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Applying Control Theory to the Design of Cancer Therapy

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.


Aniruddha DattaAniruddha 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

About the Newsletter

The IEEE Life Sciences Newsletter is a new initiative to bring forth interesting articles and informative interviews within the exciting field of life sciences every month. Please subscribe to the Newsletter to receive notification each month when new articles are published.

November 2013 Contributors

Aniruddha DattaAniruddha 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

Christopher C. YangChristopher C. Yang is an associate professor in the College of Computing and Informatics at Drexel University. He received his PhD in computer engineering from the University of Arizona. His recent research interests include healthcare informatics, social intelligence and technology, Web search and mining, knowledge management, and information visualization. Read more

Rebecca ChiuRebecca Chiu heads up Business Development for MedHelp, the leading online social media and mobile health platform. She works with strategic partners to help them engage patients and personalize their services, as well as building the partner ecosystem for MedHelp's platform service. Ms. Chiu holds an M.A. and a B.A. in Economics from Yale University and an M.B.A. from The Wharton School. Read more

Simon LinSimon Lin is Director of the Biomedical Informatics Research Center at Marshfield Clinic Research Foundation and he holds the Dr. John Melski Endowed Physician Scientist at Marshfield Clinic. Dr Lin received an MD degree in Medical Informatics at the School of Medicine, Peking University, Peking. Read more

Akhil KumarAkhil Kumar is a Professor of Information Systems at the Smeal College of Business at Penn State University. He received his Ph.D. from the University of California, Berkeley. His research interests are in healthcare IT, business process management systems, process mining and web services. Read more

Prasanna DesikanPrasanna Desikan is currently Senior Research Scientist at Division of Applied Research, Office of Clinical Excellence, Allina Health. He received his Ph.D in Computer Science from University of Minnesota, Twin Cities, USA. Read more

Ritu Khare is a Research Fellow with the National Center for Biotechnology Information at the National Institutes of Health (NIH). She conducts health informatics research focused on data and text mining, natural language processing, information extraction, and data integration. She earned her Doctorate in Information Science in 2011 from the iSchool at Drexel University, in collaboration with the Drexel University College of Medicine. Read more

Jaideep SrivastavaJaideep Srivastava is Professor of Computer Science & Engineering at the University of Minnesota, where he directs a laboratory focusing on research in Web Mining, Social Media Analytics, and Health Analytics. He has a PhD from the University of California, Berkeley. Read more

Robert M. KaplanRobert M. Kaplan, Ph.D. is Associate Director for Behavioral and Social Sciences and Director of the Office of Behavioral and Social Sciences Research (OBSSR) in the National Institutes of Health (NIH) Office of the Director. He is the author, co-author or editor of more than 18 books and over 500 articles or chapters. Read more

Joydeep GhoshJoydeep Ghosh is Joydeep Ghosh is the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. Dr. Ghosh's research interests lie primarily in data mining and web mining, predictive modeling / predictive analytics and their applications to a wide variety of complex real-world problems, including extracting value from a variety of healthcare data. He received the Ph.D. at The University of Southern California. Read more

Longjian LiuLongjian Liu, MD, PhD, MSc, FAHA, is an associate professor of Epidemiology and Biostatistics at Drexel University School of Public Health, and associate professor of medicine at Drexel University College of Medicine. Dr. Liu's main research covers cardiovascular disease and diabetes prevention, and the usage of hospital electronic medical records to monitor and predict disease risk and outcomes. Read more

Vipin GopalVipin Gopal is the Vice President of Clinical Analytics at Humana, a Fortune 100 company. He is an expert in developing differentiating analytic competencies, and has previously led analytic functions in diverse companies ranging from industrial conglomerates to healthcare. Dr. Gopal obtained his Ph.D. from Carnegie Mellon University. Read more

Christian SeegerChristian Seeger is currently a Ph.D. student in the Databases and Distributed Systems group led by Prof. Alejandro Buchmann at TU Darmstadt. His research interests are middleware approaches and applications for on-body and ambient sensor networks. Read more

Kristof Van LaerhovenKristof Van Laerhoven obtained his Ph.D. at Lancaster University (UK) He heads the Embedded Sensing Systems lab at the TU Darmstadt (Germany), funded by the Emmy Noether Programme of the German research foundation DFG. His research combines sensing systems with pattern recognition and machine learning, to obtain adaptive and power-efficient systems. Read more