Biomedical Engineering Reference
In-Depth Information
Microarray-based expression profiling was provided, for the first time, by means of
monitoring genome-wide gene expression changes in a single experiment
[
20
,
27
,
75
,
86
,
87
]. This technology has been widely employed to reveal molecular
portraits of gene expression in various cancer subtypes, for correlations with
disease progression, as well as response to drug treatments. Recent improvements
in the efficiency, quality, and cost of genome-wide sequencing have prompted
biologists and biomedical researches to dive into ultra high-throughput, massively
parallel genomic sequencing (Next Generation Sequencing, NGS) technology for
data acquisition. NGS technology opens up new research avenues for the investi-
gation of a wide range of biological and medical questions across the entire genome
at single base resolution. Therefore, NGS technology shifts the bottleneck in
sequencing processes from experimental data production to computationally inten-
sive informatics-based data analysis. To gain insights, novel computational
algorithms and bioinformatics methods represent a critical component in modern
biomedical research to analyze and interpret these massive “omics” data.
As biological systems are complex in nature, merely characterizing individual
molecular components (such as genes and proteins) by high-throughput
technologies is not sufficient to understand the functional properties of these
systems. Classically, efforts have been made to perform linear integration beginning
at the level of gene, RNA, and protein to infer function. However, it is becoming
clear that to understand the function of the biological systems, integrative
approaches are required to delineate the complex interactions both within and across
different hierarchical levels of biological organization [
6
,
41
]. To exert their
functions, individual molecular components interact with each other, which can be
located either in the same cell or across cells, and even across organs. Feedback
interaction and cross-talk between pathways and networks are common properties in
biological systems [
57
,
89
]. These complex interactions are fully exerted in cancer
cells to provide a robust system under the attack of therapeutics treatment. When
cancer cells are exposed to treatment, they can rewire their signaling networks
extensively to provide an escape mechanism for the continuation of growth and
survival in therapy-specific manner [
50
]. Therefore, a systems biology approach is
required to identify these critical and functional nodes in the oncogenic and escape
networks whose inhibition will result in “total systems failure” upon drug treatment.
In this chapter, we will review the technologies and computational algorithms to
acquire and analyze these multi-level “omics” data and provide an introduction to
systems-based approaches, bridging molecular to cellular to organ to organism
scales, in translational research. Here, our emphasis is focusing on quantitative
systems pharmacology (QSP), a translational medicine approach that integrates
computational and experimental methods to elucidate, validate, and apply new
pharmacological concepts to the development and use of small molecule and
biologic drugs [
74
]. In QSP “omics” data can be used to inform molecular model
building, which may provide detailed information on drug-target binding, down-
stream effects, and/or molecular network interactions; that can be incorporated into
multi-scale models that describe organism level drug interactions (i.e., pharmaco-
kinetics) and efficacy (Fig.
12.1
).
Search WWH ::
Custom Search