A Mathematical model to guide Genetic Engineering of Photosynthetic Metabolism

open5noThe optimization of algae biomass productivity in industrial cultivation systems requires genetic improvement of wild type strains isolated from nature. One of the main factors affecting algae productivity is their efficiency in converting light into chemical energy and this has been a major target of recent genetic efforts. However, photosynthetic productivity in algae cultures depends on many environmental parameters, making the identification of advantageous genotypes complex and the achievement of concrete improvements slow. In this work, we developed a mathematical model to describe the key factors influencing algae photosynthetic productivity in a photobioreactor, using experimental measurements for the WT strain of Nannochloropsis gaditana. The model was then exploited to predict the effect of potential genetic modifications on algae performances in an industrial context, showing the ability to predict the productivity of mutants with specific photosynthetic phenotypes. These results show that a quantitative model can be exploited to identify the genetic modifications with the highest impact on productivity taking into full account the complex influence of environmental conditions, efficiently guiding engineering efforts.embargoed_20181201Perin, Giorgio; Bernardi, Andrea; Bellan, Alessandra; Bezzo, Fabrizio; Morosinotto, TomasPerin, Giorgio; Bernardi, Andrea; Bellan, Alessandra; Bezzo, Fabrizio; Morosinotto, Toma

A rule-based functional-structural model of rice considering source and sink functions

As a first step towards a generic genotype-phenotype model of rice, we present here a model of the growth and morphology of rice in combination with ecophysiological processes using the technique of functional-structural plant modelling (FSPM) and the interactive modelling platform GroIMP along with the graph-based Relational Growth Grammar formalism. The model constitutes a simple yet functionally coherent phenotype model of rice, consisting of a set of morphogenetic RGG rules describing an “average” developmental course and final morphology, partially linking yield traits to processes (tiller and grain number, stem length, grain filling rate, grain weight)

Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants : a computational model

Peer reviewedPublisher PD

Cryptic photosynthesis, Extrasolar planetary oxygen without a surface biological signature

On the Earth, photosynthetic organisms are responsible for the production of virtually all of the oxygen in the atmosphere. On the land, vegetation reflects in the visible, leading to a red edge that developed about 450 Myr ago and has been proposed as a biosignature for life on extrasolar planets. However, in many regions of the Earth, and particularly where surface conditions are extreme, for example in hot and cold deserts, photosynthetic organisms can be driven into and under substrates where light is still sufficient for photosynthesis. These communities exhibit no detectable surface spectral signature to indicate life. The same is true of the assemblages of photosynthetic organisms at more than a few metres depth in water bodies. These communities are widespread and dominate local photosynthetic productivity. We review known cryptic photosynthetic communities and their productivity. We link geomicrobiology with observational astronomy by calculating the disk-averaged spectra of cryptic habitats and identifying detectable features on an exoplanet dominated by such a biota. The hypothetical cryptic photosynthesis worlds discussed here are Earth-analogs that show detectable atmospheric biomarkers like our own planet, but do not exhibit a discernable biological surface feature in the disc-averaged spectrum.Comment: 23 pages, 2 figures, Astrobiology (TBP) - updated Table 1, typo in detectable O2 correcte

Phenotypic landscape inference reveals multiple evolutionary paths to C4_4 photosynthesis

C$_4$ photosynthesis has independently evolved from the ancestral C$_3$ pathway in at least 60 plant lineages, but, as with other complex traits, how it evolved is unclear. Here we show that the polyphyletic appearance of C$_4$ photosynthesis is associated with diverse and flexible evolutionary paths that group into four major trajectories. We conducted a meta-analysis of 18 lineages containing species that use C$_3$, C$_4$, or intermediate C$_3$-C$_4$ forms of photosynthesis to parameterise a 16-dimensional phenotypic landscape. We then developed and experimentally verified a novel Bayesian approach based on a hidden Markov model that predicts how the C$_4$ phenotype evolved. The alternative evolutionary histories underlying the appearance of C$_4$ photosynthesis were determined by ancestral lineage and initial phenotypic alterations unrelated to photosynthesis. We conclude that the order of C$_4$ trait acquisition is flexible and driven by non-photosynthetic drivers. This flexibility will have facilitated the convergent evolution of this complex trait

Surface and Temporal Biosignatures

Recent discoveries of potentially habitable exoplanets have ignited the prospect of spectroscopic investigations of exoplanet surfaces and atmospheres for signs of life. This chapter provides an overview of potential surface and temporal exoplanet biosignatures, reviewing Earth analogues and proposed applications based on observations and models. The vegetation red-edge (VRE) remains the most well-studied surface biosignature. Extensions of the VRE, spectral "edges" produced in part by photosynthetic or nonphotosynthetic pigments, may likewise present potential evidence of life. Polarization signatures have the capacity to discriminate between biotic and abiotic "edge" features in the face of false positives from band-gap generating material. Temporal biosignatures -- modulations in measurable quantities such as gas abundances (e.g., CO2), surface features, or emission of light (e.g., fluorescence, bioluminescence) that can be directly linked to the actions of a biosphere -- are in general less well studied than surface or gaseous biosignatures. However, remote observations of Earth's biosphere nonetheless provide proofs of concept for these techniques and are reviewed here. Surface and temporal biosignatures provide complementary information to gaseous biosignatures, and while likely more challenging to observe, would contribute information inaccessible from study of the time-averaged atmospheric composition alone.Comment: 26 pages, 9 figures, review to appear in Handbook of Exoplanets. Fixed figure conversion error

The operation of two decarboxylases, transamination, and partitioning of C4 metabolic processes between mesophyll and bundle sheath cells allows light capture to be balanced for the maize C4 pathway.

The C4 photosynthesis carbon-concentrating mechanism in maize (Zea mays) has two CO2 delivery pathways to the bundle sheath (BS; via malate or aspartate), and rates of phosphoglyceric acid reduction, starch synthesis, and phosphoenolpyruvate regeneration also vary between BS and mesophyll (M) cells. The theoretical partitioning of ATP supply between M and BS cells was derived for these metabolic activities from simulated profiles of light penetration across a leaf, with a potential 3-fold difference in the fraction of ATP produced in the BS relative to M (from 0.29 to 0.96). A steady-state metabolic model was tested using varying light quality to differentially stimulate M or BS photosystems. CO2 uptake, ATP production rate (JATP; derived with a low oxygen/chlorophyll fluorescence method), and carbon isotope discrimination were measured on plants under a low light intensity, which is considered to affect C4 operating efficiency. The light quality treatments did not change the empirical ATP cost of gross CO2 assimilation (JATP/GA). Using the metabolic model, measured JATP/GA was compared with the predicted ATP demand as metabolic functions were varied between M and BS. Transamination and the two decarboxylase systems (NADP-malic enzyme and phosphoenolpyruvate carboxykinase) were critical for matching ATP and reduced NADP demand in BS and M when light capture was varied under contrasting light qualities

Optimal allocation patterns and optimal seed mass of a perennial plant

We present a novel optimal allocation model for perennial plants, in which assimilates are not allocated directly to vegetative or reproductive parts but instead go first to a storage compartment from where they are then optimally redistributed. We do not restrict considerations purely to periods favourable for photosynthesis, as it was done in published models of perennial species, but analyse the whole life period of a perennial plant. As a result, we obtain the general scheme of perennial plant development, for which annual and monocarpic strategies are special cases. We not only re-derive predictions from several previous optimal allocation models, but also obtain more information about plants' strategies during transitions between favourable and unfavourable seasons. One of the model's predictions is that a plant can begin to re-establish vegetative tissues from storage, some time before the beginning of favourable conditions, which in turn allows for better production potential when conditions become better. By means of numerical examples we show that annual plants with single or multiple reproduction periods, monocarps, evergreen perennials and polycarpic perennials can be studied successfully with the help of our unified model. Finally, we build a bridge between optimal allocation models and models describing trade-offs between size and the number of seeds: a modelled plant can control the distribution of not only allocated carbohydrates but also seed size. We provide sufficient conditions for the optimality of producing the smallest and largest seeds possible

Delving deeper: metabolic processes in the metalimnion of stratified lakes

Many lakes exhibit seasonal stratification, during which they develop strong thermal and chemical gradients. An expansion of depth-integrated monitoring programs has provided insight into the importance of organic carbon processing that occurs below the upper mixed layer. However, the chemical and physical drivers of metabolism and metabolic coupling remain unresolved, especially in the metalimnion. In this depth zone, sharp gradients in key resources such as light and temperature co-occur with dynamic physical conditions that influence metabolic processes directly and simultaneously hamper the accurate tracing of biological activity. We evaluated the drivers of metalimnetic metabolism and its associated uncertainty across 10 stratified lakes in Europe and North America. We hypothesized that the metalimnion would contribute highly to whole-lake functioning in clear oligotrophic lakes, and that metabolic rates would be highly variable in unstable polymictic lakes. Depth-integrated rates of gross primary production (GPP) and ecosystem respiration (ER) were modelled from diel dissolved oxygen curves using a Bayesian approach. Metabolic estimates were more uncertain below the epilimnion, but uncertainty was not consistently related to lake morphology or mixing regime. Metalimnetic rates exhibited high day-to-day variability in all trophic states, with the metalimnetic contribution to daily whole-lake GPP and ER ranging from 0% to 87% and<1% to 92%, respectively. Nonetheless, the metalimnion of low-nutrient lakes contributed strongly to whole-lake metabolism on average, driven by a col- linear combination of highlight, low surface-water phosphorous concentration and high metalimnetic volume. Consequently, a single-sensor approach does not necessarily reflect whole-ecosystem carbon dynamics in stratified lakes