The short version is that I’m an anti-disciplinary scientist. The slightly longer version is that I’m a postdoctoral fellow at Stanford sitting in the lab of Jonas Cremer where I use principles of bacterial physiology to make predictive models of evolution. I firmly believe that the future of biology relies on an intuition for the physics that governs it, especially in evolutionary biology.

Being the progeny of two paleontologists, I grew up in rural Utah where I was raised in a concoction of contradictions. While my weekends were spent with my parents helping dig up dinosaur bones and grappling with geology of my surroundings, my weekdays were spent in the rural public education system where I was taught evolution was a lie, humans can’t impact the Earth, and that dinosaur bones were buried by either the devil or the government (or maybe both). Contending with these diametrically opposed views of science and experiencing its influence on public discourse has strongly influenced the way I want to understand the world; through the cold, unforgiving, and objective lens of math.

After studying biology and chemistry at the University of Utah, I earned a PhD in Biochemistry and Molecular Biophysics under the tutelage of Rob Phillips at the California Institute of Technology. Through studying how bacterial cells control the action of their own genes, I learned how to approach biological problems from a physical and probabalistic perspective. I have carried this manner of scientific study with me where I bring it to bear on the complex phenomena that emerge at the intersection of bacterial physiology, ecology, and evolution.

Beyond quantitative science, I am an amateur web developer and help build and maintain a number of scientific resources, such as the Human Impacts Database. Beyond science, I love taking photographs, making programmatically generated art, vector based illustration (like those on my research page), and exploring the wild lands of California. I also watch my fair share of films and television about which I have hard-headed opinions, such as an affinity for Alejandro Jodorowsky and Julia Ducournau and a disdain for Star Wars and Marvel.

Honors & Awards

  • Postdoctoral Research Fellowship in Biology, National Science Foundation (2021-2022)

Professional Education

  • Bachelor of Science, University of Utah (2013)
  • Doctor of Philosophy, California Institute of Technology (2020)
  • Ph.D., California Institute of Technology, Biochemistry & Molecular Biophysics (2020)
  • B.Sc., University of Utah, Biology (2013)
  • B.Sc., University of Utah, Chemistry (2013)

Stanford Advisors

All Publications

  • An optimal regulation of fluxes dictates microbial growth in and out of steady-state. eLife Chure, G., Cremer, J. 2023; 12


    Effective coordination of cellular processes is critical to ensure the competitive growth of microbial organisms. Pivotal to this coordination is the appropriate partitioning of cellular resources between protein synthesis via translation and the metabolism needed to sustain it. Here, we extend a low-dimensional allocation model to describe the dynamic regulation of this resource partitioning. At the core of this regulation is the optimal coordination of metabolic and translational fluxes, mechanistically achieved via the perception of charged- and uncharged-tRNA turnover. An extensive comparison with 60 data sets from Escherichia coli establishes this regulatory mechanism's biological veracity and demonstrates that a remarkably wide range of growth phenomena in and out of steady state can be predicted with quantitative accuracy. This predictive power, achieved with only a few biological parameters, cements the preeminent importance of optimal flux regulation across conditions and establishes low-dimensional allocation models as an ideal physiological framework to interrogate the dynamics of growth, competition, and adaptation in complex and ever-changing environments.

    View details for DOI 10.7554/eLife.84878

    View details for PubMedID 36896805

  • A quantitative database of human impacts on Planet Earth. Patterns (New York, N.Y.) Chure, G., Banks, R. A., Flamholz, A. I., Sarai, N. S., Kamb, M., Lopez-Gomez, I., Bar-On, Y., Milo, R., Phillips, R. 2022; 3 (9): 100552


    The Human Impacts Database ( is a curated, searchable resource housing quantitative data relating to the diverse anthropogenic impacts on our planet, with topics ranging from sea-level rise to livestock populations, greenhouse gas emissions, fertilizer use, and beyond. Each entry in the database reports a quantitative value (or a time series of values) along with clear referencing of the primary source, the method of measurement or estimation, an assessment of uncertainty, and links to the underlying data, as well as a permanent identifier called a Human Impacts ID (HuID). While there are other databases that house some of these values, they are typically focused on a single topic area, like energy usage or greenhouse gas emissions. The Human Impacts Database facilitates access to carefully curated data, acting as a quantitative resource pertaining to the myriad ways in which humans have an impact on the Earth, for practicing scientists, the general public, and those involved in education for sustainable development alike. We outline the structure of the database, describe our curation procedures, and use this database to generate a graphical summary of the current state of human impacts on the Earth, illustrating both their numerical values and their intimate interconnections.

    View details for DOI 10.1016/j.patter.2022.100552

    View details for PubMedID 36124305

  • Fundamental limits on the rate of bacterial growth and their influence on proteomic composition. Cell systems Belliveau, N. M., Chure, G., Hueschen, C. L., Garcia, H. G., Kondev, J., Fisher, D. S., Theriot, J. A., Phillips, R. 2021


    Despite abundant measurements of bacterial growth rate, cell size, and protein content, we lack a rigorous understanding of what sets the scale of these quantities and when protein abundances should (or should not) depend on growth rate. Here, we estimate the basic requirements and physical constraints on steady-state growth by considering key processes in cellular physiology across a collection of Escherichia coli proteomic data covering 4,000 proteins and 36 growth rates. Our analysis suggests that cells are predominantly tuned for the task of cell doubling across a continuum of growth rates; specific processes do not limit growth rate or dictate cell size. We present a model of proteomic regulation as a function of nutrient supply that reconciles observed interdependences between protein synthesis, cell size, and growth rate and propose that a theoretical inability to parallelize ribosomal synthesis places a firm limit on the achievable growth rate. A record of this paper's transparent peer review process is included in the supplemental information.

    View details for DOI 10.1016/j.cels.2021.06.002

    View details for PubMedID 34214468

  • First-principles prediction of the information processing capacity of a simple genetic circuit. Physical review. E Razo-Mejia, M., Marzen, S., Chure, G., Taubman, R., Morrison, M., Phillips, R. 2020; 102 (2-1): 022404


    Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.

    View details for DOI 10.1103/PhysRevE.102.022404

    View details for PubMedID 32942428

  • Sequence-dependent dynamics of synthetic and endogenous RSSs in V(D)J recombination. Nucleic acids research Hirokawa, S., Chure, G., Belliveau, N. M., Lovely, G. A., Anaya, M., Schatz, D. G., Baltimore, D., Phillips, R. 2020; 48 (12): 6726-6739


    Developing lymphocytes of jawed vertebrates cleave and combine distinct gene segments to assemble antigen-receptor genes. This process called V(D)J recombination that involves the RAG recombinase binding and cutting recombination signal sequences (RSSs) composed of conserved heptamer and nonamer sequences flanking less well-conserved 12- or 23-bp spacers. Little quantitative information is known about the contributions of individual RSS positions over the course of the RAG-RSS interaction. We employ a single-molecule method known as tethered particle motion to track the formation, lifetime and cleavage of individual RAG-12RSS-23RSS paired complexes (PCs) for numerous synthetic and endogenous 12RSSs. We reveal that single-bp changes, including in the 12RSS spacer, can significantly and selectively alter PC formation or the probability of RAG-mediated cleavage in the PC. We find that some rarely used endogenous gene segments can be mapped directly to poor RAG binding on their adjacent 12RSSs. Finally, we find that while abrogating RSS nicking with Ca2+ leads to substantially shorter PC lifetimes, analysis of the complete lifetime distributions of any 12RSS even on this reduced system reveals that the process of exiting the PC involves unidentified molecular details whose involvement in RAG-RSS dynamics are crucial to quantitatively capture kinetics in V(D)J recombination.

    View details for DOI 10.1093/nar/gkaa418

    View details for PubMedID 32449932

    View details for PubMedCentralID PMC7337519

  • Theoretical investigation of a genetic switch for metabolic adaptation. PloS one Laxhuber, K. S., Morrison, M. J., Chure, G., Belliveau, N. M., Strandkvist, C., Naughton, K. L., Phillips, R. 2020; 15 (5): e0226453


    Membrane transporters carry key metabolites across the cell membrane and, from a resource standpoint, are hypothesized to be produced when necessary. The expression of membrane transporters in metabolic pathways is often upregulated by the transporter substrate. In E. coli, such systems include for example the lacY, araFGH, and xylFGH genes, which encode for lactose, arabinose, and xylose transporters, respectively. As a case study of a minimal system, we build a generalizable physical model of the xapABR genetic circuit, which features a regulatory feedback loop via membrane transport (positive feedback) and enzymatic degradation (negative feedback) of an inducer. Dynamical systems analysis and stochastic simulations show that the membrane transport makes the model system bistable in certain parameter regimes. Thus, it serves as a genetic "on-off" switch, enabling the cell to only produce a set of metabolic enzymes when the corresponding metabolite is present in large amounts. We find that the negative feedback from the degradation enzyme does not significantly disturb the positive feedback from the membrane transporter. We investigate hysteresis in the switching and discuss the role of cooperativity and multiple binding sites in the model circuit. Fundamentally, this work explores how a stable genetic switch for a set of enzymes is obtained from transcriptional auto-activation of a membrane transporter through its substrate.

    View details for DOI 10.1371/journal.pone.0226453

    View details for PubMedID 32379825

    View details for PubMedCentralID PMC7205307

  • Predictive shifts in free energy couple mutations to their phenotypic consequences. Proceedings of the National Academy of Sciences of the United States of America Chure, G., Razo-Mejia, M., Belliveau, N. M., Einav, T., Kaczmarek, Z. A., Barnes, S. L., Lewis, M., Phillips, R. 2019; 116 (37): 18275-18284


    Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find that the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions.

    View details for DOI 10.1073/pnas.1907869116

    View details for PubMedID 31451655

    View details for PubMedCentralID PMC6744869

  • Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression. Annual review of biophysics Phillips, R., Belliveau, N. M., Chure, G., Garcia, H. G., Razo-Mejia, M., Scholes, C. 2019; 48: 121-163


    It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles' heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input-output function describing how the level of expression depends upon the parameters of the regulated gene-for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles' heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life.

    View details for DOI 10.1146/annurev-biophys-052118-115525

    View details for PubMedID 31084583

    View details for PubMedCentralID PMC7001876

  • Connecting the Dots between Mechanosensitive Channel Abundance, Osmotic Shock, and Survival at Single-Cell Resolution. Journal of bacteriology Chure, G., Lee, H. J., Rasmussen, A., Phillips, R. 2018; 200 (23)


    Rapid changes in extracellular osmolarity are one of many insults microbial cells face on a daily basis. To protect against such shocks, Escherichia coli and other microbes express several types of transmembrane channels that open and close in response to changes in membrane tension. In E. coli, one of the most abundant channels is the mechanosensitive channel of large conductance (MscL). While this channel has been heavily characterized through structural methods, electrophysiology, and theoretical modeling, our understanding of its physiological role in preventing cell death by alleviating high membrane tension remains tenuous. In this work, we examine the contribution of MscL alone to cell survival after osmotic shock at single-cell resolution using quantitative fluorescence microscopy. We conducted these experiments in an E. coli strain which is lacking all mechanosensitive channel genes save for MscL, whose expression was tuned across 3 orders of magnitude through modifications of the Shine-Dalgarno sequence. While theoretical models suggest that only a few MscL channels would be needed to alleviate even large changes in osmotic pressure, we find that between 500 and 700 channels per cell are needed to convey upwards of 80% survival. This number agrees with the average MscL copy number measured in wild-type E. coli cells through proteomic studies and quantitative Western blotting. Furthermore, we observed zero survival events in cells with fewer than ∼100 channels per cell. This work opens new questions concerning the contribution of other mechanosensitive channels to survival, as well as regulation of their activity.IMPORTANCE Mechanosensitive (MS) channels are transmembrane protein complexes which open and close in response to changes in membrane tension as a result of osmotic shock. Despite extensive biophysical characterization, the contribution of these channels to cell survival remains largely unknown. In this work, we used quantitative video microscopy to measure the abundance of a single species of MS channel in single cells, followed by their survival after a large osmotic shock. We observed total death of the population with fewer than ∼100 channels per cell and determined that approximately 500 to 700 channels were needed for 80% survival. The number of channels we found to confer nearly full survival is consistent with the counts of the numbers of channels in wild-type cells in several earlier studies. These results prompt further studies to dissect the contribution of other channel species to survival.

    View details for DOI 10.1128/JB.00460-18

    View details for PubMedID 30201782

    View details for PubMedCentralID PMC6222198

  • Tuning Transcriptional Regulation through Signaling: A Predictive Theory of Allosteric Induction. Cell systems Razo-Mejia, M., Barnes, S. L., Belliveau, N. M., Chure, G., Einav, T., Lewis, M., Phillips, R. 2018; 6 (4): 456-469.e10


    Allosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains, but also enables us to derive analytic expressions for key properties such as the dynamic range and [EC50]. Finally, we derive an expression for the free energy of allosteric repressors that enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.

    View details for DOI 10.1016/j.cels.2018.02.004

    View details for PubMedID 29574055

    View details for PubMedCentralID PMC5991102