Nick Flann is an Associate Professor in the Department of Computer Science at Utah State University (USU) and in 2010 completed a sabbatical at ISB in the Shmulevich lab.
Systems biology has been successful in understanding cell components and their complex interactions through the integration of high throughput data sources with computational analysis. The challenge is to extend systems biology over multiple scales to comprehend how subcellular processes control cell behavior and in turn, how interactions among cells lead to large scale organization at the tissue level. Such knowledge is key to unlocking the genetic foundations of morphological development and disease.
Dr. Flann’s research interests lie in developing mechanistic multiscale models that bridge the gap between regulatory network dynamics and morphological outcomes. The work focuses on applying high-fidelity methods that implement the diversity of cell physiology, not directly as high level descriptions, but as combinations of modular subcellular mechanisms. One such modeling approach is the Cellular Potts Model (CPM) that represents 2D and 3D cellular systems as lattices of simple mesoscopic particles and model components as additive energy terms over cell and sub-cell configurations. The advantage for multiscale modeling is in its simplicity and realism since, just as in living systems, organization at the cell, multicell and tissue scale emerges through the complex interaction of lower-level mechanisms.
Flann’s work in applying multicellular cancer modeling for drug discovery and optimization is featured in the Nature article “Modeling: Computing Cancer” Nature 491, S62-S63 (22 November 2012) doi:10.1038/491S62a
Modeling Multiscale Systems
PhD, Computer Science, Oregon State University, 1992
MS, Computer Science, Oregon State University, 1986
Research in Dr. Flann’s lab is directed to the development and application of multiscale models to significant biological subsystems in cancer, immunity and yeast colony development. Through active collaboration with multiple labs at ISB, common application-independent methodologies are being developed and applied to these specific domains as pilots systems. Some of the questions driving the research are:
- What are the impacts of integrating models of intracellular regulatory networks into the CPM?
This research seeks to understand how the temporal dynamics of regulatory networks at the subcellular scale influence the multi-cell spatiotemporal dynamics of morphology development. By linking regulation to morphology, the influence of small molecule interventions on tissue level manifestations of disease can be predicted and potential treatments discovered through high-throughput simulations. Previous work has demonstrated the feasibility of this approach in discovering potential subcellular interventions in angiogenesis that lead to disruptions in the organization of the vessel network and subsequent nutrient delivery to micro-tumors.
- How do the network dynamics and the attractor landscape of regulatory networks lead developmental systems to convergence to robust attractors in morphological space?
Study of multiscale network dynamics aims to expand the established body of work in criticality of regulatory networks to include morphodynamic feedback among mechanisms such as cell/cell signaling and cell motility, apoptosis and proliferation. With such an extension, the tools of complexity could be applied to large-scale dynamic systems in order to recognize criticality in robust development and chaos in tissue level diseases such as cancer.
- How can multiscale experimental data directly inform and validate the models?
Data sources span scales from regulatory networks induced from RNA-seq, microfluidic cytometry, multicell in vitro time-lapse images, to colorimetric markers that report spatial and temporal patterns of RNA expression over developing tissues. While methods exist for analyzing and validating data when viewed individually, methods are needed that link data sources over multiple scales so that data at one level can constrain interpretations of data at another level. Methods are under development to address this problem that work by identifying suggested model corrections as discrepancies between simulated and actual outcomes at one scale, and then perform model-based error propagation to other scales.
- How can high performance and cloud computing technology enable high-throughput multiscale model executions over large complex configurations of thousands of cells?
As models incorporate more subcellular detail, cell/cell interactions and progress from the multicell scale to whole tissues, computational resources become a limiting bottleneck. Previous work has proved the value of massively parallel grid computing for model space exploration, but utilization of parallelism within individual simulations is an open problem. Collaboration between ISB, USU and Pacific Northwest National Laboratory (PNNL) high performance computing group is underway to develop effective solutions.
Projects include: (a) a multidisciplinary study of how glioma development is influenced by the interactions among the immune, vascular and micro-tumor systems. This work is in collaboration with Dr. Wei Zhang at MD Anderson Cancer Center and involves the integrated of in vitro experimentation, image analysis and multiscale modeling; (b) understanding criticality at multiple scales in morphological and disease development; and (c) the designing of new methods for model fitting and validation from multiscale images.