Belgium

Practical skills development is a core aspect of undergraduate biosciences degrees, creating graduates who can excel in a range of careers, including those that involve laboratory work. However, when we consider the global picture, it is clear that not all students have equality of opportunity to access laboratory spaces to develop these skills and so may not aspire to careers with a practical element. This project describes how we can take lessons learned during the pandemic and apply these to build skills capacity in different areas. In this case, the project described focuses on how learning pipetting and serial dilution skills can be taught outside of the classroom through a “pixel art” competition.
Students at the University of Benin in Nigeria who undertook this skills training were surveyed before and after the training. In addition to increased confidence in pipetting and making serial dilutions, 79% of students who took the survey after the training said that they would be more likely to consider a career with a practical element to it than before they did beforehand. Not only this but 83% of students strongly agreed that they appreciated the opportunity to be creative: with a third of students including the creativity as the most enjoyable aspect of the training. Whilst this project focussed on one cohort of students, the potential for building skills capacity across courses now that local staff can deliver the training independent has the potential to make a significant impact on the country’s infrastructure.

SEB Conference 2025

When Pixel art meets bioskills

practical skills

skill development

creativity

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SEB Conference 2024

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Human beings and other biological creatures navigate unpredictable and dynamic environments by combining compliant mechanical actuators (skeletal muscle) with neural control and sensory feedback. Disease or damage that impacts neuromuscular tissues thus has a severe negative impact on health, mobility, and quality-of-life, motivating the development of tissue engineered multicellular models of the motor control system. We have engineered optogenetic skeletal muscle actuators and shown that light can be used to non-invasively and precisely control muscle contraction, and moreover, that repeated light stimulation "exercise" can program tissue strength, endurance, and regeneration after trauma in vitro and in vivo. Leveraging these tissues, we have interrogated how exercise programs crosstalk between muscle and other surrounding cells, such as peripheral nerves and vasculature, to better understand how mechanical and biochemical signaling can be manipulated in physiological and pathological states. In addition to applications of this work in disease modeling and regenerative medicine, we will also discuss how we use engineered muscle to power adaptive biohybrid robots that demonstrate a range of functional behaviors such as walking and gripping. This talk will cover the advantages, challenges, and future directions of using tissue engineered models to understand and manipulate the mechanics of biological motor control systems.

Tissue engineering skeletal muscle for regenerative medicine and robotics

Climate change is predicted to cause rapid increases in temperature which will drive losses in freshwater biodiversity. Organisms will have to adapt to warming water conditions. Predicting the outcomes of climate change adaptation is important for species conservation but is challenging as most natural populations have yet to experience these anticipated conditions. However, come contemporary natural populations can experience warming through geothermal activity and provide direct insights into adaptation to warmed environments. We leveraged Icelandic threespine stickleback (Gasterosteus aculeatus) from populations undergoing adaptive divergence along a geothermal-ambient habitat axis. We aim to uncover the genomic and developmental underpinnings of adaptation to natural geothermal habitats. This research will further our understanding of the evolutionary processes promoting adaptation to warming conditions.

Metabolic adaptation in Icelandic threespine stickleback

It is widely appreciated that early developmental stages are highly sensitive to environmental stressors, resulting both in immediate impacts (including death) and more pervasive, long-term effects through to adulthood. The extent to which such heightened plasticity reflects adaptive responses or unavoidable vulnerability due to the nature of developmental processes is highly debated and depends on several aspects, including the organism’s life history, type of stressor and patterns of environmental change. Here, I give an overview of our work on maternal responses to stressors in pregnancy (heat, nutrition and infection) from an evolutionary perspective, including developing theoretical models; comparative studies across taxa, and experiments on insect models of viviparity, the tsetse fly (Glossina species) and Pacific beetle mimic cockroach (Diploptera punctata). Our work highlights the insights to be gained from considering stressors across different stages of gestation, whether maternal responses are always in the best interest of current offspring, and the challenges in establishing vulnerability versus adaptation.

Pregnancy as a critical window for understanding vulnerability and adaptation to stress: from models to maggots

Artificial intelligence (AI) encompasses methods and algorithms that allow machines to perform tasks typically requiring human intelligence, such as image recognition, complex data analysis, and decision-making. Machine learning (ML), a central component of AI, relies on analysing large datasets to identify patterns and make predictions. Unlike traditional methods based on predefined rules, ML enables algorithms to learn from data and improve over time. In plant sciences, ML offers new opportunities for optimising varietal selection, predicting the biological activity of plants relevant to humans, and anticipating their applications in health, cosmetics, or agriculture.
The Camellia genus, known for its polyphenols and oils in cosmetics and its aesthetic value in horticulture, serves as a key example where ML can be impactful. The goal of its study is to identify promising varieties by optimising selection based on their compounds of interest and aesthetic traits. One major challenge is the long wait of 3 to 7 years for the camellia to flower, a critical phase for validating phenotypic attributes. ML applied to predictive metabolomics provides a solution by training models on leaf metabolomic profiles to predict floral characteristics before flowering. This method has achieved prediction accuracies of over 80% for flower shape and colour, revealing key families of metabolites involved.
Beyond accelerating varietal selection, this method could be used to predict biological activities of interest to humans, presenting new possibilities in health, cosmetics, and agriculture.

Metabolomic Analysis of Camellia: Unlocking Phenotypic Insights and Industrial Applications

Large-scale kinetic models of photosynthesis offer precise, time-resolved predictions of photosynthesis, gas exchange, and metabolic processes. Thereby, they provide the means to determine factors limiting photosynthesis under different environmental conditions. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of kinetic parameters that may differ between genotypes. Here, we present an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from A/CO2  and A/light curves. The model was trained on a diverse artificial dataset comprising parameter samples and associated curve simulations from a dynamic C4  photosynthesis model. Model training was guided by considering both the predicted parameters and resulting curve simulations. To increase the efficiency of curve simulations during the training process, we trained a surrogate neural network to predict A/CO2  and A/light curves directly from the model parameters and environmental inputs. Despite the high sensitivity of the ODE model to changes in parameter values, over 99% of the predicted parameter vectors could be used directly by the kinetic model. Therefore, the developed framework presents a fast and precise approach for parameter estimation in large-scale photosynthesis models, based on minimal datasets. Further, the flexible structure of the model allows for an easy adjustment in the number of relevant parameters as well as the environmental inputs.

Efficient parameter estimation for a large-scale C4 photosynthesis model using neural networks

Heavy metals (HMs) such as cadmium (Cd) and zinc (Zn) disrupt photosynthesis and plant physiology. This study assesses their impact on hydroponically grown industrial hemp (Cannabis sativa) and examines the potential role of silicon (Si) as a protective agent, the latter being known to improve resistance to biotic and abiotic stresses. Various experimental conditions were established, including controls, exposure to Cd and Zn, and treatment with silicon. Physiological effects were assessed by analysing the light response curve, gas exchange and chlorophyll fluorescence, while pigment content was compared using a proteomic approach. The results show a significant decrease in electron transport, net CO₂ assimilation, PSII effective quantum yield, transpiration and stomatal conductance. These perturbations reflect stomatal closure as well as altered electron transport under metal stress. In addition, analysis of light saturation highlights a reduction in maximum CO₂ uptake and PSII quantum efficiency, as well as an increase in dark respiration in the face of HM stress. Cd and Zn exposure led to a reduction in chlorophyll and carotenoid levels, indicating pigment degradation. The increase in the chlorophyll a/b ratio under metal stress reflects a specific inhibition of chlorophyll b. Contrary to expectations, silicon supplementation did not mitigate the effects of pollutants. These results highlight the negative impact of HMs on the physiology of industrial hemp and reveal the ineffectiveness of silicon as a protective agent in this context. Proteomic analysis provides additional information on the differential accumulation of proteins and the mechanisms underlying the effects of Cd and Zn.

Impact of cadmium and zinc on the physiology of industrial hemp and evaluation of the protective effect of silicon in hydroponic solution

Chlorophyll (Chl) synthesis and degradation govern light-harvesting capacity and photosynthetic efficiency in winter wheat. Stay-green (SG) cultivars exhibit prolonged Chl retention, extending post-anthesis photosynthesis and assimilate transport. However, whether this trait results from enhanced synthesis or reduced degradation remains unclear, challenging breeding strategies. To investigate the mechanisms underlying Chl dynamics, we apply a protein-turnover model focusing on three key parameters: maximum synthesis rate (Smax), aging effect (td) reducing synthesis over time, and degradation rate (Dr). We assess these dynamics in 60 winter wheat genotypes spanning recent German breeding history, using periodic SPAD measurements over five weeks of leaf life under constant and fluctuating light and temperature conditions.
Our results reveal that modern cultivars maintain greenness longer due to higher Smax, lower td, and reduced Dr, demonstrating distinct mechanisms driving improved chlorophyll retention. Notably, these parameters do not show a strong correlation with each other, suggesting they can be selected independently in breeding strategies. Smax shows the greatest independence from environmental variation, making it a promising target for future breeding strategies aimed at enhancing chlorophyll retention regardless of changing environmental conditions. Our findings provide functional insights into SG mechanisms from a high-throughput and easily accessible measurement, guiding breeding strategies to improve wheat productivity and resilience under climate change. Future work will extend the model to include subsequent steps in photosynthesis, such as proteins involved in electron transport and carboxylation, offering a more comprehensive understanding of leaf photosynthetic dynamics.

Exploring chlorophyll dynamics in stay-green winter wheat using a protein-turnover model to assess breeding progress and strategies.

Global climate change, characterised by rising atmospheric CO₂ and increasing temperatures, has profoundly affected ecosystems, biodiversity, water resources, and human health. Extreme weather events, desertification, and ocean acidification threaten global stability, while agricultural systems face shifting growth patterns, altered pest dynamics, and declining crop nutritional quality. Rising CO₂—from pre-industrial levels of ~200 ppm to over 400 ppm today, potentially exceeding 550 ppm by 2050—modifies plant physiology, altering photosynthesis and biomass accumulation, disrupting carbon-nitrogen balance, and reducing bioactive compound synthesis. Combined with high temperatures, these changes pose risks to global food security and nutrition. 
This study examines the physiological and biochemical responses of kale, rocket, and spinach to elevated CO₂ and temperature. Crops are grown in controlled chambers under ambient (400 ppm) and elevated CO₂ (1000 ppm) conditions, with photosynthetic performance monitored using MultispeQ v2.0 at key growth stages. Growth parameters, including leaf number, plant height, leaf area, biomass accumulation, carbon-nitrogen ratio, and inorganic carbon content, are determined. Biochemical analyses quantify flavonoid, phenolic, and protein content, antioxidant activity, vitamin A and E, polyphenols (HPLC), and minerals (ICP). Bioavailability and digestibility are assessed using Caco-2 cell models. 
Preliminary results from this study indicate that elevated CO₂ may enhance photosynthesis but modulates nutrient composition, with potential trade-offs between biomass accumulation and bioactive compound synthesis. Altered sugar, mineral, and vitamin content could affect global diabetes and micronutrient deficiencies. Further high-temperature analysis is underway to clarify climate stress interactions. Findings aim to inform climate-resilient crop production and food security strategies.

IS CLIMATE CHANGE AFFECTING THE NUTRITIONAL QUALITY OF OUR FOOD?

Plants interact with their microclimate, simultaneously responding to and influencing it. A key element in this interaction is the leaf boundary layer: a stagnant air layer enveloping the leaf, creating a resistance for heat and gas exchange within canopy air. Its thickness, altered mainly by airflow and leaf size, determines the leaf-to-air interaction. Field crops typically experience wind speeds of 0-8 m s-1  at the canopy top, with wind gusts up to 20 m s-1 . Conversely, indoor-grown crops always encounter low wind speeds (0-1 m s-1 ). These low speeds, especially with larger leaves, restrict heat and gas exchange, impacting photosynthesis and transpiration. Although the effect of the leaf boundary layer on plant exchange processes has been defined, its magnitude remains poorly characterized and is frequently underestimated. This review re-examines its role, underlying processes are further explained by using an existing modelling approach informed with published physiological parameters, and the potential impact on leaf photosynthesis is quantified. This model suggests that in greenhouses, a 0.4 m s-1  increase in wind speed could boost diurnal photosynthesis by 23%, although with possible detrimental side effects. Leaves in the field wider than 8 cm may experience thick boundary layers 50% of time with stronger effects within the canopy. The role of the boundary layer needs to be reevaluated, but this will require new tools, methods and models to make a breakthrough in understanding this overlooked process.

Beyond the boundary: a new road to improve photosynthesis by wind

The irradiance of natural daylight  constantly fluctuates on the seconds to the hours scale, and it is unsurprising that photosynthesis acclimates differently under a fluctuating irradiance compared to one where the irradiance is constant. Understanding the response and acclimation of photosynthesis to fluctuating irradiance is essential for understanding crop carbon gain and crop productivity. The acclimatory responses of photosynthesis have however been shown to be species dependent. Understanding the differences in acclimation can better inform how plants adapt to fluctuating irradiance, which can be used to develop crops that adapt well to similar environments. We investigated the long-term effect of fluctuating light on five different species of tomato. The species were subject to multiple irradiance fluctuations to understand how the photosynthetic capacity and short-term photosynthetic response acclimates (referred to as ‘performance’). The performance was measured by how fast CO2  assimilation responds to a step increase and step decrease in irradiance, while the photosynthetic capacity was measured via an Irradiance Response Curve. To investigate the effects of these fluctuations on the light-reaction side of photosynthesis, PSII and PSI electron transport, rate constant of linear electron transport and antennae size were measured . The observed effect of the fluctuating light on the differences in capacity and performance of the 5 species of tomato provides insight into differences in limitations and trade-offs of photosynthesis.

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