Algal Research

MATLAB m-files containing the mathematical model described in this manuscript.

Raw images used for creation of Figure 4 and 5.

Experimental data for light and temperature experiments for biomass and phycocyanin production under turbidostat cultivation conditions for Leptolyngbya sp. QUCCCM 56 Statistical Analysis data files for phycocyanin extraction from A. platensis and Leptolyngbya sp. QUCCCM 56

The present work starts from a previous experiment where a culture medium for Dunaliella tertiolecta is developed, aiming its use as biofuel feedstock. The effect of the addition of fertilizer NPK-10: 26: 26, NaCl, NaOH, and the intensity of light incident on algal biomass growth, lipid productivity and CO2 sequestration were analyzed. The experimental data set, is first graphed using the graphical outputs of Engineering Equation Solver (EES), then is adjusted into an Adaptive Neuro Diffuse Inference System (ANFIS), obtaining a simulation of the cultivation process which is an easy to use and very accurate tool for instant evaluation of the process under study. The obtained ANFIS facilitates the analysis of the simultaneous influence of independent variables on the output variables. It is thus shown that the most recent computational facilities are of fundamental interest for the analysis of fermentative processes and in particular to model the cultivation of microalgae to be used as fuel feedstock. The results of the ANFIS model are compared with the experimental data and the effective evaluation of the performed simulation is proved.

Validation Data UV-Induction experiments data

Algae Fluorescence and Algae Cell Concentration

Raw chromatograms and FTIR spectra of polysaccharide preparations from different HA producing strains and WT Synechococcus sp. PCC 7002.

Total concentration in the reactor originated from both the concentration value measured before start-up of the reactors (will be referred as initial concentration) and the dissolution of nutrients at the early stages of the experiment (dissolution is discussed in detail under Results section). Thus, dissolution was calculated from the difference in total concentration and initial concentration. The related calculations are given in supplementary file (S1).

Figure 1. Schematic of an automatic microalgae treatment and detection system. Figure 2. (a) Basic configuration of a Christmas-tree types microfluidic concentration gradient generator. (b) The equivalent electric circuit model corresponding to the fluid conduits in concentration gradient generator. (c) The flow equivalent diagram of the concentration gradient generator. (d) The simulated concentration profile in the concentration gradient generator, where the color scale denotes the relative concentration varying from 0% to 100%. Figure 3. The simulated concentration distribution and the relative fluorescence intensity measured at 6 different outlets of the concentration gradient generator using sodium fluorescein s a tracer. The inset fluorescence images are corresponding to six outlets. Experimental data are shown as Mean ± SD (n = 6). Figure 4. Normalized Platymonas cell activity under 20 min of NaClO treatment with gradient concentrations. Experimental data are shown as Mean ± SD (n = 6). Figure 5. The relative activity of (a) Platymonas, (b) Pyramimonas sp, (c) Chlorella, and (d) a mixture of all these species after treatment with NaClO under gradient concentrations for 20 min.

Supplemental data for "Nuclear genome shuffling significantly increases production of chloroplast-based recombinant protein in Chlamydomonas reinhardtii" by Fields et al.