Reverse engineering of genetic networks with time delayed recurrent neural networks and clustering techniques [Elektronische Ressource] / presented by David Camacho Trujillo

Reverse engineering of genetic networks
with time delayed recurrent neural networks
and clustering techniques


Dissertation

submitted to the
Combined Faculties
for the Natural Sciences and for Mathematics
of the Ruperto-Carola University of Heidelberg, Germany

for the degree of


Doctor of Natural Sciences










presented by

M. Sc. David Camacho Trujillo
born in México City, México
Oral-examination: ................................................ Reverse engineering of genetic networks with 2
time delayed recurrent neural networks and clustering techniques

Reverse engineering of genetic networks with 3
time delayed recurrent neural networks and clustering techniques

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Referees: Prof. Dr. Ursula Kummer
. P.D. Dr. Ursula Klingmüller


Reverse engineering of genetic networks with 4
time delayed recurrent neural networks and clustering techniques

Reverse engineering of genetic networks with 5
time delayed recurrent neural networks and clustering techniques




D edicated to:

Sarah

&

Tere

&


A rturito




Reverse engineering of genetic networks with 6
time delayed recurrent neural networks and clustering techniques

Reverse engineering of genetic networks with 7
time delayed recurrent neural networks and clustering techniques
INDEX

Summary.......................................................................... 9
Zusammenfassung ....................................................... 10
Personal Words............................................................. 11
List of abbreviations ..................................................... 13
General Motivation........................................................ 17
1. Biological context ..................................................... 19
1.1 Gene regulation ............................................................................................. 19
1.2 Basal transcription apparatus ......................................................................... 19
1.3 Transcription factors...................................................................................... 21
1.4 Enhancers-Insulators...................................................................................... 22
1.5 Post-transcriptional regulation of the mRNA ................................................. 23
1.5.1 Alternative splicing................................................................................. 23
1.5.2 RNA interference.................................................................................... 24
1.5.3 Dimensional in-homogeneities................................................................ 26
2. Reverse engineering and modelling of genetic
network modules........................................................... 29
2.1 Related work ................................................................................................. 29
2.2 General concepts ........................................................................................... 30
2.3 Dimensionality reduction by data selection.................................................... 32
2.4 Theoretical works .......................................................................................... 36
2.4.1 Boolean Networks................................................................................... 36
2.4.2 Differential equation systems.................................................................. 38
2.4.3 Stochastic Models................................................................................... 44
2.4.4 Bayesian networks .................................................................................. 45
3. Methods ..................................................................... 50
3.1 Workflow ...................................................................................................... 50
3.2 Data pre-processing, Quality control.............................................................. 51
3.3 Data normalization ........................................................................................ 53

Reverse engineering of genetic networks with 8
time delayed recurrent neural networks and clustering techniques
3.4 Dimensionality problem. The use of interpolation approaches ....................... 55
3.5 Data fitting .................................................................................................... 57
3.6 Models........................................................................................................... 62
3.6.1 The CTRNN model................................................................................. 62
3.6.2 The TDRNN model................................................................................. 66
3.6.3 Robust parameter determination.............................................................. 67
3.6.4 Graph generation and error distance measurements ................................. 68
3.6.5 Clustering of results ................................................................................ 68
3.6.6 Dynamic Bayesian Network.................................................................... 71
4. Results ....................................................................... 73
4.1 Synthetic benchmark: The Repressilator ........................................................ 74
4.1.1 Parameter space selection........................................................................ 75
4.1.2 Required data length. .............................................................................. 86
4.1.3 Robustness against noise......................................................................... 92
4.1.4 Robustness against incomplete information: Clustering improves the
standard reverse engineering task, quantitatively and qualitatively................... 97
4.2 The yeast cell cycle...................................................................................... 103
4.2.1 TDRNN shows superior inference and predictive power than previous
models on experimental data.......................................................................... 104
4.2.2 Bootstrapping validation ....................................................................... 106
4.2.3 Clustering improves the RE process with real data ................................ 107
4.3 Reverse engineering of keratinocyte-fibroblast communication.................... 109
5. Discussion ............................................................... 127
5.1 Model choice and data driven experiments................................................... 128
5.2 Data selection .............................................................................................. 129
5.3 Data interpolation, implications ................................................................... 130
5.4 Data fitting and inference power relationship............................................... 131
5.5 Reverse engineering framework, improving the robust parameter selection.. 135
6. Conclusions............................................................. 137
7. Bibliography ............................................................ 139


Reverse engineering of genetic networks with 9
time delayed recurrent neural networks and clustering techniques

Summary

In the iterative process of experimentally probing biological networks and
computationally inferring models for the networks, fast, accurate and flexible
computational frameworks are needed for modeling and reverse engineering
biological networks. In this dissertation, I propose a novel model to simulate gene
regulatory networks using a specific type of time delayed recurrent neural networks.
Also, I introduce a parameter clustering method to select groups of parameter sets
from the simulations representing biologically reasonable networks. Additionally, a
general purpose adaptive function is used here to decrease and study the connectivity
of small gene regulatory networks modules.

In this dissertation, the performance of this novel model is shown to simulate the
dynamics and to infer the topology of gene regulatory networks derived from
synthetic and experimental time series gene expression data. Here, I assess the quality
of the inferred networks by the use of graph edit distance measurements in
comparison to the synthetic and experimental benchmarks. Additionally, I compare
between edition costs of the inferred networks obtained with the time delay recurrent
networks and other previo