IamA We are Profs. Ravi Iyengar and Marc Birtwistle at the Icahn School of Medicine at Mount Sinai in New York. We do research in the area of systems biology and teach courses on systems biology and its application to biotechnology on Coursera (an...

What do you understand the least about systems biology?

Two examples, there are probably many more:

1. Quantitative relationships between scales of organization. For example we do not know what the minimum number of any type of ion channels in a heart cell is needed for a normal ECG which is characteristic of a whole heart response.

2. Even on the level of a single cell in isolation, we have quite incomplete knowledge of how proteins within cells interact to give rise to some phenotypic behavior, such as movement, division, etc, in response to a perturbation (such as a drug).

In light of your answer, do you see a need for closer integration between the fields of systems biology and quantitative biology?

For me, they're kind of one in the same, but yes you are right, there are sub-fields of systems biology that are not so quantitative. I think a major rate limiting step is the ability to use the wealth of currently available "prior knowledge" to specify structures of models, i.e. what interacts with what, so that we can subsequently put a quantitative view on that.

Thank you for your reply! Making latent prior knowledge operational, especially in the era of permanent big data, has fascinated me for a while, especially extracting "mechanism" from scientific literature. Many people are trying to do that, including DARPA's Big Mechanism, but it looks like there is a serious gap between the mental models expert human beings work from and the usefulness of these models when encoded in an external medium. How can we make the latter as rich and flexible as the former?

That's a great question, and I know some people working in this DARPA Big Mechanism area. I wish I had a good answer for you, but its really such a challenging and wide open research question right now. I think part of the solution includes making data accessible through computation and in a defined format, such as the LINCS project is doing.

Hi! Fellow researcher in NYC here (Columbia). Thanks for doing this AMA. I'd love to hear more about what sort of data you're making available through LINCS, and what kind of research you hope this availability leads to going forward?

Going forward, I think our particular data could be useful for a few things. One is to generate hypotheses for detailed follow up with mechanistic studies, for various labs in the research area. Second, is to generate predictive signatures for pharmaceutical drug development, to have some idea of whether a putative drug might have some toxicity earlier in the development pipeline. I'm sure there will be many more unforeseen uses.

More generally, LINCS seems to be serving as a general vehicle for making biomedical research more transparent and reproducible, by defining metadata and sharing standards. Also, the availability of cellular response to so many perturbations (across all six centers) will surely lead to new unanticipated learning.

Have you been working with other schools/potential employers on these courses? Will they realize what/if any weight the certificate carries?

Yes, I would also add that we widely advertise our courses to all our colleagues, industry and academia alike. As our particular sequence becomes better known, in addition to other MOOC sequences, I think the quality of the education one can receive from such vehicles will become more and more accepted and appreciated.

Do you see this type of learning extending to other disciplines besides science?

Most definitely. There are all sorts of MOOCs in literature and music as well, I believe, already on Coursera. Their breadth at this early stage is really quite amazing. I only see it growing.

Do you see this form of learning completely replacing traditional colleges?

Not completely. I envision MOOCs fixing the content access problem, and in a major way that gives access to the best content the world can put out. But there remains a strong need for a teacher to answer questions and facilitate discussion to enhance learning in smaller groups. And also to verify identity and administer quality exams.

Hello, I'm a medical student looking to go into medical technology or biotechnology as a start up founder. How would this specialization benefit me most in terms of broadening of my perspective and other relevant skills? Could you provide very specific examples as it makes things much clearer for me?

I think a person such as yourself needs to have a deep and broad understanding of the current experimental state of the art, and how computation can enhance your ability to leverage such data for new knowledge or abilities. Otherwise, how can you expect to come up with transformative, profitable ideas? For example, mRNA sequencing has basically replaced microarrays within a few years for transcriptome analysis. Now, new mRNAseq technologies developed within the past year or two are replacing the first version. You need to stay on this cutting edge. Our courses will help you do that.

thank you for your reply Professor, I'm almost done with the fundamentals of computing specialization from Rice and about to begin the bioinformatics specialization where i will apply all the computer science concepts i learnt in a biological/genomic context. I'm planning on doing the systems biology and biotechnology specialization after that. Do you have any advice on how i can apply that knowledge or if i should consider something else?

With Coursera (and that's one of the beauties of it), just give it a whirl and see if its your cup of tea. If you don't find it interesting or useful, you can walk away without spending a dime. Personally, I think you will find our sequence quite useful, but it will require some time input from you. We strive to be on the forefront of these fields and challenge students who put in the time to learn.

Do you consider MOOCs to be generally a success story? What does the future hold for them?

Prof. Iyengar mentions our MOOCs, and I would just add that many MOOCs have been similarly successful for the same reasons. As to the future, I would speculate that MOOCs will start to replace traditional means of delivering educational content because there seems to be quite artificial barriers between students / learners and content given current technology. My hope is that MOOCs eventually provide a means to start driving down the soaring costs of higher education.

Thanks for doing the Coursera course! I'm enrolled and enjoying the experience thoroughly!

I have a chemistry degree and have about 10yrs experience in the pharma industry. Specifically, I have a lot of experience modelling PK profiles. But we generally use modelling to generate a hypothesis and then follow up with experimentation to see if the hypothesis is valid. Or if we find contradictory evidence we can use the new data to help refine our model. Generally, for small molecules a reasonable model with decent predictive power can be achieved with sufficient effort.

As someone with less Systems Biology background, can you comment on how good current biological models are? Can they currently be used as a means of generating a hypothesis, or are they too rudimentary at this point. i.e. What percent of cellular or subcellular phenomenon are currently predicatable using Systems Biology models?

I also live in the NYC area, are there any live lectures or courses you would recommend to enhance the Systems Biology Coursera education?

Or maybe Open-Source volunteer groups that we could participate in to get some hands on experience with Systems Biology?

Thanks!!

As someone with less Systems Biology background, can you comment on how good current biological models are? Can they currently be used as a means of generating a hypothesis, or are they too rudimentary at this point. i.e. What percent of cellular or subcellular phenomenon are currently predicatable using Systems Biology models?

Hmmm...that's a tough question. It really depends on the system. A great example of a good simple model is Hodgkin-Huxley, around for decades, a few equations, yet extremely precise and predictive, and has generated numerous testable hypotheses. For other more complex behaviors (e.g. cell migration), the range is from, lets say, very good to not good at all. But as with most models and model building endeavors, hypotheses can always be made based on simulations, and one learns quite a bit regardless of whether the hypothesis turned out true or not. Actually, I find I learn much more when simulations don't match experimental tests.

I also live in the NYC area, are there any live lectures or courses you would recommend to enhance the Systems Biology Coursera education? Or maybe Open-Source volunteer groups that we could participate in to get some hands on experience with Systems Biology?

Surprisingly I do not know of any, outside of those organized by academia. Perhaps, we need something like that, but of course something like that needs a champion to push it, with the time to push it! If you want to get involved with some groups, try DREAM challenges organized mainly by Gustavo Stolovitzky at IBM and now adjunct with us here at Sinai. They crowdsource computational solutions to some of the biggest outstanding problems in systems and computational biology.

I am a computer science undergraduate. I wish to know what knowledge of Biology one should have to be able to contribute to Biology/Biotech society ?

You can start with Cell and Molecular Biology. A good 200 level course will be very useful. Be sure to do the problem sets!

So is it sufficient to be able to contribute to Biotech applications in computer science ? As far I know Image Processing/Computer Vision are hugely applied in your area where extensive programming is done behind the underlaying principles.

I would argue that unless you have a good fundamental understanding of the biology that you're applying algorithms to, you're almost certainly destined to underachieve, if not fail.