Biomedical Engineering Reference
In-Depth Information
Chapter 6
Summary and Future Work
6.1
Summary
With more diseases and health related issues being attributed to genetics, it is im-
perative to improve our knowledge of gene regulation within the biological system.
While single-point measurement of gene expression/detection is relatively simple
using micro-arrays or gene chips, measuring or determining the dynamic character-
istics of genes in the lab is time and labor intensive, and even impossible in many
cases. Understanding the dynamic interaction of genes is essential in the medical
field to study and control cancer and other genetic diseases. As a result, in recent
times genomics has become a popular field of research within computational and
molecular biology, for modeling and analyzing gene networks and regulation. While
biological systems have been observed to exhibit circuit-like properties, there has
been little existing work that exploits logic synthesis to model such systems.
Systems engineering approaches are gradually becoming more accepted and nec-
essary as a means to tackle gene regulatory networks and genetic diseases. In our
research, we show how several techniques from the field of logic synthesis can be
used to model, infer, and control the GRN related to cancer. In particular, this topic
presents logic synthesis and SAT based approaches to help infer the predictor sets for
GRNs, to determine the gene regulating function, and to determine the "best" set of
drugs for cancer therapy. The results from these algorithms can be used by clinicians
to determine an optimal drug therapy, by drug developers to target drugs for specific
genes, and by biologists to design experiments to extract specific gene interactions.
Our research have applied our approaches and presented results for gene networks
involving the melanoma, p. 53, mammalian, and growth factor pathways.
6.2
Future Work
Our work in applying logic synthesis to GRNs only touches the surface of research in
genomics. Through this interdisciplinary work, we hope to inspire several additional
lines of research using logic synthesis to fundamentally improve and expand our
understanding of gene regulation and control.
