Systems Biology: An Evolving Approach in Drug Discovery and Development
Authors: Ho, R.L.1; Lieu, C.A.2
Source: Drugs in R&D, Volume 9, Number 4, 2008 , pp. 203-216(14)
Publisher: Adis International
Abstract:
Investments in systems biology approaches by the pharmaceutical industry have not yet yielded the payoffs envisaged by many. In most cases, a plethora of novel drug targets arising from genomics led to many more failed projects in the pipeline, suggesting that target-based drug discovery may not be an optimal strategy for the industry. Before high-throughput `-omics' technologies and computer analysis became commonplace, most drug candidates were laboriously screened in animal systems to identify compounds that produced useful responses. Interestingly, the targets of many of the compounds that became drugs are still uncertain to this day. It is likely that drugs act on multiple targets in concert over time, the identification of which will require not only system level cataloguing and measurements, but next generation multiscale systems modelling. The concept of a `differentiated drug response' - elucidating and integrating responses composed of a range of effects on different tissues and, importantly, different time scales - may eventually prove to be the dominant paradigm of systems biology research. In this article, we explore key relevant concepts and technologies that we believe are critical for the future of systems biology and its place in pharmaceutical research.Keywords: Research and development; Research Tools; Systems biology
Document Type: Leading article
Affiliations: 1: 1 Rosa & Co., La Jolla, California, USA 2: 2 PRTM, Newport Beach, California, USA
Molecular Systems Biology 3 Article number: 140 doi:10.1038/msb4100180
Published online: 16 October 2007
Citation: Molecular Systems Biology 3:140
Network-based classification of breast cancer metastasis
There is a News and Views associated with this document.
Han-Yu Chuang1,a, Eunjung Lee2,3,a, Yu-Tsueng Liu4, Doheon Lee3 & Trey Ideker1,2,4
- Bioinformatics Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Cancer Genetics Program, Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
Correspondence to: Trey Ideker1,2,4 Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA. Tel.: +1 858 822 4558; Fax: +1 858 534 5722; Email: trey@bioeng.ucsd.edu
Received 11 June 2007; Accepted 20 August 2007; Published online 16 October 2007
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission.
aThese authors contributed equally to this work
Abstract
Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
| Harvard Medical School researchers have successfully synthesized a DNA-based memory loop in yeast cells, findings that mark a significant step forward in the emerging field of synthetic biology. |
After constructing genes from random bits of DNA, researchers in the lab of Professor Pamela Silver, a faculty member in Harvard Medical School’s Department of Systems Biology, not only reconstructed the dynamics of memory, but also created a mathematical model that predicted how such a memory “device” might work. “Synthetic biology is an incredibly exciting field, with more possibilities than many of us can imagine,” says Silver, lead author of the paper to be published in the September 15 issue of the journal Genes and Development. “While this proof-of-concept experiment is simply one step forward, we’ve established a foundational technology that just might set the standard of what we should expect in subsequent work.” Like many emerging fields, there’s still a bit of uncertainty over what, exactly, synthetic biology is. Ask any three scientists for a definition, and you’ll probably get four answers. Some see it as a means to boost the production of biotech products, such as proteins for pharmaceutical uses or other kinds of molecules for, say, environmental clean-up. Others see it as a means to creating computer platforms that may bypass many of the onerous stages of clinical trials. In such a scenario, a scientist would type the chemical structure of a drug candidate into a computer, and a program containing models of cellular metabolism could generate information on how people would react to that compound. Either way, at it’s core, synthetic biology boils down to gleaning insights into how biological systems work by reconstructing them. If you can build it, it forces you to understand it. A team in Silver’s Harvard Medical School lab led by Caroline Ajo-Franklin, now at Lawrence Berkeley National Laboratory, and postdoctoral scientist David Drubin decided to demonstrate that not only could they construct circuits out of genetic material, but they could also develop mathematical models whose predictive abilities match those of any electrical engineering system. “That’s the litmus test,” says Drubin, “namely, building a biological device that does precisely what you predicted it would do.” The components of this memory loop were simple: two genes that coded for proteins called transcription factors. Transcription factors regulate gene activity. Like a hand on a faucet, the transcription factor will grab onto a specific gene and control how much, or how little, of a particular protein the gene should make. The researchers placed two of these newly synthesized, transcription factor-coding genes into a yeast cell, and then exposed the cell to galactose (a kind of sugar). The first gene, which was designed to switch on when exposed to galactose, created a transcription factor that grabbed on to, and thus activated, the second gene. It was at this point that the feedback loop began. The second gene also created a transcription factor. But this transcription factor, like a boomerang, swung back around and bound to that same gene from which it had originated, reactivating it. This caused the gene to once again create that very same transcription factor, which once again looped back and reactivated the gene. In other words, the second gene continually switched itself on via the very transcription factor it created when it was switched on. The researchers then eliminated the galactose, causing the first synthetic gene, the one that had initiated this whole process, to shut off. Even with this gene gone, the feedback loop continued. “Essentially what happened is that the cell remembered that it had been exposed to galactose, and continued to pass this memory on to its descendents,” says Ajo-Franklin. “So after many cell divisions, the feedback loop remained intact without galactose or any other sort of molecular trigger.” Most important, the entire construction of the device was guided by the mathematical model that the researchers developed. “Think of how engineers build bridges,” says Silver. “They design quantitative models to help them understand what sorts of pressure and weight the bridge can withstand, and then use these equations to improve the actual physical model. We really did the same thing. In fact, our mathematical model not only predicted exactly how our memory loop would work, but it informed how we synthesized the genes.” For synthetic biology, this kind of specificity is crucial. “If we ever want to create biological black boxes, that is, gene-based circuits like this one that you can plug into a cell and have it perform a specified task, we need levels of mathematical precision as exact as the kind that go into creating computer chips,” she adds. The researchers are now working to scale-up the memory device into a larger, more complex circuit, one that can, for example, respond to DNA damage in cells. “One day we’d like to have a comprehensive library of these so-called black boxes,” says Drubin. “In the same way you take a component off the shelf and plug it into a circuit and get a predicted reaction, that’s what we’d one day like to do in cells.” Source: Harvard Medical School |
생명과학 업계를 위한 경영 컨설팅과 시장조사를 전문으로 하는 Front Line Strategic Consulting Inc．는 최신 영문조사 보고서 「In Silico Biology A Strategic Business Outlook and Market Analysis」를 발행하였다. 보고서에는 시장 견인력, 현재와 장래의 어플리케이션, 주요기업, 시장점유율 등의 상세한 분석과 In Silico Biology 시장의 전략적 분석이 기술되어 있다. 2002년부터 2007년까지 기존의 2배 이상의 속도로 시장이 확대되어, 연간 복합성장률은 세계적으로 50%가 될 것으로 예측한다. 현재 국가별 점유율은 미국 70%, 유럽 20%, 일본 5%, 기타 국가 5%로 추정된다.
Front Line Strategic Consulting Inc．의 전략적 시장보고서 담당이사인 Molly Varnau씨는 「In Silico Biology는 대부분의 주요 기술이 구사되는 유망한 분야이다. 이 기술에 따르면, 시장은 순조롭게 확대되어, 성공 가능성이 높아지며, 동시에 필요 노동시장과 노력이 삭감될 것이다. 생물학적 시뮬레이션의 가능성은 무한대로 확대될 것이다. 최종 목표는 가상 환자의 창출이라고 하는 사람도 많다. 비록 그 실현이 아직 멀다 하더라도」라고 그 장래성을 지적한다.
In Silico Biology는 유전체학, 기능 단백질체학, 발현 어레이, 문헌 등에서 데이터를 수집해, 그 모두를 하나의 일관된 모델로 통합한다. 그렇게 해서 완성된 모델은 대상 약품 후보를 지정해서, 주요성분, 약리학적 가치, 성분 특성을 예측하기 위해 사용된다. 이 분야는 아직 초기단계에 있지만, 주요 목표는 컴퓨터 모델을 사용해, 시간과 비용을 절약하고, 전통적인 Wet Lab으로 실험함으로써 보다 빠르게 저비용으로 실험을 실시하는 것이다. 여러 단계로 모델링하여, 세포경로, 세포 네트워크, 세포막 전체, 다세포조직, 기관계를 포함한다. 신약개발 프로세스로 예측의 ADMET(흡수, 분포, 대사, 배설, 독성예측)가 채용됨으로써, 2007년까지 시장 점유율의 2/3를 차지할 것이다.
Entelos, LION Biosciences, Physiome Sciences를 비롯한 제약회사가 현재 시장을 지배하고 있지만, 곧, 제약, IT, 생명 정보과학 등의 관련 부문의 기업이 경합하여 참가할 것이다. 이 시장의 난점이라고 한다면, In Silico Biology는 아직 실제로 증명되지 않았다 라고 하는 많은 제약, 바이오 테크놀러지 관련 기업에서의 회의론과, 현재 경기침체에 따른 생명과학 업계의 자금 부족이다.
