Multiscale Modeling


A hallmark of living systems is their multiscale nature – their structure and behavior, in time and space, functions on multiple scales of biological organization. Furthermore, these scales are interlinked in that system behaviors on one scale influence and constrain behaviors on another scale. Consider that individual cells are themselves complex systems of molecular interactions that comprise networks among nucleic acids, proteins, lipids, metabolites, and other biomolecules. Cells process information from their environment, make decisions in response to that information, and exhibit complex behaviors. In multicellular organisms, cells do not function independently. Rather, they interact and communicate physically and chemically as they move, adhere, divide, or secrete and uptake diffusible molecules.

Complex structures involving billions of cells emerge from such interactions, such as networks of blood vessels in healthy or diseased tissues. Such structures impact the system at a “higher” scale – for example, by supplying blood to the growing tumor more efficiently, promoting cellular growth or immune infiltration. Importantly, these emergent structures, which themselves are products of intercellular and intracellular interactions, influence the “lower” scales by causing cells to alter the states of their intracellular molecular networks. These different states in turn affect cellular behaviors such as cell division, programmed cell death, motility, and others.


Although the molecular and multicellular scales are interlinked, it is important to recognize that we may very well wish to be able to predict and control complex systems at different scales of interest. For example, on the multicellular or tissue scale, we may wish to predict a general property of a vascular network in a tumor, such as the expected distribution of blood flow to the tumor or the distribution of proximities of tumor cells to the vasculature, potentiating metastasis. At the same time, the control of the system happens at the lower, molecular, scale, say, by chemically inhibiting one or more specific molecular interactions, leading to a reduction of vascular growth or cellular motility.


Cancer research demands multiscale modeling. On the one hand, the enormous power and success of the genomic and molecular paradigm of cancer has made it possible to comprehensively measure genomic, transcriptional, proteomic, and epigenomic information in multiple cancers. This underpins our understanding of how molecular systems in cancer cells are disrupted and is a central goal of large-scale cancer genomics projects, such as The Cancer Genome Atlas (TCGA). On the other hand, the emerging consensus that the tumor microenvironment, in all its complexity, plays a crucial role in cancer onset and progression calls for major efforts to integrate the vast amounts of information on the molecular and cellular scales into multiscale models. Such models could then be used to develop personalized therapies for cancer patients.


B. Aguilar, D. L. Gibbs, D. L. Reiss, M. McConnell, S. A Danziger, A. Dervan, M. Trotter, D. Bassett, R. Hershberg, A. V. Ratushny, I. Shmulevich, “A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma,” GigaScience (in review).

R. Tasseff, B. Aguilar, S. Kahan, S. Kang, C. C. Bascom, R. J. Isfort, “An integrated multiscale, multicellular skin model,” bioRxiv, 10.1101/830711, 2019.

B. Aguilar, A. Ghaffarizadeh, C. D. Johnson, G. J. Podgorski, I. Shmulevich, N. S. Flann, “Cell death as a trigger for morphogenesis,” PLoS ONE 13(3): e0191089, 2018.

S. Kang, S. Kahan, J. McDermott, N. Flann, I. Shmulevich, “Biocellion: Accelerating Computer Simulation of Multicellular Biological System Models,” Bioinformatics, Vol. 30, No. 21, pp. 3101-3108, 2014.

Featured Projects

  • biocellion

    A high-performance computing framework for simulation of agent-based multicellular models