GCSB 2020 AS VIRTUAL CONFERENCE

Due to the uncertenties about meeting regulations and to ensure the safety of all participants, we decided that this year’s GASB conference will be virtual! We are working on adapting the program to still be exciting, diverse, and interactive!

Besides keynote talks we have open slots for talks from applicants and we will also provide a digital poster session. Just submit your abstract during registration!

(To those who already registered, we will fully refund your tickets!)

INFO: If you registered and haven’t received information on how to participate, please send an email to: conference@ga-sb.de.

Keynotes

We are happy to announce the following confirmed keynote speakers:

Víctor de LorenzoKeynote

SynBio at a global scale: from the Petri dish to planet Earth:
Since the birth of Synthetic Biology in the early 2000s, new challenges have acquired an unanticipated relevance owing to their impact on the global Earth's homeostasis. They include the unacceptably high atmospheric levels of green house gases, the worrying pollution of the oceans with very recalcitrant plastics and microplastics and the noxious effects of micropollutants on many ecosystems. Global problems ask for global solutions and the environmental microbiome—because of its dimension and its amazing activities—may end up being out best instrument to both counter the impact of industrial development and enable a new, sustainable partnership with Nature. While the whole planet is afflicted at a global scale by chemical pollution and anthropogenic emissions, the ongoing development of systems and synthetic biology, modern chemistry and some key concepts from ecological theory allow us to tackle this phenomenal challenge and propose large-scale interventions aimed at reversing and even improving this state of affairs. This involves [i] identification of key reactions or processes that need to be re-established (or altogether created) for ecosystem reinstallation, [ii] implementation of such reactions in natural or designer hosts able to self-replicate and deliver the corresponding activities when/where needed in a fashion guided by sound ecological modelling, [iii] dispersal of niche-creating agents at a global scale and [iv] containment, monitoring and risk assessment of the whole process. The pillar of this new scenario includes a deep engineering of microorganisms as live chassis for delivering beneficial activities and multi-scale environmental interventions for pollution prevention/remediation (including climatic change). Current advances in the use of environmental bacteria as SynBio chassis of choice for meeting some of these environmental objectives will be addressed.

Víctor de LorenzoKeynote

SynBio at a global scale: from the Petri dish to planet Earth:
Since the birth of Synthetic Biology in the early 2000s, new challenges have acquired an unanticipated relevance owing to their impact on the global Earth's homeostasis. They include the unacceptably high atmospheric levels of green house gases, the worrying pollution of the oceans with very recalcitrant plastics and microplastics and the noxious effects of micropollutants on many ecosystems. Global problems ask for global solutions and the environmental microbiome—because of its dimension and its amazing activities—may end up being out best instrument to both counter the impact of industrial development and enable a new, sustainable partnership with Nature. While the whole planet is afflicted at a global scale by chemical pollution and anthropogenic emissions, the ongoing development of systems and synthetic biology, modern chemistry and some key concepts from ecological theory allow us to tackle this phenomenal challenge and propose large-scale interventions aimed at reversing and even improving this state of affairs. This involves [i] identification of key reactions or processes that need to be re-established (or altogether created) for ecosystem reinstallation, [ii] implementation of such reactions in natural or designer hosts able to self-replicate and deliver the corresponding activities when/where needed in a fashion guided by sound ecological modelling, [iii] dispersal of niche-creating agents at a global scale and [iv] containment, monitoring and risk assessment of the whole process. The pillar of this new scenario includes a deep engineering of microorganisms as live chassis for delivering beneficial activities and multi-scale environmental interventions for pollution prevention/remediation (including climatic change). Current advances in the use of environmental bacteria as SynBio chassis of choice for meeting some of these environmental objectives will be addressed.

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Nicola PatronPlant Synthetic Biology

Recoding Plant Metabolism
Plants provide the potential for rapid production of complex molecules from water and light but, until recently, we lacked the tools and data necessary for complex engineering of plant systems. The application of engineering principles to plant biology has enabled us to establish platforms for high­-throughput automated experimentation at nanoscales. We are combining these technologies with genome editing and comparative genomics to investigate how regulatory functions are encoded in plant DNA. We are applying our knowledge to engineer plants as photosynthetic platforms for manufacturing high-value products for health and industry and to improve agricultural and nutritional traits in crops.

Nicola PatronPlant Synthetic Biology

Recoding Plant Metabolism
Plants provide the potential for rapid production of complex molecules from water and light but, until recently, we lacked the tools and data necessary for complex engineering of plant systems. The application of engineering principles to plant biology has enabled us to establish platforms for high­-throughput automated experimentation at nanoscales. We are combining these technologies with genome editing and comparative genomics to investigate how regulatory functions are encoded in plant DNA. We are applying our knowledge to engineer plants as photosynthetic platforms for manufacturing high-value products for health and industry and to improve agricultural and nutritional traits in crops.

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Heike SiebertComputational Synthetic Biology

Designing molecular circuits using logical networks:

Mathematical modeling has become an important tool in systems biology
research. Models help to structure and formalize available knowledge on
the systems, to analyze complex behavior and to test hypotheses in silico.
In synthetic biology, in particular in molecular circuit design,
mathematical approaches can help to explore the usually vast design spaces
and identify optimal solutions. Oftentimes, the functional requirements
and desired mechanisms are, a priori, described using processing logic and
boolean gates, giving rise to a formalization in terms of boolean, or more
generally speaking, logic networks. Here, the system components are
described as variables only taking few values representing activity
levels, in the simplest case just distinguishing between active and
non-active. Effects of interactions are described using logical functions.
While constituting an idealized description, these models are well suited
for systems where steep response profiles are utilized for robust
performance and, more generally, for a first analysis of suitable network
topologies. Moreover, a wide variety of sophisticated tools is available
for extensive analysis of huge design spaces, allowing for the necessary
flexibility to incorporate constraints reflecting demands of circuit
construction in the lab in the search for optimal solutions.

In this talk, I want to give some insights into the capabilities of this
approach by discussing two applications. First, we will look at the
problem of designing classifier circuits that can be introduced into
living cells, sense endogenous molecular signals, classify them as
type-specific and finally trigger a response such as the production of a
protein. Advances have been made that allow to construct circuits that
detect presence or absence of cancer biomarkers and are capable of setting
off processes resulting in cell death accordingly. Design of such
classifier circuits is a complex task since optimal circuits should not
only be capable of accurate classification, but their architecture needs
to be amenable to construction in the lab, resulting in complex
constraints on the search problem. I will present design approaches for
Boolean classifiers and discuss different classifier concepts and
optimization strategies as well as performance scores. The second
application aims at finding optimal designs for Turing pattern generators,
i.e., multi-cell systems capable of self-ordering and giving rise to
tissue patterns. In a team comprising mathematicians, synthetic and
developmental biologists, we combed through the space of two and three
element systems capable of generating patterns, complementarily using
logical and differential equation models. The models were evaluated with
respect to different performance and robustness measures using analytical
approaches and simulation. One of the optimal candidates has now been
constructed in the lab, showing first promising results.

Heike SiebertComputational Synthetic Biology

Designing molecular circuits using logical networks:

Mathematical modeling has become an important tool in systems biology
research. Models help to structure and formalize available knowledge on
the systems, to analyze complex behavior and to test hypotheses in silico.
In synthetic biology, in particular in molecular circuit design,
mathematical approaches can help to explore the usually vast design spaces
and identify optimal solutions. Oftentimes, the functional requirements
and desired mechanisms are, a priori, described using processing logic and
boolean gates, giving rise to a formalization in terms of boolean, or more
generally speaking, logic networks. Here, the system components are
described as variables only taking few values representing activity
levels, in the simplest case just distinguishing between active and
non-active. Effects of interactions are described using logical functions.
While constituting an idealized description, these models are well suited
for systems where steep response profiles are utilized for robust
performance and, more generally, for a first analysis of suitable network
topologies. Moreover, a wide variety of sophisticated tools is available
for extensive analysis of huge design spaces, allowing for the necessary
flexibility to incorporate constraints reflecting demands of circuit
construction in the lab in the search for optimal solutions.

In this talk, I want to give some insights into the capabilities of this
approach by discussing two applications. First, we will look at the
problem of designing classifier circuits that can be introduced into
living cells, sense endogenous molecular signals, classify them as
type-specific and finally trigger a response such as the production of a
protein. Advances have been made that allow to construct circuits that
detect presence or absence of cancer biomarkers and are capable of setting
off processes resulting in cell death accordingly. Design of such
classifier circuits is a complex task since optimal circuits should not
only be capable of accurate classification, but their architecture needs
to be amenable to construction in the lab, resulting in complex
constraints on the search problem. I will present design approaches for
Boolean classifiers and discuss different classifier concepts and
optimization strategies as well as performance scores. The second
application aims at finding optimal designs for Turing pattern generators,
i.e., multi-cell systems capable of self-ordering and giving rise to
tissue patterns. In a team comprising mathematicians, synthetic and
developmental biologists, we combed through the space of two and three
element systems capable of generating patterns, complementarily using
logical and differential equation models. The models were evaluated with
respect to different performance and robustness measures using analytical
approaches and simulation. One of the optimal candidates has now been
constructed in the lab, showing first promising results.

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Yaakov "Kobi" BenensonMammalian Synthetic Biology

Cancer cell classifier as a therapeutic prototype for hepatocellular carcinoma

Research in biomolecular computing and synthetic biology has long sought to enable new types of therapeutic approaches based on multi-input sensing of molecular disease indicators, a molecular level computation to determine the intensity of the therapeutic response, and the potentiation of a therapy in situ in highly precise and coordinated fashion. Cell classifier gene circuits were introduced to enable precise identification of heterogeneous cell types via complex logical integration of multiple cellular inputs. Cancer has been expected to benefit the most from cell classifier approaches due to tumor similarity to healthy cells, tumor heterogeneity, and its dissemination both at primary and secondary loci. With that, experimental demonstrations of cell classifiers’ preclinical translation have been rare. Here we engineer and characterize a candidate cancer gene therapy agent for hepatocellular carcinoma (HCC) powered by a cell classifier gene circuit integrating transcriptional and microRNA inputs. We prove that a multi-input circuit is necessary and sufficient to enable tumor-specific expression of a therapeutic agent in vivo, while ensuring lack of expression in non-tumor liver and other healthy organs despite systemic delivery and broad tropism of the viral vector. We also prove that the expression specificity correlates with efficacy and toxicity of the therapeutic candidate in the orthotopic mouse model of HCC, and that the candidate furnished with the full classifier program leads to a substantial reduction in the tumor load without causing obvious adverse effects. Our findings support the notion that multi-input gene circuits for precise cell targeting are a promising avenue for the next generation of cancer gene therapies.

Yaakov "Kobi" BenensonMammalian Synthetic Biology

Cancer cell classifier as a therapeutic prototype for hepatocellular carcinoma

Research in biomolecular computing and synthetic biology has long sought to enable new types of therapeutic approaches based on multi-input sensing of molecular disease indicators, a molecular level computation to determine the intensity of the therapeutic response, and the potentiation of a therapy in situ in highly precise and coordinated fashion. Cell classifier gene circuits were introduced to enable precise identification of heterogeneous cell types via complex logical integration of multiple cellular inputs. Cancer has been expected to benefit the most from cell classifier approaches due to tumor similarity to healthy cells, tumor heterogeneity, and its dissemination both at primary and secondary loci. With that, experimental demonstrations of cell classifiers’ preclinical translation have been rare. Here we engineer and characterize a candidate cancer gene therapy agent for hepatocellular carcinoma (HCC) powered by a cell classifier gene circuit integrating transcriptional and microRNA inputs. We prove that a multi-input circuit is necessary and sufficient to enable tumor-specific expression of a therapeutic agent in vivo, while ensuring lack of expression in non-tumor liver and other healthy organs despite systemic delivery and broad tropism of the viral vector. We also prove that the expression specificity correlates with efficacy and toxicity of the therapeutic candidate in the orthotopic mouse model of HCC, and that the candidate furnished with the full classifier program leads to a substantial reduction in the tumor load without causing obvious adverse effects. Our findings support the notion that multi-input gene circuits for precise cell targeting are a promising avenue for the next generation of cancer gene therapies.

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Eriko TakanoSynBio-based compound production

Potentials of synthetic biology for fine and speciality chemical production:
We aim to design and construct organisms with new functionalities by exploiting synthetic biology for metabolic engineering in the context of chemical production. As a first step towards re-engineering these chemical pathways for enhanced productivity and diversity, we aim to understand the interchangeability of biosynthetic parts (Hernandez Ortega et al., Scientific Reports 2018) and have designed and assembled pathways using these parts (Cummings et al., PLOS Biol, 2019) and will engineer orthogonal circuits based on signalling molecule circuits (Biarnes-Carrera et al., ACS Synth Biol 2018). In addition, we are expanding our collection of computational tools for the detection and analysis of secondary metabolite biosynthesis gene clusters, to enrich our library of parts and building blocks for pathway engineering (Del Carratore et al., Commun Biol 2019). We use computational constraint-based modelling to pinpoint biosynthetic bottlenecks to target for further cellular engineering in a synthetic biology strategy (Amara et al., BMC Genomics. 2018). We combine this analysis with high-resolution MS analysis, which we also employ for the debugging of the engineered systems. This finally leads to the completion of the cycle by learning rules for efficient design (Jervis et al., ACS Synth Biol 2018). By exploiting all these tools in the Design/Build/Test/Learn cycle, the Manchester Synthetic Biology Research Centre, SYNBIOCHEM, provides a platform for the high-throughput engineering of fine and speciality chemicals production in microbial systems (Carbonell et al., Communications Biology 2018).

Eriko TakanoSynBio-based compound production

Potentials of synthetic biology for fine and speciality chemical production:
We aim to design and construct organisms with new functionalities by exploiting synthetic biology for metabolic engineering in the context of chemical production. As a first step towards re-engineering these chemical pathways for enhanced productivity and diversity, we aim to understand the interchangeability of biosynthetic parts (Hernandez Ortega et al., Scientific Reports 2018) and have designed and assembled pathways using these parts (Cummings et al., PLOS Biol, 2019) and will engineer orthogonal circuits based on signalling molecule circuits (Biarnes-Carrera et al., ACS Synth Biol 2018). In addition, we are expanding our collection of computational tools for the detection and analysis of secondary metabolite biosynthesis gene clusters, to enrich our library of parts and building blocks for pathway engineering (Del Carratore et al., Commun Biol 2019). We use computational constraint-based modelling to pinpoint biosynthetic bottlenecks to target for further cellular engineering in a synthetic biology strategy (Amara et al., BMC Genomics. 2018). We combine this analysis with high-resolution MS analysis, which we also employ for the debugging of the engineered systems. This finally leads to the completion of the cycle by learning rules for efficient design (Jervis et al., ACS Synth Biol 2018). By exploiting all these tools in the Design/Build/Test/Learn cycle, the Manchester Synthetic Biology Research Centre, SYNBIOCHEM, provides a platform for the high-throughput engineering of fine and speciality chemicals production in microbial systems (Carbonell et al., Communications Biology 2018).

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Chase BeiselTools & Genome Editing

Harnessing CRISPR-Cas9 for massively multiplexed diagnostics
CRISPR-Cas systems represent diverse defense systems in prokaryotes and the source of CRISPR technologies that are revolutionizing basic research, medicine, agriculture, and biotechnology. The ongoing exploration of these systems has revealed a myriad of different mechanisms and functions that in turn have been translated into widely varying technologies. Here, I will describe my group’s ongoing work exploring CRISPR-Cas9, the source of the Cas9 nuclease widely used for genome editing. I will show how this otherwise well-characterized system has additional tricks up its sleeve that extend beyond its canonical function of employing CRISPR-encoded guide RNAs for DNA targeting. One recent insight in particular became the foundation an entirely novel diagnostic platform that can readily detect hundreds to thousands of distinct RNA transcripts at one time, allowing the detection of RNAs associated with SARS-CoV-2 as well as many other respiratory viruses in a single reaction. This new technology could bring a new level of multiplexing to point-of-care diagnostics and highlights how basic biological research can continue to drive advances in synthetic biology.

Chase BeiselTools & Genome Editing

Harnessing CRISPR-Cas9 for massively multiplexed diagnostics
CRISPR-Cas systems represent diverse defense systems in prokaryotes and the source of CRISPR technologies that are revolutionizing basic research, medicine, agriculture, and biotechnology. The ongoing exploration of these systems has revealed a myriad of different mechanisms and functions that in turn have been translated into widely varying technologies. Here, I will describe my group’s ongoing work exploring CRISPR-Cas9, the source of the Cas9 nuclease widely used for genome editing. I will show how this otherwise well-characterized system has additional tricks up its sleeve that extend beyond its canonical function of employing CRISPR-encoded guide RNAs for DNA targeting. One recent insight in particular became the foundation an entirely novel diagnostic platform that can readily detect hundreds to thousands of distinct RNA transcripts at one time, allowing the detection of RNAs associated with SARS-CoV-2 as well as many other respiratory viruses in a single reaction. This new technology could bring a new level of multiplexing to point-of-care diagnostics and highlights how basic biological research can continue to drive advances in synthetic biology.

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Verena SiewersMetabolic Engineering

Metabolite biosensors in yeast metabolic engineering:
The yeast Saccharomyces cerevisiae has been engineered to produce a plethora of industrially relevant compounds including biofuels, chemicals and pharmaceuticals. However, the initial production levels of these engineered strains are often very low.
To improve the performance of such cell factories, biosensors represent a useful asset. For instance, sensors that respond to environmental or endogenous signals related to the state of the culture enable a separation of growth and production phase. Of specific importance are metabolite sensors that sense the target compound itself or one of its precursors. These make it possible to easily detect and select individual cells with superior production. On the other hand, they allow for the regulation of pathway enzymes dependent on the metabolic state of the cell and thus the incorporation of feed-forward and feed-back regulation into heterologous metabolic pathways. Different examples of biosensors responding to lipid precursors, isoprenoids or flavonoids and their application will be presented.

Verena SiewersMetabolic Engineering

Metabolite biosensors in yeast metabolic engineering:
The yeast Saccharomyces cerevisiae has been engineered to produce a plethora of industrially relevant compounds including biofuels, chemicals and pharmaceuticals. However, the initial production levels of these engineered strains are often very low.
To improve the performance of such cell factories, biosensors represent a useful asset. For instance, sensors that respond to environmental or endogenous signals related to the state of the culture enable a separation of growth and production phase. Of specific importance are metabolite sensors that sense the target compound itself or one of its precursors. These make it possible to easily detect and select individual cells with superior production. On the other hand, they allow for the regulation of pathway enzymes dependent on the metabolic state of the cell and thus the incorporation of feed-forward and feed-back regulation into heterologous metabolic pathways. Different examples of biosensors responding to lipid precursors, isoprenoids or flavonoids and their application will be presented.

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Andreas MöglichProtein Engineering

Optogenetic Control of Nucleic Acids - Mechanisms and Applications

Sensory photoreceptors underpin diverse adaptations of organismal behavior, lifestyle and physiology to incident light. In optogenetics, photoreceptors double as genetically encoded, light-gated actuators and enable the noninvasive control of cellular circuits with spatiotemporal precision. Against this backdrop, we investigate and engineer blue-light-responsive receptors of the light-oxygen-voltage (LOV) family. Certain of these photoreceptors unlock novel optogenetic modalities, in particular the control of nucleic-acid-based processes, e.g., transcription, translation and endonuclease activity. Biochemical analyses of receptor structure, function and signaling mechanism unravel the molecular bases for light-dependent allostery, thus informing protein engineering efforts and paving the way towards innovative applications in synthetic biology.

Andreas MöglichProtein Engineering

Optogenetic Control of Nucleic Acids - Mechanisms and Applications

Sensory photoreceptors underpin diverse adaptations of organismal behavior, lifestyle and physiology to incident light. In optogenetics, photoreceptors double as genetically encoded, light-gated actuators and enable the noninvasive control of cellular circuits with spatiotemporal precision. Against this backdrop, we investigate and engineer blue-light-responsive receptors of the light-oxygen-voltage (LOV) family. Certain of these photoreceptors unlock novel optogenetic modalities, in particular the control of nucleic-acid-based processes, e.g., transcription, translation and endonuclease activity. Biochemical analyses of receptor structure, function and signaling mechanism unravel the molecular bases for light-dependent allostery, thus informing protein engineering efforts and paving the way towards innovative applications in synthetic biology.

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Katharina LandfesterMinimal Synthetic Bioiology

Polymeric modules for synthetic biology

Katharina Landfester

Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany

Since many years, there is a quest for minimal cells in the field of synthetic biology, potentially allowing a maximum of efficien¬cy in biotechnological processes. Although the so-called “protocells” are usually referred to in all papers that attempt a cumulative definition of Synthetic Biology, research in this area has been largely under-represented. Our aim is at developing vesicular structures, i.e. protocells, based on block copolymer self-assembly and engulfed nanocontainers with incorporated functions, such as energy production and the control of transport properties through nanomembranes. Therefore, we have designed and developed nanocapsules that act as cell-like compartments and can be loaded with enzymes for synthetic biology and chemistry. In addition, self-assembly of well-defined diblock copolymers has been used to generate polymersomes and hybrid liposomes/polymersomes. Both strategies allow the compartimentalization on the nano- or microscale and conducting enzymatic or chemical reactions in the confinement of the polymersomes/ nanocarriers. New block copolymers and permeable nanocarriers have been synthesized and optimized. With these protocols we were able to establish an enzymatic reaction cascade within droplet-based compartments. These compartments can act as cell-like functions to regenerate NAD. For these tasks, novel conductive polymer nanoparticles have been developed which will be included into the protocells for the NAD regeneration by light. Enzyme-complexes are assembled that will fulfill these requirements.

Katharina LandfesterMinimal Synthetic Bioiology

Polymeric modules for synthetic biology

Katharina Landfester

Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany

Since many years, there is a quest for minimal cells in the field of synthetic biology, potentially allowing a maximum of efficien¬cy in biotechnological processes. Although the so-called “protocells” are usually referred to in all papers that attempt a cumulative definition of Synthetic Biology, research in this area has been largely under-represented. Our aim is at developing vesicular structures, i.e. protocells, based on block copolymer self-assembly and engulfed nanocontainers with incorporated functions, such as energy production and the control of transport properties through nanomembranes. Therefore, we have designed and developed nanocapsules that act as cell-like compartments and can be loaded with enzymes for synthetic biology and chemistry. In addition, self-assembly of well-defined diblock copolymers has been used to generate polymersomes and hybrid liposomes/polymersomes. Both strategies allow the compartimentalization on the nano- or microscale and conducting enzymatic or chemical reactions in the confinement of the polymersomes/ nanocarriers. New block copolymers and permeable nanocarriers have been synthesized and optimized. With these protocols we were able to establish an enzymatic reaction cascade within droplet-based compartments. These compartments can act as cell-like functions to regenerate NAD. For these tasks, novel conductive polymer nanoparticles have been developed which will be included into the protocells for the NAD regeneration by light. Enzyme-complexes are assembled that will fulfill these requirements.

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Testimonials

Read here what participants of previous SynBio conferences said about us:

Sebastian WormsUC Louvain, Belgium

"Can heartily recommend the GASB II Conference! Probably the nicest synbio conference I've been to. Great speakers, great organization. Thought-provoking."

Tom SpeedyIntegrated DNA Technologies, Oxford, UK

"Excellent first day at GASB II Conference! Great speakers, amazing audience, in the wonderful city Berlin! IDT is looking forward to day 2!"

Simon KelterbornHU Berlin, Germany

"The conference was really a blast, everything was so well organized and went smooth, I really enjoyed it! Perfect mix for me of talk density, breaks, break-out sessions, senior keynote speakers, young speakers, nice people, nice atmosphere,… can’t name it all. Really great great job, Keep it up!"

About the organizers

This is a conference organized by GASB
(the German Association for Synthetic Biology e.V.), in collaboration with Barbara Di Ventura.
We are proud in hosting this conference as a service for the growing SynBio community in Germany.

 

This conference and the work of GASB are supported by:

 

BioMarket Insights is GASB media partner

 

Click here to learn more about the organizer GASB e.V.

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Contact

For more information, feedback, or if you have further questions, please contact us at: info@gcsb.info