Biology subjects

Pfennig, T.

Publications and source records attributed to Pfennig, T..

3 recordsLinked to original sources

bioRxiv2025

MxlPy - Python Package for Mechanistic Learning in Life Science

SummaryRecent advances in artificial intelligence have accelerated the adoption of ML in biology, enabling powerful predictive models across diverse applications. However, in scientific research, the need for interpretability and mechanistic insight remains crucial. To address this, we introduce MxlPy, a Python package that combines mechanistic modelling with ML to deliver explainable, data-informed solutions. MxlPy facilitates mechanistic learning, an emerging approach that integrates the transparency of mathematical models with the flexibility of data-driven methods. By streamlining tasks such as data integration, model formulation, output analysis, and surrogate modelling, MxlPy enhances the modelling experience without sacrificing interpretability. Designed for both computational biologists and interdisciplinary researchers, it supports the development of accurate, efficient, and explainable models, making it a valuable tool for advancing bioinformatics, systems biology, and biomedical research. AvailabilityMxlPy source code is freely available at https://github.com/Computational-Biology-Aachen/MxlPy. The full documentation with features and examples can be found here https://computational-biology-aachen.github.io/MxlPy.

systems biology↗

bioRxiv2025

Hackathons as essential tools in an interdisciplinary biological training - report from trainings for Sub-Saharan students

Hackathons are collaborative, fast-paced events where participants from various fields work together to solve real-world problems. They are increasingly used in higher education to foster collaboration, problem-solving and applied computational skills, yet their role in interdisciplinary biological training remains under-documented. We report on the design and implementation of two computational biology summer schools in Kenya (2022, 2023), each culminating in a hackathon that integrated biological problems, quantitative methods, and coding. Both events targeted early-career researchers from multiple sub-Saharan countries and combined intensive teaching in programming and modelling with a time-bound group challenge using authentic marine conservation and synthetic epidemiological datasets. We describe the educational design, including its grounding in project-based learning, authentic learning, and Self-Determination Theory, and we document how performance-based assessment and structured participant feedback were used to evaluate learning outcomes. We present a critical reflective account of what worked, our teaching philosophy, and how hackathons can be embedded responsibly within biological curricula. We argue that, when embedded in sustained training and supported by appropriate mentoring, hackathons provide a practical and effective way to help biologists build computational skills, communicate across disciplines, and gain confidence in shaping their own research.

scientific communication and education↗

bioRxiv2023

Shedding Light On Blue-Green Photosynthesis: A Wavelength-Dependent Mathematical Model Of Photosynthesis In Synechocystis sp. PCC 6803

Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.

plant biology↗