1,5-Anhydro-2-de­oxy-1,2-C-dichloro­methyl­ene-3,4,6-tri-O-(4-meth­oxy­benz­yl)-d-glycero-d-gulo-hexitol

The pyranosyl ring in the title compound, C31H34Cl2O7, adopts a twist-boat conformation. The 4-meth­oxy­benzyl groups are located in equatorial positions with the meth­oxy groups nearly coplanar with their respective rings [dihedral angles of 0.2 (3) and 9.4 (2)°]. The aromatic rings adopt orientations enabling them to participate in C—H⋯π inter­actions with neighboring meth­oxy groups. The crystal structure is additionally stabilized by weak C—H⋯O inter­actions

METABOLIC NETWORK ANALYSIS OF MYCO-BACTERIUM TUBERCULOSIS (MTB)

Tuberculosis is a multisystem disorder that is characterized by the formation of hematomas, a type of swelling that is filled with blood that is caused due to breakage in the wall of a blood vessel. This hematomas occurs in different organ of the infected victim has claimed the life of most of its victims. This disease is caused by a bacterium known as Mycobacterium Tuberculosis (MTB) which can be represented as a metabolic system. Every biological system is made up a metabolites which include genes, proteins and enzymes that are inter-connected which define the function, features and characteristics of the biological system. These biological systems can be analysed using different computational techniques among which is flux balance analysis. Flux balance analysis is a constraint based approach to metabolic network analysis. It’s based on the steady state assumption of S.v = 0. A more grounded understanding of this features, characteristics and nature of this bacterium will lead to better approaches to reduce the damage of the disease. The flux balance analysis of MTB involves the conversion of the metabolic network into a matrix format known as a stoichiometric matrix. This matrix is formed by using the metabolites in the metabolic network as rows and the reactions as the columns. The stoichiometric matrix used in this research is an 828 by 1027 matrix. The analysis of the stoichiometric matrix resulted into a linear problem where the number of unknown is greater than the number of equations. This linear problem was solved using “extreme pathways” and “simplex method” algorithms which makes up a Flux Balance Analysis approach to metabolic network analysis. The extreme pathways algorithm help extract the independent paths in the network while the simplex method is used to optimize the metabolic network to extract metabolites peculiar to an objective function. 15 At applying the constraint of the steady state assumption, the result showed 1022 distinct pathways instead of the initial 1027 eliminating 5 other reactions. The output from the extreme pathways was used in the optimization process using biomass as the target flux to get metabolites peculiar to biomass production. After the optimization, the result shows 32 new metabolites that become activated when a value of 1 is used to represent the biomass components. The optimization result also shows two categories of metabolites: those that are part of the biomass that become inactive after optimization, those that remain active after the optimization test. The output of this research only focus on the analysis of the metabolic network using biomass as the optimization target