Research in the lab is built on our multi-disciplinary experience in computational bioengineering, cancer and systems biology, quantitative pharmacology, and single-cell measurement and analysis, and benefits from active collaboration with leaders in clinical oncology, epigenetics and proteomics research. The lab is currently focused on the following areas of research:

Single-Cell Quantitative Biology – Multi-parametric analysis of the heterogeneous and dynamic cellular response to perturbation
Classical pharmacology has been relying primarily on population-average measurements to address variance between responses of genetically different cell populations to stimuli and therapeutic agents. Clonal populations of cells, however, behave non-identically when exposed to a uniform concentration or dose of a stimulus or drug. Variability with respect to the amplitude and timing of response and the consequential phenotypes is observed among genetically identical cells. Although not obvious from the most frequently studied dose-response metrics (e.g. potency; IC50), cell-to-cell variability can limit maximal effect of a drug both in the short-term (e.g. Emax in cell culture) and in the long-term (e.g. residual disease). We use a range of multiplexed, time-lapse live and fixed single-cell assays and quantitative modeling as a means to identify the origins of heterogeneity in cellular response at a single-cell level, and determine whether or when heterogeneity arises from stochastic or feedback-regulated differences in biochemical processes, differences in cell cycle state, epigenetic state reprogramming, or the pre-existing subpopulations of stem-like cells. In addition to our fundamental understanding of cellular signaling mechanisms, this approach is likely to provide a significant impact on our choice of which drug or drug combinations to explore therapeutically.

  • Fallahi-Sichani M, Honarnejad S, Heiser LM, Gray JW, Sorger PK. Metrics other than potency reveal systematic variation in responses to cancer drugs, Nature Chemical Biology (2013). (PubMed Link) (PDF)
  • Lin JR, Fallahi-Sichani M, Sorger PK. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method, Nature Communications (2015). (PubMed Link) (PDF)
  • Lin JR, Fallahi-Sichani M, Chen JY, Sorger PK. Cyclic immunofluorescence (CycIF), a highly multiplexed method for single-cell imaging, Current Protocols in Chemical Biology (2016). (PubMed Link) (PDF)

Systems and Cancer Biology – Adaptive regulation of tumor cell fate decisions
The discovery of driver oncogenes provides new opportunities for the development of molecularly targeted therapies. However, patient benefit is often temporary and limited by drug adaptation, partial response and ultimately drug resistance. Understanding and preventing adaptation is likely to be key to durable therapy. Despite a wealth of data on signaling networks involved in adaptation, current understanding of these networks is incomplete and many questions remain unanswered: Is adaptation fundamentally similar across different cell types or individual cells that experience the same oncogenic pathway? How informative is the initial (pre-treatment) state for drug adaptation or resistance? How do adaptive changes in network state depend on the biochemical node inhibited and how do they translate to subsequent phenotypic states (i.e. cell death, proliferation, quiescence or differentiation)? What is the relative contribution of cell-autonomous versus non-cell-autonomous factors? Can we identify single or multi-component biomarkers of adaptation to guide precision treatment strategies that mitigate drug resistance? In addition, most of our knowledge about adaptive resistance comes from studying bulk tumor cell populations. It therefore remains unclear whether individual cells in a population adapt to drug effect (i.e. inhibition of oncogenic signaling) in the same way or in different ways. To address these important questions, we use a combination of long-term live-cell microscopy, high-throughput biochemical measurement, multiplex single-cell analysis and computational modeling to quantify mechanisms involved in adaptive drug responses and their consequences for cancer cell fate and to use that knowledge to propose novel approaches to enhance drug maximal effect and to prevent or overcome drug resistance. Our work is currently focused on BRAF-mutant cancers and drugs targeting the BRAF oncoprotein or other components of ERK signaling, but the approach is applicable to other cancers and other oncogenic pathways.

  • Fallahi-Sichani M#, Becker V, Izar B, Baker GJ, Lin JR, Boswell SA, Shah P, Rotem A, Garraway LA, Sorger PK#. Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de-differentiated state, Molecular Systems Biology (2017). (PubMed Link) (PDF (#Co-corresponding authors)
  • Fallahi-Sichani M, Moerke NJ, Niepel M, Zhang T, Gray NS, Sorger PK. Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis, Molecular Systems Biology (2015). (PubMed Link) (PDF)
  • Moerke N, Fallahi-Sichani M. Reverse phase protein arrays for compound profiling, Current Protocols in Chemical Biology (2016). (PubMed Link) (PDF)
  • Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, Fallahi-Sichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CGK, Kazer SW, Gaillard A, Kolb KE, Villani AC, Johannessen CM, Andreev AY, Van Allen EM, Bertagnolli M, Sorger PK, Sullivan RJ, Flaherty KT, Frederick DT, Jané-Valbuena J, Yoon C, Rozenblatt-Rosen O, Shalek AK, Regev A, Garraway LA. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq, Science (2016). (PubMed Link) (PDF)

Computational Biology – Multi-scale modeling of bio-molecular networks and reactions
Advance in biomedical research is increasingly dependent on quantitative experimental methods that use emerging technologies to profile complex and multi-scale biological systems. At the same time sophisticated analytical methods are required to dissect the complex interactions between the measured components and uncover how these interactions at a range of spatial and temporal scales give rise to the function and behavior of the system. We have contributed to the development of both experimental and computational procedures for analyzing cellular phenotypes and biochemical networks and modeling multi-scale molecular, cellular and tissue-level mechanisms. In the past, we have developed multi-scale computational modeling approaches that combine stochastic (Monte Carlo) and deterministic (ordinary or partial differential equation) modeling to investigate the dynamics of G-protein coupled receptor signaling, and to study the mechanisms by which TNF signaling determines immunity to M. tuberculosis infection. We are working to apply similar and novel approaches to the studies of other complex diseases, particularly cancer, where there is a critical need for bridging studies of therapeutic targets at the molecular scale to the effects of modulation of these targets at the cellular and tissue (tumor) levels. More recently, we have used statistical modeling approaches such as partial least squares regression and mutual information analysis with the goal of uncovering important determinants of regulation from complex high-dimensional data spaces and to identify measurements of network activity that are most predictive of a relevant phenotype. These and other mathematical approaches that capture dynamic, multi-factorial differences in signaling and biochemical networks across cell types and drugs are also essential components of our research.

  • Kirschner DE, Hunt CA, Marino S, Fallahi-Sichani M, Linderman JJ. Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models, WIREs Systems Biology and Medicine (2014). (PubMed Link) (PDF)
  • Fallahi-Sichani M, Moerke NJ, Niepel M, Zhang T, Gray NS, Sorger PK. Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis, Molecular Systems Biology (2015). (PubMed Link) (PDF)
  • Fallahi-Sichani M, Kirschner DE, Linderman JJ. NF-kappaB signaling dynamics play a key role in infection control in tuberculosis, Frontiers in Physiology 3:170 (2012). (PubMed Link) (PDF)
  • Fallahi-Sichani M, Flynn JL, Linderman JJ, Kirschner DE. Differential risk of tuberculosis reactivation among anti-TNF therapies is due to drug binding kinetics and permeability, Journal of Immunology (2012). (PubMed Link) (PDF)
  • Fallahi-Sichani M, El-Kebir M, Marino S, Kirschner DE, Linderman JJ. Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation, Journal of Immunology (2011). (PubMed Link) (PDF)
  • Fallahi-Sichani M, Schaller MA, Kirschner DE, Kunkel SL, Linderman JJ. Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma, PLoS Computational Biology (2010). (PubMed Link) (PDF)
  • Fallahi-Sichani M, Linderman JJ, Lipid raft-mediated regulation of G-protein coupled receptor signaling by ligands which influence receptor dimerization: A computational study, PLoS ONE (2009). (PubMed Link) (PDF)
  • Fallahi-Sichani M, Marino S, Flynn JL, Linderman JJ, Kirschner DE. A systems biology approach for understanding granuloma formation and function in tuberculosis, In McFadden J, Beste D, Kierzek A (Ed.), Systems biology of tuberculosis. Springer (2013). (Publisher Link) (PDF)
  • Marino S, Fallahi-Sichani M, Linderman JJ, Kirschner DE. Mathematical models of anti-TNF therapies and their correlation with tuberculosis, In Pathak Y, Benita S (Ed.), Antibody-mediated drug delivery systems: Concepts, Technology and Applications. John Wiley and Sons (2012). (Publisher Link) (PDF)