Applying Systems Biology to Benefit Human Health forum: topic
This is a public forum
On the paper "A network solution"
Francisco Azuaje
Thursday, 09 October 2008 13:25 UTC
Dear Moderators / Colleagues,
A few initial thoughts on the recommendations reported in “A network solution”.
1) On standards and guidelines. The definition of community guidelines for aiding in the reporting of systems biology findings in health may be another priority in the near future. Key recommendations and guidelines are available for major areas, such as clinical trials, and more specific domains, such as the reporting of diagnostic accuracy of biomarker studies. But the “systems” dimension seems to be missing.
2) On key applications. The investigation of systems approaches to disease biomarker development will be fundamental not only for advancing screening, diagnostic and prognostic procedures, but also to assist in drug development. This aspect may fit into your recommendations on “Modelling drug actions”, “Predictive toxicology”, and “Amenable therapeutic areas”.
3) On communication and outreach (I). I am not sure that in the near future the biggest challenge will be to convince other researchers, managers and policy makers that systems biology is important for improving health. Like in the case of bioinformatics, at some point most of them will agree that “yes!” is important or even necessary. A tougher task is to be able to tell how far they are prepared to go to demonstrate their interest and support. This is not only about funding, but also in terms of attitudes, e.g. how to give recognition, share time/resources, etc.
4) On communication and outreach (II). I agree that it is fundamental to highlight successful stories, especially those that may improve public trust in science. But this will require going beyond the idea of stressing the sophistication or novelty of approaches. There is a need to allow scientists and the public to answer “So What?” questions.
5) On education. Despite the proliferation of undergraduate and postgraduate courses in computational biology, bioinformatics and more recently systems biology, it will be necessary to ensure that training in systems biology truly involves a “systems” thinking, a problem solving culture and engineering approaches to designing solutions and interpreting findings.
6) A final point, related to 4 and 5, is that there is a need to move from an emphasis on “novel observations” (independently of their sophistication) to critical “understanding”. Should we further encourage the publication of findings that allow one to answer “Why”, “So What”, “Where now” questions? There is no doubt that over the past 5 years a number of high profile papers have reported sound, exciting and even beautiful contributions. However, significant progress in systems biology for preventing and curing disease will also require going beyond the publication of “patterns” that mainly confirm the complexity of biological systems.
-
Replies
-
Frncisco, I agree with everything you say. Compltely to the point. We need ideas on how to implement and go beyond talking and “initial papers” to what can impress the biopharma community really.
-
Just a FYI, the link to the article being discussed is here. It’s a Commentary in the Oct 9 issue of Nature.
-
Dear Drs. Henney and Superti-Furga:
Francisco Azuaje made a thought comment, which I agree. Here, based on my nearly 10 years experience in systems biology, ranging from wet to dry labs, from applications to basic biology, I would like to share a few thoughts, and adding some points.
Yes, I believe the network thinking is THE way to go. Here are two our approaches at the endogenous and metabolic networks, two hierarchical layers minimum for drug discovery and development purposes.
a) Towards Kinetic Modeling of Global Metabolic Networks: Methylobacterium extorquens AM1 Growth as Validation,
P Ao, LW Lee, ME. Lidstrom, Lan Yin, and XM Zhu,
Chinese Journal of Biotechnology 24 (2008) 980 – 994.
http://arxiv.org/ftp/arxiv/papers/0808/0808.0220.pdf
b) Cancer as Robust Intrinsic State of Endogenous Molecular-Cellular Network Shaped by Evolution. P. Ao, D. Galas, L. Hood, X.-M. Zhu,
Medical Hypotheses 70 (2008) 678–684.
http://dx.doi.org/10.1016/j.mehy.2007.03.043My experience shows to me that a few agents, or too few a pathways, i are nearly impossible to understand complex diseases such as cancer. DNA sequence or gene analysis alone will not lead us any further, either. Fortunately, this latter point of view begins to become a mainstream, showing by a recent group of high profile papers in Nature and Science.
One main feature missing in the discussion so far is a general theoretical framework.
My impression is that, despite the name “systems”, most approaches proposed so far in systems biology are of receipt type. While they are important, we need an understanding on the mechanisms of complex biological phenomena on a global level. Such understanding can of course greatly enhance our ability to discover and to develop new drugs. I have raised this theoretical framework issue recently in a broader perspective:
c) Borges Dilemma, Fundamental Laws, and Systems Biology,
P. Ao, Bioinformatics and Biology Insights 2 (2008) 201-202
http://la-press.com/article.php?article_id=693In the following, let me make some comments on the suggested topics.
Setting Data Standards—This is absolute important if we wish to understand each other and to avoid unnecessary mistakes, when the network becomes large. Our criteria for standardization are: sound biological, chemical, and physical base, and, easy to use.
We have some initial success here on metabolic networks:
d) Generic Enzymatic Rate Equation under Living Conditions,
Lee, Yin, Zhu, and Ao, J. Biol. Syst. 15 (2007) 495-514. http://ejournals.wspc.com.sg/journals/jbs/15/1504/S0218339007002295.htmlOptimising the application of existing tools—Indeed this is important. Above d) can be regarded as an example of optimization of existing tools. But, it is not enough. New tools need to be developed.
We have been doing this, too, as indicated in a) and b).Predictive Toxicology—Good framework is predictive. Our above on kinetic modeling of metabolic network, a), has this feature.
Therapeutic Area Focus—The endogenous network modeling on cancer genesis and progression has a few very interesting predictions connected to this, even at our initial modeling stage.
This can be regarded as another important evidence for network thinking and modeling.Communication and Outreach—There is a need to design suitable courses to educate next generation biologists. In my view, evolution theory should be the unifying foundation. Unfortunately, there have been a great deal of confusions among biologists. For example, what the natural selection really means is still a subject of controversy.
Many biologists only pay a lip service to evolution.I will be happy for further discussions ( aoping@u.washngton.edu ). P. Ao .
-
Concerning standards and guidelines: May I draw your attention on the STRENDA commission website ?
STRENDA is a Commission created by the Beilstein Institute. The name represents *St*andards for *R*eporting *En*zymology *Da*ta. The group addressed recently the the problem of non-comparable, non-standardised and less reliable functional enzyme data and is trying to establish standards of reporting enzyme data, to allow a full understanding of the conditions under which they were obtained (1st step). The guidelines worked out are in the process of adoption by biochemistry journals.
STRENDA is very interested in cooperating with other standardization initiatives in pertinent subjects. Timely cooperation between the various initiatives is desirable to avoid duplication of effort and diversity of recommendations made by different groups. -
Dear Drs. Henney and Superti-Furga,
I agree with you and the previous comments that setting data standards is absolutely necessary for systems based drug discovery. The development of fast, precise and quantitative measurement methods are essential to speed up the process. I would like to comment more on modelling drug actions and possible therapeutic areas based on our own experience in carrying out systems based approaches to decipher optimal invention solutions to control disease related molecular networks.
We feel that only the initial ideas are not enough to show the power of systems based approaches. Practical computational tools and solid application examples are necessary. We have developed a computational method for finding multiple-target optimal intervention (MTOI) solutions in a disease network. For a given disease network, the method tries to identify effective drug targets and the combination of multiple invention that can best restore the disease network to a desired normal state. As a concrete example, we have applied MTOI to an inflammation related network—the arachidonic acid metabolic network and correctly predicted the known side-effects of popular anti-inflammatory medicines. A number of optimal multi-target strategies were found that are both effective in controlling the inflammation mediators and safe with minimized side effects.
a) Finding multiple target optimal intervention in disease-related molecular network, Yang, K., Bai, H. J., Ouyang，Q., Lai, L. H., Tang, C., Mol. Syst. Biol. 2008, 4:228.
b) Dynamic simulations on the arachidonic acid metabolic network, Yang, K.; Ma, W. Z.; Liang, H. H.; Qi, O. Y.; Tang, C.; Lai, L. H., PLoS Comp. Biol. 2007, 3, 523-530.
In addition to combination therapy using drug cocktails, medicinal chemists are now putting more efforts on identifying multi-functional drugs which can treat a system more efficiently. There is also a call for developing drug design tools for multi-functional drug discovery.
For therapeutic areas, I agree that metabolic disorders, inflammation/infectious diseases are workable areas with the currently available data. As relatively much data is known for human metabolic networks, I am optimistic on more successful examples of systems based approaches in treating metabolic disorders in the near future.
-
