The Gutenkunst Group

Publications

Listed are our peer-reviewed journal publications and other publications. For citation information, see Google Scholar.

Wordle based on publication titles and abstracts, as of September 2011.

Peer-reviewed journal publications

2012

Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes
X Xu*, X Liu*, S Ge*, JD Jensen*, F Hu*, X Li*, Y Dong*, RN Gutenkunst, L Fang, L Huang, J Li, W He, G Zhang, X Zheng, F Zhang, Y Li, C Yu, K Kristiansen, X Zhang, J Wang, M Wright, S McCouch, R Nielsen, J Wang, W Wang
Nature Biotechnology 30:105 (2012); link; pdf; abstract

Rice is a staple crop that has undergone substantial phenotypic and physiological changes during domestication. Here we resequenced the genomes of 40 cultivated accessions selected from the major groups of rice and 10 accessions of their wild progenitors (Oryza rufipogon and Oryza nivara) to >15× raw data coverage. We investigated genome-wide variation patterns in rice and obtained 6.5 million high-quality single nucleotide polymorphisms (SNPs) after excluding sites with missing data in any accession. Using these population SNP data, we identified thousands of genes with significantly lower diversity in cultivated but not wild rice, which represent candidate regions selected during domestication. Some of these variants are associated with important biological features, whereas others have yet to be functionally characterized. The molecular markers we have identified should be valuable for breeding and for identifying agronomically important genes in rice.

2011

Guidelines for visualizing and annotating rule-based models
LA Chylek, H Bin, ML Blinov, T Emonet, JR Faeder, B Goldstein, RN Gutenkunst, JM Haugh, T Lipniacki, RG Posner, J Yang, WS Hlavacek
Molecular BioSystems 7:2779 (2011); link; pdf; abstract

Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.
Demographic history and rare allele sharing among human populations
S Gravel, BM Henn, RN Gutenkunst, AR Indap, GT Marth, AG Clark, The 1000 Genomes consortium, CD Bustamante
Proceedings of the National Academy of Sciences USA 108:11983 (2011); link; pdf; abstract
• Featured in the New York Times; pdf

High-throughput sequencing technology enables population-level surveys of human genomic variation. Here, we examine the joint allele frequency distributions across continental human populations and present an approach for combining complementary aspects of whole-genome, low-coverage data and targeted high-coverage data. We apply this approach to data generated by the pilot phase of the Thousand Genomes Project, including whole-genome 2–4× coverage data for 179 samples from HapMap European, Asian, and African panels as well as high-coverage target sequencing of the exons of 800 genes from 697 individuals in seven populations. We use the site frequency spectra obtained from these data to infer demographic parameters for an Out-of-Africa model for populations of African, European, and Asian descent and to predict, by a jackknife-based approach, the amount of genetic diversity that will be discovered as sample sizes are increased. We predict that the number of discovered nonsynonymous coding variants will reach 100,000 in each population after ∼1,000 sequenced chromosomes per population, whereas ∼2,500 chromosomes will be needed for the same number of synonymous variants. Beyond this point, the number of segregating sites in the European and Asian panel populations is expected to overcome that of the African panel because of faster recent population growth. Overall, we find that the majority of human genomic variable sites are rare and exhibit little sharing among diverged populations. Our results emphasize that replication of disease association for specific rare genetic variants across diverged populations must overcome both reduced statistical power because of rarity and higher population divergence.
A biophysical model of cell adhesion mediated by immunoadhesin drugs and antibodies
RN Gutenkunst, D Coombs, T Starr, ML Dustin, B Goldstein
PLoS ONE 6:e19701 (2011); link; pdf; abstract

A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data. We also quantitatively describe the parameter space in which binding occurs. Our results point toward mechanisms relating epitope immobility to cell adhesion and offer insight into the activity of an important class of drugs.
Comparative and demographic analysis of orang-utan genomes
DP Locke, LW Hillier, WC Warren, KC Worley, LV Nazareth, DM Muzny, SP Yang, Z Wang, AT Chinwalla, P Minx, M Mitreva, L Cook, KD Delehaunty, C Fronick, H Schmidt, LA Fulton, RS Fulton, JO Nelson, V Magrini, C Pohl, TA Graves, C Markovic, A Cree, HH Dinh, J Hume, CL Kovar, GR Fowler, G Lunter, S Meader, A Heger, CP Ponting, T Marques-Bonet, C Alkan, L Chen, Z Cheng, JM Kidd, EE Eichler, S White, S Searle, AJ Vilella, Y Chen, P Flicek, J Ma, B Raney, B Suh, R Burhans, J Herrero, D Haussler, R Faria, O Fernando, F Darré, D Farré, E Gazave, M Oliva, A Navarro, R Roberto, O Capozzi, N Archidiacono, G Della Valle, S Purgato, M Rocchi, MK Konkel, JA Walker, B Ullmer, MA Batzer, AFA Smit, R Hubley, C Casola,DR Schrider, MW Hahn, V Quesada, XS Puente, GR Ordoñez, C López-Otín, T Vinar, B Brejova, A Ratan, RS Harris, W Miller, C Kosiol, HA Lawson, V Taliwal, AL Martins, A Siepel, A Roychoudhury, X Ma, J Degenhardt, CD Bustamante, RN Gutenkunst, T Mailund, JY Dutheil, A Hobolth, MH Schierup, OA Ryder, Y Yoshinaga, PJ de Jong, GM Weinstock, J Rogers, ER Mardis, RA Gibbs, RK Wilson
Nature 469:529 (2011); link; pdf; abstract
• Featured in UANews

"Orang-utan" is derived from a Malay term meaning "man of the forest" and aptly describes the southeast Asian great apes native to Sumatra and Borneo. The orang-utan species, Pongo abelii (Sumatran) and Pongo pygmaeus (Bornean), are the most phylogenetically distant great apes from humans, thereby providing an informative perspective on hominid evolution. Here we present a Sumatran orang-utan draft genome assembly and short read sequence data from five Sumatran and five Bornean orang-utan genomes. Our analyses reveal that, compared to other primates, the orang-utan genome has many unique features. Structural evolution of the orang-utan genome has proceeded much more slowly than other great apes, evidenced by fewer rearrangements, less segmental duplication, a lower rate of gene family turnover and surprisingly quiescent Alu repeats, which have played a major role in restructuring other primate genomes. We also describe a primate polymorphic neocentromere, found in both Pongo species, emphasizing the gradual evolution of orang-utan genome structure. Orang-utans have extremely low energy usage for a eutherian mammal1, far lower than their hominid relatives. Adding their genome to the repertoire of sequenced primates illuminates new signals of positive selection in several pathways including glycolipid metabolism. From the population perspective, both Pongo species are deeply diverse; however, Sumatran individuals possess greater diversity than their Bornean counterparts, and more species-specific variation. Our estimate of Bornean/Sumatran speciation time, 400,000 years ago, is more recent than most previous studies and underscores the complexity of the orang-utan speciation process. Despite a smaller modern census population size, the Sumatran effective population size (Ne) expanded exponentially relative to the ancestral Ne after the split, while Bornean Ne declined over the same period. Overall, the resources and analyses presented here offer new opportunities in evolutionary genomics, insights into hominid biology, and an extensive database of variation for conservation efforts.
Daily sampling of an HIV-1 patient with slowly progressing disease displays persistence of multiple env subpopulations consistent with neutrality
H Skar*, RN Gutenkunst*, KW Ramsay, A Alaeus, J Albert, T Leitner
PLoS ONE 6:e21747 (2011); link; pdf; abstract

The molecular evolution of HIV-1 is characterized by frequent substitutions, indels and recombination events. In addition, a HIV-1 population may adapt through frequency changes of its variants. To reveal such population dynamics we analyzed HIV-1 subpopulation frequencies in an untreated patient with stable, low plasma HIV-1 RNA levels and close to normal CD4+ T-cell levels. The patient was intensively sampled during a 32-day period as well as approximately 1.5 years before and after this period (days −664, 1, 2, 3, 11, 18, 25, 32 and 522). 77 sequences of HIV-1 env (approximately 3100 nucleotides) were obtained from plasma by limiting dilution with 7–11 sequences per time point, except day −664. Phylogenetic analysis using maximum likelihood methods showed that the sequences clustered in six distinct subpopulations. We devised a method that took into account the relatively coarse sampling of the population. Data from days 1 through 32 were consistent with constant within-patient subpopulation frequencies. However, over longer time periods, i.e. between days 1…32 and 522, there were significant changes in subpopulation frequencies, which were consistent with evolutionarily neutral fluctuations. We found no clear signal of natural selection within the subpopulations over the study period, but positive selection was evident on the long branches that connected the subpopulations, which corresponds to >3 years as the subpopulations already were established when we started the study. Thus, selective forces may have been involved when the subpopulations were established. Genetic drift within subpopulations caused by de novo substitutions could be resolved after approximately one month. Overall, we conclude that subpopulation frequencies within this patient changed significantly over a time period of 1.5 years, but that this does not imply directional or balancing selection. We show that the short-term evolution we study here is likely representative for many patients of slow and normal disease progression.
Effect of 1918 PB1-F2 expression on influenza A virus infection kinetics
AM Smith, FR Adler, JL McAuley, RN Gutenkunst, RM Ribeiro, JA McCullers, AS Perelson
PLoS Computational Biology 7:e1001081 (2011); link; pdf; abstract

Relatively little is known about the viral factors contributing to the lethality of the 1918 pandemic, although its unparalleled virulence was likely due in part to the newly discovered PB1-F2 protein. This protein, while unnecessary for replication, increases apoptosis in monocytes, alters viral polymerase activity in vitro, enhances inflammation and increases secondary pneumonia in vivo. However, the effects the PB1-F2 protein have in vivo remain unclear. To address the mechanisms involved, we intranasally infected groups of mice with either influenza A virus PR8 or a genetically engineered virus that expresses the 1918 PB1-F2 protein on a PR8 background, PR8-PB1-F2(1918). Mice inoculated with PR8 had viral concentrations peaking at 72 hours, while those infected with PR8-PB1-F2(1918) reached peak concentrations earlier, 48 hours. Mice given PR8-PB1-F2(1918) also showed a faster decline in viral loads. We fit a mathematical model to these data to estimate parameter values. The model supports a higher viral production rate per cell and a higher infected cell death rate with the PR8-PB1-F2(1918) virus. We discuss the implications these mechanisms have during an infection with a virus expressing a virulent PB1-F2 on the possibility of a pandemic and on the importance of antiviral treatments.

2010

A map of human genome variation from population scale sequencing
The 1000 Genomes Project Consortium
Nature 467:1061 (2010); link; pdf; abstract

The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother-father-child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10^-8 per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research.
RuleMonkey: Software for stochastic simulation of rule-based models
J Colvin, MI Monine, RN Gutenkunst, WS Hlavacek, DD Von Hoff, RG Posner
BMC Bioinformatics 11:404 (2010); link; pdf; abstract

The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specied using model-specication languages, such as the BioNetGen language (BNGL). A set of rules implicitly denes a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.

Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specic codes implementing network-free simulation methods.

RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application (http://public.tgen.org/rulemonkey). It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.

2009

Inferring the joint demographic history of multiple populations from multidimensional SNP data
RN Gutenkunst, RD Hernandez, SH Williamson, CD Bustamante
PLoS Genetics 5:e1000695 (2009); link; pdf; supporting info; abstract

Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40-270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17-43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3-26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).
Targets of balancing selection in the human genome
AM Andrés, MJ Hubisz, A Indap, D Torgerson, J Degenhardt, AR Boyko, RN Gutenkunst, TJ White, ED Green, CD Bustamante, AG Clark, R Nielsen
Molecular Biology and Evolution 26:2755 (2009); link; pdf; abstract

Balancing selection is potentially an important biological force for maintaining advantageous genetic diversity in populations, including variation that is responsible for long-term adaptation to the environment. By serving as a means to maintain genetic variation, it may be particularly relevant to maintaining phenotypic variation in natural populations. Nevertheless, its prevalence and specific targets in the human genome remain largely unknown. We have analyzed the patterns of diversity and divergence of 13,400 genes in two human populations using an unbiased single-nucleotide polymorphism data set, a genome-wide approach, and a method that incorporates demography in neutrality tests. We identified an unbiased catalog of genes with signatures of long-term balancing selection, which includes immunity genes as well as genes encoding keratins and membrane channels; the catalog also shows enrichment in functional categories involved in cellular structure. Patterns are mostly concordant in the two populations, with a small fraction of genes showing population-specific signatures of selection. Power considerations indicate that our findings represent a subset of all targets in the genome, suggesting that although balancing selection may not have an obvious impact on a large proportion of human genes, it is a key force affecting the evolution of a number of genes in humans.
Darwinian and demographic forces affecting human protein coding genes
R Nielsen, MJ Hubisz, I Hellmann, D Torgerson, AM Andrés, A Albrechtsen, R Gutenkunst, MD Adams, M Cargill, X Hu, A Boyko, A Indap, CD Bustamante, AG Clark
Genome Research 19:838 (2009); link; pdf; abstract

Past demographic changes can produce distortions in patterns of genetic variation that can mimic the appearance of natural selection unless the demographic effects are explicitly removed. Here we fit a detailed model of human demography that incorporates divergence, migration, admixture, and changes in population size to directly sequenced data from 13,400 protein coding genes from 20 European-American and 19 African-American individuals. Based on this demographic model, we use several new and established statistical methods for identifying genes with extreme patterns of polymorphism likely to be caused by Darwinian selection, providing the first genome-wide analysis of allele frequency distributions in humans based on directly sequenced data. The tests are based on observations of excesses of high frequencyâ€“derived alleles, excesses of low frequencyâ€“derived alleles, and excesses of differences in allele frequencies between populations. We detect numerous new genes with strong evidence of selection, including a number of genes related to psychiatric and other diseases. We also show that microRNA controlled genes evolve under extremely high constraints and are more likely to undergo negative selection than other genes. Furthermore, we show that genes involved in muscle development have been subject to positive selection during recent human history. In accordance with previous studies, we find evidence for negative selection against mutations in genes associated with Mendelian disease and positive selection acting on genes associated with several complex diseases.
Global distribution of genomic diversity underscores rich complex history of continental human populations
A Auton, K Bryc, AR Boyko, KE Lohmueller, J Novembre, A Reynolds, A Indap, MH Wright, J Degenhardt, RN Gutenkunst, KS King, MR Nelson, CD Bustamante
Genome Research 19:795-803 (2009); link; pdf; abstract

Characterizing patterns of genetic variation within and among human populations is important for understanding human evolutionary history and for careful design of medical genetic studies. Here, we analyze patterns of variation across 443,434 single nucleotide polymorphisms (SNPs) genotyped in 3845 individuals from four continental regions. This unique resource allows us to illuminate patterns of diversity in previously under-studied populations at the genome-wide scale including Latin America, South Asia, and Southern Europe. Key insights afforded by our analysis include quantifying the degree of admixture in a large collection of individuals from Guadalajara, Mexico; identifying language and geography as key determinants of population structure within India; and elucidating a north-south gradient in haplotype diversity within Europe. We also present a novel method for identifying long-range tracts of homozygosity indicative of recent common ancestry. Application of our approach suggests great variation within and among populations in the extent of homozygosity, suggesting both demographic history (such as population bottlenecks) and recent ancestry events (such as consanguinity) play an important role in patterning variation in large modern human populations.

2008

Sloppiness, robustness, and evolvability in systems biology
BC Daniels, YJ Chen, JP Sethna, RN Gutenkunst, CR Myers
Current Opinion in Biotechnology 19:389 (2008); link; pdf; abstract

The functioning of many biochemical networks is often robust - remarkably stable under changes in external conditions and internal reaction parameters. Much recent work on robustness and evolvability has focused on the structure of neutral spaces, in which system behavior remains invariant to mutations. Recently we have shown that the collective behavior of multiparameter models is most often sloppy: insensitive to changes except along a few "stiff" combinations of parameters, with an enormous sloppy neutral subspace. Robustness is often assumed to be an emergent evolved property, but the sloppiness natural to biochemical networks offers an alternative nonadaptive explanation. Conversely, ideas developed to study evolvability in robust systems can be usefully extended to characterize sloppy systems.
Variational method for estimating the rate of convergence of Markov Chain Monte Carlo algorithms
FP Casey, JJ Waterfall, RN Gutenkunst, CR Myers, JP Sethna
Physical Review E 78:046704 (2008); link; pdf; abstract

We demonstrate the use of a variational method to determine a quantitative lower bound on the rate of convergence of Markov chain Monte Carlo (MCMC) algorithms as a function of the target density and proposal density. The bound relies on approximating the second largest eigenvalue in the spectrum of the MCMC operator using a variational principle and the approach is applicable to problems with continuous state spaces. We apply the method to one dimensional examples with Gaussian and quartic target densities, and we contrast the performance of the random walk Metropolis-Hastings algorithm with a "smart" variant that incorporates gradient information into the trial moves, a generalization of the Metropolis adjusted Langevin algorithm. We find that the variational method agrees quite closely with numerical simulations. We also see that the smart MCMC algorithm often fails to converge geometrically in the tails of the target density except in the simplest case we examine, and even then care must be taken to choose the appropriate scaling of the deterministic and random parts of the proposed moves. Again, this calls into question the utility of smart MCMC in more complex problems. Finally, we apply the same method to approximate the rate of convergence in multidimensional Gaussian problems with and without importance sampling. There we demonstrate the necessity of importance sampling for target densities which depend on variables with a wide range of scales.

2007

Extracting falsifiable predictions from sloppy models
RN Gutenkunst, FP Casey, JJ Waterfall, CR Myers, JP Sethna
in G. Stolovitsky, A. Califano, J. Collins, editors, Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference
Annals of the New York Academy of Sciences 1115:203 (2007); link; pdf; abstract

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.
Universally sloppy parameter sensitivities in systems biology
RN Gutenkunst, JJ Waterfall, FP Casey, KS Brown, CR Myers, JP Sethna
PLoS Computational Biology 3:e189 (2007); link; pdf; supporting info; abstract
• Featured in Biomedical Computation Review; pdf
• "Exceptional" evaluation on Faculty of 1000: Biology

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model
FP Casey, D Baird, Q Feng, RN Gutenkunst, JJ Waterfall, CR Myers, KS Brown, RA Cerione, JP Sethna
IET Systems Biology 1:190 (2007); link; pdf; abstract

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (Β-Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.
Python unleashed on systems biology
CR Myers, RN Gutenkunst, JP Sethna
Computing in Science and Engineering 9:34 (2007); link; pdf; abstract

Researchers at Cornell University have built an open source software system to model biomolecular reaction networks. SloppyCell is written in Python and uses third-party libraries extensively, but it also does some fun things with on-the-fly code generation and parallel programming.
Inferring resource distributions from Atlantic bluefin tuna movements: an analysis based on net displacement and length of track
R Gutenkunst, N Newlands, M Lutcavage, L Edelstein-Keshet
Journal of Theoretical Biology 245:243 (2007); link; pdf; abstract

We use observed movement tracks of Atlantic bluefin tuna in the Gulf of Maine and mathematical modeling of this movement to identify possible resource patches. We infer bounds on the overall sizes and distribution of such patches, even though they are difficult to quantify by direct observation in situ. To do so, we segment individual fish tracks into intervals of distinct motion types based on the ratio of net displacement to length of track (ΔD/ΔL) over a time window Δt. To find the best segmentation, we optimize the fit of a random-walk movement model to each motion type. We compare results from two distinct movement models: biased turning and biased speed, to check the model-dependence of our inferences, and find that uncertainty in choice of movement model dominates the uncertainties of our conclusions. We find that our data are best described using two motion types: "localized" (ΔD/ΔL small) and "long-ranged" (ΔD/ΔL large). The biased turning model leads to significantly better resolution of localized movement intervals than the biased speed model. We hypothesize that localized movement corresponds to exploitation of resource patches. Comparison with visual behavior observations made during tracking suggests that many inferred intervals of localized motion do indeed correspond to feeding activity. From our analysis, we estimate that, on average, bluefin tuna in the Gulf of Maine encounter a resource patch every 2 h, that those patches have an average radius of 0.7-1.2 km, and that, overall, there are at most 5-9 such patches per 100 km² in the region studied.

2006

Sloppy-model universality class and the Vandermonde matrix
JJ Waterfall, FP Casey, RN Gutenkunst, KS Brown, CR Myers, PW Brouwer, V Elser, JP Sethna
Physical Review Letters 97:150601 (2006); link; pdf; abstract

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy: the system behavior depends only on a few "stiff" combinations of the parameters and is unchanged as other "sloppy" parameter combinations vary by orders of magnitude. We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.

2003

An introduction to signal extraction in interferometric gravitational wave detectors
ED Black, RN Gutenkunst
American Journal of Physics 71:365 (2003); link; pdf; abstract

In the very near future gravitational wave astronomy is expected to become a reality, giving us a completely new tool for exploring the universe around us. We provide an introduction to how interferometric gravitational wave detectors work, suitable for students entering the field and teachers who wish to cover the subject matter in an advanced undergraduate or beginning graduate level course.

Other publications

Discussion of "Riemannian manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead
MK Transtrum, RN Gutenkunst, Y Chen, BB Machta, JP Sethna
Journal of the Royal Statistical Society: Series B 73:199 (2011); link; pdf
Genetics and genomics of human population structure
S Ramachandran, H Tang, RN Gutenkunst, CD Bustamante
in M Speicher et al., editors, Vogel and Motulsky's Human Genetics: Problems and Approaches, Springer Verlag (2010); link; pdf; abstract

Recent developments in sequencing technology have created a flood of new data on human genetic variation, and this data has yielded new insights into human population structure. Here we review what both early and more recent studies have taught us about human population structure and history. Early studies showed that most human genetic variation occurs within populations rather than between them, and that genetically related populations often cluster geographically. Recent studies based on much larger data sets have recapitulated these observations, but have also demonstrated that high-density genotyping allows individuals to be reliably assigned to their population of origin. In fact, for admixed individuals, even the ancestry of particular genomic regions can often be reliably inferred. Recent studies have also offered detailed information about the composition of specific populations from around the world, revealing how history has shaped their genetic makeup. We also briefly review quantitative models of human genetic history, including the role natural selection has played in shaping human genetic variation.
Sloppiness, Modeling, and Evolution in Biochemical Networks
RN Gutenkunst
Ph.D. thesis, Cornell University (2008); link; pdf; abstract

The wonderful complexity of livings cells cannot be understood solely by studying one gene or protein at a time. Instead, we must consider their interactions and study the complex biochemical networks they function in.

Quantitative computational models are important tools for understanding the dynamics of such biochemical networks, and we begin in Chapter 2 by showing that the sensitivities of such models to parameter changes are generically "sloppy", with eigenvalues roughly evenly spaced over many decades. This sloppiness has practical consequences for the modeling process. In particular, we argue that if one's goal is to make experimentally testable predictions, sloppiness suggests that collectively fitting model parameters to system-level data will often be much more efficient that directly measuring them.

In Chapter 3 we apply some of the lessons of sloppiness to a specific modeling project involving in vitro experiments on the activation of the heterotrimeric G protein transducin. We explore how well time-series activation experiments can constrain model parameters, and we show quantitatively that the T177A mutant of transducin exhibits a much slower rate of rhodopsin-mediated activation than the wild-type.

All the preceding biochemical modeling work is performed using the SloppyCell modeling environment, and Chapter 4 briefly introduces SloppyCell and some of the analyses it implements. Additionally, the two appendices of this thesis contain preliminary user and developer documentation for SloppyCell.

Modelers tweak network parameters with their computers, and nature tweaks such parameters through evolution. We study evolution in Chapter 5 using a version of Fisher's geometrical model with minimal pleiotropy, appropriate for the evolution of biochemical parameters. The model predicts a striking pattern of cusps in the distribution of fitness effects of fixed mutations, and using extreme value theory we show that the consequences of these cusps should be observable in feasible experiments.

Finally, this thesis closes in Chapter 6 by briefly considering several topics: sloppiness in two non-biochemical models, two technical issues with building models, and the effect of sloppiness on evolution beyond the first fixed mutation.
Adaptive mutation in a geometrical model of chemotype evolution
RN Gutenkunst, JP Sethna
arXiv:0712.3240 (2007); link; pdf; abstract

The distribution of fitness effects of adaptive mutations remains poorly understood, both empirically and theoretically. We study this distribution using a version of Fisher's geometrical model without pleiotropy, such that each mutation affects only a single trait. We are motivated by the notion of an organism's chemotype, the set of biochemical reaction constants that govern its molecular constituents. From physical considerations, we expect the chemotype to be of high dimension and to exhibit very little pleiotropy. Our model generically predicts striking cusps in the distribution of the fitness effects of arising and fixed mutations. It further predicts that a single element of the chemotype should comprise all mutations at the high-fitness ends of these distributions. Using extreme value theory, we show that the two cusps with the highest fitnesses are typically well-separated, even when the chemotype possesses thousands of elements; this suggests a means to observe these cusps experimentally. More broadly, our work demonstrates that new insights into evolution can arise from the chemotype perspective, a perspective between the genotype and the phenotype.
Extracting light from water: sonoluminescence
R Gutenkunst
Caltech Undergraduate Research Journal 2:16 (2002); pdf; abstract

Water is one of the most abundant compounds on Earth. No known form of life can survive without it, and for a human, a day without water is a day of suffering. Water is such a familiar element in everyday life that one might assume that it is completely understood. Unfortunately, that is not true. Our ignorance is well illustrated by sonoluminescence, the astounding ability of water to concentrate energy from sound waves into flashes of light. After over a decade of intense study, the scientific community is still unable to provide a complete explanation for this phenomenon, but its work has pushed the frontiers of fluid mechanics and expanded our understanding of physics at high pressures and temperatures.

* authors contributed equally

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