Hands-on A
Exploring Metabolic Cross-Feeding in a Synthetic Pseudomonas Consortium Vittorio Venturi (ICGEB), Mihael Špacapan (ICTP), William R. Shoemaker (ICTP)
In this experiment, a synthetic consortium of two _Pseudomonas_ strains is designed to degrade ferulic acid via a metabolically complementary pathway. Ferulic acid, a common phenolic compound in the rhizosphere, is initially degraded by _Pseudomonas_ Strain A, which expresses key enzymes such as feruloyl-CoA synthetase and vanillin dehydrogenase to convert ferulic acid into vanillic acid. This intermediate is then utilized by _Pseudomonas_ Strain B, which carries enzymes like vanillate O-demethylase and protocatechuate dioxygenase to further degrade vanillic acid into protocatechuic acid. Protocatechuic acid subsequently enters the β-ketoadipate pathway, ultimately feeding into central carbon metabolism via TCA cycle intermediates. The consortium reflects metabolic cross-feeding, with each strain performing a distinct step in the degradation pathway. Growth dynamics and substrate conversion can be monitored through OD₆₀₀, CFU counts, FACS and metabolite profiling (i.e. presence of ferulic and/or vanillic acid in the minimal medium), allowing inference on the interdependence, efficiency, and stability of this two-strain system under defined conditions.
 
Hands-on B A primer on evolutionary inference from cancer sequencing datasets Giulio Caravagna, Riccardo Bergamini, Nicola Calonaci (University of Trieste) Understanding the evolutionary history of cancer is essential for interpreting its progression, therapeutic resistance, and clonal architecture. In this hands-on session, participants will be introduced to the fundamental concepts and practical tools used to infer evolutionary dynamics from sequencing data of human tumours. The session is designed for researchers with a background in quantitative methods and will combine concise theoretical introductions with guided exploration of real data. We begin by examining the types of sequencing data commonly used for evolutionary inference, focusing on variant allele frequencies (VAFs) and somatic copy number alterations (CNAs). Participants will learn to process variant calls and CNA files to derive inputs for downstream analysis and visualise the mutational landscape of tumours through interactive plots. Building on this foundation, we will explore methods for clonal decomposition using unsupervised learning tools. Attendees will utilise clustering algorithms to identify clonal populations. We will then use these to reconstruct evolutionary models, introducing core principles such as the clonal evolution model and the distinction between trunk and branch alterations. During this session, we will also make use of a synthetic tumour growth model, which will allow students to simulate a solid tumour that grows in space. The session will include a discussion of common pitfalls, limitations of current models, and suggestions for further exploration. Participants will leave with a set of notebooks and tools that can be readily adapted to their research, as well as a practical understanding of how to extract evolutionary insights from cancer sequencing datasets.

Hands-on A
Exploring Metabolic Cross-Feeding in a Synthetic Pseudomonas Consortium Vittorio Venturi (ICGEB), Mihael Špacapan (ICTP), William R. Shoemaker (ICTP)
In this experiment, a synthetic consortium of two _Pseudomonas_ strains is designed to degrade ferulic acid via a metabolically complementary pathway. Ferulic acid, a common phenolic compound in the rhizosphere, is initially degraded by _Pseudomonas_ Strain A, which expresses key enzymes such as feruloyl-CoA synthetase and vanillin dehydrogenase to convert ferulic acid into vanillic acid. This intermediate is then utilized by _Pseudomonas_ Strain B, which carries enzymes like vanillate O-demethylase and protocatechuate dioxygenase to further degrade vanillic acid into protocatechuic acid. Protocatechuic acid subsequently enters the β-ketoadipate pathway, ultimately feeding into central carbon metabolism via TCA cycle intermediates. The consortium reflects metabolic cross-feeding, with each strain performing a distinct step in the degradation pathway. Growth dynamics and substrate conversion can be monitored through OD₆₀₀, CFU counts, FACS and metabolite profiling (i.e. presence of ferulic and/or vanillic acid in the minimal medium), allowing inference on the interdependence, efficiency, and stability of this two-strain system under defined conditions.
 
Hands-on B 
A primer on evolutionary inference from cancer sequencing datasets Giulio Caravagna, Riccardo Bergamini, Nicola Calonaci (University of Trieste) Understanding the evolutionary history of cancer is essential for interpreting its progression, therapeutic resistance, and clonal architecture. In this hands-on session, participants will be introduced to the fundamental concepts and practical tools used to infer evolutionary dynamics from sequencing data of human tumours. The session is designed for researchers with a background in quantitative methods and will combine concise theoretical introductions with guided exploration of real data. We begin by examining the types of sequencing data commonly used for evolutionary inference, focusing on variant allele frequencies (VAFs) and somatic copy number alterations (CNAs). Participants will learn to process variant calls and CNA files to derive inputs for downstream analysis and visualise the mutational landscape of tumours through interactive plots. Building on this foundation, we will explore methods for clonal decomposition using unsupervised learning tools. Attendees will utilise clustering algorithms to identify clonal populations. We will then use these to reconstruct evolutionary models, introducing core principles such as the clonal evolution model and the distinction between trunk and branch alterations. During this session, we will also make use of a synthetic tumour growth model, which will allow students to simulate a solid tumour that grows in space. The session will include a discussion of common pitfalls, limitations of current models, and suggestions for further exploration. Participants will leave with a set of notebooks and tools that can be readily adapted to their research, as well as a practical understanding of how to extract evolutionary insights from cancer sequencing datasets.

Hands-on C The role of mechanical forces in inhibiting cancer cell proliferation in the heart.
Serena Zacchigna (ICGEB)
Both primary and secondary cardiac cancer are extremely rare. While the low incidence of primary tumors is expected, due to the low proliferation rate of cardiomyocytes, the low incidence of metastasis is enigmatic, considering that the heart is highly vascularized and blood constantly flows through it. We recently demonstrated that cancer cells ectopically implanted into the heart grow less than in any other organ. However, the mechanisms that inhibit cancer cell proliferation in the heart remain elusive. Mechanical forces operating in a beating heart have been proposed to blunt the proliferative potential of cardiomyocytes. We hypothesized that the same forces inhibit cancer cell proliferation in the heart. Consistently, our preliminary data indicate that cancer cells grow massively in mechanically unloaded hearts. In this experiment, cancer cells and primary health fibroblasts will be mechanically stimulated using a custom device able to stretch cells in 3D, to mimic a beating heart, at multiple pressures and frequencies, followed by quantification of cancer cell apoptosis and proliferation. We would like to generate a model to infer the best combination of pressure and frequency to use in order to reduce proliferation and induce apoptosis in cancer cells, while minimally affecting the behavior of healthy cells.
 

 

 
Hands-on D Image-based analysis of drug-induced cell competition dynamics
Luca Braga, Giannino del Sal (ICGEB)
Cell competition is increasingly recognized as a critical mechanism shaping tissue homeostasis, tumor evolution, and therapeutic response. In the context of cancer, pharmacological pressure can shift the balance between competing cell populations, selectively promoting survival or elimination based on genotype-specific vulnerabilities. Participants will be introduced to the principles and basic techniques of cell-based high-throughput functional screenings. These approaches will be applied to explore how different drugs modulate cell competition dynamics using fluorescence-based quantitative imaging. Participants will quantitatively analyze the interaction between two genetically distinct cell populations exposed to a panel of small molecules, focusing on how drug-induced shifts in proliferation or survival drive clonal dominance. By integrating time-lapse imaging with endpoint assays, the course aims to build a framework for interpreting competitive interactions as a functional and measurable readout of drug responsiveness. The program combines theoretical modules with hands-on activities in imaging, quantification, and data interpretation. Attendees will gain insights into how image-based approaches can be leveraged to investigate cell behavior in complex, heterogeneous systems.

Hands-on C The role of mechanical forces in inhibiting cancer cell proliferation in the heart.
Serena Zacchigna (ICGEB)
Both primary and secondary cardiac cancer are extremely rare. While the low incidence of primary tumors is expected, due to the low proliferation rate of cardiomyocytes, the low incidence of metastasis is enigmatic, considering that the heart is highly vascularized and blood constantly flows through it. We recently demonstrated that cancer cells ectopically implanted into the heart grow less than in any other organ. However, the mechanisms that inhibit cancer cell proliferation in the heart remain elusive. Mechanical forces operating in a beating heart have been proposed to blunt the proliferative potential of cardiomyocytes. We hypothesized that the same forces inhibit cancer cell proliferation in the heart. Consistently, our preliminary data indicate that cancer cells grow massively in mechanically unloaded hearts. In this experiment, cancer cells and primary health fibroblasts will be mechanically stimulated using a custom device able to stretch cells in 3D, to mimic a beating heart, at multiple pressures and frequencies, followed by quantification of cancer cell apoptosis and proliferation. We would like to generate a model to infer the best combination of pressure and frequency to use in order to reduce proliferation and induce apoptosis in cancer cells, while minimally affecting the behavior of healthy cells.
 

 

 
Hands-on D Image-based analysis of drug-induced cell competition dynamics
Luca Braga, Giannino del Sal (ICGEB)
Cell competition is increasingly recognized as a critical mechanism shaping tissue homeostasis, tumor evolution, and therapeutic response. In the context of cancer, pharmacological pressure can shift the balance between competing cell populations, selectively promoting survival or elimination based on genotype-specific vulnerabilities. Participants will be introduced to the principles and basic techniques of cell-based high-throughput functional screenings. These approaches will be applied to explore how different drugs modulate cell competition dynamics using fluorescence-based quantitative imaging. Participants will quantitatively analyze the interaction between two genetically distinct cell populations exposed to a panel of small molecules, focusing on how drug-induced shifts in proliferation or survival drive clonal dominance. By integrating time-lapse imaging with endpoint assays, the course aims to build a framework for interpreting competitive interactions as a functional and measurable readout of drug responsiveness. The program combines theoretical modules with hands-on activities in imaging, quantification, and data interpretation. Attendees will gain insights into how image-based approaches can be leveraged to investigate cell behavior in complex, heterogeneous systems.