Research Center

Complexity, Cooperation and Community in Cancer ([email protected])


Center Title

Complexity, Cooperation and Community in Cancer ([email protected])

Center Website

Center Summary

The overall goal for the UCI Center for Cancer Systems Biology ([email protected]), also known as the Center for Complexity, Cooperation and Community in Cancer, is to understand the principles that underlie why cancers are organized as they are. Our approaches stem from the idea that cancer cells proliferate and evolve in complex environments that have been highly selected for the robust control of growth and differentiation, and thus the behaviors of cancer cells can only be fully understood in the context of the design principles underlying such control. [email protected] is a joint effort by the systems biology and cancer biology communities at the University of California Irvine (UCI), as represented by two campus-wide research organizations, the Center for Complex Biological Systems and the Cancer Research Institute of the UCI Chao Family Comprehensive Cancer Center.

[email protected] is carrying out three coordinated, team-oriented research projects on the role of context, cooperation and community in the initiation and progression of cancer. All three projects seek to understand how the in vivo behaviors of transformed cells are constrained by rules inherited from the communities of diverse, interacting cell types and lineage hierarchies within which those cells arise. Project 1 leverages new observations from xenograft models of colon cancer to investigate non-genetic heterogeneity in solid tumors, both its origins and its relevance to tumor growth and response to therapy. Project 2 investigates the cellular origins of melanoma, seeking to clarify the relationship between melanoma and the benign lesions (melanocytic nevi) that are driven by a common oncogenic event. This work focuses on interactions among melanocyte precursors, within the skin environment, and under conditions that promote progression from benign to malignant. Project 3 focuses on improving models of chronic myeloid leukemia (CML) and its treatment, taking into account interactions between hierarchical lineages, intercellular feedback, and dynamics.

All three projects combine mathematical modeling, genomics, and experimental manipulation of animal models. Mathematical modeling is central to all three projects, not just as a means to analyze large data sets, but as a way of identifying, with the most generality, the architectures of cell interaction and feedback that can explain generic features of cancer cell behavior. All three projects develop models with design principles built on similar concepts: cell state transitions, proliferation and quiescence, positive and negative feedback, but in different contexts that derive from the different cancer types (spatial vs. non-spatial; focus on self-organizing pattern vs. focus on control; emphasis on model and parameter identification vs. emphasis on replicating qualitative behaviors). A common theme is the idea that bi- and multistability that arises as a result of feedback can potentially explain bifurcating system behaviors, such as nevi and melanoma in the same mouse, spatial patterns of heterogeneity, or resistance to cancer therapy, without having to attribute such events to new mutational “hits.”

All three projects are served by a core facility for investigating tumor cells at the single cell level, providing access to the latest in single-cell genomic, transcriptomic and other technologies, which are needed to provide the kind of data that can adequately constrain models.

An Administrative Core provides the organizational framework and logistic support for the center. The Administrative Core also solicits, reviews and administers pilot project grants initiated both by faculty and by trainees (graduate students and postdoctoral fellows). An Outreach Core promotes cancer systems biology to the research community, targeting researchers and trainees at all career stages through a wide variety of educational and professional development activities.


Principal Investigators

John Lowengrub, Ph.D.

John Lowengrub, Ph.D., Chancellor’s Professor of Mathematics and Biomedical Engineering, is a systems biologist and mathematician whose research efforts focus on developing mathematically rigorous and biologically-justified models of solid tumors. His research is highly interdisciplinary and has involved collaborations with many of the faculty participating in this center. In general, Dr. Lowengrub’s research aims to use mathematical modeling to understand how misregulated feedback signaling, metabolic reprogramming and interactions between a vascularized tumor and its microenvironment can drive tumor progression and dictate the optimal course of treatment. Recently, Dr. Lowengrub’s research group has provided an explanation of how cancer stem cells drive the development of invasive fingers (joint work with MPI Lander) and crosstalk with the stroma and vascular network. In addition, Dr. Lowengrub’s research group has developed models that explain how Wnt-driven spatiotemporal patterns of different metabolic states can emerge in vascularized tumor xenografts with genetically identical cells (joint work with MPI Waterman). Dr. Lowengrub currently serves as a co-leader of the Systems, Pathways and Targets (SPT) program of the Chao Family Comprehensive Cancer Center, a program that brings together cell biologists, immunologists, geneticists, developmental biologists, systems biologists, computational scientists, and clinicians. He is deeply committed to education in mathematical biology, cancer biology and systems biology, and spearheaded the development of an interdisciplinary M.S./Ph.D. graduate program at UCI that spans 10 departments and 5 schools; he now serves as Director of that program.

Arthur Lander, M.D., Ph.D.

Arthur Lander, M.D., Ph.D., Bren Professor of Developmental and Cell Biology and Professor of Biomedical Engineering and Logic & Philosophy of Science, originally trained in biochemistry, medicine, neurobiology and developmental biology, but his research over the last 15 years has focused primarily on the systems biology of morphogenesis and growth. His interests in the feedback control of growth, as well as his early clinical training, led him to an interest in cancer biology that ultimately resulted in several collaborative papers with the Lowengrub group, and his service as an MPI on a multi-investigator RC2 grant from the NCI. He has 18 years of experience as director of T32 training grants (three different ones), 8 years of service as the Chair of Developmental and Cell Biology, 10 years of experience as founding director of the UCI transgenic mouse core facility, and 16 years of experience as the founding director of the Center for Complex Biological Systems, UCI’s NIGMS-designated National Center for Systems Biology, which supports the research and educational activities of over 100 Systems Biology-affiliated faculty. Dr. Lander has served as chair of an NIH Developmental Biology study section and, from 2013 to 2017, as a member and Chair of the MABS (modeling and analysis of biological systems) NIH study section.

Marian Waterman, Ph.D.

Marian Waterman, Ph.D., Professor of Microbiology and Molecular Genetics, is the Director of the UCI Cancer Research Institute and Deputy Director of the Chao Family Comprehensive Cancer Center. She focuses her research on mechanisms of Wnt signaling that direct cellular phenotypes in cancer and stem cells. Dr. Waterman discovered one of the first members of the LEF/TCF transcription factor family and since then has focused on how these factors function in normal and diseased tissue. Dr. Waterman collaborates with physician scientists, mass spectroscopists, biomedical engineers, biophysicists at the Laboratory for Fluorescence Dynamics (Gratton, Digman), and mathematicians (including MPI Lowengrub). The overarching goal of her research is to understand how gene expression, metabolism, development, carcinogenesis and Wnt signaling are connected. As Director of the Cancer Research Institute, she also coordinates member leadership for a T32 training grant in Cancer Biology and Therapeutics; an American Cancer Society seed grant mechanism for junior faculty; the Cancer Biology track for the Molecular and Cellular Biology cross-campus graduate program; and a popular and competitive summer internship program for high school students. As Deputy Director of the Chao Family Comprehensive Cancer Center, Dr. Waterman coordinates the basic science programs and Shared Resources and works with the Director Richard Van Etten to advance strategic initiatives of the center and aims of the NCI cancer center support grant. At the national level, Dr. Waterman has served as chair of an NIH cancer biology study section (CAMP; 2010-2016), as chair of an American Cancer Society review panel (DMC; 2003-2007), and a member of ACS council for extramural grants (2010-2014).

Participating Investigators

Steven D. Allison, Ph.D.

Steven D. Allison, Ph.D., is an Associate Professor in the Departments of Ecology and Evolutionary Biology and Earth System Science. He is broadly trained as a biologist but with a focus on the role of micro-organisms in ecosystem scale processes, such as carbon cycling. His research spans scales from genomes to the globe, and he applies both experimental and theoretical approaches to analyze microbial functioning. Most of his experimental work has focused on the regulation, environmental responses, and ecosystem consequences of extracellular enzymes in environmental systems. More recently, his research has increasingly focused on theory and models. There is a clear need for new models that scale up microbial processes, and he brings a unique experimental perspective to the models he has developed. One set of individual-based models he created has potential applications to human health. These models are spatially-explicit and represent interactions among different cell types. The variation across cell types is flexible and informed by empirical data. One of the main results from this set of models is that emergent properties at the ecosystem scale are dependent on microbial interactions involving metabolite production. In some cases, evolutionary processes that optimize individual cell performance serve to undermine system-level functioning. This type of prediction is relevant in cancer treatments where targeting particular cellular interactions might provide a means of arresting tumor development and other system-scale symptoms.

Michelle Digman, Ph.D.

Michelle Digman, Ph.D., is an Assistant Professor in the Department of Biomedical Engineering. Her research focuses on developing fluctuation imaging techniques to study protein dynamics in real time and space inside live cells and tissues. In addition, her lab is developing a metabolic index for organs, tissue sections, tumors, embryos and single cells using the FLIM/Phasor method. This fast computational approach allows for the detection of metabolic states at the single cell level will help create a reference for metabolically active or non-active cells under specific conditions. She is also working on characterizing the competitive recruitment and binding activity of DNA repair proteins upon DNA single strand and double strand laser induced DNA damage using high spatiotemporal resolution scanning correlation spectroscopies. She has extensive expertise in measuring protein dynamics in both nuclear and cytosolic compartments in living cells as her background is multidisciplinary in the areas in biochemistry and biophysics. She has coauthored over 80 peer reviewed manuscripts including PNAS, Biophysical Journal, EMBO, and Nature Communication. She set up her lab as Assistant Professor in July 2013 and is focused in the quantitative understanding of cell function including cell invasion, proliferation, differentiation and apoptosis. She has several peer reviewed papers on using the pair correlation method to calculate the diffusive rout of proteins in the nucleus to understand nuclear epigenetic regulation in cancer cells. She is developing imaging methods with high spatial-temporal resolution in correlation analysis to elucidate signaling protein network interactions in the 2D and 3D microenvironment. Using a bioengineering approach, she can mimic the tissue microenvironment and quantitatively measure changes in matrix reorganization to dissect key molecular mechanisms that govern tumor cell invasion. She also developed the phasor/FLIM method for mapping and identifying intrinsic autofluorescence markers in tissues and for fluorescent FRET biosensors (Rho, Rac and CDC42) to measure signaling and activation. She has extensive expertise in measuring protein dynamics in live cells. Among these techniques, which she co-developed, the raster image correlation spectroscopy (RICS) method, the Number and Molecular Brightness (N&B) technique, the Phasor/FLIM and the pair correlation spectroscopy (pCF) method, are now being used by researchers in Biology.

Robert A. Edwards, M.D., Ph.D.

Robert A. Edwards, M.D., Ph.D., is an Associate Professor in the Department of Pathology & Laboratory Medicine. Dr. Edwards is the Director of the Experimental Tissue Resource (ETR) within the Chao Family Comprehensive Cancer Center. Research in his laboratory is focused on understanding how chronic inflammatory signals in the intestine promote colorectal cancer (CRC). As a Pathology resident and post-doctoral fellow, he studied a new mouse tumor model that links inflammation and colon cancer, via knockout of the G-protein subunit Gi2. Upon establishing his laboratory at UC Irvine, he developed collaborations with Dr. Steve Lipkin and MPI Waterman to study how this mouse model connects to human colon cancer. For the past 10 years, his work has highlighted network links between inflammation, hypoxia, Wnt and Notch signals in the tumor microenvironment, both in animal models and in clinical isolates of human CRC.

Anand K. Ganesan, M.D., Ph.D.

Anand K. Ganesan, M.D., Ph.D., is an Associate Professor of Dermatology and Biological Chemistry. His research focuses on understanding how the melanocyte interacts with the environment and other cells within the skin during the process of normal tanning and transformation. He has recently published studies identifying novel pathways that drive the early steps in melanoma progression, studies identifying novel pathways that regulate melanogenesis in vivo, and studies characterizing how melanoma tumors modulates the immune response during progression. His published work has used in vitro cell based approaches to understand the cell biology of melanocytes, in vivo approaches to understand how melanocytes interact with other skin and immune cells in mouse models, and studies with human tissue to ensure that the findings in these models are relevant to human disease. His clinical practice focuses on caring for patients with atypical moles and determining whether these lesions have malignant potential or not. He has recently redirected his research efforts to answer this important question and developed models to study nevus development, regression, and progression non-invasively. The eventual goal of this line of investigation is to develop better algorithms to identify and remove nevi evolving to melanoma at an early stage before they become a problem.

Christopher C.W. Hughes, Ph.D.

Christopher C.W. Hughes, Ph.D., is the Francisco J. Ayala Chair of the Department of Molecular Biology & Biochemistry and a Professor in the Department of Biomedical Engineering, where he serves as Director of the Edwards Lifesciences Center for Advanced Cardiovascular Technology. Dr. Hughes is also co-Leader of the Onco-Imaging and Biotechnology (OIB) Program, part of the Chao Family Comprehensive Cancer Center at UCI, and in 2014 was elected a Fellow of the American Association for the Advancement of Science (AAAS). His research focuses on the development and growth of blood vessels. The work in his lab spans multiple scales – from understanding the basic molecular mechanisms of angiogenesis (the growth of new blood vessels), to engineering of artificial tissues. Recently his lab has been pioneering “Body-on-Chip” technology, which allows for micro-organs – heart, pancreas, tumor, etc. – to be grown in the lab, each with its own blood vessel network. In addition to his research, Dr. Hughes works extensively with the non-profit organization, cureHHT, which provides patient support and research advocacy on behalf of those suffering from the rare vascular disorder Hereditary Hemorrhagic Telangiectasia. Professor Hughes serves as Chair of the foundation’s Global Research and Medical Advisory Board. He has worked with Marian Waterman on several projects related to tumor angiogenesis and wnt signaling, and has co-mentored a student with John Lowengrub

Kai Kessenbrock, Ph.D.

Kai Kessenbrock, Ph.D., is an Assistant Professor in the Department of Biological Chemistry. His research experiences include: a) 5 years of working on inflammatory diseases using mouse models and human samples during his PhD thesis; and b) 6 years of postdoctoral training studying the role of microenvironmental factors in the regulation of breast epithelial stem cell function and breast cancer in single cell resolution. His lab at UC Irvine is focusing on single cell analyses of the epigenetic landscapes and the transcriptional signatures in individual cancer cells in order to ultimately understand how one may therapeutically tackle the clinical problem of tumor heterogeneity. Dr. Kessenbrock is highly experienced in tissue dissociation, cell preparation, cell screening and analysis using the C1 Fluidigm and 10X Chromium platforms. The goals of his research are to use enhanced understanding of tumor initiation to improve methods for early detection of cancer in order to treat cancer patients before it turns into a life-threatening condition.

Natalia Komarova, Ph.D.

Natalia Komarova, Ph.D., is a Professor in the Departments of Mathematics and Ecology and Evolutionary Biology. She has extensive experience with mathematical models that describe the in vivo dynamics of human diseases, most notably cancer. In particular, she is very familiar with stochastic modeling approaches to describe cancer and with the types of approaches required to predict age-incidence patterns from mathematical models of in vivo biological processes. She has a strong background in applied mathematics, the area in which I performed my doctoral studies at the University of Arizona. She has been working extensively in various fields of mathematical biology since her postdoctoral position at the Institute for Advanced Study in Princeton, including biomedical fields, as well as general evolutionary dynamics and problems in social and behavioral sciences. She has been PI and investigator on a number of NIH and NSF grants, has collaborated with a variety of experimental groups, and has successfully led and coordinated scientific work in the context of larger projects.

Tatiana B. Krasieva, Ph.D.

Tatiana B. Krasieva, Ph.D., is a Project Scientist at the Beckman Laser Institute and the Department of Surgery. She has been involved with the development of optical microscopy methods and its applications through Laser Microbeam and Medical Program (LAMMP) since 1991. She has extensive expertise in developing in-vitro, ex-vivo, and in-vivo microscopy applications, experimental design and data interpretation in the field of optical microscopy, including conventional techniques, multiphoton excited fluorescence, second harmonic generation, fluorescence life-time imaging, optical coherence microscopy and spectroscopy. She has developed an optical method based on fluorescence spectroscopy and fluorescence lifetime imaging for identification and separation of two pigments – eumelanin and pheomelanin, two major constituents of human skin. She also has expertise in development of novel murine model imaging in vivo microscopy applications (skin, brain) and in laser ablation of tissue in the laboratory setting and experience in imaging regeneration after laser injury.

Devon A. Lawson, Ph.D.

Devon A. Lawson, Ph.D., is an Assistant Professor, Department of Physiology and Biophysics. The goal of her research program is to understand the cellular and molecular basis of breast cancer metastasis, with an emphasis on using new single-cell genomics technologies to investigate the role of genetic and phenotypic diversity in different phases of the metastatic process. Her research takes an interdisciplinary approach, combining recent advances in human in mouse modeling, sequencing technology, bioinformatics and computational biology, systems biology, and mathematical modeling to investigate the metastasis problem at an integrated level. Since metastasis remains the cause of most cancer-related mortality, enhanced understanding of the metastatic process is needed to effectively treat and prevent metastatic progression. The ultimate goal of her research is to utilize new insights to develop biomarkers for early detection of metastatic cells, and identify new therapeutic strategies to prevent and treat metastatic disease.

Harry Mangalam, Ph.D.

Harry Mangalam, Ph.D., is a research computing specialist in the Office of Information Technology. He is deeply familiar with a wide range of research computing domains and works with researchers to help them accelerate their work in large scale visualization, bioinformatics, evolutionary biology, high-throughput sequencing, large scale data processing, compute cluster planning & implementation, system administration, data center planning, and grant preparation. He is experienced at integrating multiple pieces of software into a pipeline or complex script, usually addressed with Perl, Python, and R/Bioconductor. He is an expert in C and is familiar with the GNU build toolchain. Dr. Mangalam has performed bioinformatics contract work for the Epidemic Outbreak Surveillance taskforce (now part of the Homeland Security Department), GeneCodes, the CDC, Allergan, Accelerys, and startups. He has also worked in the Science group on their Genomics Knowledge Platform (GKP), which provided both syntactic and semantic integration of biological information from a number of sources through their “Biological Object Model”. In addition, Dr. Mangalam has significant experience in storing and analyzing results from large scale gene expression through projects involving the GeneX gene expression database project, an Open Source Gene Expression database. He has created the sequence analysis application tacg and it’s CGI Web interface tacgi to make a small, fast (~30X faster than GCG or EMBOSS), free, and capable molecular biology tool available for Linux/Unix. Dr. Mangalam has taught introductory linux classes, written online documents on various subjects in scientific computing, and written useful Open Source applications, including the clusterfork, a cluster administration tool, parsyncfp, a parallel, load-balancing wrapper for rsync, and scut, a regular-expression-aware data slicer.

Edwin S. Monuki, M.D., Ph.D.

Edwin S. Monuki, M.D., Ph.D., is the Warren L. Bostick Professor and Chair of the Department of Pathology & Laboratory Medicine. His laboratory investigates basic forebrain development to inform human disorders and stem cell culture strategies with clinical potential. The two forebrain structures of particular interest to his group are the cerebral cortex, the seat of higher cognitive functions, and the choroid plexus, the source of cerebrospinal fluid (CSF) that bathes and nourishes the brain and spinal cord. Over the past several years, the experimental work in his lab has benefitted tremendously from collaborations with systems biologists. Dr. Monuki is working closely with systems biologists to develop an educational program in systems pathology in the School of Medicine at UC Irvine.

Ali Mortazavi, Ph.D.

Ali Mortazavi, Ph.D., is an Assistant Professor of Developmental and Cell Biology. His research interests are in the application of genomic methods to answer fundamental questions in the transcriptional regulation of development using a combination of functional sequencing assays and computational methods. He is particularly interested in understanding how homologous gene regulatory networks are encoded in the human and mouse genomes. He also has a separate interest in comparative genomics and development of nematode parasites with a particular focus on the genus Steinernema. His lab combines experimental work and computational analysis primarily in hematopoietic, skeletal muscle, and embryonic stem cells in human, mouse, and other mammals to understand which regulatory elements are conserved, which elements are not conserved but functional, and which elements regulates what genes. His laboratory has focused over the last five years on doing comparative analyses of regulatory elements and their long-range interactions in human and mouse using a combination of ChIP-seq, ChIA-PET, DNase-seq, ATAC-seq and RNA-seq using ever lower amounts of starting material down to single cells when practical. His long term goals are (a) to develop models that take into account the complex interplay of promoters and enhancers in controlling gene expression and (b) to understand the common and species specific parts of the human and mouse Gene Regulatory Networks for homologous cell types and tissues in order to translate seamlessly results between human and mouse models of disease.

Qing Nie, Ph.D.

Qing Nie, Ph.D., is a Professor of Mathematics, Biomedical Engineering, and Developmental and Cell Biology. He is a fellow of the American Association for the Advancement of Science (AAAS) and American Physical Society (APS). Dr. Nie is also the Director of the Mathematical and Computational Biology Graduate Gateway Program and an Associate Director of the Mathematical, Computational and Systems Biology interdisciplinary graduate program. Originally trained in scientific computing and mechanics during my Ph.D. study and as a postdoctoral fellow working on fluids and materials, during the past 15 years Dr. Nie has devoted his research effort to systems biology of morphogenesis, regulatory networks, cell signaling, and stem cells with emphasis on addressing challenging and complex biological questions in close collaboration with experimentalists. One of his major research aims is to develop predictive models and powerful computational tools that target specific and important questions including developmental patterning, stem cells, spatial dynamics, and stochastic dynamics in cell signaling. Dr. Nie is a leader in interdisciplinary training at the interface between mathematics and biology and is a MPI of a NIH T32 pre-doctoral training grant on Mathematical, Computational, and Systems Biology.

Nilamani Jena, Ph.D.

Nilamani Jena, Ph.D., is a Senior Project Scientist in the Department of Hematology and Oncology. His graduate and postdoctoral studies focused on the molecular pathogenesis of human hematologic malignancies. As a graduate student with Dr. Carlo Croce, he identified a novel pathway of regulation of the BCL-2 antiapoptotic protein via phosphorylation. As a postdoctoral fellow with Dr. George Daley, he demonstrated a specific role for Cyclin D2 in transformation and proliferation of BCR-ABL1-transformed B lymphoid cells. In his subsequent work as a postdoctoral fellow and now as a Senior Project Scientist in the research group of Dr. Rick Van Etten at UC Irvine, he has gained expertise on study of lymphoid development, hematopoietic stem cell isolation, retrovirus mediated gene transfer, bone marrow transplantation, analysis of development of leukemia in mice, and treatment of mice with leukemia with experimental therapy. He also has extensive experience in recombinant DNA techniques, in vitro cell culture, production of recombinant retrovirus, analysis of cells using FACS, analysis of proteins by immunoblotting.

Melanie L. Oakes, Ph.D.

Melanie L. Oakes, Ph.D., is a Project Scientist in the Department of Biological Chemistry and the Facilities Manager of the UCI Genomics High Throughput Facility (GHTF). As manager of the UCI Genomics High Throughput Facility, she brings the latest technologies to the UCI campus research community. She supervises a staff of five research associates in applications including current Affymetrix microarrays, next generation sequencing using the Illumina HiSeq 2500 and 4000 and Pacific Biosciences Sequel sequencers, Nanostring RNA analysis and BioNano Irys genome mapping system. Additionally, her team at the GHTF supports single cell gene expression with the Fluidigm C1 single cell auto prep system and the 10x Genomics Chromium platform. Her team tests and develops custom approaches to assist users in optimal applications of emergent technologies. Her research focused on using yeast as a model system to explore regulation of cellular growth with a specific focus on the regulation of transcription of ribosomal RNA. During the course of the work, she identified and characterized polymerase I transcription factors and took advantage of yeast genetics to create mutants and subsequently analyze ribosomal RNA synthesis, nucleolar structure and cell growth.

Bruce J. Tromberg, Ph.D.

Bruce J. Tromberg, Ph.D., is a Professor of Biomedical Engineering and Surgery. Dr. Tromberg is also the Director of the Beckman Laser Institute and Medical Clinic (BLI) and is the Director of the Laser Microbeam and Medical Program (LAMMP), an NIH P41 National Biomedical Technology Center. His lab has been involved in the development of biophotonic technologies for cancer detection, diagnosis, and treatment for more than 25 years. A major emphasis has been on the clinical translation of in vivo imaging technologies based on nonlinear optical microscopy (NLOM) and diffuse optical spectroscopic imaging (DOSI). He has extensive experience in building and supporting optical imaging technologies, including leading an ECOG-ACRIN national clinical trial of DOSI in breast cancer imaging and a UCI trial of NLOM in melanoma.

Richard Van Etten, M.D., Ph.D.

Richard Van Etten, M.D., Ph.D., is a Professor of Medicine and Biological Chemistry and the Director of the Chao Family Comprehensive Cancer Center at UCI. Dr. Van Etten has been involved in basic and translational cancer research for over 25 years as a faculty member at academic medical centers, initially at the Dana-Farber/Harvard Cancer Center and subsequently at Tufts University. He currently serves as a regular member of NCI Cancer Centers Subcommittee A. He is also a member of the Leukemia Committee of the Eastern Cooperative Oncology Group/ACRIN. His lab studies the molecular pathogenesis of human leukemia with a heavy emphasis on modeling these diseases in laboratory mice using retroviral/lentiviral gene transfer and bone marrow transplantation, and using conditional transgenic mouse technology. He has extensive experience in murine hematopoietic stem cell transplantation and in the analysis of healthy and diseased BM chimeric mice. Dr. Van Etten’s group has modeled adoptive immunotherapy of chronic myeloid leukemia (CML) and B-cell acute lymphoblastic leukemia (B-ALL), discovered novel signaling pathways in CML and B-ALL, and tested new approaches to targeted therapy of myeloid and lymphoid leukemia. Dr. Van Etten also a clinician specializing in hematologic malignancies, currently caring for many CML patients in various phases of the disease, and serving as institutional co-investigator on multiple clinical trials in blood cancer.

Dominik Wodarz, D. Phil.

Dominik Wodarz, D. Phil., is a Professor in the Department of Ecology and Evolutionary Biology. He has a broad background in mathematical models that describe biological processes, especially in the context of diseases and biomedical questions. He has extensive experience with modeling the in vivo dynamics of carcinogenesis, as well as with modeling the in vivo dynamics of viral infections and immune responses. He works with a variety of modeling approaches, including ordinary differential equations, stochastic models, as well as a variety of spatial modeling approaches including agent based models. He began this work as a D. Phil. student at the University of Oxford, expanded on it as a postdoctoral researcher at the Institute for Advanced Study in Princeton. Dr. Wodarz has been working in this area of research as a faculty member both at the Fred Hutchinson Cancer Research Center, and at the University of California Irvine. He has worked successfully with a number of experimental laboratories, and have previously led dual PI projects that involved combinations of mathematics and experiments.

Jie (Jenny) Wu, Ph.D.

Jie (Jenny) Wu, Ph.D., is a Project Scientist in the Department of Biological Chemistry and the UCI Genomics High Throughput Facility. She has a broad background in computational biology, with specific training and expertise in key research areas such as sequence analysis, network analysis, statistical analysis and software development. As a postdoctoral researcher at Boston University, she carried out whole genome sequence data analysis to automatically annotate newly sequenced genomes and developed software for visualization and exploration of functional association networks. At Delta Search Labs, she focused on integrating heterogeneous data types such as microarray data, genomics data, proteomics and metabolomics data from toxicological treatments, using pathway and network analysis with GeneGO metacore and IPA. As a Sr. Scientist at CODA and Verdezyne, she designed pipelines for whole genome sequencing data analysis to optimize protein expression using MATLAB, R and Perl. At UC Irvine, she has performed next generation sequencing data analysis including exome-sequencing, WGS and RNA-seq data, pathway and network analysis with WGCNA and IPA. Working with huge data sets on a daily basis, she has experience with Unix environment scripting, high performance computing and parallel programing. She is also familiar with popular NGS tools such as Bowtie, Samtools, IGV etc.

Xiaohui Xie, Ph.D.

Xiaohui Xie, Ph.D., is a Professor of Computer Science. His main research and teaching interests are in computational biology, bioinformatics and machine learning. He has extensive experience in sequence analysis, genomics, statistics including 1) large-scale genome analysis; 2) development of deep learning methods; 3) development of machine learning algorithms for probabilistic models; 4) data structures and algorithms for sequence alignment; 5) genome-wide regulatory element discovery; and 6) development of analysis tools for CLIP-seq, ChIP-seq, RNA-seq, epigenomics, and genetic variation detection. His group has developed popular tools for cancer genome analysis, including TEMT, a software package for analyzing RNA-seq in heterogeneous cancer tissues, and PyLOH and MixClone, two packages for analyzing cancer genome heterogeneity. Xie has also pioneered in the application of deep learning to genomic analysis. Dr. Xie also been actively involved in teaching machine learning, computational and systems biology to both undergraduate and graduate students.

Core Director

Suzanne B. Sandmeyer, Ph.D.

Suzanne B. Sandmeyer, Ph.D., is the Grace Beekhuis Bell Chair of Biological Chemistry and a Professor of Microbiology & Molecular Genetics and Chemical Engineering & Materials Science. She is the Director of the UCI Genomics and High-Throughput Facility (GHTF), the Associate Director of the UCI Institute for Genomics and Bioinformatics and the Vice Dean for Research in the School of Medicine. As Director of the UCI Genomics High-Throughput Facility, Dr. Sandmeyer strives to make emerging technologies available to investigators at UCI, and where possible beyond, and to facilitate development of such technologies. The GHTF currently provides Affymetrix microarray, Illumina HiSeq 4000 and PacBio Sequel sequencing, Nanostring RNA analysis, BioNano Long Range mapping, and 10X Genomics Chromium single-cell DNA sequencing to the UCI campus and outside clients. The GHTF also provides workshops for users in wet bench techniques and bioinformatics. Dr. Sandmeyer discovered the Ty3 retrotransposon in budding yeast which became a molecular model for Ty3/gypsy elements, one of the largest families of retrotransposons. Using this system and the power of yeast genetics and biochemistry she described the essential features of the Ty3 genome and encoded proteins, Ty3 host factors, virus-like particle assembly in association with RNA processing bodies, and integration targeting by transcription factors. She has expanded her research interests to development of the oleaginous yeast model, for metabolism, Yarrowia lipolytica. She recently published an annotated version of the genome for this organism and a study using transposon mutagenesis to identify essential genes of Yarrowia and test the requirements for growth under different conditions.

Project Manager

Sohail Jahid, Ph.D.

Sohail Jahid, Ph.D., is an Academic Coordinator at the Center for Complex Biological Systems. Her research experiences have included studying the role of microRNAs in the development of colon cancer. She identified a recurrent amplicon on mouse chromosome 8 that encodes microRNAs (miRs) 23a and 27a. miRs-23a and 27a levels are upregulated in mouse intestinal adenocarcinomas, primary tumors from stage I/II CRC patients, as well as in human CRC cell lines and cancer stem cells. She also studied the role of DNA mismatch repair proteins in genomic recombination. Mammalian mismatch repair (MMR) complexes include MLH/PMS proteins, which heterodimerize to form three distinct complexes: MLH1/PMS1, MLH1/PMS2, and MLH1/MLH3. MMR suppresses tumor formation via three mechanisms: repair of base substitution, repair of frameshift, and repair of small insertion-deletion mutation. She became interested in next generation cancer imaging, and joined Dr. Enrico Gratton’s laboratory to develop novel techniques to image cancer cell metastasis in vivo. While in his laboratory, she gained expertise in advanced imaging techniques and how they can be utilized to image the extravasation of cancer cells from the bloodstream. She has also studied RhoJ (a Cdc42 homologue), as a novel regulator of melanoma chemoresistance, and has investigated whether Pak inhibitors are useful agents to treat metastatic melanomas through both transgenic mouse and translational human biomarker studies.


Project 1: Patterned Heterogeneity in Colon Cancer

PIs: Christopher C.W. Hughes, Marian Waterman
Key Personnel: Steven D. Allison, Michelle Digman, Robert A. Edwards, Kai Kessenbrock, Arthur Lander, John Lowengrub, Qing Nie

Solid tumors are complex masses of cancer cells with a multitude of genetic, epigenetic, morphologic and metabolic phenotypes. This heterogeneous condition is a formidable barrier to treating cancer as it underlies the ability of tumors to adapt to nutrient starvation, immune challenges and to develop resistance to cancer treatments – the most common cause of mortality. Non-genetic heterogeneity in gene expression, signaling and metabolism are considered to be some of the most dynamic forms of heterogeneity and the most responsive to the tumor microenvironment. But we currently understand little about such heterogeneity, both mechanistically (what drives it) and functionally (how it helps the tumor). Non-genetic heterogeneity is very challenging to study, partly because of a limited toolbox and partly for lack of tractable model systems. Consequently, there are fundamental unknowns about how such heterogeneity arises and what its role in tumor growth and drug resistance really is.
In preliminary and published work, we observed heterogeneity in a xenograft model of colon cancer where the heterogeneity is patterned in a manner suggestive of a spatially self-organizing process (such as Turing-patterning). What is heterogeneous in these tumors is both Wnt signaling (thought to be the essential driver of proliferation in these cells), and metabolism (the balance between glycolysis and oxidative phosphorylation). In particular, the pattern consists of cell clusters, or spots, in which biomarkers of Wnt signaling are higher than in surrounding regions. These spots also mark regions of glycolytic metabolism. These patterns are likely connected through Wnt regulation of the expression of genes that control metabolism, as identified in our earlier work, and possibly through Wnt-Turing patterning of cancer cell subpopulations. Interestingly, glycolytic heterogeneity has recently been proposed to serve as the basis for resistance to anti-angiogenic therapy, one of the most important clinical problems in colorectal cancer. The short time scale of the xenografting (14-21 days), the reproducibility of the heterogeneity across genetically identical cell lines, and sites of injection, all suggest that this heterogeneity is not genetic.

In this project, Hughes, Waterman and their team seek to understand the causes of the observed heterogeneity in colon cancer, the reason why spatial patterns of heterogeneity develop spontaneously, the consequences of such heterogeneity for the growth of tumor cells, and whether this, or possibly other, forms of heterogeneity indeed drive resistance to therapy (and if so, why). They are addressing these questions by 1) explaining the relationships between heterogeneity, patterning and growth of colon tumors; 2) defining the general principles linking heterogeneity, patterning and growth in colon tumors; and 3) defining the link between heterogeneity and drug resistance.

Figure 1. Xenograft colon tumors reveal a spotted pattern of metabolic (A) and Wnt signaling (B) heterogeneity; concordant heterogeneity in metabolism and Wnt signaling is present in primary patient tumors (C). (D) shows a new vascularized microtumor (VMT) platform that supports human vessels (red) and colon tumors (green), NADH-fluorescence lifetime imaging (FLIM) of colon tumors in the VMT shows metabolic heterogeneity (D: lower right). (E) Single Cell Sequence analysis using Seurat/tSNE clustering reveals that SW480 xenograft tumors are strikingly heterogeneous with a cancer stem cell-like cluster (0: top left) that is missing in tumors expressing dominant negative LEF1 (dnLEF: bottom left). In (E/Middle Panel) Ligand and receptor pairs among several cell clusters of the human and mouse (right panels in E) cells are shown. (F) Candidate mathematical model for simulating SW480 tumors.

The foundation of the project is multi-scale modeling of stochastic and self-organizing processes that potentially explains overt differences in tumor growth, patterning of heterogeneity and metabolism, and the most likely mechanisms for drug resistance. The experimental tools pair xenograft studies with a novel platform for generating fully vascularized micro-tumors in vitro; the use of fluorescence lifetime imaging to read out the metabolic states of unlabeled, living cells, and the use of single cell transcriptomics to identify cell states, the gene expression signatures that define them and signaling and adhesion molecules that mediate communication among the cells. Modeling predictions of strategies that re-establish drug sensitivity will be tested via genetic engineering (CRISPR/Cas9) or small molecule drug therapies. The overarching goal of the work is to discover deep insights into the origins and consequences of tumor heterogeneity in an especially manipulable, and clinically relevant tumor system. The integration of this work with Projects 2 and 3, which focus on different cancer types (melanoma and chronic myeloid leukemia, respectively), enables us to identify general principles that underlie how the in vivo behaviors of transformed cells are constrained by rules inherited from the communities of diverse, interacting cell types and lineage hierarchies within which those cells arise.

Project 2: Understanding the Cellular Origins of Melanoma

PIs: Anand Ganesan, Arthur Lander
Key Personnel: Devon A. Lawson, John Lowengrub, Bruce Tromberg, Tatiana Krasieva

Melanoma, a tumor resistant to therapy in late stages, is curable by excision when caught early. Early melanomas can be difficult to distinguish from benign, pigmented “moles”, i.e. melanocytic nevi; this leads to unnecessary excision of many normal nevi while early melanomas are often missed. Nevi and melanomas share more than morphological features: Clinical and experimental data show that ~90% of nevi are initiated when melanocytes acquire an activating mutation in the BRAF oncogene, the same oncogenic mutation observed in >60% of melanomas. Yet nevi spontaneously stop growing. This is usually attributed to “oncogene-induced senescence,” but the fact that nevi readily re-grow after incomplete excision, or in response to UV-irradiation, and can sometimes evolve to melanoma, suggest nevi are not “senescent” but reversibly growth-arrested. Nevi also spontaneously regress, a process that appears to involve the immune system.

In preliminary work, we investigated nevus dynamics in a mouse model of inducible Braf activation, which mimics human nevus formation and also produces melanomas either at low frequency or when additional oncogenic mutations are added (e.g., in Pten). We found that as we activate Braf in more melanocytes, such that nevi become more numerous and closely-spaced, the smaller individual nevi become—as if nevi, when close together enough, inhibit each other’s growth. Such behavior is predicted by mathematical models of growth control based on feedback through diffusible signaling molecules. Such models achieve robust control when feedback regulates decisions between self-renewal and progression to alternate cell states or fates. Interestingly, when we look closely at the nevi in this model, we see that there are, in fact, two distinct cell types: highly pigmented nevus body cells and a scattered, lightly pigmented melanocyte population that forms a “veil” around the pigmented cells that had not been observed before. These veil cells are usually not seen unless the melanocyte lineage is fluorescently-labeled with GFP. Single cell RNA-sequencing suggests that these cells likely communicate through ligands and receptors they differentially express.

In this project, Ganesan, Lander and their team seek to understand the role of the nevus body and veil cell types in mouse models that produce both nevi and melanoma, and to identify both the nature of how growth is controlled in nevi and the means by which melanoma cells escape from it. The goal of the work is to build a solid molecular and cellular framework on which to base clinical decisions about melanoma prevention, detection and treatment. They are addressing these issues by 1) Explaining feedback growth dynamics in melanocytic nevi; 2) Elucidating how melanomas escape growth control mechanisms that arrest nevi; and 3) Revealing how the immune system targets nevi, and how this affects melanoma development.

Figure 2. (A) BRAF-mutant (Tyrosinase::CreERT2; BraffloxV600E/+) mice are crossed with ROSAmT/mG mice (TCBR) to GFP-label BRAF-mutant melanocytes. (B) Fluorescence emission (confocal and MPM) 3-D imaging of skin of live mice (as in Fig. 1; total depth = 90, 115 and 190μm for 1x, 2x, and 3x representative stacks, respectively). Green = GFP; red = td Tomato. (C,D). Continuum modeling, in which cells at an arrested (quiescent) lineage stage feedback on the self-renewal probability of dividing cells. Model snapshots (D) show cell density (see heatmap) as a function of time (T= cell cycles) and location. Top and bottom rows are for low and high seeding densities, respectively, that represent different levels of nevi induction. (E) Single Cell RNA-seq of melanocytic nevi. Control mice (Tyrosinase::CreERT2; ROSAmTmG; TCR) and TCBR mice, as labeled. Left: tSNE clustering of gene expression for individual cells. Right: A portion of the tSNE map, showing contributions of TCR and TCBR mice as well as expression of selected marker genes.


This project integrates multiscale mathematical modeling with experiments in mice using a nevus-forming inducible activated Braf model, and a version of the same model that combines Braf activation with inducible loss of one allele of Pten, leading to the reliable production of both nevi and melanoma tumors. We are developing hypotheses that can explain the spatiotemporal dynamics and spatial statistics of nevus and melanoma development in these models, including potential bifurcations that account for the development of both nevi and melanoma in the same mouse. We are investigating the reasons why some cells escape from growth control, while others do not. We anticipate that this is unlikely to be due to a requirement for inactivation of the other Pten allele, and instead believe that escape may more likely be due to the spatial dynamics of collective feedback. The results are expected to shed light on signaling pathways that could be manipulated to prevent or treat melanoma. Live cell imaging, focused laser ablation, immunohistochemistry, and time-course single cell RNA-sequencing are used to identify potential positive and negative feedback regulators that drive the mathematical models, and experiments are used to test model-based predictions concerning the roles that such molecules play. Finally, an investigation of spontaneous regression, which occurs with both mouse and human nevi, provides clues into how the immune system efficiently recognizes melanocyte overgrowth. Since immunotherapy has recently emerged as a promising therapy for melanoma, this study is expected to reveal whether immunotherapy leverages an existing immune program for eliminating nevi, and if so, how that program is carried out, and how melanomas typically escape from it. Such information should aid in developing new prevention and therapeutic strategies for this devastating disease. The integration of this work with Projects 1 and 3 occurs through the use of scRNA-seq as a tool for hypothesis generation and development and through the application of mathematical models that have similar underlying structures (cell state transitions, proliferation and quiescence, positive and negative feedback), but differ in their context (e.g., spatial in Projects 1 and 2 vs. non-spatial in Project 3).

Project 3: Modeling malignant myelopoiesis to increase efficacy of targeted leukemia therapy

PIs: Richard Van Etten

Key Personnel: Kai Kessenbrock, Natalia Komarova, John S. Lowengrub, Qing Nie, Dominik Wodarz, Xiaohui Xie, Nilamani Jena
Chronic myeloid leukemia (CML), one of the most prevalent of human leukemias, is a natural model of dysregulated granulopoiesis driven by a single genetic abnormality in a hematopoietic stem cell, the BCR-ABL1 gene fusion. Although therapy with tyrosine kinase inhibitors (TKIs) such as imatinib mesylate has dramatically lowered the death rate in CML, lifelong treatment is needed and the associated economic costs are significant. Two major unaddressed questions are to understand the mechanism of primary resistance to TKI therapy (affecting ~10-15% of newly diagnosed CML patients), and to identify strategies to increase the frequency of complete molecular remission (CMR) in patients treated with TKIs and subsequently the rate of treatment-free remission (TFR), which may represent a surrogate for permanent cure of the disease. The scientific premise of this project is that new and clinically relevant insights into the biology of CML and its response to therapy can be gained by a more physiologically accurate mathematical model of the disease.

While mathematical models of CML have been developed by several groups, these models tend to be highly simplified and largely omit feedback interactions among the different hematopoietic components. These models are designed to fit clinical data sets of the response of patient populations to TKI therapy but unfortunately, they have not proven useful for understanding primary TKI resistance or for predicting TFR. In preliminary work, we found that nonlinear models that incorporate feedback are more robust, have a better fit to alternative patterns of patient response than simple linear models used previously, and allow predictions about the effects of interventions affecting parameters (such as cell cycle status) subject to feedback mechanisms on the response to TKI therapy. Preliminary analyses from prototype feedback models have already raised two provocative hypotheses about CML. The first is that the initial response to TKI therapy may depend on the relative size of the leukemic stem cell clone, which will be analyzed by TKI treatment of mice engrafted with different levels of BCR-ABL1+ stem cells. The second is that interventions that increase leukemic stem cell cycling may sensitize this population to killing by TKIs.

In this project, Van Etten and his team seek to develop improved mathematical models of chronic phase CML and the response to TKI therapy, to validate these models using data from a binary transgenic mouse model of CML and from human CML patients, and to utilize the models to test several hypotheses about the response of CML to therapy and to predict strategies for improving the treatment-free remission rate in CML. We address these issues by 1) Developing data-driven, dynamic models of CML hematopoiesis incorporating feedback control; 2) Measuring granulocytopoiesis parameters in a CML mouse model and in human CML patients; 3) Testing model-driven hypotheses about the response of CML to therapy and informing strategies for improving the treatment-free remission rate in CML.

Figure 3. (A) Illustrative nonlinear feedback models of hematopoiesis. The red box indicates the model used in (B). (B) Increased leukemic stem cell (LSC) cycling predicts faster LSC decline on TKI therapy. The results correspond to different LSC cycling parameters applied (expressed as ratio of LSC to multipotential progenitor (MPP) cycling rate). Note the increasing negative slope of the second linear phase with increased cycling. (C,D) Selective decrement of HSC compartment increases MPP proliferation. Mice were irradiated (50 cGy), injected 24h post-radiation with BrdU and analyzed 12h later. (C) 50 cGy irradiation significantly (P=0.018) decreased the size (%LSK) of the HSC compartment (right panel) at 36h without a significant effect on the MPP compartment (left panel). (D) Reduction in HSC pool by low-dose radiation dramatically increases proliferation of the MPP compartment, supporting the existence of a negative feedback loop in the model above. (E) t-SNE analysis of scRNA-seq data from the MPP population isolated from mice with BCR-ABL1-induced CML shows eight separate subpopulations. (F) Expression of sorted lineage markers mapped on the t-SNE plot from (E). (G) Expression of GFP (marking leukemic MPP) and MPP/HSC markers mapped onto the t-SNE plot from (E).

Machine-based automated model selection methods are being utilized to arrive at a mathematical model that maintains appropriate stability and homeostasis, responds physiologically to stress and depletion of different cell compartments, and conforms to the limited existing qualitative data on CML hematopoiesis derived from mouse models and patient studies. To validate and inform potential models, binary/conditional BCR-ABL1 transgenic donor mice are being used to generate mixed BM chimeras via transplantation of high doses of unfractionated marrow cells without use of conditioning radiation. Recipients bearing a clone of BCR-ABL1+ cells have leukemia induced by withdrawal of doxycycline, leukemic mice are treated with BrdU or subjected to short-term stable isotope labeling with D2-glucose, marrow and spleen stem and progenitor compartments isolated by flow cytometry, and cell cycle and kinetic parameters estimated to inform the mathematical models. Single cell transcriptome profiling is used to investigate the heterogeneity of the MPP compartment and to discover potential regulatory mechanisms mediated by cytokine/receptor interactions. Feedback relationships are tested via direct in vivo manipulation using depleting monoclonal antibodies and through a novel mouse that targets metronidazole cytotoxicity to specific cell compartments. Parallel studies of human CML progenitor flux will be carried out through a clinical protocol of short-term D2-glucose labeling in patients presenting with suspected CML prior to diagnostic BM biopsy. The response to TKI therapy is also being analyzed. The integration of this work with

Projects 1 and 2 occurs through the use of scRNA-seq as a tool for hypothesis generation and development and through the application of nonlinear mathematical models that have similar underlying structures (cell state transitions, proliferation and quiescence, positive and negative feedback), although the models in Project 3 are non-spatial.


Single Cell Analysis Core

Core Director: Suzanne Sandmeyer
Key personnel: Kai Kessenbrock, Devon A. Lawson, Melanie Oakes, Ali Mortazavi, Jie (Jenny) Wu

This core provides technical, bioinformatic, and training support for the Center. The goal of the Single Cell Analysis Core technical support is to make cost-effective high-throughput analysis of single cells optimal for and accessible to each of the three Projects. Technical support consists of providing staff and instrument infrastructure, as well as advising on design of experimental strategies, facilitating sharing of protocols for process development; providing single-cell services including microscopy, protein localization, cell sorting, library production, and sequencing; and working with investigators to innovate in these technologies

Administrative Core

Core Director: John S. Lowengrub

Key personnel: Arthur Lander, Ed Monuki, Marian Waterman
The Administrative Core (AC) provides the administrative, communication and oversight needs for the three projects and the single cell analysis and outreach cores. The AC defines, approves and reviews membership in the Center and provides logistical support for Center members, including assisting investigators with meeting regulatory hurdles associated with research. In addition, the AC solicits, reviews and administers two types of pilot project grants: one directed at graduate students and postdocs and the other directed at faculty. The AC guides the integration of the Center into the Cancer Systems Biology Consortium by facilitating the sharing of Center resources and member expertise across the Consortium, by participating in the Annual Consortium meeting and by matching Center members with counterparts in the Consortium to foster new collaborations.

Outreach Core

Core Director: Arthur Lander
Key personnel: Ed Monuki; Sohail Jahid

The Outreach Core of the UCI cancer systems biology center promotes cancer systems biology to the research community, targeting researchers and trainees at all career stages, and disseminates advances and capabilities of cancer systems biology to the cancer research and broader communities. These goals are accomplished through a variety of activities including symposia, seminars, “short courses”, interest groups, “bootcamps”, a visiting scientist program, and an annual retreat. Included among the activities of the core are programs aimed at mentoring junior faculty, programs to provide undergraduate and pre-college students with exposure to cancer systems biology research, and activities to increase public awareness of the advances and capabilities of cancer systems biology. The core monitors its effectiveness through periodic evaluation, and coordinates and integrates with activities of the larger NCI Cancer Systems Biology consortium