Correction: Kis, Z. et al. Resources, Production Scales and Time Required for Producing RNA Vaccines for the Global Pandemic Demand. 2021, , 3.

The authors wish to make the following corrections to this paper [...

On the liquid demixing of water + elastin-like polypeptide mixtures: bimodal re-entrant phase behaviour.

Water + elastin-like polypeptides (ELPs) exhibit a transition temperature below which the chains transform from collapsed to expanded states, reminiscent of the cold denaturation of proteins. This conformational change coincides with liquid-liquid phase separation. A statistical-thermodynamics theory is used to model the fluid-phase behavior of ELPs in aqueous solution and to extrapolate the behavior at ambient conditions over a range of pressures.

Resources, Production Scales and Time Required for Producing RNA Vaccines for the Global Pandemic Demand.

To overcome pandemics, such as COVID-19, vaccines are urgently needed at very high volumes. Here we assess the techno-economic feasibility of producing RNA vaccines for the demand associated with a global vaccination campaign. Production process performance is assessed for three messenger RNA (mRNA) and one self-amplifying RNA (saRNA) vaccines, all currently under clinical development, as well as for a hypothetical next-generation saRNA vaccine.

Immune reconstitution and clinical recovery following anti-CD28 antibody (TGN1412)-induced cytokine storm.

Cytokine storm can result from cancer immunotherapy or certain infections, including COVID-19. Though short-term immune-related adverse events are routinely described, longer-term immune consequences and sequential immune monitoring are not as well defined. In 2006, six healthy volunteers received TGN1412, a CD28 superagonist antibody, in a first-in-man clinical trial and suffered from cytokine storm.

Exploring cyclic networks of multisite modification reveals origins of information processing characteristics.

Multisite phosphorylation (and generally multisite modification) is a basic way of encoding substrate function and circuits/networks of post-translational modifications (PTM) are ubiquitous in cell signalling. The information processing characteristics of PTM systems are a focal point of broad interest. The ordering of modifications is a key aspect of multisite modification, and a broad synthesis of the impact of ordering of modifications is still missing.

Predictive models for the phase behaviour and solution properties of weak electrolytes: nitric, sulphuric, and carbonic acids.

The distribution of ionic species in electrolyte systems is important in many fields of science and engineering, ranging from the study of degradation mechanisms to the design of systems for electrochemical energy storage. Often, other phenomena closely related to ionic speciation, such as ion pairing, clustering and hydrogen bonding, which are difficult to investigate experimentally, are also of interest. Here, we develop an accurate molecular approach, accounting for reactions as well as association and ion pairing, to deliver a predictive framework that helps validate experiment and guides future modelling of speciation phenomena of weak electrolytes.

The interplay of spatial organization and biochemistry in building blocks of cellular signalling pathways.

Biochemical pathways and networks are central to cellular information processing. While a broad range of studies have dissected multiple aspects of information processing in biochemical pathways, the effect of spatial organization remains much less understood. It is clear that space is central to intracellular organization, plays important roles in cellular information processing and has been exploited in evolution; additionally, it is being increasingly exploited in synthetic biology through the development of artificial compartments, in a variety of ways.

Correction: Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-γ Mie.

[This corrects the article DOI: 10.1039/C9RA07057E.].

Mathematical Modeling for the Design and Scale-Up of a Large Industrial Aerosol-Assisted Chemical Vapor Deposition Process under Uncertainty.

Aerosol-assisted chemical vapor deposition (AACVD) can be used to produce coatings and thin films such as transparent conducting oxide (TCO) films, which are used in self-cleaning surfaces, solar cells, and other electronic and optoelectronic applications. A process based on AACVD consists of a number of steps: aerosol generation, aerosol transport, aerosol delivery, and chemical deposition. Predicting the behavior of such a process at an industrial scale is challenging due to a number of factors: the aerosol generation creates droplets of different sizes, losses are incurred in the transport, the delivery must evaporate the solvent to release the precursors, and the reactions on the surface of the deposition target may be complex.

Equation of state and force fields for Feynman-Hibbs-corrected Mie fluids. II. Application to mixtures of helium, neon, hydrogen, and deuterium.

We extend the statistical associating fluid theory of quantum corrected Mie potentials (SAFT-VRQ Mie), previously developed for pure fluids [Aasen et al., J. Chem.

Autologous CAR T-cell therapies supply chain: challenges and opportunities?

Chimeric antigen receptor (CAR) T cells are considered a potentially disruptive cancer therapy, showing highly promising results. Their recent success and regulatory approval (both in the USA and Europe) are likely to generate a rapidly increasing demand and a need for the design of robust and scalable manufacturing and distribution models that will ensure timely and cost-effective delivery of the therapy to the patient. However, there are challenging tasks as these therapies are accompanied by a series of constraints and particularities that need to be taken into consideration in the decision-making process.

Application of Photo-Electrochemically Generated Hydrogen with Fuel Cell Based Micro-Combined Heat and Power: A Dynamic System Modelling Study.

Photo-electrochemical (PEC) hydrogen generation is a promising technology and alternative to photovoltaic (PV)-electrolyser combined systems. Since there are no commercially available PEC cells and very limited field trials, a computer simulation was used to assess the efficacy of the approach for different domestic applications. Three mathematical models were used to obtain a view on how PEC generated hydrogen is able to cover demands for a representative dwelling.

Efficient Screening for Ternary Molecular Ionic Cocrystals Using a Complementary Mechanosynthesis and Computational Structure Prediction Approach.

The discovery of molecular ionic cocrystals (ICCs) of active pharmaceutical ingredients (APIs) widens the opportunities for optimizing the physicochemical properties of APIs whilst facilitating the delivery of multiple therapeutic agents. However, ICCs are often observed serendipitously in crystallization screens and the factors dictating their crystallization are poorly understood. We demonstrate here that mechanochemical ball milling is a versatile technique for the reproducible synthesis of ternary molecular ICCs in less than 30 min of grinding with or without solvent.

Network-based piecewise linear regression for QSAR modelling.

Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression.

Destabilization of Eukaryote mRNAs by 5' Proximal Stop Codons Can Occur Independently of the Nonsense-Mediated mRNA Decay Pathway.

In eukaryotes, the binding of poly(A) binding protein (PAB) to the poly(A) tail is central to maintaining mRNA stability. PABP interacts with the translation termination apparatus, and with eIF4G to maintain 3'-5' mRNA interactions as part of an mRNA closed loop. It is however unclear how ribosome recycling on a closed loop mRNA is influenced by the proximity of the stop codon to the poly(A) tail, and how post-termination ribosome recycling affects mRNA stability.

Comparison of physics-based and data-driven modelling techniques for dynamic optimisation of fed-batch bioprocesses.

The development of digital bioprocessing technologies is critical to operate modern industrial bioprocesses. This study conducted the first investigation on the efficiency of using physics-based and data-driven models for the dynamic optimisation of long-term bioprocess. More specifically, this study exploits a predictive kinetic model and a cutting-edge data-driven model to compute open-loop optimisation strategies for the production of microalgal lutein during a fed-batch operation.

A Predictive Mathematical Model of Cell Cycle, Metabolism, and Apoptosis of Monoclonal Antibody-Producing GS-NS0 Cells.

The monoclonal antibody (mAb) industry is witnessing unprecedented growth, with an increasing range of new molecules and biosimilars as well as disease targets approved than ever before. Competition necessitates pharmaceutical companies to reduce development/production costs and time-to-market. To this aim, mathematical modeling can aid traditional experiment-only-based process development by reducing the design space, integrating scales, and assisting in identifying optimal operating conditions in less time and with lower expense.

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization.

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors.

Powering sustainable development within planetary boundaries.

The concept of planetary boundaries identifies a safe space for humanity. Current energy systems are primarily designed with a focus on total cost minimization and bounds on greenhouse gas emissions. Omitting planetary boundaries in energy systems design can lead to energy mixes unable to power our sustainable development.

Design Principles for Compartmentalization and Spatial Organization of Synthetic Genetic Circuits.

Compartmentalization is a hallmark of cellular systems and an ingredient actively exploited in evolution. It is also being engineered and exploited in synthetic biology, in multiple ways. While these have demonstrated important experimental capabilities, understanding design principles underpinning compartmentalization of genetic circuits has been elusive.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs.

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute.

Global optimization in Hilbert space.

We propose a complete-search algorithm for solving a class of non-convex, possibly infinite-dimensional, optimization problems to global optimality. We assume that the optimization variables are in a bounded subset of a Hilbert space, and we determine worst-case run-time bounds for the algorithm under certain regularity conditions of the cost functional and the constraint set. Because these run-time bounds are independent of the number of optimization variables and, in particular, are valid for optimization problems with infinitely many optimization variables, we prove that the algorithm converges to an -suboptimal global solution within finite run-time for any given termination tolerance .