Panoptic dual-use management: preventing deliberate pandemics in an age of synthetic biology and artificial intelligence

Powerful new technologies can have profound global security implications. In this thesis, I investigate how advances in synthetic biology and artificial intelligence could have dual-use potential and enable the deliberate release of pandemic pathogens. I review risks from synthetic biology based on case studies on wildlife virus discovery, viral engineering for vaccine design, and viral engineering for gene therapy. For assessing impacts of artificial intelligence, I consider large language models and biodesign tools. I find that related advances can create new methods to engineer pathogens and make such capabilities increasingly accessible to non-specialists. These risks are not well captured by existing risk mitigation measures. I argue that the management of dual-use virological research is currently defined by oversight of individual research projects. This is effective for addressing high-risk research but fails to address risks from a more diffuse set of research and technologies with dual-use potential. To help mitigate these risks, I introduce the idea of panoptic dual-use management. Inspired by methodologies to reduce carbon emissions, panoptic dual-use management involves treating associated dual-use risks as negative externalities and creating appropriate incentives so they are accounted for in decisions between projects. I explore ways in which such incentives could be created for various stakeholders. For instance, funding bodies could use dual-use risks as a tiebreaker between projects on the brink of getting funded, a practice which would incentivise researchers to preferentially propose projects with lower dual-use risks. To realise this proposal, I sketch out a framework for assigning tiered dual-use scores to virological research. I conclude by highlighting the importance of combining different dual-use management approaches across stakeholders and geographies to establish an effective complex of overlapping mitigation regimes

Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools

As advancements in artificial intelligence propel progress in the life sciences, they may also enable the weaponisation and misuse of biological agents. This article differentiates two classes of AI tools that pose such biosecurity risks: large language models (LLMs) and biological design tools (BDTs). LLMs, such as GPT-4, are already able to provide dual-use information that could have enabled historical biological weapons efforts to succeed. As LLMs are turned into lab assistants and autonomous science tools, this will further increase their ability to support research. Thus, LLMs will in particular lower barriers to biological misuse. In contrast, BDTs will expand the capabilities of sophisticated actors. Concretely, BDTs may enable the creation of pandemic pathogens substantially worse than anything seen to date and could enable forms of more predictable and targeted biological weapons. In combination, LLMs and BDTs could raise the ceiling of harm from biological agents and could make them broadly accessible. The differing risk profiles of LLMs and BDTs have important implications for risk mitigation. LLM risks require urgent action and might be effectively mitigated by controlling access to dangerous capabilities. Mandatory pre-release evaluations could be critical to ensure that developers eliminate dangerous capabilities. Science-specific AI tools demand differentiated strategies to allow access to legitimate users while preventing misuse. Meanwhile, risks from BDTs are less defined and require monitoring by developers and policymakers. Key to reducing these risks will be enhanced screening of gene synthesis, interventions to deter biological misuse by sophisticated actors, and exploration of specific controls of BDTs.Comment: 15 pages, 1 figur

Phase structure of the N=1 supersymmetric Yang-Mills theory at finite temperature

Supersymmetry (SUSY) has been proposed to be a central concept for the physics beyond the standard model and for a description of the strong interactions in the context of the AdS/CFT correspondence. A deeper understanding of these developments requires the knowledge of the properties of supersymmetric models at finite temperatures. We present a Monte Carlo investigation of the finite temperature phase diagram of the N=1 supersymmetric Yang-Mills theory (SYM) regularised on a space-time lattice. The model is in many aspects similar to QCD: quark confinement and fermion condensation occur in the low temperature regime of both theories. A comparison to QCD is therefore possible. The simulations show that for N=1 SYM the deconfinement temperature has a mild dependence on the fermion mass. The analysis of the chiral condensate susceptibility supports the possibility that chiral symmetry is restored near the deconfinement phase transition.Comment: 26 pages, 12 figure

N=1 supersymmetric Yang-Mills theory on the lattice

Numerical simulations of supersymmetric theories on the lattice are intricate and challenging with respect to their theoretical foundations and algorithmic realisation. Nevertheless, the simulations of a four-dimensional supersymmetric gauge theory have made considerable improvements over the recent years. In this contribution we summarise the results of our collaboration concerning the mass spectrum of this theory. The investigation of systematic errors allows now a more precise estimate concerning the expected formation of supersymmetric multiplets of the lightest particles. These multiplets contain flavour singlet mesons, glueballs, and an additional fermionic state.Comment: presented at the 31st International Symposium on Lattice Field Theory (Lattice 2013), 29 July - 3 August 2013, Mainz, German

APP Expression in Primary Neuronal Cell Cultures fromP6 Mice during in vitro Differentiation

Primary neuronal cell cultures from P6 mice were investigated in order to study amyloid protein precursor (APP) gene expression in differentiating neurons. Cerebellar granule cells which strongly express APP 695 allowed the identification of three distinct isoforms of neuronal APP 695. The high-molecular-weight form of APP 695 is sialylated. The expression pattern of neuronal APP 695 changes during in vitro differentiation. Sialylated forms become more abundant upon longer cultivation time. The secreted forms of sialylated, neuronal APP 695 are shown to comigrate with APP isolated from cerebrospinal fluid. We suggest that the different sialylation states of APP 695 may reflect the modulation of cell-cell and cell-substrate interactions during in vitro differentiation and regeneration

Ziele und Empfehlungen für die Entwicklung der gemeinwohlorientierten Weiterbildung in NRW

Über den Hintergrund des zweijährigen Prozesses der Evaluation von Weiterbildungs mitteln in NRW habe ich bereits im forum erwachsenenbildung 2/2011 berichtet. Anlass der Vorgänge war eine Prüfmitteilung des Landesrechnungshofes zur Berechnung des Förderanspruchs. Evaluiert wurde die Wirksamkeit der Weiterbildungsmittel des Weiterbildungsgesetztes (WbG) durch das Deutsche Institut für Erwachsenenbildung (DIE)

Promoting versatile vaccine development for emerging pandemics

The ongoing COVID-19 pandemic has demonstrated the importance of rapid and versatile development of emergency medical countermeasures such as vaccines. We discuss the role of platform vaccines and prototype pathogen research in modern vaccine development, and outline how previous pathogen-specific funding approaches can be improved to adequately promote vaccine R&D for emerging pandemics. We present a more comprehensive approach to financing vaccine R&D, which maximises biomedical pandemic preparedness by promoting flexible vaccine platforms and translatable research into prototype pathogens. As the numerous platform-based SARS-CoV-2 vaccines show, funders can accelerate pandemic vaccine development by proactively investing in versatile platform technologies. For certain emerging infectious diseases, where vaccine research can translate to other related pathogens with pandemic potential, investment decisions should reflect the full social value of increasing overall preparedness, rather than just the value of bringing a vaccine to market for individual pathogens

Numerische Bestimmung von Quarkpotential, Glueball-Massen und Phasenstruktur in der N = 1 supersymmetrischen Yang-Mills-Theorie

Eines der vielversprechendsten Modelle für Physik jenseits des Standardmodells ist die Supersymmetrie. Diese Arbeit entstand im Rahmen der DESY-Münster-Kollaboration, die sich insbesondere mit der N=1 supersymmetrischen Yang-Mills-Theorie (SYM) beschäftigt. Der Schwerpunkt dieser Arbeit liegt auf der numerischen Bestimmung von Quarkpotential, Glueball-Massen und der Phasenstruktur in der N=1 supersymmetrischen Yang-Mills-Theorie mit Hilfe von Monte-Carlo-Simulationen auf dem Gitter. Es werden verschiedene Methoden untersucht, um die Unsicherheiten bei der Massenbestimmung der Gluebälle zu verringern. Der Fokus liegt dabei auf den Smearing-Methoden und ihrem Einsatz beim variational smearing sowie der Verwendung verschiedener Glueball-Operatoren. Parallel zu den Simulationen bei Temperatur Null wurden Simulationen bei endlicher Temperatur durchgeführt, um das Verhalten der Polyakov-Schleifen und des Gluino-Kondensats im Phasendiagramm genauer zu analysieren

Differential technology development: A responsible innovation principle for navigating technology risks

Responsible innovation efforts to date have largely focused on shaping individual technologies. However, as demonstrated by the preferential advancement of low-emission technologies, certain technologies reduce risks from other technologies or constitute low-risk substitutes. Governments and other relevant actors may leverage risk-reducing interactions across technology portfolios to mitigate risks beyond climate change. We propose a responsible innovation principle of “differential technology development”, which calls for leveraging risk-reducing interactions between technologies by affecting their relative timing. Thus, it may be beneficial to delay risk-increasing technologies and preferentially advance risk-reducing defensive, safety, or substitute technologies. Implementing differential technology development requires the ability to anticipate or identify impacts and intervene in the relative timing of technologies. We find that both are sometimes viable and that differential technology development may still be usefully applied even late in the diffusion of a harmful technology. A principle of differential technology development may inform government research funding priorities and technology regulation, as well as philanthropic research and development funders and corporate social responsibility measures. Differential technology development may be particularly promising to mitigate potential catastrophic risks from emerging technologies like synthetic biology and artificial intelligence