Systems Biologists Seek Fuller Integration of Systems Biology Approaches in New Cancer Research Programs

Defining Cancer Systems Biology

Aiming at understanding the dynamic interactions among components of a cell, among cells and their interaction with the environment, cancer systems biology is an approach by which biomedical questions are addressed through integrating experiments in iterative cycles with mathematical modeling, simulation, and theory. Modeling is not the final goal, but is a tool to increase understanding of the system, to develop more directed experiments, and finally to enable predictions. This definition of cancer systems biology matches closely the definition of the EraSysBio consortium of 16 European ministries, funding bodies, and project management agencies from 13 countries.²⁹

The most popular definitions of systems biology refer to “dynamics,” “mechanisms,” “principles,” and “behaviors.” The complexity of biological systems and/or functions arises from the interaction of a myriad of nonlinear spatio-temporal phenomena and components. The fact that most cellular processes, such as cell-cycle control, cell differentiation, and apoptosis, are inherently dynamical highlights the need for integrating mathematical modeling into life science and clinical research. A systems biology approach can help identify and analyze the principles, laws, and mechanisms underlying the behavior of biological systems.

The participants of the strategic workshop concluded that advancing biomedical applications through systems biology approaches requires the development of new theoretical methodologies, such as novel techniques for data-based system identification, theoretical concepts for the design of experiments, good methods for hypothesis testing, theoretical frameworks to couple processes occurring at (and across) different spatial and temporal scales, and effective algorithms to solve problems of computational complexity.

Approaching Tumor Complexity by Integrating Experiments with Mathematical Modeling

In order for systems biology to succeed in cancer research, and ultimately become an integrated part of it, rather than a separate discipline, it has to overcome significant barriers to the acceptance of mathematical modeling in the life sciences. Such models are necessary because of the dynamic nature of many biological processes involved in the initiation and progression of cancer. In order to allow the formulation and calibration of mathematical models, quantitative data have to be generated.

Because the systems approach is, above all, a different way of thinking about the organization and behavior of dynamical systems, the impact of systems biology approaches is more difficult to measure. This situation is further confounded by the fact that all models are abstractions of reality, because all the hypotheses on which they are based will eventually prove to be incomplete in one way or another, and science progresses by uncovering these shortcomings and looking for models that are improved (if never perfect).

Mathematical models, however, have the advantage of being quantitative and interactive rather than solely descriptive. The process by which models are formulated, and which may include the representation of genetic, epigenetic, cellular, and tissue effects across the various physical and temporal scales during tumorigenesis, helps to articulate hypotheses and thereby supports the design of appropriate experiments to test them. Consequently, models cannot be regarded as “deliverables” or “final products” that unambiguously mark the success of a systems biology project.

The role of mathematical modeling is to provide a conceptual framework for the formulation and quantitative testing of hypotheses. However, the initial purpose is not prediction but to enhance our “understanding” of a biological system. Generating predictions is one way to test whether this understanding is consistent with the behavior of the system of interest.

An Unlikely End to a New Discipline

Rather than defining systems biology as a separate discipline, the workshop participants' recommendation is to encourage the incorporation of “systems biology approaches” across new research programs, particularly those of medical relevance to cancer. Systems biology emphasizes the integration and coordination between theoretical and experimental efforts. If systems biology approaches were widely adopted in the life sciences as a powerful tool for hypothesis testing, there would be no need to consider systems biology as a separate discipline. However, considering the skepticism that exists toward mathematical modeling in the life sciences, it remains important to emphasize and show its value in dealing with biological complexity.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.