Quorum, density or efficiency sensing, as well as other forms of inter-cellular communication, have been found to impact many of the physiological processes that are important in biology, medicine and human health. Working in yeast we have shown that cell cycle dependent feedback of a non-local form can produce an oscillating population density. The mathematical results, observed across a wide range of models, with and without randomness, suggest that the phenomena is robust. The phenomenon is observed when cells in one part of the cell cycle (G0,G1,S,G2,M) communicate with cells in another via metabolites and/or pheromones, with the consequence that the later population experiences advances and/or delays in their growth rate. The outcome of the advances or delays is that the population density becomes multi-modal and clustered. (Consider how traffic lights produce clusters of cars.) Experimental data support the observation that feedback of this form produces clusters of pseudo-synchronized cells that traverse the cell cycle in unison. Our prior work shows that the size and location of signaling and responsive regions within the cell cycle, and cell age, influences the integer number of emergent clusters. These factors suggests a picture in which the geometry of the cell cycle is more influential than the explicit form and strength of the feedback. By (1) focusing narrowly on phenomenological models of feedback that involve density dependence and the geometry of the cell cycle, and (2) tightly coupling measurement with modeling and analysis, we will make progress toward understanding the phenomenon of clustered populations and lay the foundations of a bifurcation theory in this setting that describes how the number of clusters can and will respond to external and genetic variation. Oscillating population density can be exploited in the laboratory and the clinic to prolong cell cycle synchrony or to reduce the cost of protein purification. Cell cycle dependence can con found virtually any assay involving populations of proliferating cells, from diagnostic measurements of bio molecules and biomarkers to metabolic studies of tumor progression. Models that can predict and de convolve the effects of population structure are likely to be useful across systems biology and biomedicine.
The behavior of individual microorganisms, pathogens and tumor cells is influenced by the process of growth and division. When observed in populations, as is normal the case, this variation can influence the outcome of tests and experiments. The variation within a population can be influenced by communication among members of the population. Therefore, models of the process of growth and division and how they are effected by communication is important for the correct interpretation of tests and experiments.
|Gong, Xue; Moses, Gregory; Neiman, Alexander B et al. (2014) Noise-induced dispersion and breakup of clusters in cell cycle dynamics. J Theor Biol 355:160-9|
|Burger, Joanna (2013) Role of self-caught fish in total fish consumption rates for recreational fishermen: Average consumption for some species exceeds allowable intake. J Risk Res 16:1057-1075|
|Just, Winfried; Korb, Mason; Elbert, Ben et al. (2013) Two classes of ODE models with switch-like behavior. Physica D 264:|
|Sun, Jiashu; Gao, Yandong; Isaacs, Richard J et al. (2012) Simultaneous on-chip DC dielectrophoretic cell separation and quantitative separation performance characterization. Anal Chem 84:2017-24|
|Young, Todd R; Fernandez, Bastien; Buckalew, Richard et al. (2012) Clustering in cell cycle dynamics with general response/signaling feedback. J Theor Biol 292:103-15|
|Afraimovich, Valentin; Young, Todd; Muezzinoglu, Mehmet K et al. (2011) Nonlinear dynamics of emotion-cognition interaction: when emotion does not destroy cognition? Bull Math Biol 73:266-84|
|Boczko, Erik M; Gedeon, Tomas; Stowers, Chris C et al. (2010) ODE, RDE and SDE models of cell cycle dynamics and clustering in yeast. J Biol Dyn 4:328-45|
|Sun, Jiashu; Stowers, Chris C; Boczko, Erik M et al. (2010) Measurement of the volume growth rate of single budding yeast with the MOSFET-based microfluidic Coulter counter. Lab Chip 10:2986-93|