Abstract
Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.
| Original language | English |
|---|---|
| Article number | bbs026 |
| Pages (from-to) | 430-445 |
| Number of pages | 16 |
| Journal | Briefings in Bioinformatics |
| Volume | 13 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jul 2012 |
| Externally published | Yes |
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Keywords
- -omic data
- Analysis-by-synthesis
- High-throughput technology
- Reverse engineering biological systems
- Synthetic biology
ASJC Scopus subject areas
- Molecular Biology
- Information Systems
Cite this
Reverse engineering biomolecular systems using -omic data : Challenges, progress and opportunities. / Quo, Chang F.; Kaddi, Chanchala; Phan, John H.; Zollanvari, Amin; Xu, Mingqing; Wang, May D.; Alterovitz, Gil.
In: Briefings in Bioinformatics, Vol. 13, No. 4, bbs026, 07.2012, p. 430-445.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Reverse engineering biomolecular systems using -omic data
T2 - Challenges, progress and opportunities
AU - Quo, Chang F.
AU - Kaddi, Chanchala
AU - Phan, John H.
AU - Zollanvari, Amin
AU - Xu, Mingqing
AU - Wang, May D.
AU - Alterovitz, Gil
PY - 2012/7
Y1 - 2012/7
N2 - Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.
AB - Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.
KW - -omic data
KW - Analysis-by-synthesis
KW - High-throughput technology
KW - Reverse engineering biological systems
KW - Synthetic biology
UR - http://www.scopus.com/inward/record.url?scp=84865091206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865091206&partnerID=8YFLogxK
U2 - 10.1093/bib/bbs026
DO - 10.1093/bib/bbs026
M3 - Article
VL - 13
SP - 430
EP - 445
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
SN - 1467-5463
IS - 4
M1 - bbs026
ER -