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An Engineering Approach to Cancer Therapy Design

By Aniruddha Datta

Cancer encompasses various diseases associated with loss of control in the mechanisms that regulate the cell numbers in a multicellular organism. It is usually caused by malfunction(s) in the cellular signaling pathways. Malfunctions occur in different ways and at different locations in a pathway. Consequently, therapy design should first identify the location and type of malfunction and then arrive at a suitable drug combination. We first model the interactions between different pathway components using Boolean logic gates. The input-output behavior of the faulty circuit is used to carry out fault classification and arrive at the subsequent choice of the appropriate combination therapy.

Introduction to Molecular Biology and Cancer
Multicellular organisms such as ourselves are made up of about 100 trillion cells. A cell is the basic unit of life and nothing smaller than a cell can be considered to be truly living. Each cell is like a massive factory where thousands of reactions are performed every second inside compartmentalized organelles. By far the largest organelle in a human cell is the nucleus which contains the genetic information written using the four-letter language of DNA. The information in the DNA codes for proteins which are made up of units called amino acids that are linked together. There are twenty different amino acids that occur over and over again in nature. A gene is a stretch of DNA that codes for a protein. The sequence of specific amino acids in a protein determines its three-dimensional conformation which in turn plays a crucial role in determining its function.

Unicellular organisms such as bacteria and yeast reproduce if there are sufficient nutrients and the ambient conditions are favorable. In the case of multicellular organisms, however, cell division is under very tight control and takes place only when new cells are needed. Futhermore, in the absence of survival signals from its neighbors, a cell will activate an intracellular suicide mechanism, called apoptosis, and eliminate itself from the population. It is this dynamic equilibrium between controlled cell proliferation and cell death that maintains the tissue architecture in adult multicellular organisms. Disruption of this dynamic equilibrium can lead to the disease called cancer.

Under normal conditions, growth factors (or mitogens) external to a cell come and bind their respective transmembrane receptors and this leads to a signal transduction cascade inside the cell which ultimately results in the activation of genes involved in cell proliferation. Aberrant behavior , such as mutations, in some of the genes in the signal-transduction cascade can cause the cell proliferation genes to be activated even when the external growth factor stimulus is missing, and this is one of the mechanisms by which uncontrolled cell proliferation and possibly cancer can develop. Another mechanism by which cancer can develop is through the mutational inactivation of genes that serve as molecular brakes on cell division. Thus under normal conditions, in the absence of growth factors and the molecular brakes on cell division being intact, a cell should not proliferate, nor should there be a drop in programmed cell death. When this dynamic equilibrium between cell proliferation and programmed cell death is disrupted it leads to the formation of tumors, which are initially benign. Subsequently, these tumors can become malignant or cancerous by acquiring the ability to invade surrounding tissue. Finally metastases can occur as these tumors develop the ability to spread to distant sites via the blood or lymphatic system.

Genetic Regulatory Networks and Pathways
Genes (and other biological molecules such as proteins) interact with each other in a multivariate fashion. However, historically biologists have focused on experimentally studying the marginal cause-effect interactions between a small number of biological molecules, leading to what is called biological pathway information. This piecemeal approach has been very successful in unraveling the sequences of steps involved in metabolic processes, primarily studied using simpler organisms. However, it has failed to completely elucidate the intricate cellular signaling mechanisms that are associated with higher organisms such as ourselves. Motivated by this need and enabled by the advent of high throughput technologies, such as microarrays (which can simultaneously provide measurements of the activity status of thousands of genes), several approaches have been recently proposed for modeling the multivariate interactions between genes leading to what are called genetic regulatory networks [1]. The study of genetic regulatory networks has been carried out using differential equations, Bayesian networks, Boolean networks, and their stochastic generalizations, the so-called probabilistic Boolean networks (PBNs). Of these, the PBNs can be equivalently represented as homogenous Markov Chains. By introducing external treatment as a control variable in the PBN, we obtain a controlled Markov Chain or a Markov Decision Process. By formulating cancer treatment as the problem of moving a genetic regulatory network from an undesirable state to a desirable one, and trading off the costs involved, one can formulate an optimal control problem which can be solved using dynamic programming and its variants. Translating the solution to experimental validation, however, faces a challenging hurdle since the amount of data needed to reliably infer a genetic regulatory network is huge.

Combination Therapy Design Based on Pathway Information
The practical considerations summarized above forced us to refocus on cancer therapy design using pathway information. The particular pathway that we focused on is the growth factor signaling pathway Since this pathway came to us without any feedback loops, we were able to model it as a digital circuit using logic gates. The input signals feeding into this circuit are the growth factors while the outputs are proteins/genes reporting on cell proliferation and programmed cell death activity. From an input-output point of view, cancer could be characterized by enhanced cell proliferation or suppressed apoptosis even when growth factors are absent. This can only happen if there is signaling breakdown along the pathways being studied. A key goal here is to extract information about where the signaling breakdown may have occurred using only input-output information. Computer simulation of the digital circuit can certainly aid in doing that. A second goal is to predict the efficacy of different drug combinations in treating the disease, once again relying only on input-output information. Towards this end, the effect of different anti-cancer drugs, whose main mechanism of action is to cut off the downstream signaling, can be superimposed on the digital circuit, at the known appropriate points of intervention. Thereafter, this circuit can be used to make predictions about efficacy of different drug combinations. For further details, the reader is referred to [2].

Future Challenges in Experimental Validation
The predictions that we have made regarding combination therapy for cancer merit experimental validation, perhaps using cancer cell lines. However, it should be pointed out that in carrying out the experimental validation, we anticipate encountering several challenges such as (i) possible inaccuracies in the pathway model; (ii) the presence of feedback loops that have not been accounted for here; (iii) the presence of multiple faults; and (iv) the heterogeneity of cancer tissue. Addressing each of these problems is a research issue in its own right and could significantly contribute to the ultimate validation of the approach discussed here. Here, it is encouraging to note that issues of this type such as uncertainty and robustness have been extensively studied in engineering disciplines such as Control Theory [3], although adapting the ideas to the current context will still be a challenge.

This work was carried out in collaboration with Ritwik Layek, Michael Bittner and Edward Dougherty, and was supported by the National Science Foundation, the W.M. Keck Foundation and the Qatar National Research Fund.

For Further Reading

1. A. Datta and E. R. Dougherty, Introduction to Genomic Signal Processing with Control, CRC Press, 2007.

2. R. Layek, A. Datta, M.L. Bittner and E. R. Dougherty, “Cancer Therapy Design Based on Pathway Logic,” Bioinformatics, Vo. 27, No.4, 548-555, 2011

3. M. Vidyasagar, Control System Synthesis: A Factorization Approach, MIT Press, Cambridge, MA, 1985.


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

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October 2013 Contributors

Mary Capelli-SchellpfefferMary Capelli-Schellpfeffer, MD, MPA, is Medical Director of Loyola University Health System's Occupational Health Services, and Associate Professor, Department of Medicine, Loyola University Chicago Stritch School of Medicine. Dr. Mary Capelli-Schellpfeffer guides Loyola's occupational medicine programs. Read more

Brian T. CunninghamBrian T. Cunningham is a Professor in the Department of Electrical and Computer Engineering and the Department of Bioengineering at the University of Illinois at Urbana-Champaign, where he also serves as the Interim Director of the Micro and Nanotechnology Laboratory, and as Director of the NSF Center for Agricultural, Biomedical, and Pharmaceutical Nanotechnology. His research is in the development of biosensors and detection instruments for pharmaceutical high throughput screening, disease diagnostics, point-of-care testing, life science research, and environmental monitoring. Read more

Aniruddha DattaAniruddha Datta received the B. Tech degree in Electrical Engineering from IIT Kharagpur in 1985, the M.S.E.E. degree from Southern Illinois University, Carbondale in 1987 and the M.S. (Applied Mathematics) and Ph.D. degrees 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. Read more