Abstracts

Professor Birgit Strodel
Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Research Centre Jülich, Germany
Computational Biochemistry, Heinrich Heine University Düsseldorf, Germany

Peptides Under the Microscope: From Amyloid Aggregation Mechanics to Computational Design

This talk bridges fundamental aggregation mechanics with computationally driven design strategies for therapeutic development. We begin by examining amyloid-beta (Aβ) aggregation through the lens of free energy landscapes. In solution, Aβ monomers occupy a funnel-to-disorder state, but upon interaction with membranes or other Aβ molecules, the landscape transforms into a funnel-to-order regime that drives structured aggregation. To overcome the severe timescale limitations of simulating full-length Aβ assembly, we employ a fragment-based sliding window approach, systematically studying 7- and 10-residue homo- and heterodimers. This reductionist strategy successfully recapitulates experimentally observed PDB fibril structures, revealing how local sequence segments dictate global assembly.

Building on these structural insights, we transition to therapeutic peptide design. We introduce an in silico saturation mutagenesis protocol that systematically explores sequence space to optimize peptide-protein interactions. This approach is demonstrated through the design of peptide inhibitors targeting SARS-CoV-2 proteases. Crucially, while this computational pipeline screens hundreds of candidate peptides, it narrows the selection so effectively that fewer than ten variants need to be synthesized and tested in the wet lab. The methodology exemplifies how a mechanistic understanding of molecular recognition directly accelerates and streamlines therapeutic development.

A mathematical model for rocking bioreactors using feedback control

Cultivated meat, also known as cell-based or artificial meat, is an alternative protein source created from animal cells grown outside of their natural environment. Cells are initially grown on a very small scale in so-called seed trains. Once a sufficiently large number of cells with the desired properties have formed, they are then transferred to larger vessels known as bioreactors for proliferation (growth and multiplication). This stage, combining multi-phase fluid mechanics with advection-diffusion processes, is the focus of my research and this talk. The most common bioreactors are stirred bioreactors, where the media is agitated by stirring with propellers. Previous research has suggested that this type of reactor may create high shear stresses, which have a particularly noticeable impact for the production of cultivated meats, as mammalian cells are particularly shear sensitive. To mediate these issues, an alternative reactor design is the rocking-wave bioreactor. Here, the whole reactor, a small 5-10L bag half-filled with liquid, is gently rocked back and forth, generating waves and vortical structures in the flow which induce mixing and oxygen transfer at the surface, allowing cells to proliferate while reducing shear. I will present our mathematical model and dedicated direct numerical simulation (DNS) framework of rocking bioreactors, which is then used to develop a feedback control strategy to optimise mixing while maintaining a target value for the shear stress, aiding cell growth

Characteristic scattering exponents for electronic transport in complex thermoelectric materials from ab initio calculations

A typical way to understand the electronic transport mechanism that dominates the properties of a material, is to fit experimental data into transport models where the scattering times are treated by a simple exponential energy dependence as τ = τ0 (E/E0)r. Here, r, is a characteristic exponent which depends on the dominant scattering mechanism. For example, transport processes dominated by acoustic deformation potential (ADP), optical deformation potential (ODP), polar optical phonon (POP) and ionized impurity scattering (IIS), are considered to follow r = -0.5, -0.5, 0.5, and 1.5, respectively [1, 2]. Although these values are based on single band considerations, they are often used without sufficient justification. Modern thermoelectric (TE) materials, however, have complex electronic structures interacting via complex scattering mechanisms. To better characterize transport measurements, this scattering exponent approach needs to be re-examined, validated, and corrected. In this work we use Boltzmann Transport theory and start with re-evaluating the exponent approximation for a single parabolic band material. We then evaluate a two-band system, where the effect of the energy separation and the effective mass ratio between the two bands is examined. Finally, we investigate the exponent validity for many real materials from the half-Heusler group using ab initio DFT electronic structures and Boltzmann transport with full energy/momentum/band dependent scattering, as implemented in the code ElecTra [3]. In the case of a single band, we find that the standard literature scattering exponent values are in good agreement for ADP, only at high energies for ODP and POP, and only at low carrier densities for IIS. In the case of a two-band material, significant deviations are seen for all scattering mechanisms, which increase with band mass ratio and band energy separation. In the case of half-Heusler materials at high carrier densities, where carrier screening is significant, the scattering exponents vary at such a degree, that it is nearly impossible to link transport trends (i.e. mobility) to a specific scattering mechanism. From a statistical analysis for 11 different half-Heusler materials, we find that the scattering exponents follow a gaussian distribution with μ = −0.102 and σ = 0.175, which does not fit any of the standard exponents. In conclusion, our study will help improve the characterization of TE measurements, leading to better understanding and optimization. Alongside my presentation, I will be bringing a poster which will describe other work done using our code ElecTra.

Progress towards phase-field modeling of anisotropic crack propagation using physics-informed deep learning

In this study, we investigate the ability of variational physics-informed neural networks (VPINNs) to learn complex fracture processes in anisotropic media. VPINNs have recently been explored in the context of crack propagation in isotropic brittle solids, demonstrating key fracture mechanisms such as crack nucleation, propagation, kinking, branching, and coalescence through phase-field damage modelling, which represents the current state of the art [1]. However, their extension to crack propagation in anisotropic materials remains an open research challenge, which this work seeks to address. Unlike the second-order approximations used for isotropic cases, modelling anisotropic crack propagation in the phase-field framework requires fourth-order approximations of fracture energy [2]. This is accomplished by using NURBS elements to compute higher-order gradients. A neural network is then trained to minimise the system’s variational energy computed via finite element (FE) calculations. The proposed methodology is applied to several benchmark problems, such as a 2D square plate under pure tension and 2D L-shaped section with the right side forced upward. [1] Manav M., Molinaro R., Mishra S., De Lorenzis L., (2024). Phase-field modeling of fracture with physics-informed deep learning, Computer Methods in Applied Mechanics and Engineering, 429, 117104, DOI: https://doi.org/10.1016/j.cma.2024.117104 [2] Kakouris E.G, Triantafyllou S.P. (2018), Material point method for crack propagation in anisotropic media: a phase field approach, Archive of Applied Mechanics, 88 (1), 287-316, DOI: https://doi.org/10.1007/s00419-017-1272-7