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Microalgae-based processes are considered one of the most promising alternative technologies for the production of fuels and chemicals. However, what may look like as the Holy Grail for sustainable energy production is currently just a mirage. The real issue is that average efficiency in sunlight conversion in production equipment is not higher than 5%. Although giant steps have been carried out in the biological understanding of light conversion mechanisms, no suitable formal representation (model) is currently available for a quantitative assessment and optimisation of those phenomena.
In this research project knowledge-based methodologies and a novel experimental set up based on microscale photobioreactors will be synergically exploited for paving the way to a more effective approach to microalgae modelling and design. The project core is based on technologies reconstructing biologically sound niches along with tight control of the culture environment. In this way, also thanks to advanced design of experiments techniques it will be possible to isolate and mathematically describe the complex multiscale mechanisms determining the interactions between light and growth and eventually fuel production in a microalgae cultures.

• OBJECTIVES
Although several models have appeared over the last years to represent algae growth, we still do not have the capability of representing whole key phenomena in a reliable way. Unfortunately, the processes governing microalgae growth are intricate and span multiple time scales: photoproduction occurs within milliseconds; photoinhibition, i.e. the loss of production due to excess of light, acts on a time scale of minutes to hours; photoregulation, i.e. the set of mechanisms by which microalgae protect themselves, occurs within minutes; photoacclimation, i.e. the ability of the cells to adjust under varying light conditions, occurs within hours or days. Experimental results demonstrate the tight interaction between different mechanisms and the need for a highly tailored investigation where different phenomena are somewhat “decoupled” and the “reassembled”. We have already demonstrated (Nikolaou et al (2014), IFAC World Congress) that it is possible to model the complex dynamics of fluorescence measurements (Figure 1), which may be used to shed light on some photosynthetic mechanisms. Now the objective it to go further and describe the above phenomena according to a multiscale approach. One key aspect will be the strong adherence between the experimental and modelling activities.

Exploiting experimental data is not an obvious statement, since biological measurements may be scarce, affected by uncertainty, and “non-conventional” (e.g., images). Rigorous identification techniques and advanced model-based design of experiments (MBDoE) can help tackling this issue (Galvanin et al (2013), J Pharmacokinet Pharmacodyn, 40, 451). Effective modelling and optimization require effective experimental facilities. Here, the objective is to design and built a novel platform for generating data. In fact, a typical issue in the cultivation of microalgae is represented by: i) the long duration of experimental trials; ii) the difficulty at singling out the contribution of multiple inputs; iii) the cost of conducting multiple experiments where inputs are manipulated independently. We propose to tackle the issue according to a lab-on-a-chip (Kim et al (2014), Lab Chip,14, 1415) approach where an array of photobioreactors is designed to assess the concurrent effects of light and nutrients. Microscale platforms bring significant advantages largely due to the small transport distances, small volumes being handled, and the ability to introduce and measure fast dynamic changes in cellular responses.

• ACTIVITIES
We propose the use of a microphotobioreactor array for the study of algae behaviour under different conditions of light intensity, nutrient concentration, CO2.
We will adapt and optimise a platform combining some of the advantages of multi-well plates and perfusion bioreactors while respecting the constraints dictated by the biological system of choice. This system will always work at steady state, ensuring that all established concentrations are invariant with time.
The proposed microbioreactor platform generates stable concentration gradients over a large number of cell samples. These microbioreactors (Figure 2B) provide multiple gradients of molecular factors, with complex sequences of time- and space- resolved gradients, and the application of fast dynamic changes of environmental signals.
The microbioreactor platform will meet a set of specific requirements: (i) generation of multiple concentration gradients, (ii) support of long-term culture of algae cells in a no-shear environment, (iii) high-throughput studies with large numbers of replicates, (iv) compatibility with on-line imaging and standard analytics, (v) capability to apply fast dynamic changes of environmental signals. The base device will be a microfluidic platform with an array of microwells containing cells that are exposed to stable concentration gradients. Microwells within a row communicate between each other, while separate rows represent replicates of the same conditions. A light-intensity control unit will be placed on top of the microbioreactor and aligned to the microwells array. Different steady or dynamic (e.g., pulsed) light conditions can be easily applied and tested, for example to assess mixing effects. During the design optimization phases we will determine the ideal microwell size. Statistical significance of each experimental run will thus be improved when compared with standard static culture plates.
The platform is designed with the aid of 3D CAD software (Figure 2A).Computational modelling will confirm that the platform assures the stable establishment of complex patterns of stimulation easily modifiable in space and time (Figure 2C). We will print photomasks with varying color intensities thus exposing selected microwells, and thus algae samples, to different light intensities within the same experimental run.

The modelling activity will be carried out based on data generated by the photobioreactor-on-a-chip platform. The high flexibility will allow applying MBDoE techniques for both model discrimination and identification. Modelling hypotheses will be validated against the experimental data so as to build up a modelling framework where key photosynthetic mechanisms can be represented in quantitative and reproducible way, which represents the key preliminary step for photobioreactor design and operation. Thus, once a set of mechanistic assumptions has been proposed, MBDoE will be exploited and developed anew in order to: i) discriminate among competing modelling assumptions; ii) quantify and “locate” the mismatch between the model and the actual experimental response so as to direct further investigation; iii) guarantee model robustness (identifiability) so that the model may be trusted for design in compliance with experimental constraints.

• SCHEDULE
- Semester I: after acquiring the necessary equipment (see below), the design of the microphotobioreactor array will be carried out through model optimisation. Some initial modelling photoproduction, photoinhibition, photoregulation also initiated.
- Semester II: tuning and assessment of the experimental facility. First tests at constant light. Model assessment and validation.
- Semester III: Photoacclimation model development and design of specific experiments for model discrimination and identification. First tests at variable light to mimic mixing phenomena
- Semester IV: Model refinement and assessment of its capability of predicting pulsed light and dynamic in illumination conditions. Design of tailored experiments for final model validation.

PUBBLICAZIONI:

Articoli su rivista: G. Perin, E. Cimetta, F. Monetti, T. Morosinotto, F. Bezzo (2016). Novel micro-photobioreactor design and monitoring method for assessing microalgae response to light intensity. Algal Res., 19, 69-76
Articoli in atti di congressi: A. Bernardi, A. Meneghesso, T. Morosinotto, F. Bezzo (2016). A model-based investigation of genetically modified microalgae strains. In: Computer-Aided Chemical Engineering 38, Proc. Of the 26th European Symposium on Computer Aided Process Engineering (Z. Kravanja and M. Bogataj, Ed.), Elsevier, Amsterdam (The Netherlands) 607-612