Project description:Characterization of the fitness landscape, a representation of fitness for a large set of genotypes, is key to understanding how genetic information is interpreted to create functional organisms. Here, we reconstruct the evolutionarily-relevant segment of the fitness landscape of His3, a gene coding for an enzyme in the histidine synthesis pathway, focusing on combinations of amino acid states found at orthologous sites of extant species. We find that the His3 fitness landscape is dominated by synergistic epistasis, such that the cumulative effect of amino acid substitutions causes a dramatic decline in fitness. Furthermore, in 63% of sites substitutions were strongly positive in one genetic background and strongly negative in another, with 41% of sites showing reciprocal sign epistasis. This sign epistasis, present in proportionally few genotypes, was caused by simultaneous interaction of multiple sites with demonstrating a complex multidimensional nature of the His3 fitness landscape.
Project description:Characterization of transcriptional landscape of HELLS in T-cell lymphoma. ChIP-seq experiments against Histone Marks and RNA-Polymerase II were performed in both control and HELLS knockdown (DOX) cells. ChIP-seq against HELLS was performed in control cells.
Project description:In order to identify the direct target of TFEB, we performed a ChIP-seq in HEK293-PFL-TFEB cells where, after removal of tetracycline, samples were collected in duplicates at 18, 36 and 90 hours
Project description:We characterized the transcriptomic profiles of cells from the substantia nigra (SN) of mice with co-injection with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and selective inhibitor of GCase activity (conduritol-β-epoxide, (CBE)) to mimic PD bearing GCase dysfunction (MPTP+CBE), mice treated with MPTP, mice treated with CBE and control mice treated with injec-tion of sodium chloride (NaCl) (vehicle).
Project description:Youfang Cao & Jie Liang. Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability. BMC Systems Biology 2 (2008).
Stochasticity plays important roles in many molecular networks when molecular concentrations are in the range of 0.1 muM to 10nM (about 100 to 10 copies in a cell). The chemical master equation provides a fundamental framework for studying these networks, and the time-varying landscape probability distribution over the full microstates, i.e., the combination of copy numbers of molecular species, provide a full characterization of the network dynamics. A complete characterization of the space of the microstates is a prerequisite for obtaining the full landscape probability distribution of a network. However, there are neither closed-form solutions nor algorithms fully describing all microstates for a given molecular network.
