The Algae in silico project is organized to tackle four challenges, and thus improve our understanding of the mechanisms driving productivity, to finally better represent and optimise them.

  • Understand the strong link between hydrodynamics and biology, to assess the growth conditions actually percept by the cells: The link between the hydrodynamics and the light signals percept by the cells has been explored for a decade in small scale closed photobioreactors (Perner-Nochta&Posten, 2007).The difficulty is that it is currently not possible to experimentally measure the light received by a cell in a culturing process, and only a numerical model can reconstruct this information.Nevertheless, current hydrodynamic models are too demanding in computing power.They are most often based on Fluent like software integrating the Navier-Stokes equations. Recently, several hydrodynamics modelling studies have been carried out in raceways (Hadiyanto et al. 2013, Mendoza, et al. 2013 , Heiz et al. 2014). These simulators make it difficult to study large processes (such as industrial raceways) over long periods of time (a week).The multilayer Saint-Venant approach (Audusse et al. 2009) leads to refined predictions for these large systems, which can be traced back to lagrangian trajectories, and thus to the light history of microalgae (Bernard et al. 2013).
  • Understand and represent the metabolism of algae metabolism of bacteria or yeast has been extensively studied, validated and used in the models over the past decades.Metabolic and genomic knowledge to microalgae is much more recent, partial and limited to a few species (Boyle & Morgan, 2009). Beyond the reconstruction of the metabolism of these phototrophic microorganisms from the knowledge of their genomes, identifying their ability to adapt to the strong temporal variability of their environment remains a limit to the reconstruction of metabolic networks.An associated challenge is that the algae are rarely in a steady state.Traditional approaches relying on the balanced growth assumptions and metabolic equilibrium are thus inadequate for microalgae.
  • Industrial culturing processes are not axenic, and many unwanted microorganisms grow. This results in a natural biodiversity, involving both bacteria and other microalgal species whose overall effect on productivity is misidentified (Cardinale et al., 2011). Evaluate and represent the positive or negative influence of the natural flora on productivity is now a delicate task for which the metabolic and genomic tools developed in the framework of single-species populations are not well suited. This aspect is of outmost importance when coupling microalgae to depollution processes to capture nitrogen and phosphorus from wastes.
  • Finally, it is difficult to use these nonlinear models of high dimension subject to periodic forcings (light, temperature) for optimization purpose. Conventional methods are often inadequate, and we must develop new approaches dedicated to these forced nonlinear systems (Bernard, 2011).It would probably be illusory to identify, from experimental data, the whole set of parameters of a mechanistic model coupling hydrodynamics and metabolism.Similarly, to optimize the functioning of these systems, we must first develop dedicated methods to reduce these models to coarser grains models which are more tractable analytically.

Grand challenge

By unifying our efforts, we developed coherent and realistic models that incorporate the key aspects of a microalgae production process. These models support:

  • Representing and understanding the effect of light variations induced by hydrodynamic mechanisms on phenotypic and genotypic adaptations,
  • Simulating  and trigger the metabolic-genomic functioning related to the accumulation of lipids or sugars,
  • Simulate the benefit of a multi-population cultivation.

The models, of different levels of complexities are used in distributed software, and support optimisation strategies guiding upstream process development. They also contribute to online control and optimisation through monitoring software (for example using the ODIN advanced monitoring platform developed within BIOCORE).

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