Society and synthetic cells:A position paper by the Future Panel on Synthetic Life

The BaSyC consortium, whose acronym stands for building a synthetic cell, proposes to develop a synthetic cell from the bottom up. In the context of this joined effort, the Rathenau Instituut and Radboud University Nijmegen have organised the Future Panel on Synthetic Life, consisting of societal experts, to explore the social challenges, dilemmas, and possible societal impacts of synthetic cell research, and to advise how this research may contribute to a fair and sustainable future. The goal for the Future Panel is to create an initial agenda for future political, academic, and public debate on the synthetic cell.The profile of science and technology is two-sided. On the one hand, they act as drivers for problem-solving, progress, and emancipation, but techno-scientific innovation can also give rise to disruptive threats. Therefore, societal reflection should be timely and anticipatory. Rather than asking what risks and benefits are involved, the question will be how to engage society in such a way that synthetic cell research can become a joint endeavour, responsive to societal hopes and concerns. Consequently, the Future Panel aimed to:• map the social challenges and dilemmas in a society where a synthetic cell exists;• identify conditions under which synthetic cell technology can be considered beneficial for society; and• advice on how these conditions can be realised.To contribute to this, the Future Panel discussed the role and perspectives of key stakeholders (academia, government and governance, industry, and civil society), besides more specific issues like public responses, biosafety, biosecurity, and intellectual property rights during multiple online and offline meetings within a period of two years. This position paper summarises the most important points of conversation, shared insights, key challenges, dilemmas that were discussed during these meetings, resulting in four recommendations, as a starting point for further analysis and debate.Key challengesDuring the deliberations, the Future Panel encountered four overarching challenges.1. The novelty of synthetic cell research makes it challenging to devise amethodology capable of anticipating public concerns in a domain where overt public attitudes do not exist as of yet.Society and synthetic cells 132. As long as the existing power structures within the contexts that shape developments in science and technology are not explicitly addressed, the development of a synthetic cell will inevitably reproduce and may even strengthen existing power inequalities.3. In order to involve civil society and allow citizens to articulate their views and concerns, besides factual information, the synthetic cell has to be positioned in a proper context: how to develop a responsible narrative that allows the public to actively relate to these developments?4. Even though the BaSyC project is halfway, there are still many unknowns, even unknown unknowns. A key challenge is to connect social, ethical, and science perspectives, and dilemmas, ambitions, and uncertainties related to the building of a synthetic cell.DilemmasDuring the panel discussions, many reasons have arisen, from different perspectives, for involving the general public, governments, industry and NGOs in an anticipatory way. However, doing this reveals some fundamental dilemmas and tensions that should be addressed.1. The BaSyC project is curiosity-driven, aspiring to deepen our understanding of life. At the same time, our desire to know is driven by an impetus to control. How to practice synthetic cell research as a dialogue with nature rather than an appropriation and instrumentalisation of the living cell?2. Many aspects of synthetic cell research are yet unknown. How to allow space for the unknown while, at the same time, opt for an anticipatory and imaginative approach to take the future social and ethical implications and concerns into account?3. How to make research more inclusive by involving public, politics and policy in such a way that it is fostering and inspirational rather than detrimental for curiosity-driven experimentation and exploration?4. Curiosity-driven science requires a great deal of specialism and thrives on serendipity. How to achieve convergence in science, involving multiple stakeholders and taking into account societal expectations and concerns, without frustrating the process of discovery?5. Deliberation requires a dialogue across disciplines, languages, and levels of information. How to combine different vocabularies, perspectives, socio- cultural and time horizons in a meaningful way?6. Within science and technology, and in particular biotechnology, there has long been a discussion about how to deal with knowledge and intellectual property rights. Should life be considered patentable or should life be seen as a common heritage that belongs to everybody?7. How to deal with researchers who need to make their work openly accessible, and companies, incubators, and organisations that want to protect their invention?8. Within projects of four to five years, researchers are under pressure to focus on and deliver scientific publications, while at the same time being encouraged to actively reflect on and engage with the potential societal impact of their work. How to balance conflicting expectations related to different time horizons?RecommendationsThe Future Panel proposes four recommendations for fostering a socially responsible development of the synthetic cell:1. Ensure that the synthetic cell contributes to a fair and sustainable futureTo foster sustainable synthetic cells, we need co-constructed narratives that allow us to explore how synthetic cells may contribute to a sustainable future. It is not enough to stimulate techno-scientific innovation as such. Governments must simultaneously stimulate social innovation, and promote broad stakeholder involvement in synthetic cell research.2. Organise participation of civil society in synthetic cell researchIn order to ensure that synthetic cell research contributes to a fair and sustainable society, an inclusive and participatory process of reflection is required, open to public intelligence, and sensitive to societal expectations and concerns. This requires innovative methods to engage the wisdom of the crowd. Meetings with societal stakeholders should be organised on relevant issues at different moments of the project and should be designed as in-between spaces in which different meanings, interests, and societal values come together and are made explicit.3. Foster a socially responsive academic ecosystemRather than endorsing the status quo, synthetic cell research emphasises the importance of rethinking the university of the 21st century, where research and education must become more inclusive and interactive, bent on developing long- term partnerships with society: with industry and governmental organisations, but first and foremost with society at large. Societal reflection and interaction with society should be an integral part of academic research and education. Therefore, researchers must be empowered to engage with society in such a way that dialogue and interaction become an inherent part of their work, from design to publication.4. Design social governance experiments aimed at renewing the regulatory landscape for new biotechnologies, including the synthetic cellEnsuring that the synthetic cell may contribute to a more sustainable and socially equitable world requires an adequate social understanding of governance and regulatory systems. The current regulatory system is not prepared for that task. We need a new system, which does not reproduce previous polemics. Besides looking at risks, a more comprehensive regulatory regime would integrate questions concerning sustainability, human rights, ethics, and societal desirability. Governance experiments co-designed with societal actors are needed to gain insight into the contours of such a new regulatory landscape on synthetic biology or new biotechnologies, including the synthetic cell

Das Stigma von Suchterkrankungen verstehen und überwinden = Understanding and overcoming the stigma of substance use disorders

Understanding and overcoming the stigma of substance use disorders Abstract. Stigma does harm to individuals with substance use disorders (SUD), and it increases the burden of SUDs. It presents a barrier to help seeking, results in lower treatment quality and increases social and health related consequences of SUDs. This applies to both the individual, societal and economic consequences of substance use. Moreover, stigmatizing persons with addictions is an ethical problem, since it discriminates against a certain group and infringes on their human dignity. Dealing with substance use disorders without stigma is possible. Eliminating the stigma of SUDs means finding better ways to deal with SUDs and to make these ways available to everyone. Instead of devaluing, marginalizing and disciplining persons with SUD, empowerment and appreciation need to be at the core of dealing with SUD in prevention, treatment and every day life

A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or “mutation signatures” at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5′ to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with “mixed-membership models” which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/

A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures.

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or "mutation signatures" at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5' to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with "mixed-membership models" which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/

Examples of visualizations and parameter values for the mutation signatures of the unconstrained (full) model and our independent model, where substitution patterns, two 5′ and 3′ bases and transcription strand direction are considered as mutation features.

<p>(A) The barplots are divided by 6 substitution patterns and transcription strand direction. In each division, 256 bars show joint probabilities of up to two base 5′ and 3′ bases (ApApNpApA, ApApNpApC, ApApNpApG, ApApNpApT, ⋯, TpTpNpTpT). (B, C) An example mutation signature representation and parameter values from our independent model, where mutation features (substitution patterns, two 5′ and 3′ bases and strand direction) are assumed to be independent (<i>L</i> = 6, <i><b>M</b></i> = (6, 4, 4, 4, 4, 2)). In the bottom five rectangles, the width of each box represents the frequencies of bases (A, C, G and T) at the substitution and flanking site. To highlight the most informative flanking sites, the heights of flanking site boxes are scaled by <math><mrow><mn>1</mn><mo>+</mo><mn>0</mn><mo>.</mo><mn>5</mn><mo>×</mo><mo>log</mo><msub><mo>∑</mo><mrow><mi>n</mi><mo>=</mo><mi>A</mi><mo>,</mo><mi>C</mi><mo>,</mo><mi>G</mi><mo>,</mo><mi>T</mi></mrow></msub><msubsup><mi>f</mi><mi>n</mi><mn>2</mn></msubsup></mrow></math>, where <i>f</i>_<i>n</i> is the parameter for each base, which can be interpreted as 1 − 0.5 × Rényi entropy [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005657#pgen.1005657.ref017" target="_blank">17</a>]. This is analogous to the information content scaling used in sequencing logos. In the top rectangle, the height of each box represents the conditional frequencies of mutated bases for each original base (C and T). In the upper right, the height of the + box represents the frequencies of mutations in the coding strand (the plus strand, the sense strand or the untranscribed strand in other words) whose nucleotide sequences directly corresponds to mRNA, whereas the height of − box represents those in the template strand (the minus strand, the antisense strand, the transcribed strand or the noncoding strand in other words) whose sequences are copied during the synthesis of mRNA.</p

Relationships among mutation signature model, topic models, and population structure models.

<p>Relationships among mutation signature model, topic models, and population structure models.</p