Lawrence Berkeley National Laboratory, University of California, Berkeley
To meet the goal of creating reliable, predictable, efficient, and transparent methods to harness cellular capabilities for human benefit, it is necessary both to have standard libraries of elements from which useful pathways can be constructed and an understanding of the how host physiology and the environment impacts the functioning of these heterologous circuits. We show how variations in cellular and environmental context affect the operation of the basic central dogma functions underlying gene expression. Then we describe progress on creating a complete, scalable, and relatively homogeneous and designable sets of part families that can control central dogma function predictably in the face of varying configurations, genetic contexts, and environments. We show the challenges that arise in attempting this in applications such as a tumor destroying bacteria
Speaker biography: Adam Arkin is division director of the Physical Biosciences Division at the Lawrence Berkeley National Laboratory and a Full Professor in the Department of Bioengineering, UC Berkeley. He is director of the Synthetic Biology Institute launched this year at Berkeley and codirector of the International Open Facility Advancing Biotechnology (BIOFAB). In addition, he directs the Joint Bioenergy Institute’s Bioinformatics Group and Berkeley Lab’s Virtual Institute of Microbial Stress. He is a professor of bioengineering at the University of California (UC), Berkeley and was an investigator with the Howard Hughes Medical Institute (HHMI) until 2007. Prof. Arkin has served on many academic and government committees including the US Air Force Science Advisory Board and the Defense Science Study Group. The thrust of Arkin’s research has focused on developing the physical theory, computational tools, and experimental approaches for understanding cellular processes critical to life. The goal is to provide a framework that will facilitate the design and engineering of new functions and behaviors in cells through synthetic and systems biology.
Stanford University School of Medicine, Howard Hughes Medical Institute
The discovery of extensive transcription of long noncoding RNAs (lncRNAs) provides an important new perspective on the centrality of RNA in gene regulation. I will discuss genome-scale strategies to discover and characterize lncRNAs. An emerging theme from multiple model systems is that lncRNAs form extensive networks of ribonucleoprotein (RNP) complexes with numerous chromatin regulators, and target these enzymatic activities to appropriate locations in the genome. Consistent with this notion, long noncoding RNAs can function as modular scaffolds to specify higher order organization in RNP complexes and in chromatin states. The importance of these modes of regulation is underscored by the newly recognized roles of long RNAs in developmental patterning and cancer.
Speaker Biography: Howard Y. Chang, MD, PhD, is professor of dermatology at Stanford University School of Medicine and an Early Career Investigator of the Howard Hughes Medical Institute. Chang earned a PhD in biology from MIT and his MD from Harvard Medical School, and completed dermatology residency and postdoctoral training at Stanford University. His research addresses how individual cells know where they are located in the human body, which is important in normal development and in cancer metastasis. Chang discovered a new class of genes, termed long noncoding RNAs, that can control gene activity throughout the genome, illuminating a new layer of biological regulation.
Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.
Speaker Biography: Markus Covert's main interests focus on integrating experimental and computational approaches to study large biological systems. He began his career with Bernhard Palsson, working in metabolic and transcriptional regulatory modeling of Escherichia coli, and became the first graduate of the bioinformatics program at UCSD with a combined degree in bioengineering and bioinformatics. He then became a postdoctoral fellow with David Baltimore at Caltech, where he used a combined experimental/computational approach to study the NF-kappaB signaling network. He started as an assistant professor in Stanford's Bioengineering Department in January 2007, and won the NIH Director's Pioneer Award for transformative research in 2009.
University of Massachusetts Medical School
My laboratory studies how chromosomes are organized in three dimensions. The three-dimensional organization of the genome is critical for regulating gene expression by bringing genes in close spatial proximity to distal regulatory elements such as enhancers. We have developed powerful molecular approaches, based on our Chromosome Conformation Capture technology, to determine the folding of genomes at unprecedented resolution (Kb) and scale (genome-wide).
We have applied these methods to determine the spatial folding of 1% of the human genome (the ENCODE pilot regions) across a panel of cell lines. We discovered that chromosomes fold into extensive long-range interaction networks in which genes are interacting with distal gene regulatory elements. These results start to place genes and regulatory elements, that are often separated by large genomic distances, in three-dimensional context to reveal their functional relationships.
Our analysis of chromosome folding also revealed that chromosomes are compartmentalized in a series of “Topological Association Domains” (TADs) that are hundreds of Kb in size. Loci located within a TAD mingle freely, but interact far less frequently with loci located outside their TAD. TADs appear involved in gene expression, as we found that genes located within the same TAD tend to be co-expressed, but the mechanism(s) by which these domains affect gene regulation is still unknown. TADs represent novel universal and genetically encoded building blocks of chromosomes.
Speaker Biography: Job Dekker is a professor and co-director of the Program in Systems Biology at the University of Massachusetts Medical School. His laboratory studies how genomes are folded in three dimensions. He has invented the Chromosome Conformation Capture (3C) technology and since then has pioneered development and application of a series of molecular, genomic, and computational approaches to map and analyze the spatial organization of genomes at Kb resolution. His work has led to new insights into the internal organization of chromatin fibers, the formation of chromatin looping interactions involved in long-range gene regulation, and the general folding principles of complete genomes.
Harvard Medical School
Enormous progress has been made in identifying non-coding DNA sequences that control gene expression. We now know the locations of hundreds of thousands of enhancers in a myriad of species. Comparative studies indicate that gene expression variation can contribute to phenotypic divergence between species, and that in general, enhancer sequences change rapidly over evolutionary time. Variant non-coding sequences are also increasingly being associated with human disease, and a majority of these variants map to putative enhancers. Therefore, a fundamental challenge is to understand how sequence variation in enhancers impacts their function.
Here I will discuss how we have used quantitative measurements to model the gene regulatory function encoded by multiple developmental enhancers from Drosophila. These models relate the concentration of input transcription factors to output gene expression levels directly. They operate at cellular resolution and are simple to apply and interpret. We have applied these models to wild type embryos from multiple species to uncover sources of gene expression divergence, and to wild-type and mutant embryos from a single species to uncover potential molecular mechanisms. Because of their simplicity, these models can also be used to discover relevant regulators for a given enhancer. I will discuss the implications of our findings for enhancer architecture and evolution, and the role these models can play in dissecting the relationship between enhancer sequence and function.
Speaker Biography: Angela DePace is an assistant professor of systems biology at Harvard Medical School, where her lab studies the mechanism and evolution of gene expression in animals, using quantitative experimental techniques and computational frameworks to contextualize results. She is also the co-author of Visual Strategies: A Practical Guide to Graphics for Scientists and Engineers. She did her graduate work at UCSF, where she elucidated the structural basis of yeast prion strains. She did her postdoctoral work at UC Berkeley, where she worked with the Berkeley Drosophila Transcription Network Project to develop quantitative imaging techniques for the Drosophila blastoderm embryo.
Salk Institute for Biological Sciences, Howard Hughes Medical Institute
Natural epigenetic variation provides a source for the generation of phenotypic diversity, but to understand its contribution to phenotypic diversity, its interaction with genetic variation requires further investigation. We have carried out population-wide analyses of genomes, transcriptomes, and methylomes of wild strains of Arabidopsis. Association analyses of the epialleles with genetic variants identified thousands methylQTL, providing the first population estimate of genetically dependent methylation variation. Analysis of invariably methylated transposons and genes across this population indicates that hundreds of silenced loci are epigenetically reactivated during male gametogenesis, which facilitates their silencing in future generations.
Speaker Biography: Joseph Ecker earned his PhD in microbiology at the Pennsylvania State University College of Medicine and carried out postdoctoral studies at the Department of Biochemistry at Stanford University. He served on the faculty at the University of Pennsylvania (1987-2000) before joining the Salk Institute for Biological Studies (2000) where he is a professor in the Plant Biology Laboratory and director of the Salk Institute Genomic Analysis Laboratory. His research focuses on genomic and epigenomic regulation in plants and mammals and on the application of DNA sequencing technologies for genome-wide analysis of DNA methylation, transcription, and gene function.
Professor Ecker currently serves on the editorial board of Proceedings of the National Academy of Science. From 2005-2008, he served as president of the International Society for Plant Molecular Biology. He has been the recipient of multiple honors, including the Kumho Science International Award in Plant Molecular Biology and Biotechnology (2001), the International Plant Growth Substances Association Distinguished Research Award (2004), and the American Society for Plant Molecular Biology Martin Gibbs Medal (2005), and was chosen as the Scientific American 50: Research Leader of the Year in Agriculture in 2004. He was elected to the US National Academy of Sciences in 2006. In 2007 he received the John J. Carty Award for the Advancement of Science from the US National Academy of Sciences. In 2009, research from his laboratory on the sequencing of the first human DNA methylomes was ranked the #2 scientific discovery of the year by Time magazine. In 2011, he was awarded the George W. Beadle Award from the Genetics Society of America for outstanding contributions to the community of genetics researchers and was selected as a Howard Hughes Medical Institute/Gordon and Betty Moore Foundation Investigator.
California Institute of Technology, Howard Hughes Medical Institute
The cell contains specialized gene circuits that enable it to respond to specific inputs, including extracellular signals and stresses. Despite much work, it usually remains unclear how a given input is represented dynamically inside the cell, and what capabilities these dynamics provide. In order to address these issues, we are analyzing natural circuits dynamically at the level of individual cells, in bacteria, yeast, and mammalian systems, while constructing new circuits and rewiring natural ones. These methods reveal unexpected, often noisy, intracellular dynamics in both prokaryotic and eukaryotic cells, show how they arise from specific features of underlying circuit architectures, and enable the discovery of basic gene circuit design principles for critical regulatory and developmental functions.
Speaker Biography: Michael Elowitz is a professor of biology, and bioengineering and applied physics at the California Institute of Technology. He is also a Bren Scholar and a Howard Hughes Medical Institute Investigator. His laboratory studies how genetic circuits enable individual cells to make decisions, oscillate, and communicate with one another, using several experimental techniques, especially time-lapse movies.
Stanford University School of Medicine
Cancers can exhibit marked tumor regression after oncogene inhibition through a phenomenon called “oncogene addiction.” The ability to predict when a tumor will exhibit oncogene addiction would be useful in the development of targeted therapeutics. Oncogene addiction is likely the consequence of many cellular programs.
Many of these biological inputs may converge on aggregate survival and death signals. To test this, we examined conditional transgenic models of lung tumors and lymphoma combined with quantitative imaging and an in situ analysis of biomarkers of proliferation and apoptotic signaling. We then used two computational modeling based approaches. First, we mathematically modeled oncogene addiction presuming that it would be a function of a global difference in survival and death signaling. Indeed, we found that upon oncogene suppression, when oncogene addiction is elicited, tumors exhibit a marked and rapid decrease in both survival and death signaling, however, survival signaling drops much more rapidly and earlier, than the drop in death signaling. The consequence is that there is a net overall increase in tumor cell death more because of the absence of survival signaling than the induction of death signaling.
Our model could be confirmed by using multiple imaging methods (computed tomography and bioluminescence imaging), different oncogenes (K-rasG12D and MYC), and several tumor types (lung and lymphoma). Moreover, we confirmed are model by measuring several prosurvival and prodeath signaling factors [phosphorylated extracellular signal–regulated kinase 1 and 2, Akt1, Stat3/5 (signal transducer and activator of transcription 3/5), and p38] that contribute to the the survival and death signals after oncogene inactivation. Furthermore, we could predict the influence of specific genetic lesions (p53−/−, Stat3-d358L, and myr-Akt1) on tumor regression after oncogene inactivation.
Second, we used another mathematical approach in our mouse models to distinguish oncogene addicted versus non-oncogene addicted tumors. This enabled us to define a specific threshold in the rate of change of tumor regression very shortly after therapeutic oncogene inactivation. We could apply this threshold kinetics of tumor regression to a human dataset to predict which human patients would have the best clinical response to the therapeutic inactivation mutant EGFR using erlotinib. Hence, our results suggest the possibility that it is possible to predict oncogene addiction in a clinical setting, right after the initiation of a therapy.
Our results suggest the surprising possibility that oncogene addiction is strongly associated with the suppression of growth/survival signals rather than the induction of death/apoptotic signals as well as provides strategies to more rapidly predict when a targeted therapeutic is likely to be effective in human patients.
Massachusetts Institute of Technology
Proteomic technologies, next-generation sequencing, and RNAi screens are providing increasingly detailed descriptions of the molecular changes that occur in diseases. However, it is difficult to use these data to reveal new therapeutic insights for several reasons. Despite their power, each of these methods still only captures a small fraction of the cellular response. Moreover, when different assays are applied to the same problem, they provide apparently conflicting answers. I will show that network modeling reveals the underlying consistency of the data by identifying small, functionally coherent pathways linking the disparate observations. We have used these methods to analyze how oncogenic mutations alter signaling and transcription and to prioritize experiments aimed at discovering therapeutic targets.
Speaker Biography: Ernest Fraenkel was first introduced to computational biology in high school when the field did not yet have a name. His early experiences with Professor Cyrus Levinthal of Columbia University taught him that biological insights often come from unexpected disciplines. After graduating summa cum laude from Harvard College in chemistry and physics he obtained his PhD at the Massachusetts Institute of Technology in the Department of Biology and did postdoctoral work at Harvard. As the field of systems biology began to emerge, he established a research group in this area at the Whitehead Institute and then moved to the Department of Biological Engineering at MIT. His research group takes a multidisciplinary approach involving tightly connected computational and experimental methods to uncover the molecular pathways that are altered in cancer, neurodegenerative diseases, and diabetes.
Speaker Biography: Stephen Friend is the President of Sage Bionetworks. He was previously Senior Vice President and Franchise Head for Oncology Research at Merck & Co., Inc. where he led Merck's Basic Cancer Research efforts. In 2005, he led the Advanced Technologies and Oncology groups to firmly establish molecular profiling activities throughout Merck's laboratories around the world, as well as to coordinate oncology programs from Basic Research through phase IIA clinical trials. Prior to joining Merck, Dr. Friend along with Dr. Leland Hartwell founded and co-led the Fred Hutchinson Cancer Research Center's "Seattle Project," an advanced institute for drug discovery. While there Drs. Friend and Hartwell developed a method for examining large patterns of genes that led them to co-found Rosetta Inpharmatics in 2001. Dr. Friend has also held faculty positions at Harvard Medical School from 1987 to 1995 and at Massachusetts General Hospital from 1990 to 1995. He received his BA in philosophy, his PhD in biochemistry and his MD from Indiana University.
University of Chicago
Changes in gene regulation are thought to play an important role in adaptation and speciation, notably in primates. However, the extent to which changes in different regulatory mechanisms underlie gene expression evolution is not yet known. To address this gap, we comparatively characterized gene expression (using RNA sequencing) and genetic and epigenetic regulatory mechanisms in humans, chimpanzees, and rhesus macaques, using LCLs from 8 individuals from each species. Specifically, we used ChIP-seq to obtain genome-wide profiles of H3K4me3, H3K4me1, H3K27me3 and H3K27ac histone modifications, as well as binding of RNA polymerase II. We also collected DNaseI-sequencing from the same LCLs, and by using the CENTIPEDE algorithm we measured the strength of transcription factor binding for over 200 transcription factors in all three species. These data allowed us to identify both conserved and species-specific enhancer and repressor regulatory elements, and to characterize similarities and differences across species in transcription factor binding to these regulatory elements. We found that that transcription factor binding and histone modifications in more than 67% of regulatory elements in putative promoter regions is conserved across the three species. In turn, by considering sequence conservation at genomic locations that showed differences in regulatory mechanisms across species we were able to better understand the extent to which changes in transcription factor binding are due to either cis- or trans- differences across species. Finally, we analyzed correlations between inter-species differences in the genetic and epigenetic regulatory mechanisms and variation in gene expression levels across species using a system of logistic regression models. Assuming that these correlations do imply a causal regulatory relationship, we estimate that up to 50% of inter-species gene expression differences can be accounted for by corresponding changes in transcription factor binding and/or the presence of histone modification marks.
Speaker Biography: Yoav Gilad is professor of human genetics at the University of Chicago. Gilad earned a PhD in molecular genetics from the Weizmann Institute of Science in Israel, and completed an EMBO postdoctoral fellowship training at Yale University. His research focuses on the evolution of gene regulation and associated biological mechanisms in humans and closely related primates, as well as on understanding how regulatory variation underlies variation in complex phenotypes.
Oregon Health & Science University
Speaker Biography: Dr. Joe W. Gray, a physicist and an engineer by training, is known for breakthroughs that have changed clinical practices for patients. He has been employed as a staff scientist in the Biomedical Sciences Division of the Lawrence Livermore National Laboratory (1972-1991), professor of laboratory medicine at the University of California San Francisco (1991-2011), Associate Laboratory Director for Biosciences and Life Sciences Division Director at the Lawrence Berkeley National Laboratory (2003-2011). In 2011, he joined the Oregon Health and Science University, where he holds the Gordon Moore Endowed Chair, and serves as Chair, Department of Biomedical Engineering; Director, Center for Spatial Systems Biomedicine; and Associate Director for Translational Research, Knight Cancer Institute. He also holds positions as Emeritus Professor, University of California San Francisco; and as Senior Scientist, Lawrence Berkeley National Laboratory.
The Gray Laboratory explores mechanisms by which genomic, transcriptional and proteomic abnormalities occur in selected cancers. Current studies focus on developing: (a) integrated analyses of the spectrum of recurrent abnormalities that influence cancer behavior (b) mathematical models that describe how cancer-associated molecular abnormalities influence individual responses to therapeutic inhibitors (c) novel therapeutic approaches to treat cancer subpopulations that do not respond well to current aggressive chemotherapeutic strategies (d) automated functional assessment of genes deregulated by genomic abnormalities in cancers, (e) molecular imaging for early detection of metastasis prone breast cancer and (f) spatial systems biomedicine. Research in these areas is typically multi-institutional and multi-disciplinary.
Pacific Northwest Research Institute
The genetic and systems architecture of complex traits remains largely unknown. A deep understanding of these issues is needed to determine whether modulating systems properties, rather than the activity of single RNAs or proteins, might be a more effective, safe, and robust way to treat dysfunction and disease. Surveys of more than 100 traits in a panel of chromosome substitution strains and in congenic strains derived from them revealed many more QTLs than conventional methods and that these QTLs have strong but context-dependent phenotypic effects. In addition, gene interactions are pervasive and strong, and often constrain the range of phenotypic variation, regardless of the extent of gene and protein divergence. Chromosome substitutions tend to shift phenotypes between alternative states, suggesting that these biological systems are both robust and fragile to genetic and environmental perturbations. Finally, transgenerational epistatic effects appear to be common and strong, and persist for many generations. These studies provide a unique glimpse into the genetics and systems biology of complex traits and raise the possibility of systems solution for treating and preventing common diseases.
Speaker Biography: Joseph Nadeau is an internationally recognized expert in genetic, genomic, metabolic, bioinformatics, computational, and systems studies of mouse models of development, cancer, and metabolic disease as well as in translating results to these studies in humans. He has been a pioneer in comparative genomics (comparative gene mapping), genetics and systems studies of mouse models of human disease as well as transgenerational epigenetic effects on cancer, metabolism, embryogenesis, and behavior. He is formerly James H. Jewel Professor and Chair of Genetics Department at Case Western Reserve University School of Medicine. He is founding editor of both Mammalian Genome and WIRES Systems Biology and Medicine; the latter won the RR Hawkins Award from the American Publishers Awards for Professional & Scholarly Excellence (PROSE) – this is the top award for outstanding scholarly work in all disciplines of the arts and sciences. He is an Elected Fellow of the American Association for the Advancement of Science. His work was recently recognized with an NIH Pioneer Award. Finally, his students and fellows have won numerous local, national, and international awards for their work.
DOE Joint Genome Institute
The paucity of a defined collection of mammalian transcriptional enhancers has largely precluded both our ability to develop computational methods for predicting additional tissue-specific enhancers in the human genome and to assess such sequences for their role in disease. In ongoing studies, we are leveraging extreme evolutionary sequence conservation as well as next generation ChIP-Sequencing to identify putative gene regulatory elements and are characterizing their in vivo enhancer activity in a transgenic mouse assay. To date we have tested over 2000 such sequences in animals, and observed that 1000 function reproducibly as tissue-specific enhancers of gene expression. As a community resource, we have established a database to visualize and query the activity of these enhancer sequences (http://enhancer.lbl.gov/) and continue to generate additional in vivo enhancer data for ~300 sequences per year. In recent studies directly from human tissues, we show that the conservation of enhancers across mammals varies widely depending on the specific tissue of examination. In particular, enhancers of the nervous system have high levels of evolutionary constraint while enhancers of the heart are largely not conserved. These findings highlight the importance of enhancer identification directly from human tissues for certain organs and hence disease states. In addition, this growing set of enhancers with in vivo-defined activities provides a molecular toolbox that can be used to experimentally target gene expression to organs and tissues in mammals and constitutes a starting point for studying the role of regulatory elements in human disease.
Speaker Biography: Len Pennacchio is a senior staff scientist in the Genomics Division at Lawrence Berkeley National Laboratory (LBNL) and deputy director of the DOE Joint Genome Institute. Dr. Pennacchio has an extensive background in mammalian genetics and genomics as well as with DNA sequencing technologies and their application to address outstanding issues in both the medical and energy sectors. He received his PhD in 1998 from the Department of Genetics at Stanford University and performed his postdoctoral work with Eddy Rubin as an Alexander Hollaender Distinguished Fellow at LBNL. He has authored over 100 peer-reviewed publications and in 2007 he received the Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House for his in vivo studies on mammalian gene regulation.
University of Illinois, Urbana-Champaign
In recent years there has been growing interest in quantitative models of gene regulation. One such class of models is the sequence-based models, which map the sequence of a DNA segment to expression driven by that segment or to transcription factor (TF) binding level at the segment. We have previously reported on such models: (i) the GEMSTAT model that was fit to the expression readouts of enhancers from the segmentation network in Drosophila, and (ii) the STAP model that was fit to ChIP-SEQ data from mouse ES cells. This talk will present our recent work with these models. First, I will present a model of the expression readout of an entire gene locus using sequence and trans-regulatory information (TF concentrations). I will demonstrate uses of such a model, from regulatory network prediction to answering questions about enhancer modularity and synergy. In the second part of the talk, I will describe how biophysical modeling of ChIP data can provide extensive clues into the extent of combinatorial interactions involved in TF-DNA binding in vivo.
University of California, San Francisco, Howard Hughes Medical Institute
The ability to sequence genomes has far outstripped approaches for deciphering the information they encode. We have developed a suite of techniques based on ribosome profiling (deep sequencing of ribosome protected fragments) that dramatically expand our ability to follow translation in vivo. I will present recent applications of our ribosome profiling approach including the following: (1) Development of ribosome profiling protocols for a wide variety of eukaryotic and prokaryotic organisms. (2) Uses of ribosome profiling to globally monitor when chaperones, targeting factors, or processing enzymes engage nascent chains. (3) Deciphering the driving force and biological consequences underlying the choice of synonymous codons.
Speaker biography: Jonathan Weissman is a Howard Hughes Medical Institute Investigator and a professor of cellular and molecular pharmacology and of biochemistry and biophysics at the University of California, San Francisco. He received his undergraduate physics degree from Harvard College. After obtaining a PhD in physics from the Massachusetts Institute of Technology, where he worked with Peter Kim, Dr. Weissman pursued postdoctoral fellowship training in Arthur Horwich's laboratory at Yale University School of Medicine. He was awarded the Raymond and Beverly Sackler International Prize in Biophysics and is a member of the National Academy of Sciences.