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Associate Department Head
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 http://www.nature.com/nature/
Areas of Research:
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.
Key collaborations within ISB:
Ilya Shmulevich: 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.
Aímee Dudley: Projects include: (a) the development of computational frameworks that tightly integrate whole yeast colony simulation with high-throughput experimentation; and (b) the application of multiscale models to understand sub-colony structure formation including carbohydrate matrix and its role in the development of complex and distinct colony features such as ridges, folds and aerial tubes.
Adrian Ozinsky: Projects include model-based cell tracking from in vitro time-lapse images of epithelial to mesenchymal cell transitions to understand mechanisms of transition and discover possible precursor morphology and physiology clues that predict state changes.
Ghaffarizadeh A, Eftekhari M, Esmailizadeh AK, Flann NS (2013). Quantitative Trait Loci Mapping Problem: An Extinction-Based Multi-Objective Evolutionary Algorithm Approach. In Algorithms. 2013,6(3): 546-564. link
Beijie Xu, Mimi Recker, Xiaojun Qi, Nicholas Flann, Lei Ye Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms. Journal of Educational Data Mining. 2013 Aug;5(2):38-68 link
Flann, N. S., Mohamadloun, H., Podgorski, G. J. (2013) Kolmogorov Complexity of Epithelial Pattern Formation: the role of Regulatory Network Con. Biosystems. 2013 May;112(2):131-8. doi: 10.1016/j.biosystems.2013.03.
Flann, N. S., Mohamadloun, H., Podgorski, G. J. (2012). Criticality of Spatiotemporal Dynamics in Contact Mediated Pattern Formation. In Marko Djordjevic, Konstantin Severinov, Magdalena Djordjevic (Ed.), Series: Lecture Notes in Computer Science, Vol. 7223 Subseries: Theoretical Computer Science and General Issues (vol. 7223, pp. 50-61). Springer.
Podgorski, G. J., Flann, N. S. (2012). Searching a Multicellular Model to Tame Tumor-Induced Angiogenesis (pp. 349 - 354). IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012 Digital Object Identifier: 10.1109/CIBCB.2012.6217251. Page(s): 349- 354 link
Mahoney A. W., Podgorski G.J., Flann N. S. (2012). "Multiobjective Optimization Based-Approach for Discovering Novel Cancer Therapies," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 1, pp. 169-184, January/February, 2012 link
Ghaffarizadeh A., Ahmadi K., and Flann N. S. (2011). Sorting Unsigned Permutations by Reversals using Multi-Objective Evolutionary Algorithms with Variable Size Individuals In IEEE Congress on Evolutionary Computation June. 2011. New Orleans, USA. link
Flann, N. S., Mahoney, A. W., and Podgorski, G. J. (2010) A Multi-Objective Optimization Based-Approach for Discovering Novel Cancer Therapies, IEEE/ACM Transactions on Computational Biology and Bioinformatics, May. 2010. IEEE Computer Society, link
Calmelet C., Flann N. S., & Sepich D., (2010) Notochord Development in Zebrafish. In Proceedings of 10th International Symposium on Mathematical and Computational Biology, 2010.
Flann N. S., Mahoney A. W., & Smith B. G., and Podgorski G. J. (2008). Evaluating Cancer Interventions by Simulating Tumor-Induced Angiogenesis, Blood Flow and Oxygen Delivery. In European Conference on Mathematical and Theoretical Biology.
Flann N. S., Mahoney A. W., Podgorski G. J., and Smith B. G. (2008). Discovering Novel Cancer Therapies: A Computational Modeling and Search Approach. In IEEE Conference on Computational Intelligence in Bioinformatics and Bioengineering, pp 233 - 240, November 2008. DOI: 10.1109/CIBCB.2008.4675785
Bansal, M., Podgorski, G. J., and Flann, N. S. (2007). Regular Mosaic Pattern Formation: A Study of the Interplay between Lateral Inhibition, Apoptosis, and Differential Adhesion. In Journal of Theoretical Biology and Medical Modeling, 2007, Volume 4:43 doi: 10.1186/1742-4682-4-43.
Dhanasekaran, R., Flann, N. S., and Podgorski, G. (2007). Co-option and Irreducibility in Regulatory Networks for Cellular Pattern Development. In Proceedings of First IEEE Symposium on Artificial Life 2007, Honolulu, Hawaii. DOI: 10.1109/ALIFE.2007.367794
Bansal M., Flann N. S., Hu J., Patel V., and Podgorski G. J. (2005). Biological Development of Cell Patterns: Characterizing the Space of Cell Chemistry Genetic Regulatory Networks, Eighth European Conference on Artificial Life, Canterbury, Kent, UK, September 2005. DOI: 10.1007/11553090_7