Human and Mouse Systems Biology

Systems Biology and Disease

The systems biology approach to study, diagnose, and treat human disease has become a critical and necessary method to combat the disease’s complex nature. High-throughput experimental technologies have enabled the identification of biological components at unprecedented scale, from cells and tissues to genes and proteins. Collectively, these technologies provide a “parts list” for biological systems (e.g., biochemical pathways, larger interaction networks). Systems biology employs an integrative approach to characterize biological systems, in which interactions among all these components in a system are described mathematically to establish a computable model that integrates this data into a cohesive whole. These in silico models—which complement traditional in vivo and in vitro models—can be simulated to quantitatively study the emergent behavior of a system of interacting components. Integrating heterogeneous dynamic data into quantitative predictive models holds great promise to significantly increase our ability to understand and rationally intervene in disease-perturbed biological systems. This promise—particularly with regards to personalized medicine and medical intervention—has motivated the development of new methods for systems analysis of human biology and disease.


Cancer is particularly amenable to systems biology approaches because it is an intrinsically complex and heterogeneous disease. Malignant tumors develop as a function of multiple biological interactions and events, both in the molecular domain among individual genes and proteins, and at the cellular and physiological levels between functionally diverse somatic cells and tissues. At the molecular level, genetic lesions interact synergistically to evade tumor suppression pathways, with no single mutation typically sufficient to cause transformation. The convolution of genetic effects, changes in gene and protein expression levels, and epigenetic modifications further illustrates the complex, nonlinear relationship between molecular state and cellular cancer phenotype, emphasizing the need for heterogeneous data integration through in silico models. Important efforts in sequencing the human genome and now individual cancers mean that malignant genetic transformations can be studied in the context of the entire genome. As a result, it is becoming increasingly clear that cancers result not only from multiple perturbations, but from differing sets of mutations in every patient. Such a distribution of mutations presents enormous challenges for personalized medicine, because it means that simple mutation to treatment correlations are not likely to be effective.


Glioblastoma multiforme is the most prevalent and deadly form of brain cancer. Leveraging a wide array of omics measurements generated in the Price group and at ISB, we are using a combination of statistical and mechanistic modeling approaches to interrogate the link between mutations in the cancer and key metabolic, signaling, and regulatory processes that contribute to tumorigenesis. Furthermore, we are exploring the aggressiveness and heterogeneity of different grades of human astrocytoma using both network- and gene-based approaches.