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Dr. Aniruddha Datta has been working on a novel technique to optimize cancer therapy design, using tools from classical control therapy, as outlined in an article in last month’s Newsletter. In this interview, he tells us more about his work, and reactions to it in the engineering and medical/biological communities.

IEEE tv: What is your Current area of research?

Anirudda Datta: I am doing work on translational genomics. The sequencing of the human genome has produced a vast amount of information, and we’re trying to see if some of that information that is available in the literature, as well as the information that is generated by the genomic technology, can help in the diagnosis and therapy for complex diseases like cancer. My background is in control systems, so initially, when I got into this area, it was basically by the desire to apply well-proven methodologies from the engineering literature to come to bear on problems of biological ad medical interest. Basically, I am looking at trying to see if any engineering approach or computer simulation could be used to come up with a combination of drugs for treating cancer, as opposed to trial-and-error selection of drugs, because, these cancer drugs, they have a lot of side effects, and we want to minimize the impact on the patient.

IEEE tv: Have your efforts shown promising results?

Datta: So we have at least had theoretical success working with models that most biologists have faith in, well-known biological pathways, the mitogen-activated protein kinase pathway, (or the MAP kinase pathway), and we have been able to suggest some combinations of drugs, and, at least on paper, that works out.

What I would be interested in, and that’s really the next step, is to see to what extent we can validate real work. Whether, if we work with cell lines that harbor the mutations, and we apply the combinations that we are predicting, whether we can see a degree of success greater that what we would be getting just by some random choice, random trial and error. Of course, having worked in this area for ten years, I an not naive enough to think that we’re going to be instantly successful, because a lot of the information that we are using for designing these techniques, or arriving at the combination therapies; that is based on what is called pathway information, and that is not something set in concrete, because it is based on marginal information that biologists have gathered from different experiments. So, some part of the modeling of the assumptions could be incorrect, or not 100% correct. That is just “garbage in, garbage out”. So, our technique might be fine, but it is basically the stuff that’s feeding into it that will determine whether it is ultimately successful, or not.

As an engineer, we are going to do what we always do as engineers: when you design a control system and it doesn’t work, then you go back and look and see where you need to fine tune things. In lcontrol design, it is always done. On paper you design something, then you’re going to fine tune that when you do the application, or even in simulation. And that is something that we will have to do.

IEEE tv: Please describe how the medical and engineering communities have reacted to your work.

Datta: I have given this kind of talk at many places, even at schools like Yale, and several times at Cal Tech, and so on. Wherever I give this talk to the engineers there is excitement. I think you sensed that this morning in the room. They see it as a novelty. Now, among biologist and medical people, one of them asked me that, “how far away are you from validating it?” So, the proof of the pudding is in the eating thereof, OK? So, until I can get into the stage where this technology is validated, or I have provided proof, that will say, “Hey, this worked a lot better that the trial and error.” So the engineers will appreciate it, IEEE will appreciate it, but I will always get some skepticism from the medical people, or from the biologists.

Now, I don’t want to alienate them, because we are trying to actually help them, because, they are oftentimes used to a one direction approach. I think the medical Dr., Mary, this morning, put it very aptly: you have all of these different silos of information, right? And if you are restricted within your silo, then you come up with conclusions, but it is the whole over all global thing that is more important. So I think, what we are proposing, is the over-all global picture, but it cannot be done in a way that we had thought we would do ten years ago, where you discard all of the siloed information, because this is not the actual, complete picture, discard that, and then generate data, and then build models from data. I think, really, what would be required to ensure success, is that you integrate whatever new data or weak and then we’ll have the “Big Data.” Integrate that together and then I can be in a much better position to predict and to make the kind of validation that people will demand from us, sooner or later.

Contributor

Aniruddha Datta received the Ph.D. degree from the University of Southern California in 1991. In August 1991, he joined the Department of Electrical and Computer Engineering at Texas A&M University where he is currently the J. W. Runyon, Jr. ’35 Professor II. His areas of interest include adaptive control, robust control, PID control and Genomic Signal Processing. Read more