Processes on the emergent landscapes of biochemical reaction networks and heterogeneous cell population dynamics: differentiation in living matters.

The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to intracellular dynamics of biochemical regulatory networks within an individual cell but also to tissue dynamics of heterogeneous interacting cell populations. The landscape for an individual cell, applicable to a population of isogenic non-interacting cells under the same environmental conditions, is defined on the counting space of intracellular chemical composition

Integrating multiple types of data to predict novel cell cycle-related genes

<p>Abstract</p> <p>Background</p> <p>Cellular functions depend on genetic, physical and other types of interactions. As such, derived interaction networks can be utilized to discover novel genes involved in specific biological processes. Epistatic Miniarray Profile, or E-MAP, which is an experimental platform that measures genetic interactions on a genome-wide scale, has successfully recovered known pathways and revealed novel protein complexes in <it>Saccharomyces cerevisiae</it> (budding yeast).</p> <p>Results</p> <p>By combining E-MAP data with co-expression data, we first predicted a potential cell cycle related gene set. Using Gene Ontology (GO) function annotation as a benchmark, we demonstrated that the prediction by combining microarray and E-MAP data is generally >50% more accurate in identifying co-functional gene pairs than the prediction using either data source alone. We also used transcription factor (TF)–DNA binding data (Chip-chip) and protein phosphorylation data to construct a local cell cycle regulation network based on potential cell cycle related gene set we predicted. Finally, based on the E-MAP screening with 48 cell cycle genes crossing 1536 library strains, we predicted four unknown genes (<it>YPL158C</it>, <it>YPR174C</it>, <it>YJR054W</it>, and <it>YPR045C</it>) as potential cell cycle genes, and analyzed them in detail.</p> <p>Conclusion</p> <p>By integrating E-MAP and DNA microarray data, potential cell cycle-related genes were detected in budding yeast. This integrative method significantly improves the reliability of identifying co-functional gene pairs. In addition, the reconstructed network sheds light on both the function of known and predicted genes in the cell cycle process. Finally, our strategy can be applied to other biological processes and species, given the availability of relevant data.</p

The Galactic extinction and reddening from the South Galactic Cap U-band Sky Survey: u band galaxy number counts and u−ru-r color distribution

We study the integral Galactic extinction and reddening based on the galaxy catalog of the South Galactic Cap U-band Sky Survey (SCUSS), where $u$ band galaxy number counts and $u-r$ color distribution are used to derive the Galactic extinction and reddening respectively. We compare these independent statistical measurements with the reddening map of \citet{Schlegel1998}(SFD) and find that both the extinction and reddening from the number counts and color distribution are in good agreement with the SFD results at low extinction regions ($E(B-V)^{SFD}<0.12$ mag). However, for high extinction regions ($E(B-V)^{SFD}>0.12$ mag), the SFD map overestimates the Galactic reddening systematically, which can be approximated by a linear relation $\Delta E(B-V)= 0.43[E(B-V)^{SFD}-0.12$]. By combing the results of galaxy number counts and color distribution together, we find that the shape of the Galactic extinction curve is in good agreement with the standard $R_V=3.1$ extinction law of \cite{ODonnell1994}