Arabidopsis leaves along the lifespan Science, 2009; Plant Physiology, 2016.
Construction of RA-associated networks PLOS One, 2012; PNAS, 2015
describing pannus formation, and selection of key regulators of the networks
Arthritis and Rheumatism, 2011; JEM, 2012; PNAS, 2014
Construction of T2D-associated networks, selection of key regulators of the
networks Cell Metabolism, 2013; Diabetes, 2015, and elucidation of a retrograde
signaling mechanism from the networks Science Signaling, 2012
Construction of cancer-associated networks Nature Reviews Cancer, 2011 & 2012
and selection of key regulators of the networks JCI, 2012; Oncogene, 2014;
Cancer Research, 2016; Nature Communications, 2015; JI, 2015
Construction of network models related to other diseases, such as AD Cell Death
and Differentiation, 2014, PD Nature Communications, 2013, and Interstitial
Cystitis Molecular & Cellular Proteomics, 2011, and a metabolic network
underlying pharmacokinetics of tacrolimus CPT, 2010.
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Temporal disease-perturbed networks:
Prion disease
Hwang et al., Molecular Systems Biology (2009)
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Time-evolving cell cycle networks
Kim et al., Bioinformatics (2014)
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Application of these tools for various studies
The utility of these tools has been demonstrated in various systems biology studies.
Dynamic disease-perturbed networks during the course of prion disease
progression Molecular Systems Biology, 2009 & age-related networks of
Arabidopsis leaves along the lifespan Science, 2009; Plant Physiology, 2016.
Construction of RA-associated networks PLOS One, 2012; PNAS, 2015
describing pannus formation, and selection of key regulators of the networks
Arthritis and Rheumatism, 2011; JEM, 2012; PNAS, 2014
Construction of T2D-associated networks, selection of key regulators of the
networks Cell Metabolism, 2013; Diabetes, 2015, and elucidation of a retrograde
signaling mechanism from the networks Science Signaling, 2012
Construction of cancer-associated networks Nature Reviews Cancer, 2011 & 2012
and selection of key regulators of the networks JCI, 2012; Oncogene, 2014;
Cancer Research, 2016; Nature Communications, 2015; JI, 2015
Construction of network models related to other diseases, such as AD Cell Death
and Differentiation, 2014, PD Nature Communications, 2013, and Interstitial
Cystitis Molecular & Cellular Proteomics, 2011, and a metabolic network
underlying pharmacokinetics of tacrolimus CPT, 2010.
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Mitochondrial disorders
Defects in mitochondrial DNA
Retrogradesignaling
Changes in nucleus programming
Development of diverse disease
phenotypes
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A Cybrid model with mtDNA 3243 mutation
mtDNA 3243 A>G mutation
Causes 1% of Diabetes
Cybrid Model
Dr. Park (SNU)
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System-level Analysis of Cybrid Models
High glucose
(4.5g/L)
Hetero
Mutant
Wild
Proteome analysis20 50 80 110 140 170
Transcriptomic analysis
Chae et al., Science Signaling (2013)
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-3 -1 0 1 3
C1
C2
C3
C4C6C5
Mutation-load Dependent Differential Expression Patterns
Number of DEGs UP DOWN Total
OXPHOS 2 46 48
Translation 28 69 97
0 100 200 300 400 500 600
C6
C5
C4
C3
C2
C1
Number of Genes
239
304
523
112
68
90
0 20 40 60 80 100
C6
C5
C4
C3
C2
C1
Number of regulators
(TFs / Signaling molecules)
17
22
25
9
6
6
31
46
55
27
17
21
Number of translation
related genes
Number of OXPHOS
related genes
38
53
40
5 14
7
4
1
C1C2
C3C4
C5C6
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What Regulates Changes in C3 ?
TF
Name
No. of
genes
Regulated
P-value
CREB1 124 < 1.0E-05
GABPA* 112 < 1.0E-05
ELF1 100 < 1.0E-05
NRF1* 65 < 1.0E-05
ELK1 63 < 1.0E-05
YY1* 60 < 1.0E-05
RXRA* 32 0.0003
ESRRA* 35 0.0031
ARNT 28 0.00668
RELA 29 0.00749
RXRA
PPARGC1α
NRF1
GABPA
OXPHOS Translation
9-cis RA
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Future in systems biology & medicine
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EBV+ Hypermutable Genome-stable High SCNA (Aneuploid)
Hypermutable
Multi-dimensional genomic analysis of cancers
Different size,
scale,
coverage, etc.
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Complementary nature of different omics datasets
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Single cell systems biology
전이 대장암: 생존율 감소
Stage 1
Stage 4
Stage 2
Stage 3
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Cellular heterogeneity Cancer stem cells & drug resistance
Single cell transcriptomics
35
Cancer stem cells & drug resistance
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Big data analysis
Finding relationships
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Big data analysis
Finding rules
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Other big data problems in proteomics
Finding co-occurring post-translational modifications in the same proteins and other
proteins in the same pathway???
Finding frequently co-varying proteins in different pathways (cross-talks between pathways) ???
Finding frequently co-varying network modules (pathways) that can be used as bases for
interpretation of a new dataset???
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Network architecture
Edge: Correlation in peptide ratios
Edges: - Relationships based on PTMs- Co-occurrence rate
Edge: Protein-protein interactions
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Happy Birthday !!
Happy Retirement !!