cell quANT3


Some work continues research avenues opened by the Alpha Project (2002 – 2009) at the Molecular Sciences Institute. This was an attempt to devise means to enable construction of a mechanism-based model that captured the quantitative function of the Saccharomyces cerevisiae pheromone response system (PRS). The idea behind the project was building a model that accurately predicted quantitative behaviors of the system in response to defined perturbations would be "tantamount to understanding." The further assertion was that the attempt to build such a predictive model would require us to bring into being new experimental and computational methods and that these methods would be of general use. A further assertion was that the attempt to build this model would result in a better understanding of how to conduct research into other biological problems that required interdisciplinary work of this type (Yu et al., 2008b).

Although we made a great deal of progress toward these goals, the project did not succeed in reaching the larger goal of a predictive model.  More importantly, we do not believe that the predictive modeling will be achievable within this decade. Two reasons are worth mentioning here. One complication bedeviling the construction of mechanism-based predictive models of cellular function relates to the fact that the molecules that carry out the reactions by which the system functions can exist in different states (eg. unphosophorylated or phosphorylated on different residues) and as members of different oligomeric complexes. Many of these complexes can carry out qualitatively equivalent enzymatic functions. The problem of the proliferation of complexes was first encountered by Oliver Morton-Firth, a graduate student working with Dennis Bray, who dubbed it "the combinatorial explosion" (Morton-Firth et al., 1999).  Although we developed effective computational means to deal with such ensembles of molecular species of qualitatively related function (Lok and Brent, 2005), we never developed corresponding experimental methods to quantify function of such ensembles in vivo. We believe that there are single molecule methods that in principle should allow such quantification of different complexes in different cells but their development will be a labor of some years.

A second complication is precisely that careful study of the system's quantitative performance revealed numerous, unanticipated, previously uncharacterized, quantitative behaviors. Some of these, including cell-to-cell variation in quantitative function, required us to understand them so that we could account for these in our quantitative assertions about system function (Colman-Lerner et al., 2005). Others were simply interesting and important, even fundamental, in the sense that they are widely conserved in cell signaling systems in higher eukaryotes (Brent, 2009). These included DoRA, or Dose Response Alignment, the phenomenon in which system output is identical to system input defined by percent receptors on the cell surface bound by ligand. In signaling systems that exhibit it, DoRA maximizes the amount of information cell signaling systems can transmit (Yu et al. 2008a, Andrews et al., 2016). Others, including "Pre-Equilibrium Sensing" (Ventura et al., 2014) or PES, have been uncovered in a collaborating lab. Many of these "systems level" quantitative behaviors (SLQBs) are only revealed after careful single cell physiological experimentation and are tractable to a combination of simple experimentation and simple modeling. These SLQBs form the major focus of study of the Colman-Lerner lab, one of our collaborating labs.

Current Research

The Brent lab studies the quantitative operation of the systems that living cells use to sense, represent, transmit, and act upon information to make decisions that determine their future fates.

One system under study is a prototypic cell signaling system in budding yeast, the pheromone response system. Our most important experiments in yeast concern the ability of the yeast to make accurate polarity and cell fate decisions in shallow gradients. We are addressing the mechanistic basis for these decisions by study of the dynamics of the known molecular components, and, when appropriate, which is frequently, by simulation. This work involves development and use of microfluidic devices, generously provided by Nirveek Bhattacharjee and Albert Folch in the Folch lab (Ventura et al., 2014). In this work, we are treating the polarity and fate decisions made isolated yeast cells on the floor of the gradient device as a sort of Platonic instance of a monolayer of cells comprising the simplest possible metazoan tissue, an epithelium. We are interested in quantifying composition, location, interaction of the components that make up the mechanisms by which the system operates (Thomson et al., 2011). We are particularly interested in the first few minutes of system operation (Yu et al., 2008a), when key decisions are made, and in the origins and consequences of cell-to-cell variation in those decisions. We (one undergraduate) have also pursued the idea that different allelic forms of orthologs of a particular protein involved in this processes might have small differences in quantitative function that might contribute, at the population level, to small negative impacts on public health. We are also continuing to explore the causes and consequences of the finding, opened up by work nine years ago (Colman-Lerner et el., 2005), that a great deal of cell-to-cell variation in signaling system output is due to persistent cell-to-cell differences in the ability to send signal through the system, and to persistent differences in the general ability of specific cells to express genes into proteins. Recent studies (Pesce et al. 2016a, 2106b.)

We have recently extended similar analysis to systems operating in single cells of tissues in a metazoan, Caenorhabditis elegans. Perfection of sensitive technical means (Mendenhall et al., 2015) has allowed insight into sources of variation in operation of various signaling and gene expression systems in that organism. We have gained more insight into the physiological state differences that underlie cell-to-cell and organism-to-organism differences in gene expression. Differences in these physiological states can have significant effects on the function of the organism that continue over the organism's life (Mendenhall et al., 2016).

Work requires continual development and refinement of experimental and computational methods. One area of continual development is rapid means to generate new DNA constructions and make desired changes to the genomes of yeast and higher cells (Sands and Brent, 2016). Another is development of intracellular reporters that can quantify particular molecular events in living cells, including all the individual cells that comprise living tissues (Sands et al. 2015, Cao et al. 2016). Another is microscopic, flow cytometric, and computational means to read the output of these reporters. Another is fluidic means to provide to the systems defined inputs. Much of this technology development finds application to other biological problems. Moreover, some of the current work is suggesting modalities for experimental manipulations and therapeutic interventions. Much of this technology development is in concert with closely collaborating labs in the US and abroad. The suggestions for possible therapeutic intervention are likely to lead to collaborative applied work in 2014 and 2015, first in the field of infectious disease.

For the past three years, the lab has also included an experimental social science component. Some of this work continues under the aegis of the Center for Biological Futures, a two-year pilot project that brought together biologists with scholars in the social sciences and humanities, including anthropologists and philosophers, to better understand how biological knowledge and capability are shaping human affairs in the 21st century. This work included a significant and still active collaboration with investigators at the University of Washington, in the project Biological Futures in a Globalized World. Brent and other lab members are frequently able to participate in government and other advisorial settings to help shape the overall course of future research and its application to human affairs, and all lab members are encouraged to identify and analyze how the outcomes of their research and the ongoing increases in biological knowledge and capability might shape human affairs. The lab will work to facilitate interactions with individuals and organizations in Seattle and the rest of the world to enable its members to better understand and influence these developments.


Andrews, S., Peria, W., Yu. R,. C., Colman-Lerner, A., and Brent, R. (2016). Feedback and push-pull mechanisms can align signaling system outputs with inputs. Cell Systems 3 (5): 444-455.e2

Brent, R. (2009) Cell signaling: what is the signal and what information does it carry? FEBS Lett. 583(24):4019-4024. Dec 17. Epub. Review. PMID: 19917282

Cao, R., Jenkins, P. Peria, W., Sands, B., Naivar, M., Brent, R, and Houston, J. P. (2016). Phasor plotting with time-resolved flow cytometry. Optics Express. 24(13), 14596-14607

Colman-Lerner, A., Gordon, A., Serra, E., Chin, T., Resnekov, O., Endy, D., Pesce, G. and Brent, R. (2005) Regulated cell-to-cell variation in a cell fate decision system. Nature. 437, 699-706

Lok, L. and Brent, R. (2005). Automatic generation of cellular reaction networks with Moleculizer 1.0. Nature Biotechnology, 23:131-136.

Mendenhall, A. R., Tedesco, P. M., Johnson, T.  E., and Brent, R. (2015). Single cell quantification of reporter gene expression in live adult Caenorhabditis elegans reveals reproducible cell-specific expression patterns and underlying biological variation. PLoS ONE 10(5): e0124289.

Mendenhall, A., Driscoll, M., and Brent, R. (2016). Using measures of single-cell physiology and physiological state to understand organismic aging. Aging Cell. 15(1):4-13.

Morton-Firth, C.J., Shimizu, T.S., and Bray, D. (1999). A free-energy- based stochastic simulation of the Tar receptor complex. J. Mol. Biol. 286, 1059–1074.

Pesce, C. G, Peria, W., Zdraljevic, S., Rockwell, D., Yu, R. C., Colman-Lerner, A., and Brent, R. (2016a). Cell-to-cell variability in the yeast pheromone response: high throughput screen identifies genes with different effects on transmitted signal and response. bioRxiv 093187; doi:

Pesce, C. G., Zdraljevic, S., Bush, A., Repetto, V., Peria, W., Yu, R. C., Colman-Lerner, A., and Brent, R. (2016b). Cell-to-cell variability in the yeast pheromone response: Cytoplasmic microtubule function stabilizes signal generation and promotes accurate fate choice. bioRxiv 093195; doi:

Sands, B., Jenkins, P., Peria, W. J., Naivar, M., Houston, J. P., and Brent, R. (2014) Measuring and sorting cell populations expressing isospectral fluorescent proteins with different fluorescence lifetimes. PLoS ONE. 9(10):e109940

Sands, B. and Brent, R. (2016). Overview of post Cohen-Boyer methods for single segment cloning and for multisegment DNA assembly. Curr. Protoc. Mol. Biol. 113:3.26.1-3.26.20

Thomson, T. M., Benjamin, K. R., Thomson, T. M., Love, T., Yu, R., Gordon, A., Colman-Lerner, A., Endy, D. and Brent, R. Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range (2011).  Proc. Natl. Acad. Sci., 108, 20265-20270.

Ventura, A. C., Bush, A., Vasen, G., Goldin, M., Burkinshaw, B., Bhattacharjee, N., Folch, A., Brent, R., Chernomoretz, A., and Colman-Lerner, A. Utilization of extracellular information before equilibrium receptor binding expands and shifts the input dynamic range. Proc. Natl. Acad. Sci. USA (2014) 111(37): e3860-3869. PMID: 25172920. PMCID: PMC4853029.

Yu, R., Gordon, A., Colman-Lerner, A., Benjamin, K.R., Pincus, D., Serra, E., Holl, M., Brent, R. (2008) Negative feedback optimizes information transmission in a cell signaling system. Nature, 456, 755-761

Yu RCResnekov OAbola APAndrews SSBenjamin KRBruck JBurbulis IEColman-Lerner AEndy DGordon AHoll MLok LPesce CGSerra ESmith RDThomson TMTsong AEBrent R.  The Alpha project, a model system for systems biology (2008) IET Syst Biol. 2008 Sep;2(5):222-233.