R&D priorities in the field of sustainable remediation and purification of agro-industrial and municipal wastewater

This article was presented as a position paper during the Environmental Biotechnology and Microbiology Conference in Bologna, Italy in April 2012. It indicates major and emerging environmental biotechnology research and development (R&D) priorities for EU members in the field of sustainable remediation and purification of agro-industrial and municipal wastewater. The identified priorities are: anaerobic/aerobic microbial treatment, combination of photochemical and biological treatment, phytoremediation and algae-based remediation, as well as innovative technologies currently investigated, such as enzyme-based treatment, bioelectrochemical treatment and recovery of nutrients and reuse of cleaned water. State of the art, research needs and prospective development in these domains are crucially discussed. As a result, goals of the future development of bioremediation and purification processes are defined and the way to achieve them is proposed.     

A rule-based model of barley morphogenesis, with special respect to shading and gibberellic acid signal transduction

BACKGROUND AND AIMS: Functional-structural plant models (FSPM) constitute a paradigm in plant modelling that combines 3D structural and graphical modelling with the simulation of plant processes. While structural aspects of plant development could so far be represented using rule-based formalisms such as Lindenmayer systems, process models were traditionally written using a procedural code. The faithful representation of structures interacting with functions across scales, however, requires a new modelling formalism. Therefore relational growth grammars (RGG) were developed on the basis of Lindenmayer systems. METHODS: In order to implement and test RGG, a new modelling language, the eXtended L-system language (XL) was created. Models using XL are interpreted by the interactive, Java-based modelling platform GroIMP. Three models, a semi-quantitative gibberellic acid (GA) signal transduction model, and a phytochrome-based shade detection and object avoidance model, both coupled to an existing morphogenetic structural model of barley (Hordeum vulgare L.), serve as examples to demonstrate the versatility and suitability of RGG and XL to represent the interaction of diverse biological processes across hierarchical scales. KEY RESULTS: The dynamics of the concentrations in the signal transduction network could be modelled qualitatively and the phenotypes of GA-response mutants faithfully reproduced. The light model used here was simple to use yet effective enough to carry out local measurement of red:far-red ratios. Suppression of tillering at low red:far-red ratios could be simulated. CONCLUSIONS: The RGG formalism is suitable for implementation of multi-scaled FSPM of plants interacting with their environment via hormonal control. However, their ensuing complexity requires careful design. On the positive side, such an FSPM displays knowledge gaps better thereby guiding future experimental design.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Automated analysis of three-dimensional stress echocardiography

Real-time three-dimensional (3D) ultrasound imaging has been proposed as an alternative for two-dimensional stress echocardiography for assessing myocardial dysfunction and underlying coronary artery disease. Analysis of 3D stress echocardiography is no simple task and requires considerable expertise. In this paper, we propose methods for automated analysis, which may provide a more objective and accurate diagnosis. Expert knowledge is incorporated via statistical modelling of patient data. Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results. We also present software developed especially for analysis of 3D stress echocardiography in clinical practice. Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%). The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function.     

Moving forward in the use of aerobic granular sludge for municipal wastewater treatment: an overview

Activated sludge is one of the most widely implemented technologies for municipal wastewater treatment. Yet, more restrictive environmental standards demand for more efficient technologies. Aerobic granular sludge (AGS) is a promising alternative in this context since this technology has shown potential for simultaneous organic matter and nutrient removal using smaller bioreactors and consuming less energy. However, despite such engaging claims, only ca. 40 full-scale AGS systems have been installed worldwide after 30 years of development. This reduced implementation suggests the existence of significant bottlenecks for this technology, which currently only have partially been overcome. This overview aims to analyze the recent progress in R&D concerning aerobic sludge granulation for municipal wastewater treatment via the analysis of research articles and invention patents as well as to elucidate exiting technological gaps and development opportunities. Culturing methods aiming at fast granulation, long-term stability and excellent process performance are of utmost interest for promoting massive implementation of full-scale AGS systems. Moreover, the recovery of biomaterials from waste sludge could contribute to the implementation of the biorefinery paradigm in wastewater treatment plants.

     

Process R&D under the magnifying glass: Organization,business model,challenges, and scientific context

Initially, the aim is to provide the big picture illustrating the as is situation in the pharmaceutical industry: a lack of productivity resulting in too few products reaching the market; a loss of billions in revenue over the next few years as some of the major megabrands go off patent; a spiraling cost for developing new drugs and taking them through clinical and safety studies. Following on, a look deeper into the organization will offer an insight into the state-of-the-art in a technical function accountable for chemical Process R&D (with a remit to develop scalable, robust, and cost efficient processes for small molecules). The vast majority of compounds already launched in the form of drug products on the market or still being pursued through the phases of discovery and development, fall within the category of small molecules (as opposed to biopharmaceuticals, e.g., proteins, monoclonal antibodies). This typically means molecular weights of <1000 Da and puts organic synthesis in the widest sense of the word at the forefront of technologies needed to support R&D programs in the pharma industry.Understandably, the demands on Medicinal Chemistry are quite different to what applies in a Process R&D (PR&D) organization. In the former, making large numbers of potentially interesting molecules, many of which are discarded after testing, is a key driver and for this virtually any synthetic methodology will suffice. For PR&D, however, homing in on selected compounds there is an expectation that the best synthetic routes will be delivered that meet a number of tough criteria, for instance from an environmental and safety point of view, allowing operation on large scale, offering cost competitiveness, avoiding patent infringements, showing sustainability for long-term production, etc. The intention is to focus on issues to be addressed during this transition by providing examples of changes that had to be put in place in order to make the supply of larger amounts of material feasible. At the end some forward looking conclusions will be shared.     

Managing uncertainty: a review of food system scenario analysis and modelling

Complex socio-ecological systems like the food system are unpredictable, especially to long-term horizons such as 2050. In order to manage this uncertainty, scenario analysis has been used in conjunction with food system models to explore plausible future outcomes. Food system scenarios use a diversity of scenario types and modelling approaches determined by the purpose of the exercise and by technical, methodological and epistemological constraints. Our case studies do not suggest Malthusian futures for a projected global population of 9 billion in 2050; but international trade will be a crucial determinant of outcomes; and the concept of sustainability across the dimensions of the food system has been inadequately explored so far. The impact of scenario analysis at a global scale could be strengthened with participatory processes involving key actors at other geographical scales. Food system models are valuable in managing existing knowledge on system behaviour and ensuring the credibility of qualitative stories but they are limited by current datasets for global crop production and trade, land use and hydrology. Climate change is likely to challenge the adaptive capacity of agricultural production and there are important knowledge gaps for modelling research to address.     

Towards mechanistic models of plant organ growth

Modelling and simulation are increasingly used as tools in the study of plant growth and developmental processes. By formulating experimentally obtained knowledge as a system of interacting mathematical equations, it becomes feasible for biologists to gain a mechanistic understanding of the complex behaviour of biological systems. In this review, the modelling tools that are currently available and the progress that has been made to model plant development, based on experimental knowledge, are described. In terms of implementation, it is argued that, for the modelling of plant organ growth, the cellular level should form the cornerstone. It integrates the output of molecular regulatory networks to two processes, cell division and cell expansion, that drive growth and development of the organ. In turn, these cellular processes are controlled at the molecular level by hormone signalling. Therefore, combining a cellular modelling framework with regulatory modules for the regulation of cell division, expansion, and hormone signalling could form the basis of a functional organ growth simulation model. The current state of progress towards this aim is that the regulation of the cell cycle and hormone transport have been modelled extensively and these modules could be integrated. However, much less progress has been made on the modelling of cell expansion, which urgently needs to be addressed. A limitation of the current generation models is that they are largely qualitative. The possibilities to characterize existing and future models more quantitatively will be discussed. Together with experimental methods to measure crucial model parameters, these modelling techniques provide a basis to develop a Systems Biology approach to gain a fundamental insight into the relationship between gene function and whole organ behaviour.     

The Grand Convergence: Closing the Divide between Public Health Funding and Global Health Needs

The Global Health 2035 report notes that the “grand convergence”—closure of the infectious, maternal, and child mortality gap between rich and poor countries—is dependent on research and development (R&D) of new drugs, vaccines, diagnostics, and other health tools. However, this convergence (and the R&D underpinning it) will first require an even more fundamental convergence of the different worlds of public health and innovation, where a largely historical gap between global health experts and innovation experts is hindering achievement of the grand convergence in health.The Global Health 2035 report notes that the “grand convergence”—closure of the infectious, maternal, and child mortality gap between rich and poor countries—is dependent on research and development (R&D) of new drugs, vaccines, diagnostics, and other health tools. New tools alone are estimated to deliver a 2% decline each year in the under-5 mortality rate, maternal mortality ratio, and deaths from HIV/AIDS and tuberculosis (TB) [1].However, this convergence (and the R&D underpinning it) is unlikely unless we first have an even more fundamental convergence of the parallel worlds of public health and innovation. At the moment, these worlds are often disconnected, with major gaps to be bridged at both the intellectual and practical levels before we can truly reach a grand convergence in health.     

Future-proofing regulation for rapidly changing biotechnologies

Our understanding of DNA structure and how it interacts with the environment to give form and function at the organism level is growing at an unprecedented pace which shows no sign of slowing. These developments have already led to many new products and will continue to underpin as yet unpredicted future developments in biotechnology. However, this potential will not be realised unless the mechanisms for risk assessment, regulatory approval, product claims and labelling etc. are fit for purpose, have the confidence of all stakeholders and are sufficiently agile to support this rapidly changing field. The sectors that are making particular advances in biotechnological processes include agriculture, pharmaceuticals, food, chemical and human diagnostics and therapeutics. In many of these areas the research, investment and innovation pipeline is operating well as evidenced by the many marketed products. However, developments in plant breeding methods have posed particular challenges for regulators which in turn is stifling R&D and innovation, particularly in the EU. In rapidly moving areas of research and development, it is imperative that regulatory frameworks are future-proofed by design.

     

Role of LCA in the design of research and development (R&D) of novel processes subject to IPPC and BAT

Establishing BAT (Best Available Techniques) for processes subject to IPPC is a new barrier and one that processes in the development stage need to be aware of. For multi-functional processes, the sectorial approach adopted under IPPC (Integrated Pollution Prevention and Control) increases the potential problems. Life Cycle Assessment (LCA) is an established tool to assist establishing BAT but is difficult to apply in its full form at the Research and Development (R&D) stage. A review of LCA in the context of a case study, the Trefoil kiln process, concludes that it has the flexibility to cope with multi-functionality and that use of key environmental issues and key indicators could overcome the informational gaps. Environmental burdens can be presented appropriately provided the research identifies appropriate allocation methods. The use of LCA thinking provided useful insight on the content of the research programme.     

Designing a blueprint for next-generation stem cell bioprocessing development

The creation of a blueprint for stem cell bioprocess development that it is easily readable and shareable among those involved in the construction of the bioprocess is a necessary step toward full-fledged bioprocess integration. The blueprint provides the culturing tools and methodologies, designed to highlight knowledge gaps within biological sciences and bioengineering. This review highlights a blueprint for stem cell bioprocessing development using a landscape architecture approach that can aid the development of culture technologies and tools that satisfy the demands for stem cell-derived products for use in clinical and industrial applications. This work is intended to provide insights to cell biologists, geneticists, bioengineers, and clinicians seeking knowledge outside of their field of expertise and fosters a leap from a reductionist approach to one, that is, globally integrated in stem cell bioprocessing.     

Investment in plant research and development bears fruit in China

Recent rapid progress in plant science and biotechnology in China demonstrates that China’s stronger support for funding in plant research and development (R&D) has borne fruit. Chinese groups have contributed major advances in a range of fields, such as rice biology, plant hormone and developmental biology, genomics and evolution, plant genetics and epigenetics, as well as plant biotechnology. Strigolactone studies including those identifying its receptor and dissecting its complex structure and signaling are representative of the recent researches from China at the forefront of the field. These advances are attributable in large part to interdisciplinary studies among scientists from plant science, chemistry, bioinformatics, structural biology, and agronomy. The platforms provided by national facilities facilitate this collaboration. As well, efficient restructuring of the top–down organization of state programs and free exploration of scientists’ interests have accelerated achievements by Chinese researchers. Here, we provide a general outline of China’s progress in plant R&D to highlight fields in which Chinese research has made significant contributions.     

Determining membrane protein structures: still a challenge!

Determination of structures and dynamics events of transmembrane proteins is important for the understanding of their function. Analysis of such events requires high-resolution 3D structures of the different conformations coupled with molecular dynamics analyses describing the conformational pathways. However, the solution of 3D structures of transmembrane proteins at atomic level remains a particular challenge for structural biochemists--the need for purified and functional transmembrane proteins causes a 'bottleneck'. There are various ways to obtain 3D structures: X-ray diffraction, electron microscopy, NMR and modelling; these methods are not used exclusively of each other, and the chosen combination depends on several criteria. Progress in this field will improve knowledge of ligand-induced activation and inhibition of membrane proteins in addition to aiding the design of membrane-protein-targeted drugs.     

De novo discovery of antibody drugs – great promise demands scrutiny

ABSTRACT

We live in an era of rapidly advancing computing capacity and algorithmic sophistication. “Big data” and “artificial intelligence”find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts     

PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery

Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.     

Using Citizen Science Data to Model the Distributions of Common Songbirds of Turkey Under Different Global Climatic Change Scenarios

In this study, we evaluated the potential impact of climate change on the distributions of Turkey’s songbirds in the 21st century by modelling future distributions of 20 resident and nine migratory species under two global climate change scenarios. We combined verified data from an ornithological citizen science initiative (www.kusbank.org) with maximum entropy modeling and eight bioclimatic variables to estimate species distributions and projections for future time periods. Model predictions for resident and migratory species showed high variability, with some species projected to lose and others projected to gain suitable habitat. Our study helps improve the understanding of the current and potential future distributions of Turkey’s songbirds and their responses to climate change, highlights effective strategies to maximize avian conservation efforts in the study region, and provides a model for using citizen science data for biodiversity research in a large developing country with few professional field biologists. Our results demonstrate that climate change will not affect every species equally in Turkey. Expected range reductions in some breeding species will increase the risk of local extinction, whereas others are likely to expand their ranges.     

Integration of evidence across human and model organism studies: A meeting report

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration—particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.