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Synthetic biology designs and constructs new biological parts, devices and systems with predetermined functionalities. With the unlimited ability to synthesise any DNA and RNA and transfer it to almost any organism, we are at the dawn of a new era in which biology is being recreated in ways never before possible. It has reached a maturity level that enables construction of artificial communities of synthetic organisms. Considering the strong engineering component in synthetic biology design, we highlight the role of computational models in reaching the full potential of this emerging field. Computational models have been shown to be an essential part of biology entangling the nonlinearly increasing complexity with the growing number of components, and emerging properties of biological phenomena. In recent years, great hope has been put in modelling efforts to guide synthetic biology approaches in much the same way as they guided engineers to design new technical devices. Yet, till now, modelling has not been fully integrated into the synthetic design process. Here we summarise the state of the art and discuss how synthetic biology design can be supported with an organism-free modular modelling approach focussing on designing synthetic multi-species communities. We argue that efforts should shift from organism- and context-specific complex systems or even whole-cell models to modular computational models with mathematical descriptions of parts and circuits, from which synthetic systems could be systematically assembled. Such an approach would be more compatible with synthetic biology approaches, opening the door to designing artificial communities.

Diatoms are amongst the most successful clades of oceanic phytoplankton, significantly contributing to photosynthesis on Earth. Their ecological success likely stems from their ability to acclimate to changing environmental conditions, including e.g. variable light intensity. Diatoms are outstanding at dissipating light energy exceeding the maximum photosynthetic electron transfer (PET) capacity of via Non Photochemical Quenching (NPQ). While the molecular effectors of this process, as well as the role of the Proton Motive Force (PMF) in its regulation are known, the putative regulators of the PET/PMF relationship in diatoms remain unidentified. Here, we demonstrate that the H+/K+ antiporter KEA3 is the main regulator of the coupling between PMF and PET in the model diatom Phaeodactylum tricornutum. By controlling the PMF, it modulates NPQ responses at the onset of illumination, during transients and in steady state conditions. Under intermittent light KEA3 absence results in reduced fitness. Using a parsimonious model including only two components, KEA3 and the diadinoxanthin de-epoxidase, we can describe most of the feedback loops observed between PET and NPQ. This two-components regulatory system allows for efficient responses to fast (minutes) or slow (e.g. diel) changes in light environment, thanks to the presence of a regulatory Ca2+-binding domain in KEA3 that controls its activity. This circuit is likely finely tuned by the NPQ effector proteins LHCX, providing diatoms with the required flexibility to thrive in different ocean provinces.One sentence summaryThe author(s) responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/pages/General-Instructions) is Giovanni Finazzi.

Chlorophyll a fluorescence is a powerful indicator of photosynthetic energy conversion in plants and photosynthetic microorganisms. One of the most widely used measurement techniques is Pulse Amplitude Modulation (PAM) fluorometry. Unfortunately, parameter settings of PAM instruments are often not completely described in scientific articles although their variations, however small these may seem, can influence measurements. We show the effects of parameter settings on PAM measurements. We first simulated fluorescence signals using a previously published computational model of photosynthesis. Then, we validated our findings experimentally. Our analysis demonstrates how the kinetics of non-photochemical quenching (NPQ) induction and relaxation are affected by different settings of PAM instrument parameters. Neglecting these parameters may mislead data interpretation and derived hypotheses, hamper independent validation of the results, and cause problems for mathematical formulation of underlying processes. Given the uncertainties inflicted by this neglect, we urge PAM users to provide detailed documentation of measurement protocols. Moreover, to ensure accessibility to the required information, we advocate minimum information standards that can serve both experimental and computational biologists in our efforts to advance system-wide understanding of biological processes. Such specification will enable launching a standardized database for plant and data science communities.HighlightPAM fluorometry measurement is sensitive to instrument settings and protocols. Yet, protocols are published incompletely. We urge to reach an agreement on minimal protocol information of PAM experiments to be shared publicly.

In this Letter, we report the outcomes of the third biannual Young Algaeneers Symposium (YAS) that took place in April 2016 in Qawra, Malta. We are reviewing the importance of interdisciplinary communication and benefits of discussion panels. By communicating our experience of hosting YAS2016, we would like to encourage and instill scientific exchange amongst the new generation of scientists and suggest solutions to various problems arising from a lack of mutual understanding.

The concept of plant memory triggers a quite controversial dispute on the definition, extent or even existence of such. Because plants are permanently exposed to rapidly changing environments it is evident that they had to evolve mechanisms enabling them to dynamically adapt to such fluctuations. Recognizing memory as a timed response to changes of external inputs through amplification and integration of multiple signals, here we study the short-term illumination memory in Arabidopsis thaliana by monitoring fluorescence emission dynamics. For this, we designed an experiment to systematically determine the extent of non-photochemical quenching (NPQ) after previous light exposure. We propose a simplified, mathematical model of photosynthesis that includes the key components required for NPQ activation. Due to its reduced complexity, our model is universally applicable to other species, which we demonstrate by adapting it to the shadow-tolerant plant Epipremnum aureum. We demonstrate that a basic mechanism of short-term light memory, which is based on two interacting components, can explain our experimental observations. The slow component, accumulation of zeaxanthin, accounts for the amount of memory remaining after relaxation in darkness, while the fast one, antennae protonation, increases quenching efficiency. With this combined theoretical and experimental approach we provide a unifying framework that helps to uncover general principles of key photoprotective mechanisms across species.

The photosynthetic electron transport chain (PETC) provides energy and redox equivalents for carbon fixation by the Calvin-Benson-Bassham (CBB) cycle. Both of these processes have been thoroughly investigated and the underlying molecular mechanisms are well known. However, it is far from understood by which mechanisms it is ensured that energy and redox supply by photosynthesis matches the demand of the downstream processes. Here, we deliver a theoretical analysis to quantitatively study the supply-demand regulation in photosynthesis. For this, we connect two previously developed models, one describing the PETC, originally developed to study non-photochemical quenching, and one providing a dynamic description of the photosynthetic carbon fixation in C3 plants, the CBB Cycle. The merged model explains how a tight regulation of supply and demand reactions leads to efficient carbon fixation. The model further illustrates that a stand-by mode is necessary in the dark to ensure that the carbon fixation cycle can be restarted after dark-light transitions, and it supports hypotheses, which reactions are responsible to generate such mode in vivo.

The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods to define reactions and their rates. Based on the rates and stoichiometries, the system of differential equations is assembled automatically. modelbase minimises the constraints imposed on the user, allowing for easy and dynamic access to all variables, including derived ones, in a convenient manner. A simple incorporation of algebraic equations is, for example, convenient to study systems with rapid equilibrium or quasi steady-state approximations. Moreover, modelbase provides construction methods that automatically build all isotope-specific versions of a particular reaction, making it a convenient tool to analyse non-steady state isotope-labelling experiments.

The ability of phototrophs to colonise different environments relies on robust protection against oxidative stress, a critical requirement for the successful evolutionary transition from water to land. Photosynthetic organisms have developed numerous strategies to adapt their photosynthetic apparatus to changing light conditions in order to optimise their photosynthetic yield, which is crucial for life on Earth to exist. Photosynthetic acclimation is an excellent example of the complexity of biological systems, where highly diverse processes, ranging from electron excitation over protein protonation to enzymatic processes coupling ion gradients with biosynthetic activity, interact on drastically different timescales from picoseconds to hours. Efficient functioning of the photosynthetic apparatus and its protection is paramount for efficient downstream processes, including metabolism and growth. Modern experimental techniques can be successfully integrated with theoretical and mathematical models to promote our understanding of underlying mechanisms and principles. This review aims to provide a retrospective analysis of multidisciplinary photosynthetic acclimation research carried out by members of the Marie Curie Initial Training Project, AccliPhot, placing the results in a wider context. The review also highlights the applicability of photosynthetic organisms for industry, particularly with regards to the cultivation of microalgae. It intends to demonstrate how theoretical concepts can successfully complement experimental studies broadening our knowledge of common principles in acclimation processes in photosynthetic organisms, as well as in the field of applied microalgal biotechnology.

Along with the development of several large-scale methods such as mass spectrometry or micro arrays, genome wide models became not only a possibility but an obvious tool for theoretical biologists to integrate and analyse complex biological data. Nevertheless, incorporating the dynamics of photosynthesis remains one of the major challenges while reconstructing metabolic networks of plants and other photosynthetic organisms. In this review, we aim to provide arguments that small-scale models are still a suitable choice when it comes to discover organisational principles governing the design of biological systems. We give a brief overview of recent modelling efforts in understanding the interplay between rapid, photoprotective mechanisms and the redox balance within the thylakoid membrane, discussing the applicability of a reductionist approach in modelling self-regulation in plants, and outline possible directions for further research.