Section: New Results

Fields of application

Bioenergy

Modeling microalgae production

Participants : Olivier Bernard, Antoine Sciandra, Frédéric Grognard, Walid Djema, Ignacio Lopez Munoz, David Demory, Ouassim Bara, Jean-Philippe Steyer.

Experimental developments

Running experiments in controlled dynamical environments. The experimental platform made of continuous photobioreactors driven by a set of automaton controlled by the ODIN software is a powerful and unique tool which gave rise to a quantity of very original experiments. Such platform improved knowledge of several biological processes such as lipid accumulation or cell cycle under light fluctuation, etc. [55],[19].

This experimental platform was used to control the long term stress applied to a population of microalgae. This Darwinian selection procedure generated two new strains after more than 6 months in the so called selectiostats. A strain with $+92\%$ lipids was obtained, another more transparent resulting in $+92\%$ enhancement in productivity [18].

Other experiments were carried out to reproduce the light signal percept by a cell in a raceway pond [60], derived from hydrodynamical studies [64]. An electronic platform was developed to reproduce this high frequency light signal. The experiments show that the microalgae adapt their pigments to the average light that they have received [59]. Experiments with coloured light demonstrated that the growth rate results from the absorbed light, whatever its wavelength.

On top of this, we carried out outdoor pilot experiments with solar light. We tested the impact of various temperatures, resulting from different shadowing configurations on microalgal growth rate [56],[40]. This is the topic of Bruno Assis Pessi's master thesis.

These works have been carried out in collaboration with A. Talec and E. Pruvost (CNRS/UPMC -Oceanographic Laboratory of Villefranche-sur-Mer LOV).

Metabolism of carbon storage and lipid production. A metabolic model has been set up and validated for the microalgae Isochrysis luthea, on the basis of the DRUM framework , in order to simulate autotophic, heterotropic and mixotrophic growth, and to determine how to reduce substrate inhibition [15]. The model was extended for other substrates such as glucose or glycerol. A simplified model was developped by I. Lopez to represent the dynamics of polar lipids, especially when faced to a high oxygen concentration.

Modeling the coupling between hydrodynamics and biology. In collaboration with the Inria ANGE team, a model coupling the hydrodynamics of the raceway (based on a new multilayer discretisation of Navier-Stokes equations) with microalgae growth was developed [51]. This model is supported by the work of ANGE aiming at improving the discretization scheme to more finely represent the hydrodynamics of the raceway and more accurately reconstruct Lagrangian trajectories. The statistical analysis of both theoretical properties of probability densities for perfectly mixed systems and output of Lagrangian simulations demonstrate the accurate reconstruction of the trajectories. As a consequence, more relevant experimental protocols have been proposed to more realistically design simplified light signal for experiments.

Modeling photosynthetic biofilms. Several models have been developed to represent the growth of microalgae within a biofilm. A first structured physiological model uses mixture theory to represent the microalgae growth, based on the consideration of intracellular reserves triggering the processes of growth, respiration and excretion. We consider separately the intracellular storage carbon (lipids and carbohydrates) and the functional part of microalgae [29]. Another approach accounts for the dynamics of the light harvesting systems when cells are submitted to rapid successions of light and dark phases. A simpler model was developed and used to identify the optimal working mode of a process based on photosynthetic biofilm growing on a conveyor belt.

Modeling microalgae production processes. The integration of different models developed within BIOCORE [52] was performed to represent the dynamics of microalgae growth and lipid production in raceway systems.

Using these approaches, we have developed a model which predicts lipid production in raceway systems under varying light, nutrients and temperature [72]. A simplified version of this model, describing microalgal growth under varying light and temperature conditions predicts microalgal productivity in the perspective of large scale biofuel production [23].

In the framework of the ANR project Purple Sun, we developed a thermal model of a raceway pond within a greenhouse in order to estimate the culture temperature. We also included in the microalgae model the effect of light wavelength. This model has been calibrated on experimental data from LOV and has been used to support lighting strategy in order to optimize microalgal productivity (a patent on this process has been submitted). We have shown in [40] that a control strategy based on shadowing with solar panel can significantly improve productivity, especially during the early growth stage of the culture.

A procedure for rapid outdoor model calibration, from lab data, has been proposed and applied to the microalgae Dunaliella salina [56].

Modeling thermal adaptation in microalgae. We have studied several models of microalgae growth to different temperatures [27]. In particular, we have detailed the impact of higher temperatures on cell mortality [20]. Experiments have been carried out in collaboration with A.-C. Baudoux (Biological Station of Roscoff) in order to study growth of various species of the microalgae genus Micromonas at different temperatures. After calibration of our models, we have shown that the pattern of temperature response is strongly related to the site where cells were isolated. We derived a relationship to extrapolate the growth response from isolation location. With this approach, we proved that the oceanwide diversity of Micromonas species is very similar to the oceanwide diversity of the phytoplankton. We have used Adaptive Dynamics theory to understand how temperature drives evolution in microalgae. We could then predict the evolution of this biodiversity in a warming ocean and show that phytoplankton must be able to adapt within 1000 generation to avoid a drastic reduction in biodiversity.

Modeling viral infection in microalgae. Experiments have been carried out in collaboration with A.-C. Baudoux (Biological Station of Roscoff) in order to study the impact of viral infections on the development of populations of Micromonas at different temperatures. This work revealed a qualitative change in viral infection when temperature increases. A model was developed to account for the infection of a Micromonas population, with population of susceptible, infected and also free viruses. The model turned out to accurately reproduce the infection experiments at various temperatures, and the reduction of virus production above a certain temperature [24].

Control and Optimization of microalgae production

Optimization of the bioenergy production systems. A model predictive control approach was run based on simple microalgae models coupled with thermal physical models. Optimal operation in continuous mode for outdoor cultivation was determined when allowing variable culture depth. Assuming known weather forecasts considerably improved the control efficiency [23].

Interactions between species. We had formerly proposed an adaptive controller which regulates the light at the bottom of the reactor [70]. When applied for a culture with $n$ species, the control law allows the selection of the strain with the maximum growth rate for a given range of light intensity. This is of particular interest for optimizing biomass production as species adapted to high light levels (with low photoinhibition) can be selected. We have also proposed a strategy based on light stresses in order to penalize the strains with a high pigment content and finally select microalgae with a low Chlorophyll content [69]. This characteristic is of particular interest for maximizing biomass production in dense culture. The strategy has been carried out at the LOV and eventually the productivity of Tisochrysis lutea was improved by 75%. A patent on this strategy has been submitted.

Strategies to improve the temperature response have also been studied. We modelled the adaptive dynamics for a population submitted to a variable temperature [62]. This was used at the LOV to design experiments with periodic temperature stresses during 200 days aiming at enlarging the thermal niche of Tisochrysis lutea. It resulted in an increase by 2 degrees of the thermal niche [18].

Finaly, optimal strategies when selecting the strain of interest within a set of $n$ species competing for the same substrate has been proposed [16].

Biological depollution

Control and optimization of bioprocesses for depollution

Participants : Olivier Bernard, Carlos Martinez Von Dossow, Jean-Luc Gouzé.

Although bioprocesses involve an important biodiversity, the design of bioprocess control laws are generally based on single-species models. In [68], we have proposed to define and study the multispecies robustness of bioprocess control laws: given a control law designed for one species, what happens when two or more species are present? We have illustrated our approach with a control law which regulates substrate concentration using measurement of growth activity. Depending on the properties of the additional species, the control law can lead to the correct objective, but also to an undesired monospecific equilibrium point, coexistence, or even a failure point. Finally, we have shown that, for this case, the robustness can be improved by a saturation of the control.

Coupling microalgae to anaerobic digestion

Participants : Olivier Bernard, Antoine Sciandra, Jean-Philippe Steyer, Frédéric Grognard, Carlos Martinez Von Dossow.

The coupling between a microalgal pond and an anaerobic digester is a promising alternative for sustainable energy production and wastewater treatment by transforming carbon dioxide into methane using light energy. The ANR Phycover project is aiming at evaluating the potential of this process [74].

We have proposed several models to account for the biodiversity in the microalgal pond and for the interaction between the various species. These models were validated with data from the Saur company. More specifically, we have included in the miroalgae model the impact of the strong turbidity, and derived a theory to better understand the photolimitation dynamics especially when accounting for the photo-inhibition in the illuminated periphery of the reactor. Optimal control strategies playing with the dilution rate, shadowing or modifying depth were then studied [40].

Life Cycle Assessment

Participants : Olivier Bernard, Jean-Philippe Steyer, Marjorie Alejandra Morales Arancibia.

Environmental impact assessment. In the sequel of the pioneering life cycle assessment (LCA) work of [65], we continued to identify the obstacles and limitations which should receive specific research efforts to make microalgae production environmentally sustainable.

We studied a new paradigm to improve the energy balance by combining biofuel production with photovoltaic electricity. This motivated the design of the purple sun ANR-project where electricity is produced by semi transparent photovoltaic panels [50] under which photosynthetic microalgae are growing. The LCA of a greenhouse with, at the same time, photovoltaic panels and low emissivity glasses is studied. Depending on the period of the year, changing the species can both improve productivity and reduce environmental footprint.

This work is the result of a collaboration with Arnaud Helias of INRA-LBE (Laboratory of Environmental Biotechnology, Narbonne) and Pierre Collet (IFPEN).

Design of ecologically friendly plant production systems

Controlling plant arthropod pests

Participants : Frédéric Grognard, Ludovic Mailleret, Suzanne Touzeau, Nicolas Bajeux, Bapan Ghosh.

Optimization of biological control agent introductions. The question of how many and how frequently natural enemies should be introduced into crops to most efficiently fight a pest species is an important issue of integrated pest management. The topic of optimization of natural enemies introductions has been investigated for several years [66], [73], unveiling the crucial influence of within-predator density dependent processes. Since some natural enemies may be more prone to exhibit positive density dependent dynamics rather than negative ones, we studied the impact of positive predator-predator interactions on the optimal biological control introduction strategies [13]. Extension of this result have been performed to take into account stochasticity by develeoping a master equation for the combined continuous-stochastic process and a purely stocastic model. This last part of N. Bajeux's PhD thesis mycitePhD:bajeux was performed in collaboration with Vincent Calcagno (ISA).

Characteristics of space and the behavior and population dynamics of parasitoids. We studied the influence of space on the spread of biological control agents through computer simulations and laboratory experiments on Trichogramma. This is the topic of Marjorie Haond's PhD thesis (ISA, 2015-). In particular, we showed both theoretically and experimentally how habitat richness [63] shape the spatio-temporal dynamics of populations in spatially structured environments. This work is being performed in collaboration with Elodie Vercken (ISA) and Lionel Roques (BioSP, Avignon).

Model of coffee berry borer dynamics. We built a first model describing the coffee berry borer dynamics, in order to design efficient and sustainable control strategies, including alternative methods to pesticides (cropping practices, trapping, biological control). This single-season model is based on the insect life-cycle and includes the berry availability during a cropping season. Local and global stability results, the latter using Lyapunov functions, were obtained for both the pest-free and the endemic equilibria. Furthermore, this model was extended to integrate the berry maturation age. The well-posedness of the resulting PDE model was shown. This research pertains to Yves Fotso Fotso's PhD thesis, who visited BIOCORE during 4 months in 2017 in the framework of the EPITAG associate team.

Controlling plant pathogens

Participants : Frédéric Grognard, Ludovic Mailleret, Suzanne Touzeau, Julien Guégan, Yves Fotso-Fotso, Israel Tankam-Chedjou.

Sustainable management of plant resistance. We studied other plant protection methods dedicated to fight plant pathogens. One such method is the introduction of plant strains that are resistant to one pathogen. This often leads to the appearance of virulent pathogenic strains that are capable of infecting the resistant plants.

Experiments were conducted in INRA Avignon, followed by high-throughput sequencing (HTS) to identify the dynamics of virus strains competing within host plants. Different plant genotypes were chosen for their contrasted effects on genetic drift and selection they induce on virus populations. Those two evolutionary forces can play a substantial role on the durability of plant resistance. Therefore we fitted a mechanistic-statistical model to these HTS data in order to disentangle the relative role of genetic drift and selection during within-host virus evolution [31]. Also, the Quantitative Trait Loci (QTL) controlling viruses effective population sizes (linked to genetic drift) have been identified for two different viruses, showing the genetic origin of these parameters and the presence of general and virus specific QTLs [34]. This was done in collaboration with Frédéric Fabre (INRA Bordeaux) and Benoît Moury (INRA Avignon).

We also developed an epidemiological model describing the dynamics of root-knot nematodes in a protected vegetable cropping system, to design optimal management strategies of crop resistance. The model was fitted to experimental and field data. Preliminary results show that alternating susceptible and resistant crops not only increased the resistance durability, but reduced the disease intensity over time [28], [41]. This research pertains to Samuel Nilusmas' PhD thesis.

We developed and partly calibrated a (spatio-)temporal epidemiological model of the phoma stem canker of oilseed rape, to design sustainable resistance deployment strategies. Ongoing work includes the completion of this study and the development of a user-friendly simulation tool. It will be achieved through the MoGeR project, in collaboration with BIOGER (INRA Grignon) and partners from technical institutes and cooperatives. It benefits from the resources and support of NEF computation cluster.

Model of nematodes-plantain roots dynamics. We developed and analysed a seasonal model describing the interactions between nematodes and plantain roots, to design efficient and sustainable control strategies, including alternative methods to pesticides (cropping practices, resistant or tolerant banana cultivars, biological control). It is a doubly hybrid system, so as to take into account the plantain root growth. A slow-fast dynamics approximation was used to obtain local stability results for the pest-free equilibrium and exact solutions around this equilibrium. Conditions were derived for nematode extinction, depending in particular on the delay between cropping seasons. This research pertains to Israël Tankam Chedjou's PhD thesis, who visited BIOCORE during 4 months in 2017 in the framework of the EPITAG associate team.

Mate limitation and cyclic epidemics. We studied the effect of mate limitation in parasites which perform both sexual and asexual reproduction in the same host. Since mate limitation implies positive density dependence at low population density, we modeled the dynamics of such species with both density-dependent (sexual) and density-independent (asexual) transmission rates. A first simple SIR model incorporating these two types of transmission from the infected compartment, suggested that combining sexual and asexual spore production can generate persistently cyclic epidemics [30].

Optimality/games in population dynamics

Participants : Frédéric Grognard, Ludovic Mailleret, Pierre Bernhard, Ivan Egorov, Pierre-Olivier Lamare.

Optimal resource allocation. Mycelium growth and sporulation are considered for phytopathogenic fungi. For biotrophic fungi, a flow of resource is uptaken by the fungus without killing its host; in that case, life history traits (latency-sporulation strategy) have been computed based on a simple model considering a single spore initiating the mycelium, several spores in competition and applying optimal resource allocation [42], and several spores in competition through a dynamic game. The solution of this dynamic game has been shown to be the equilibrium of two-trait adaptive dynamics in Julien Guégan's internship. Also, the obtained sporulation strategy has been put in a PDE model to evaluate how the characteristics of the fungus evolve along a colonization gradient. This work, in the framework of the ANR Funfit project, is done with Fabien Halkett of INRA Nancy.

Dynamic games as a model of animal foraging. P. Bernhard has continued his investigations of dynamic games with randomly arriving players as a model of animal foraging and of competition in open markets. He has written the chapter “Robust Control and Dynamic Games”in the Handbook of Dynamic Games Theory [54].