-
Notifications
You must be signed in to change notification settings - Fork 0
/
oversikt forelesninger systembio.txt
284 lines (194 loc) · 5.99 KB
/
oversikt forelesninger systembio.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
Section I: Networks
Lecture 1
Adjacency matrix
connectivity of node
connectivity distribution of network
average connectivity in network
clustering of node
average clustering in network
Lecture 2
average clustering in network
Network structure related to adjacency matrix
clustering coefficient
clustering degree function
distance in networks
diameter of networks
Erdös-Renyi (ER) model¨
Lecture 3
Watts-strogatz small-world model
Scale free networks
Barabási-Albert (BA) scale-free model
- global attachment rule
Preferential attachment
Biological networks, PIN in yeast
SF topology
Duplication & Diversification (DD) model
Lecture 4
Configuration model
Centrality in a network
Betweenness-centrality (BC)
identifying “bridges” in the network
Network decomposition or “peeling”
Lecture 5
Degree correlations - Pearson’s expression
quantify degree assortativity
Neighbor-degree correlation
Robustness in a network setting
Consequences of network damage
Random network (ER) node removal
Scale-free network and node failure
Lecture 6
Possible network configurations – what
is most robust?
How can networks can help us
understand disease?
Human diseasesome
Human disease network (HDN)
Disease Gene Network
Gene-correlation network analyses
Differential network analysis
Lecture 7
Differential co-expression networks
CSD-method for differential co-expression
network analysis
Applications of differential network analysis
Disease co-morbidity:
Metabolic disease network (MDN)
Assortativity
Network robustness
Weighted networks
Representation of weighted networks
Weight & topology correlations
Lecture 8
Communities in networks
Connectedness
Density
Ravasz algorithm
Agglomorative clustering method
Divisive clustering method
measure community strength
Girvan-Newman algorithm
Lecture 9
Modularity in Cellular Networks
Fractal properties
hierarchical networks
Entropy
Empirical data for predictability
Lecture 10
GLEAM modeling
Identification of epidemic origin
Oslo municipality simulation
Vaccination-herd immunity as function of R
Lecture 11
spreading on network
Protein Interaction Network
Network peeling
Section II: metabolism
Lecture 12
Metabolic Network Structure
Metabolic network representations
model genome-scale metabolism: Flux Balance Analysis (FBA)
Principle of mass conservation give reaction flux
Ex 1: Two coupled reactions - adjacency matrix from reactions
Lecture 13
Stoichiometric matrix is central to all metabolic modelling
When is FBA well suited to explain experiments?
Example: Glycolysis
Cycle of refinement
How do we optimize? Example (small) metabolic network
FBA principle
Lecture 14
Phenotype Phase-Plane (PhPP) Analysis
Shadow price
Further interpretation of Shadow Price
Structure of typical PhPP
Robustness analysis and PhPP
Modifying the genetic content of a cell
Assumptions of gene-knockout simulation
Minimization of Metabolic Adjustment (MoMA)
Why is MoMA Quadratic Programming (QP)
Lecture 15
COBRA = Constraint-Based Reconstruction and Analysis
How we can play with FBA to investigate cellular phenotypes
Epistasis
FBA & MoMA: detailed comparison
Epistasis and epistatic interactions
Algorithm for detecting epistatic network modules
Schematic reaction view of “Lazarus effect”
Lecture 16
Variety of COBRA approaches
Flux Variability Analysis (FVA) principle
Flux-Coupling Finder (FCF)
FCF Analysis of E.col
Objective function suggestions
Pareto-optimal surface
Lecture 17
Dynamic FBA -- dFBA
Constraining the amount of proteins – ecFBA
Dynamic FBA with enzymatic constraints – decFBA
GECKO framework:
sMOMENT
Applying AutoPACMEN to E.coli
Lecture 18
Using COBRA to predict organism gene content
Evolution of minimal genome
How do we simulate evolution of minimal genome using COBRA?
Buchnera minimal networks
The idea of Adaptive Laboratory Evolution (ALE)
Connection between FBA & ALE
Long-term adaptive evolution experiment (LTEE)
Principle of computer-guided ALE: EvolveX
Lecture 19
Model-driven design of microbial phenotypes
Central aim in strain design
Example uses of E. coli engineering
COBRA methods for strain engineering
Lecture 20
OptKnock approach for strain engineering
Heuristic approaches for strain design
GDLS - Genetic Design through Local Search
Example: overproduction of lysine in E. coli
Lecture 21
Phenotype phase planes
Example of application: Cryptic genes
CryptFind: A New method for identification of cryptic/pseudo genes
Common steps in genome-scale model building
AutoKEGGRec: Network extracted from KEGG
ModelExplorer: Visual tool for manual recon curation
Metabolic plasticity: Can we identify patterns?
The Metabolic Core
Single metabolite use patterns
Metabolic super-highways
Flux re-arrangements localized to HFB
Lecture 22
microbial community modelling
Human metabolic models
How do we use COBRA in modelling microbial communities?
COMETS platform
Whole-Body Metabolism reconstruction
Lecture 23
Basic complex network theory
-Statistical measures for characterizing networks
-Standard network models (scale-free, small-world, Erdös-Rényi)
-Properties of Protein Interaction Networks (PIN),
Gene Regulatory Networks (GRN)
2. Genome-scale metabolic modeling
-System-level organization
-Basics of model building from genome à network
-Basics of modeling methods (especially Flux Balance Analysis)
How do we quantify network properties?
Network theory
Random network models
Communities in networks
Community identification & network resilience
Network Robustness
Real SF networks also vulnerable!
Approaches to modeling disease spread
PIN summary
Gene co-expression networks
Flux Balance Analysis
Metabolic reconstruction
Robustness analysis and PhPP
Flux Variability Analysis
Minimization of Metabolic Adjustment (MoMA)
Adaptive evolution of E. coli on malate