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We assessed mechanistic temperature influence on flowering by incorporating temperature-responsive flowering mechanisms across developmental age into an existing model. Temperature influences the leaf production rate as well as expression of

Particles of the SARS-CoV-2 virus, the causative agent of the COVID-19 disease, can enter the sewage system through faeces of infected people. The Scottish Environment Protection Agency (SEPA) has been monitoring viral levels in wastewater since May 2020 in over 100 locations, as such data is a good indicator of COVID-19 spread throughout the population. These estimates are combined with others to provide the best overall estimate of disease prevalence and viral reproduction (R) values, at the Scottish and UK levels. These longitudinal, geospatial data are costly to obtain while they have a high potential for re-use. However, access to the data risks deteriorating over time once COVID-19 becomes an endemic infection and monitoring programmes terminate. Our team worked on the open research front of the programme monitoring SARS-CoV-2 in wastewater, with the aim to develop appropriate preservation strategies while following open science and FAIR data principles. Here we will present the curation process of making the outputs complete and unambiguous. We will describe the multiple ways in which we disseminated the data to maximize their visibility and re-usability, while assuring cost-free, long-term preservation, as well as share some recommendations for such multi-institutional initiatives. Briefly, our work included sharing data in public repositories, submitting a data paper to a scientific data journal, and curating and transcribing protocols, which were also published in online platforms. A dashboard webpage containing the links to published outputs was also created: https://biordm.github.io/COVID-Wastewater-Scotland. By making these data open and FAIR, we are supporting COVID-19 data transparency, assisting with government decisions and accountability. Additionally, the detailed data and methodologies can help with the implementation of similar surveillance programmes in the future, as well as assist with the modelling and analysis of the past SARS-CoV-2 outbreaks. Funding: CREW; Grant CD2019_06 Tracking SARS-CoV-2 via municipal wastewater

Predicting a multicellular organism’s phenotype quantitatively from its genotype is challenging, as genetic effects must propagate across scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour. Here we explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used diverse metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for the vegetative growth of Arabidopsis thaliana, sharing the model and data files in a structured, public resource. The calibrated model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants under standard laboratory conditions. Altered night-time metabolism of stored starch accounted for most of the decrease in whole-plant biomass, as previously proposed. Mobilization of a secondary store of malate and fumarate was also mis-regulated, accounting for any remaining biomass defect. The three candidate mechanisms tested did not explain this organic acid accumulation. Our results link genotype through specific processes to higher-level phenotypes, formalizing our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits., in silico Plants, 4 (2), ISSN:2517-5025

Poster presented at theEdinburgh Open Research Conference2022 Particles of the SARS-CoV-2 virus, the causative agent of the COVID-19 disease, can enter the sewage system through faeces of infected people. The Scottish Environment Protection Agency (SEPA) has been monitoring viral levels in wastewater since May 2020 in over 100 locations, as such data is a good indicator of COVID-19 spread throughout the population. These estimates are combined with others to provide the best overall estimate of disease prevalence and viral reproduction (R) values, at the Scottish and UK levels. These longitudinal, geospatial data are costly to obtain while they have a high potential for re-use. However, access to the data risks deteriorating over time once COVID-19 becomes an endemic infection and monitoring programmes terminate. Our team worked on the open research front of the programme monitoring SARS-CoV-2 in wastewater, with the aim to develop appropriate preservation strategies while following open science and FAIR data principles. Here we will present the curation process of making the outputs complete and unambiguous. We will describe the multiple ways in which we disseminated the data to maximize their visibility and re-usability, while assuring cost-free, long-term preservation, as well as share some recommendations for such multi-institutional initiatives. Briefly, our work included sharing data in public repositories, submitting a data paper to a scientific data journal, and curating and transcribing protocols, which were also published in online platforms. A dashboard webpage containing the links to published outputs was also created: https://biordm.github.io/COVID-Wastewater-Scotland. By making these data open and FAIR, we are supporting COVID-19 data transparency, assisting with government decisions and accountability. Additionally, the detailed data and methodologies can help with the implementation of similar surveillance programmes in the future, as well as assist with the modelling and analysis of the past SARS-CoV-2 outbreaks.

The original version of the chapter “Period Estimation and Rhythm Detection in Timeseries Data Using BioDare2, the Free, Online, Community Resource” was previously published non-open access. This has now been changed to open access under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), and the copyright holder updated to “The Author(s)”. For further details, see license information in the chapter. The chapter and the book have been updated with the change.

One of the key objectives of data analysis in circadian research is to quantify the rhythmic properties of the experimental data. BioDare2 is a free, online service which provides fast timeseries analysis, attractive visualizations, and data sharing. This chapter outlines the description of an experiment for BioDare2 and how to upload and analyze the numerical timeseries data.

One of the key objectives of data analysis in circadian research is to quantify the rhythmic properties of the experimental data. BioDare2 is a free, online service which provides fast timeseries analysis, attractive visualizations, and data sharing. This chapter outlines the description of an experiment for BioDare2 and how to upload and analyze the numerical timeseries data.

The circadian clock coordinates plant physiology and development. Mathematical clock models have provided a rigorous framework to understand how the observed rhythms emerge from disparate, molecular processes. However, models of the plant clock have largely been built and tested against RNA time-series data in arbitrary, relative units. This limits model transferability, refinement from biochemical data and applications in synthetic biology. Here, we incorporate absolute mass units into a detailed model of the clock gene network in Arabidopsis thaliana. We re-interpret the established P2011 model, highlighting a transcriptional activator that overlaps the function of REVEILLE 8/LHY-CCA1-LIKE 5. The new U2020 model incorporates the repressive regulation of PRR genes, a key feature of the most detailed clock model KF2014, without greatly increasing model complexity. We tested the experimental error distributions of qRT–PCR data calibrated for units of RNA transcripts per cell and of circadian period estimates, in order to link the models to data more appropriately. U2019 and U2020 models were constrained using these data types, recreating previously described circadian behaviours with RNA metabolic processes in absolute units. To test their inferred rates, we estimated a distribution of observed, transcriptome-wide transcription rates (Plant Empirical Transcription Rates, PETR) in units of transcripts per cell per hour. The PETR distribution and the equivalent degradation rates indicated that the models’ predicted rates are biologically plausible, with individual exceptions. In addition to updated clock models, FAIR (Findable, Accessible, Interoperable, Re-usable) data resources and a software environment in Docker, this validation process represents an advance in biochemical realism for models of plant gene regulation.

Biological rhythms are ubiquitous in eukaryotes, and the best understood of these occur with a period of approximately a day - circadian rhythms. Such rhythms persist even when the organism is placed under constant conditions, with a period that is close, but not exactly equal, to 24 h, and are driven by an endogenous timer - one of the many 'biological clocks'. In plants, research into circadian rhythms has been driven forward by genetic experiments using Arabidopsis. Higher plant genomes include a particularly large number of genes involved in metabolism, and circadian rhythms may well provide the necessary coordination for the control of these - for example, around the diurnal rhythm of photosynthesis - to suit changing developmental or environmental conditions. The endogenous timer must be flexible enough to support these requirements. Current research supports this notion most strongly for the input pathway, in which multiple photoreceptors have been shown to mediate light input to the clock. Both input and output components are now related to putative circadian oscillator mechanisms by sequence homology or by experimental observation. It appears that the pathways linking some domains of the basic clock model may be very short indeed, or the mechanisms of these domains may overlap. Components of the first plant circadian output pathway to be identified unequivocally will help to determine exactly how many output pathways control the various phases of overt rhythms in plants. contents Summary 175 I. the circadian system 176 II. overt circadian rhythms 177 III. photoperiodism 183 IV. oscillator theory and practice 186 V. phototransduction pathways 190 VI. conclusions 192 Acknowledgements 193 References 193.

Twenty-four-hour, circadian rhythms control many eukaryotic mRNA levels, whereas the levels of their more stable proteins are not expected to reflect the RNA rhythms, emphasizing the need to test the circadian regulation of protein abundance and modification. Here we present circadian proteomic and phosphoproteomic time series from Arabidopsis thaliana plants under constant light conditions, estimating that just 0.4% of quantified proteins but a much larger proportion of quantified phospho-sites were rhythmic. Approximately half of the rhythmic phospho-sites were most phosphorylated at subjective dawn, a pattern we term the “phospho-dawn.” Members of the SnRK/CDPK family of protein kinases are candidate regulators. A CCA1-overexpressing line that disables the clock gene circuit lacked most circadian protein phosphorylation. However, the few phospho-sites that fluctuated despite CCA1-overexpression still tended to peak in abundance close to subjective dawn, suggesting that the canonical clock mechanism is necessary for most but perhaps not all protein phosphorylation rhythms. To test the potential functional relevance of our datasets, we conducted phosphomimetic experiments using the bifunctional enzyme fructose-6-phosphate-2-kinase/phosphatase (F2KP), as an example. The rhythmic phosphorylation of diverse protein targets is controlled by the clock gene circuit, implicating posttranslational mechanisms in the transmission of circadian timing information in plants., Graphical abstract, Highlights • Circadian (phospho)proteomics time courses of plants with or without functional clock. • Most protein abundance/phosphorylation rhythms require a transcriptional oscillator. • The majority of rhythmic phosphosites peak around subjective dawn (“phospho-dawn”). • A phosphorylated serine of the metabolic enzyme F2KP has functional relevance., In Brief Plants have circadian rhythms, driven by a transcription factor network. Circadian clock research has therefore focused on transcriptional regulation. However, nontranscriptional processes also play a role. Therefore, we here present circadian (phospho)proteomics time courses. We find rhythmically phosphorylated proteins with diverse biological roles and demonstrate functional relevance of one example. Most of these rhythms require the transcriptional oscillator. Moreover, most rhythmic phosphorylations peak around dawn, which is a focus of our analysis. These results increase our knowledge of nontranscriptional circadian processes.

Tools and software that automate repetitive tasks, such as metadata extraction and deposition to data repositories, are essential for researchers to share Open Data, routinely. For research that generates microscopy image data, OMERO is an ideal platform for storage, annotation and publication according to open research principles. We present PyOmeroUpload, a Python toolkit for automatically extracting metadata from experiment logs and text files, processing images and uploading these payloads to OMERO servers to create fully annotated, multidimensional datasets. The toolkit comes packaged in portable, platform-independent Docker images that enable users to deploy and run the utilities easily, regardless of Operating System constraints. A selection of use cases is provided, illustrating the primary capabilities and flexibility offered with the toolkit, along with a discussion of limitations and potential future extensions. PyOmeroUpload is available from: https://github.com/SynthSys/pyOmeroUpload.

Plants accumulate reserves in the daytime to support growth at night. Circadian regulation of diel reserve turnover was investigated by profiling starch, sugars, glucose 6-phosphate, organic acids, and amino acids during a light-dark cycle and after transfer to continuous light in Arabidopsis wild types and in mutants lacking dawn (lhy cca1), morning (prr7 prr9), dusk (toc1, gi), or evening (elf3) clock components. The metabolite time series were integrated with published time series for circadian clock transcripts to identify circadian outputs that regulate central metabolism. (a) Starch accumulation was slower in elf3 and prr7 prr9. It is proposed that ELF3 positively regulates starch accumulation. (b) Reducing sugars were high early in the T-cycle in elf3, revealing that ELF3 negatively regulates sucrose recycling. (c) The pattern of starch mobilization was modified in all five mutants. A model is proposed in which dawn and dusk/evening components interact to pace degradation to anticipated dawn. (d) An endogenous oscillation of glucose 6-phosphate revealed that the clock buffers metabolism against the large influx of carbon from photosynthesis. (e) Low levels of organic and amino acids in lhy cca1 and high levels in prr7 prr9 provide evidence that the dawn components positively regulate the accumulation of amino acid reserves.

Practical delivery of Findable, Accessible, Reusable and Interoperable principles for research data management requires expertise, time resource, (meta)data standards and formats, software tools and public repositories. The Synthetic Biology Open Language (SBOL2) metadata standard enables FAIR sharing of the designs of synthetic biology constructs, notably in the repository of the SynBioHub platform. Large libraries of such constructs are increasingly easy to produce in practice, for example, in DNA foundries. However, manual curation of the equivalent libraries of designs remains cumbersome for a typical lab researcher, creating a barrier to data sharing. Here, we present a simple tool SynBio2Easy, which streamlines and automates operations on multiple Synthetic Biology Open Language (SBOL) designs using Microsoft Excel® tables as metadata inputs. The tool provides several utilities for manipulation of SBOL documents and interaction with SynBioHub: for example, generation of a library of plasmids based on an original design template, bulk deposition into SynBioHub, or annotation of existing SBOL component definitions with notes and authorship information. The tool was used to generate and deposit a collection of 3661 cyanobacterium Synechocystis plasmids into the public SynBioHub repository. In the process of developing the software and uploading these data, we evaluated some aspects of the SynBioHub platform and SBOL ecosystem, and we discuss proposals for improvement that could benefit the user community. With software such as SynBio2Easy, we aim to deliver a user-driven tooling to make FAIR a reality at all stages of the project lifecycle in synthetic biology research. Graphical Abstract

Plants sense light and temperature changes to regulate flowering time. Here, we show that expression of the Arabidopsis florigen gene, FLOWERING LOCUS T (FT), peaks in the morning during spring, a different pattern than we observe in the laboratory. Providing our laboratory growth conditions with a red/far-red light ratio similar to open-field conditions and daily temperature oscillation is sufficient to mimic the FT expression and flowering time in natural long days. Under the adjusted growth conditions, key light signalling components, such as phytochrome A and EARLY FLOWERING 3, play important roles in morning FT expression. These conditions stabilize CONSTANS protein, a major FT activator, in the morning, which is probably a critical mechanism for photoperiodic flowering in nature. Refining the parameters of our standard growth conditions to more precisely mimic plant responses in nature can provide a powerful method for improving our understanding of seasonal response.

Trees are carbon dioxide sinks and major producers of terrestrial biomass with distinct seasonal growth patterns. Circadian clocks enable the coordination of physiological and biochemical temporal ...

Modelling in biology becomes necessary when systems are complex but the more complex the systems are the harder the models become to read. The most common ways of writing models are by writing reactions on discrete, typed objects (e.g. molecules of different species), or writing rate equations for the populations of such species. One problem (1) with those approaches is that the number of species and reactions is often so large that the model cannot be realistically enumerated. Another problem (2) is that the number of species and reactions is fixed, whereas biology often grows new compartments which means new reactions and species. Here we develop an extension to the representation of reactions where the objects carry variables that are defined by their type (for example objects of type Leaf all have a Mass variable). The dynamics are defined by rules about types, which means they work for all objects of that type. This compact representation solves problem 1. If we think of the object variables as the analogue of reaction/rate equation species, creating a new object of some type means we are also creating new species (solving problem 2). We also developed an embedding of Chromar in the programming language Haskell and showed its applicability to two examples. Having a more compact representation can help make models a tool for knowledge representation and exchange instead of just a simulation input. Embedding Chromar in a general purpose programming language lifts some of the constraints of modelling languages while still maintaining the naturalness of a domain-specific language.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them. Fil: Hughes, Michael E.. Washington University School Of Medicine In St. Louis; Estados Unidos Fil: Abruzzi, Katherine C.. Brandeis University; Estados Unidos Fil: Allada, Ravi. Northwestern University; Estados Unidos Fil: Anafi, Ron. University of Pennsylvania; Estados Unidos Fil: Arpat, Alaaddin Bulak. Universite de Lausanne; Suiza Fil: Asher, Gad. Weizmann Institute Of Science Israel; Israel Fil: Baldi, Pierre. University of California at Irvine; Estados Unidos Fil: de Bekker, Charissa. University Of Central Florida; Estados Unidos Fil: Bell Pedersen, Deborah. Texas A&M University; Estados Unidos Fil: Blau, Justin. University of New York; Estados Unidos Fil: Brown, Steve. Universitat Zurich; Suiza Fil: Ceriani, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Chen, Zheng. University Of Texas Health Science Center At Houston; Estados Unidos Fil: Chiu, Joanna C.. University of California at Davis; Estados Unidos Fil: Cox, Juergen. Max Planck Institute Of Biochemistry; Alemania Fil: Crowell, Alexander M.. Geisel School Of Medicine At Dartmouth; Estados Unidos Fil: DeBruyne, Jason P.. Morehouse School Of Medicine; Estados Unidos Fil: Dijk, Derk-Jan. University Of Surrey; Reino Unido Fil: DiTacchio, Luciano. University Of Kansas Medical Center; Estados Unidos Fil: Doyle, Francis J.. Harvard University; Estados Unidos Fil: Duffield, Giles E.. University of Notre Dame; Estados Unidos Fil: Dunlap, Jay C.. Geisel School Of Medicine At Dartmouth; Estados Unidos Fil: Eckel Mahan, Kristin. University Of Texas Medical School At Houston; Estados Unidos Fil: Esser, Karyn A.. University Of Florida College Of Medicine; Estados Unidos Fil: FitzGerald, Garret A.. University of Pennsylvania; Estados Unidos Fil: Forger, Daniel B.. University Of Michigan; Estados Unidos Fil: Francey, Lauren J.. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Fu, Ying Hui. University of California; Estados Unidos Fil: Gachon, Frédéric. Nestlé Institute Of Health Sciences; Suiza Fil: Gatfield, David. Universite de Lausanne; Suiza Fil: de Goede, Paul. University Of Amsterdam; Países Bajos Fil: Golden, Susan S.. University of California at San Diego; Estados Unidos Fil: Green, Carla. University of Texas; Estados Unidos Fil: Harer, John. University of Duke; Estados Unidos Fil: Harmer, Stacey. University of California at Davis; Estados Unidos Fil: Haspel, Jeff. Washington University; Estados Unidos Fil: Hastings, Michael H.. The Medical Research Council Laboratory Of Molecular Biology; Reino Unido Fil: Herzel, Hanspeter. Charité Universitätsmedizin Berlin; Alemania Fil: Herzog, Erik D.. Washington University in St. Louis; Estados Unidos Fil: Hoffmann, Christy. Washington University in St. Louis; Estados Unidos Fil: Hong, Christian. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Hughey, Jacob J.. Vanderbilt University School Of Medicine; Estados Unidos Fil: Hurley, Jennifer M.. Rensselaer Polytechnic Institute; Estados Unidos Fil: de la Iglesia, Daniel Horacio. University Of Washington; Estados Unidos Fil: Johnson, Carl. Vanderbilt University; Estados Unidos Fil: Kay, Steve A.. University of California at San Diego; Estados Unidos Fil: Koike, Nobuya. Kyoto Prefectural University Of Medicine; Japón Fil: Kornacker, Karl. Ohio State University; Estados Unidos Fil: Kramer, Achim. Charité Universitätsmedizin Berlin; Alemania Fil: Lamia, Katja. The Scripps Research Institute; Estados Unidos Fil: Leise, Tanya. Amherst College; Estados Unidos Fil: Lewis, Scott A.. Washington University; Estados Unidos Fil: Li, Jiajia. Washington University; Estados Unidos Fil: Li, Xiaodong. Wuhan University; China Fil: Liu, Andrew C.. The University Of Memphis; Estados Unidos Fil: Loros, Jennifer J.. Geisel School Of Medicine At Dartmouth; Estados Unidos Fil: Martino, Tami A.. University of Guelph; Canadá Fil: Menet, Jerome S.. Texas A&M University; Estados Unidos Fil: Merrow, Martha. Ludwig Maximilians Universitat; Alemania Fil: Millar, Andrew J.. University of Edinburgh; Reino Unido Fil: Mockler, Todd. Donald Danforth Plant Science Center; Estados Unidos Fil: Naef, Felix. Ecole Polytechnique Federale de Lausanne; Suiza Fil: Nagoshi, Emi. Universidad de Ginebra; Suiza Fil: Nitabach, Michael N.. University of Yale. School of Medicine; Estados Unidos Fil: Olmedo, Maria. Universidad de Sevilla; España Fil: Nusinow, Dmitri A.. Donald Danforth Plant Science Center; Estados Unidos Fil: Ptácek, Louis J.. University of California; Estados Unidos Fil: Rand, David. University of Warwick; Reino Unido Fil: Reddy, Akhilesh B.. The Francis Crick Institute; Reino Unido Fil: Robles, Maria S.. Ludwig Maximilians Universitat; Alemania Fil: Roenneberg, Till. Ludwig Maximilians Universitat; Alemania Fil: Rosbash, Michael. Brandeis University; Estados Unidos Fil: Ruben, Marc D.. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Rund, Samuel S.C.. University of Edinburgh; Reino Unido Fil: Sancar, Aziz. University of North Carolina; Estados Unidos Fil: Sassone Corsi, Paolo. University of California at Irvine; Estados Unidos Fil: Sehgal, Amita. University of Pennsylvania; Estados Unidos Fil: Sherrill Mix, Scott. University of Pennsylvania; Estados Unidos Fil: Skene, Debra J.. University Of Surrey; Reino Unido Fil: Storch, Kai Florian. Mcgill University, Douglas Mental Health University Institute; Canadá Fil: Takahashi, Joseph S.. Ut Southwestern Medical Center; Estados Unidos Fil: Ueda, Hiroki R.. The University of Tokyo; Japón Fil: Wang, Han. Soochow University; China Fil: Weitz, Charles. Harvard Medical School; Estados Unidos Fil: Westermark, P. O.. Leibniz Institute For Farm Animal Biology; Alemania Fil: Wijnen, Herman. University of Southampton; Reino Unido Fil: Xu, Ying. Soochow University; China Fil: Wu, Gang. Cincinnati Children's Hospital Medical Center; Estados Unidos Fil: Yoo, Seung Hee. University of Texas; Estados Unidos Fil: Young, Michael. The Rockefeller University; Estados Unidos Fil: Zhang, Eric Erquan. National Institute Of Biological Sciences, Beijing; China Fil: Zielinski, Tomasz. University of Edinburgh; Reino Unido Fil: Hogenesch, John B.. Cincinnati Children's Hospital Medical Center; Estados Unidos

Recent changes in UK research funding priorities have led to the emergence of large challenge-led, societally embedded research opportunities. We used semi-structured interviews with 27 academic, management and professional staff at the University of Edinburgh to explore the life cycles of selected projects and centres from planning and preparation through to decommissioning. We observed the degree to which pursuit of challenge-led opportunities induced the emergence of new project-level organizational forms or changed academics’ modus operandi from “Mode 1” research to “Mode2” knowledge production. We further explored the levels of management input and administrative support expected and received by the project organization from its host academic schools, colleges the university. We found that the size, complexity and disciplinary, interdisciplinary or transdisciplinary scope of the initiatives influenced their emergence as autonomous project organisations. “Role strain” affected many respondents as they sought to balance Mode 1 academic and Mode 2 leadership and management roles in their project organizations. Further exploration of the distribution of leadership management and professional support functions among project organizations and the support structures of academic schools, colleges and the university is warranted. We suggest that the university might usefully act as a boundary organization and adopt a Mode 2 knowledge exchange mission in support of multi-stakeholder projects. This paper was submitted to the 35th European Group for Organizational Studies colloquium (EGOS2019). A shorter version of this paper, which focuses on the institutional logics observed among the university staff, is also available at SocArXiv ("Nurturing cross-disciplinary research: Institutional logics, hybrid Spaces and meta-organization at a research university").

The emergence of “big research” has given rise to a variety of organizational environments (e.g. powerful research labs, projects and centres) that complement and transcend the traditional departmental structures of universities. Success in building and sustaining these “organized research units” and in reconciling their competing interests is central to the mission of a research university. In this preliminary study, we focus on individual experiences of working in cross-disciplinary research projects and centres within a large research university. Through interviewing key individuals (project leaders, academic and non-academic staff) engaged in running large research projects and/or centres, we aim to unpack the diversity and dynamics of different institutional logics at work at different stages of the organized research unit (ORU) life cycle. We seek to draw general conclusions in terms of managing and reconciling the research missions of ORUs, the university, and its external stakeholders (government, industry and society in general). This paper was submitted to the 35th European Group for Organizational Studies colloquium (EGOS2019). A longer version of this paper, which interprets our results from the perspective of Mode 2 knowledge production, is available at SocArXiv ("Nurturing Project Organizations: A Mode 2 Mission for the University?").

24-hour, circadian rhythms control many eukaryotic mRNA levels, whereas the levels of their more stable proteins are not expected to reflect the RNA rhythms, emphasizing the need to test the circadian regulation of protein abundance and modification. Here we present circadian proteomic and phosphoproteomic time-series fromArabidopsis thalianaplants under constant light conditions, estimating that just 0.4% of quantified proteins but a much larger proportion of quantified phospho-sites were rhythmic. Approximately half of the rhythmic phospho-sites were most phosphorylated at subjective dawn, a pattern we term the ‘phospho-dawn’. Members of the SnRK/CDPK family of protein kinases are candidate regulators. ACCA1-over-expressing line that disables the clock gene circuit lacked most circadian protein phosphorylation. However, the few phospho-sites that fluctuated despiteCCA1-over-expression still tended to peak in abundance close to subjective dawn, suggesting that the canonical clock mechanism is necessary for most but perhaps not all protein phosphorylation rhythms. To test the potential functional relevance of our datasets, we conducted phosphomimetic experiments using the bifunctional enzyme fructose-6-phosphate-2-kinase / phosphatase (F2KP), as an example. The rhythmic phosphorylation of diverse protein targets is controlled by the clock gene circuit, implicating post-translational mechanisms in the transmission of circadian timing information in plants.

Synthetic biology aims to introduce engineering principles into biology, for example, the construction of biological devices by assembling previously-characterized, functional parts. This approach demands new resources for cataloging and sharing biological components and designs, in order to accelerate the design-build-test-learn cycle. We evaluated two free, open source software platforms for managing synthetic biology data: Joint Bioenergy Institute-Inventory of Composable Elements (JBEI-ICE) and SynBioHub. We analyzed the systems from the perspective of experimental biology research groups in academia, which seek to incorporate the repositories into their synthetic biology workflow. Here, we define the minimal requirements for a repository in this context and develop three usage scenarios, where we then examine the two platforms: (i) supporting the synthetic biology design-build-test-learn cycle, (ii) batch deposit of existing designs into the repository and (iii) discovery and reuse of designs from the repository. Our evaluation of JBEI-ICE and SynBioHub provides an insight into the current state of synthetic biology resources, might encourage their wider adoption and should guide future development to better meet the needs of this user group.

A whole-life-cycle multi-model for Arabidopsis combines phenology and physical growth models to explain reproductive success in different genotype × environment scenarios., Linking our understanding of biological processes at different scales is a major conceptual challenge in biology and aggravated by differences in research methods. Modelling can be a useful approach to consolidating our understanding across traditional research domains. The laboratory model species Arabidopsis is very widely used to study plant growth processes and has also been tested more recently in ecophysiology and population genetics. However, approaches from crop modelling that might link these domains are rarely applied to Arabidopsis. Here, we combine plant growth models with phenology models from ecophysiology, using the agent-based modelling language Chromar. We introduce a simpler Framework Model of vegetative growth for Arabidopsis, FM-lite. By extending this model to include inflorescence and fruit growth and seed dormancy, we present a whole-life-cycle, multi-model FM-life, which allows us to simulate at the population level in various genotype × environment scenarios. Environmental effects on plant growth distinguish between the simulated life history strategies that were compatible with previously described Arabidopsis phenology. Our results simulate reproductive success that is founded on the broad range of physiological processes familiar from crop models and suggest an approach to simulating evolution directly in future.

Plants use the circadian clock to sense photoperiod length. Seasonal responses like flowering are triggered at a critical photoperiod when a light-sensitive clock output coincides with light or darkness. However, many metabolic processes, like starch turnover, and growth respond progressively to photoperiod duration. We first tested the photoperiod response of 10 core clock genes and two output genes. qRT-PCR analyses of transcript abundance under 6, 8, 12 and 18 h photoperiods revealed 1-4 h earlier peak times under short photoperiods and detailed changes like rising PRR7 expression before dawn. Clock models recapitulated most of these changes. We explored the consequences for global gene expression by performing transcript profiling in 4, 6, 8, 12 and 18 h photoperiods. There were major changes in transcript abundance at dawn, which were as large as those between dawn and dusk in a given photoperiod. Contributing factors included altered timing of the clock relative to dawn, light signalling and changes in carbon availability at night as a result of clock-dependent regulation of starch degradation. Their interaction facilitates coordinated transcriptional regulation of key processes like starch turnover, anthocyanin, flavonoid and glucosinolate biosynthesis and protein synthesis and underpins the response of metabolism and growth to photoperiod.

A major challenge for biology is to extend our understanding of molecular regulation from the simplified conditions of the laboratory to ecologically relevant environments. Tractable examples are essential to make these connections for complex, pleiotropic regulators and, to go further, to link relevant genome sequences to field traits. Here, I review the case for the biological clock in higher plants. The gene network of the circadian clock drives pervasive, 24-hour rhythms in metabolism, behavior, and physiology across the eukaryotes and in some prokaryotes. In plants, the scope of chronobiology is now extending from the most tractable, intracellular readouts to the clock's many effects at the whole-organism level and across the life cycle, including biomass and flowering. I discuss five research areas where recent progress might be integrated in the future, to understand not only circadian functions in natural conditions but also the evolution of the clock's molecular mechanisms.

We assessed mechanistic temperature influence on flowering by incorporating temperature-responsive flowering mechanisms across developmental age into an existing model. Temperature influences the leaf production rate as well as expression of FLOWERING LOCUS T (FT), a photoperiodic flowering regulator that is expressed in leaves. The Arabidopsis Framework Model incorporated temperature influence on leaf growth but ignored the consequences of leaf growth on and direct temperature influence of FT expression. We measured FT production in differently aged leaves and modified the model, adding mechanistic temperature influence on FT transcription, and causing whole-plant FT to accumulate with leaf growth. Our simulations suggest that in long days, the developmental stage (leaf number) at which the reproductive transition occurs is influenced by day length and temperature through FT, while temperature influences the rate of leaf production and the time (in days) the transition occurs. Further, we demonstrate that FT is mainly produced in the first 10 leaves in the Columbia (Col-0) accession, and that FT accumulation alone cannot explain flowering in conditions in which flowering is delayed. Our simulations supported our hypotheses that: (i) temperature regulation of FT, accumulated with leaf growth, is a component of thermal time, and (ii) incorporating mechanistic temperature regulation of FT can improve model predictions when temperatures change over time.

Bioluminescence has been an important tool for gathering circadian data with the main reporter gene exploited firefly luciferase (LUC). In some circumstances the rapid inactivation of LUC could be disadvantageous, e.g. reporting total protein levels through reporter translational fusions. In the latter scenario the commercially available Nano luciferase (NanoLUC) might offer and advantage, however no data in plant has been provided so far. We tested NanoLUC under different research scenarios were LUC has been used, for example enzyme purification, expression in transient plant systems and in stable transgenic lines. We show that NanoLUC is active in these experimental scenarios. We also created a set of NanoLUC variants for example MBP-NanoLUC-3xFlag-10xHis version of NanoLUC can be easily purified and stable for several days (half-life 37.2 at 4°C) and can be used for generating calibration curves for quantifying protein as the signal is linear over a large dynamic range. In addition, we show that NanoLUC can report in-planta protein levels on circadian time scale thanks to the stability of furimazine. Therefore, opening the possibility of using NanoLUC for reporting protein dynamics in seedlings. With this new technology, we explored the dynamics of protein BROTHER OF LUX ARRHYTHMO (BOA), which has been suggested in mathematical models to be a rhythmic protein from RNA data. Using an automatic plate-reader, we were able to track BOAp:BOA-NL for an extended period of time by just adding a mix of furimazine with Triton X-100, as it is done with LUC-reporter lines. In our experimental context BOA protein does not present strong oscillatory dynamics similar to what has been reported for Phytocrhome B for which rhythmic accumulation of transcript can be observed while total protein levels remain constant under diurnal conditions. Our results support the use NanoLUC for studying the dynamics of plant proteins for extended period of time under different circumstances.

A recent initiative named 'Crops in silico' proposes that multi-scale models “have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts” in plant science, particularly directed to crop species. To that end, the group called for “a paradigm shift in plant modelling, from largely isolated efforts to a connected community” (Marshall-Colon et al., 2017). ‘Wet’ (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in ‘dry’ (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from systems biology and crop modelling (Chew et al., 2017; Chew et al., 2014b). Here we outline the approaches that seem most relevant to connected, ‘digital organism’ initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison to the SBML community, we note computational resources and community structures that will help to realise the potential for plant systems biology to connect with a broader crop science community.