Messenger RNA (mRNA) vaccines are a new alternative to conventional vaccines with a prominent role in infectious disease control. These vaccines are produced in in vitro transcription (IVT) reactions, catalyzed by RNA polymerase in cascade reactions. To ensure an efficient and cost-effective manufacturing process, essential for a large-scale production and effective vaccine supply chain, the IVT reaction needs to be optimized. IVT is a complex reaction that contains a large number of variables that can affect its outcome. Traditional optimization methods rely on classic Design of Experiments methods, which are time-consuming and can present human bias or based on simplified assumptions. In this contribution, we propose the use of Machine Learning approaches to perform a data-driven optimization of an mRNA IVT reaction. A Bayesian optimization method and model interpretability techniques were used to automate experiment design, providing a feedback loop.IVT reaction conditions were found under 60 optimization runs that produced 12 g • L −1 in solely 2 h. The results obtained outperform published industry standards and data reported in literature in terms of both achievable reaction yield and reduction of production time. Furthermore, this shows the potential of Bayesian optimization as a cost-effective optimization tool within (bio)chemical applications.

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Abstract. In real world scenarios, the formation of consensus is an autoorganisation process by which actors have to make a joint assessment about a target subject being it a decision making problem or the formation of a collective opinion. In social simulation, models of opinion dynamics tackle the opinion formation phenomena. These models try to make an assessment, for instance, of the ideal conditions that lead an interacting group of agents to opinion consensus, polarisation or fragmentation. In this paper, we investigate the role of social relation structure in opinion dynamics using an interaction model of relative agreement. We present an agent-based model that defines social relations as multiple concomitant social networks and apply our model to an opinion dynamics model with bounded confidence. Moreover, we discuss the influence of complex social network topologies that capture the complexity of real-world social scenarios where actors interact in multiple contexts simultaneously. The paper builds on previous work about social space design with multiple contexts and context switching, to determine the influence of such complex social structures in a process such as opinion formation.

The structure of social relations is fundamental for the construction of plausible simulation scenarios. It shapes the way actors interact and create their identity within overlapping social contexts. Each actor interacts in multiple contexts within different types of social relations that constitute their social space. In this article, we present an approach to model structured agent societies with multiple coexisting social networks. We study the notion of context permeability, using a game in which agents try to achieve global consensus. We design and analyse two different models of permeability. In the first model, agents interact concurrently in multiple social networks. In the second, we introduce a context switching mechanism which adds a dynamic temporal component to agent interaction in the model. Agents switch between the different networks spending more or less time in each one. We compare these models and analyse the influence of different social networks regarding the speed of convergence to consensus. We conduct a series of experiments that show the impact of different configurations for coexisting social networks. This approach unveils both the limitations of the current modelling approaches and possible research directions for complex social space simulations.

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