Biology subjects

Krause, A.

Publications and source records attributed to Krause, A..

14 recordsLinked to original sources

bioRxiv2026

Breaking the Synthesis Barrier for AI-Designed DNA Libraries

Designing DNA libraries is a key challenge from drug design to protein engineering and synthetic biology. Modern generative models offer opportunities to navigate the design space and propose specific sequences predicted to be effective in-silico. Designing deterministic libraries of specific sequences is however limited by the cost of DNA synthesis - the synthesis barrier. In contrast, high-throughput multiplexed screening can measure the function of billions of biological sequences in parallel. Harnessing this technology requires the design of randomized libraries with specific design constraints to achieve low synthesis costs. In practice, such stochastic libraries are often chosen heuristically, sacrificing control for scale. Is there a way to bridge AI-based in-silico sequence design with high-throughput experimentation? In this work, we introduce Policy Gradients for Library Design (PGLD). PGLD uses a synthesis-aware parametrization of stochastic DNA libraries and optimizes them against a specified objective function. This allows for designing massive, controlled libraries without being limited by synthesis costs. We show how PGLD enables lab-in-the-loop design of multi-round high-throughput experiments, and large-scale in-vitro DNA sampling from generative models. Finally, we use PGLD to design a library of ~ 106 unique sequences which is synthesized at a cost of ~ 700 USD to explore the mutation space of a broadly neutralizing influenza antibody.

bioengineering↗

bioRxiv2026

The impact of crystallographic plane orientation as an unexplored terrain in hemocompatible material design

Thrombogenicity causes significant complications in the application of blood-contacting implants, requiring strategies to prevent adverse coagulation reactions. The thrombotic responses to the foreign surfaces are mainly driven by surficial factors such as surface energy, topography, and electrochemical interactions. Although anticoagulation therapies reduce the risks of clotting, patients might still encounter bleeding complications. Therefore, rather than high-risk anticoagulation therapies to counteract coagulation, it is essential to ensure hemocompatibility through the materials intrinsic properties. Endothelialization is crucial in preventing thrombotic complications, with various strategies explored for facilitating endothelial cell adhesion and proliferation. We investigated the impact of crystallographic anisotropy on endothelial and blood cell interactions on four main planes (A-, C-, M-, and R-planes) of single crystalline alumina (-Al2O3, sapphire). Employing advanced surface characterization techniques, including SIMS, KPFM and Zeta potential measurements, our study sheds light on the hemocompatibility of biomaterials considering anisotropic effects. We elucidated that the A-plane of alumina promotes endothelialization and suppresses platelet activation in contrast to other crystallographic planes. Our investigation into cell-surface interactions provides valuable insights and contributes to the advanced biomaterial design, ultimately leading to enhanced clinical outcomes.

bioengineering↗

bioRxiv2024

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning

Tailored enzymes hold great potential to accelerate the transition to a sustainable bioeconomy. Yet, enzyme engineering remains challenging as it relies largely on serendipity and is, therefore, highly laborious and prone to failure. The efficiency and success rates of engineering campaigns may be improved substantially by applying machine learning to construct a comprehensive representation of the sequence-activity landscape from small sets of experimental data. However, it often proves challenging to reliably model a large protein sequence space while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning and model-guided library design. We applied this strategy to efficiently engineer an artificial metalloenzyme (ArM) catalysing a new-to-nature hydroamination reaction. By combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds according to user-defined objectives. Crucial characteristics of our enhanced enzyme engineering pipeline include i) the cost-effective generation of information-rich experimental data sets, ii) the integration of an explorative round to improve the performance of the model, as well as iii) the consideration of experimental noise during modelling. Our approach led to an order-of-magnitude boost in the hit rate of screening while making efficient use of experimental resources. Smart search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.

synthetic biology↗

bioRxiv2023

ChromaX: a fast and scalable breeding program simulator

SummaryChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding value calculations, and selection processes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware. This offers breeders and scientists new opportunities to simulate genetic gain and optimize breeding schemes. Availability and ImplementationThe documentation is available at https://chromax.readthedocs.io The code is available at https://github.com/kora-labs/chromax Contactomargallalaly.younis@inf.ethz.ch, luca.corinzia@inf.ethz.ch, matteo.turchetta@inf.ethz.ch

bioinformatics↗

bioRxiv2022

Landscape heterogeneity and soil biota are central to multi-taxa diversity for landscape restoration

How to enhance biodiversity in monoculture-dominated landscapes is a key sustainability question that requires considering the spatial organization of ecological communities (beta diversity). Here, we experimentally tested if increasing landscape heterogeneity - through tree islands - is a suitable landscape restoration strategy when aiming to enhance multi-taxa diversity. We found that multi-taxa diversity resulted from islands fostering unique species (turnover: between 0.18 - 0.73) rather than species losses and gains (nestedness: between 0.03 - 0.34), suggesting that tree islands enhance diversity at the landscape scale. Through partial correlation networks, we revealed that landscape heterogeneity is associated with multi-taxa diversity (strength = 0.84). Soil biota were also central to the overall community by connecting beta diversity patterns across taxa. Our results show that increasing landscape heterogeneity enhances multi-taxa diversity in monoculture-dominant landscapes. Furthermore, we highlight that strategies aiming to enhance multi-taxa diversity should consider that spatial distributions of above- and below-ground communities are associated.

ecology↗

bioRxiv2022

Collagen-binding integrin alpha11beta1 contributes to joint destruction in arthritic hTNFtg mice

BackgroundIn rheumatoid arthritis (RA), fibroblast like synoviocytes (FLS) undergo a "tumor-like" transformation, wherein they develop an aggressive phenotype that is characterized by increased adhesion to components of cartilage extracellular matrix (ECM) and that contributes extensively to joint destruction. The collagen binding integrin 11{beta}1 was previously shown to be involved in similar processes in cancer-associated fibroblasts mediating tumorigenicity and metastasis in certain tumors. Therefore, this study aimed to study the role of integrin 11{beta}1 in RA and to characterize the effects of 11{beta}1 deficiency on the disease course and severity in arthritic hTNFtg mice. MethodsThe expression levels of integrin 11{beta}1 were analyzed by immunohistochemistry, immunofluorescence, and western blot analysis in synovial samples and FLS of patients with RA and osteoarthritis (OA) as well as in samples from wild type (wt) and arthritic hTNFtg mice. Furthermore, the subcellular expression of integrin 11{beta}1 was investigated in co-culture experiments with cartilage explants and analyzed by transmission electron microscopy. To investigate the effects of integrin 11{beta}1 deficiency, itga11-/- mice were interbred with hTNFtg mice and disease severity was assessed by clinical scoring of grip strength and paw swelling over the disease course. Hind paws of 12-weeks-old mice of all genotypes were analyzed by {micro}CT imaging followed by stainings of paraffin-embedded tissue sections with Toluidine-blue and tartrate-resistant acid phosphatase (TRAP) to evaluate established parameters of joint destruction such as inflammation area, cartilage destaining, FLS attachment to the cartilage surface, and bone damage. ResultsExpression levels of integrin 11{beta}1 were clearly elevated in synovial tissues and FLS from RA patients and hTNFtg mice, compared to the controls derived from OA patients and wt mice. Interestingly, this expression was shown to be particularly localized in focal adhesions of the FLS. As revealed by transmission electron microscopy, integrin 11{beta}1 expression was particularly evident in areas of direct cellular contact with the ECM of cartilage. Evaluations of clinical scorings and histomorphological analyses demonstrated that itga11-/-hTNFtg displayed alleviated clinical symptoms, higher bone volume, less cartilage destruction and reduced FLS attachment to the cartilage in comparison to hTNFtg mice. ConclusionsThe collagen-binding integrin 11{beta}1 is upregulated in the context of RA and its deficiency in mice with an inflammatory hTNFtg background leads to a significant reduction in the arthritic phenotype which makes integrin 11{beta}1 an interesting target for therapeutical intervention.

molecular biology↗

bioRxiv2021

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CO_SCPLOWELLC_SCPLOWOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CO_SCPLOWELLC_SCPLOWOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

bioinformatics↗

bioRxiv2021

Growth rate as a modulator of tooth patterning during adaptive radiations

Tooth classes are a mammalian innovation that has contributed to the evolutionary success of mammals. However, our understanding of how tooth classes have evolved and diversified remains limited. Here, we use the evolutionary radiation of noctilionoid bats, the most diverse clade of mammals in terms of diet type, as a model system to show how the tooth developmental program evolved during the adaptation to new diet types. We combined morphological, developmental, cellular, and modeling approaches, to investigate the developmental differences between two tooth classes, molars and premolars and the mechanisms driving their diversification. We demonstrate that tooth classes develop through independent developmental cascades that deviate from classical models. Then we showed that the dramatic diversification of tooth number and size is driven by the modulation of the growth rate of the jaw, explaining the rapid gain/loss of teeth during the evolution of this clade. Finally, we propose a mathematical model that recapitulates the successive appearance of tooth buds and supports the hypothesis that growth acts as a key driver of the evolution of tooth number and size by tinkering with reaction/diffusion processes. Our results demonstrate developmental independence between mammalian tooth classes and provide a mechanism to explain their rapid diversification. More broadly, these results reveal how simple modifications of one developmental mechanism by another can drive the evolution of repeated structures during adaptive radiations.

evolutionary biology↗

bioRxiv2019

Pitfalls in estimating the global carbon removal via forest expansion - a comment on Bastin et al. (2019)

We believe the carbon removal potential of 205 Gt C reported by Bastin et al. (2019) to be overestimated. The authors did not consider the carbon already stored on the land with identified tree restoration potential. For instance, grasslands and degraded forests have similar soil carbon stocks as old-growth forests. Accounting for these inconsistencies in the calculation, we estimate a carbon removal potential of 81 Gt C, i.e. only 39% of the Bastin et al. estimate. In addition, some of the assumptions about potential restoration area seem questionable and an essential question remains unanswered: why do areas identified as suitable for tree growth currently lack tree cover?

ecology↗

bioRxiv2019

Quantifying geographic accessibility to improve cost-effectiveness of entomological monitoring.

BackgroundVector-borne diseases are important causes of mortality and morbidity in humans and livestock, particularly for poorer communities and countries in the tropics. Large-scale programs against these diseases, for example malaria, dengue and African trypanosomiasis, include vector control, and assessing the impact of this intervention requires frequent and extensive monitoring of disease vector abundance. Such monitoring can be expensive, especially in the later stages of a successful program where numbers of vectors and cases are low. Methodology/Principal FindingsWe developed a system that allows the identification of monitoring sites where pre-intervention densities of vectors are predicted to be high, and travel cost to sites is low, highlighting the most efficient locations for longitudinal monitoring. Using remotely sensed imagery and an image classification algorithm, we mapped landscape resistance associated with on- and off-road travel for every gridded location (3m and 0.5m grid cells) within Koboko district, Uganda. We combine the accessibility surface with pre-existing estimates of tsetse abundance and propose a stratified sampling approach to determine the most efficient locations for longitudinal data collection. Our modelled predictions were validated against empirical measurements of travel-time and existing maps of road networks. We applied this approach in northern Uganda where a large-scale vector control program is being implemented to control human African trypanosomiasis, a neglected tropical disease (NTD) caused by trypanosomes transmitted by tsetse flies. Our accessibility surfaces indicate a high performance when compared to empirical data, with remote sensing identifying a further ~70% of roads than existing networks. Conclusions/SignificanceBy integrating such estimates with predictions of tsetse abundance, we propose a methodology to determine the optimal placement of sentinel monitoring sites for evaluating control programme efficacy, moving from a nuanced, ad-hoc approach incorporating intuition, knowledge of vector ecology and local knowledge of geographic accessibility, to a reproducible, quantifiable one. Author SummaryAssessing the impact of vector control programmes requires longitudinal measurements of the abundance of insect vectors within intervention areas. Such monitoring can be expensive, especially in the later stages of a successful program where numbers of vectors and cases of disease are low. Efficient monitoring involves a prior selection of monitoring sites that are easy to reach and produce rich information on vector abundance. Here, we used image classification and cost-distance algorithms to produce estimates of accessibility within Koboko district, Uganda, where vector control is contributing to the elimination of sleeping sickness, a neglected tropical disease (NTD). We combine an accessibility surface with pre-existing estimates of tsetse abundance and propose a stratified sampling approach to determine locations which are associated with low cost (lowest travel time) and potential for longitudinal data collection (high pre-intervention abundance). Our method could be adapted for use in the planning and monitoring of tsetse- and other vector-control programmes. By providing methods to ensure that vector control programmes operate at maximum efficiency, we can ensure that the limited funding associated with some of these NTDs has the largest impact.

ecology↗

bioRxiv2019

The conserved histone chaperone LIN-53 links lifespan and healthspan regulation in C. elegans

Whether extension of lifespan provides an extended time without health deteriorations is an important issue for human aging. However, to which degree lifespan and healthspan regulation might be linked is not well understood. Chromatin factors could be involved in linking both aging aspects, as epigenetic mechanisms bridge regulation of different biological processes. The epigenetic factor LIN-53 (RBBP4/7) is required for safeguarding cell identities in Caenorhabditis elegans as well as mammals and for preventing memory loss and premature aging in humans. LIN-53 is a histone chaperone that associates with different chromatin-regulating complexes. We show that LIN-53 interacts with the Nucleosome remodeling and deacteylase (NuRD)-complex in C. elegans muscles to promote healthy locomotion during aging. While mutants for other NuRD members show a normal lifespan, animals lacking LIN-53 die early because LIN-53 depletion affects also the Histone deacetylase complex Sin3, which is required for a normal lifespan. To determine why lin-53 and sin-3 mutants die early, we performed transcriptome and metabolome analysis and found that levels of the disaccharide Trehalose are significantly decreased in both mutants. As Trehalose is required for normal lifespan in C. elegans, lin-53 and sin-3 mutants could be rescued by either feeding with Trehalose or increasing Trehalose levels via the Insulin/IGF1 signaling pathway. Overall, our findings suggest that LIN-53 is required for maintaining lifespan and promoting healthspan through discrete chromatin regulatory mechanisms. Since both LIN-53 and its mammalian homologs safeguard cell identities, it is conceivable that its implication in lifespan and healthspan regulation is also evolutionarily conserved.

cell biology↗

bioRxiv2018

A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios

Abstract.To support the assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on Scenarios and Models is carrying out an intercomparison of biodiversity and ecosystem services models using harmonized scenarios (BES-SIM). The goals of BES-SIM are (1) to project the global impacts of land use and climate change on biodiversity and ecosystem services (i.e. natures contributions to people) over the coming decades, compared to the 20th century, using a set of common metrics at multiple scales, and (2) to identify model uncertainties and research gaps through the comparisons of projected biodiversity and ecosystem services across models. BES-SIM uses three scenarios combining specific Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to explore a wide range of land-use change and climate change futures. This paper describes the rationale for scenarios selection, the process of harmonizing input data for land use, based on the second phase of the Land Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem service models used, the core simulations carried out, the harmonization of the model output metrics, and the treatment of uncertainty. The results of this collaborative modelling project will support the ongoing global assessment of IPBES, strengthen ties between IPBES and the Intergovernmental Panel on Climate Change (IPCC) scenarios and modelling processes, advise the Convention on Biological Diversity (CBD) on its development of a post-2020 strategic plans and conservation goals, and inform the development of a new generation of nature-centred scenarios.

ecology↗

bioRxiv2017

Generalization and search in risky environments

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

animal behavior and cognition↗

bioRxiv2016

A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios

This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial aims to provide an accessible introduction to these techniques. We will introduce Gaussian processes which generate distributions over functions used for Bayesian non-parametric regression, and demonstrate their use in applications and didactic examples including simple regression problems, a demonstration of kernel-encoded prior assumptions and compositions, a pure exploration scenario within an optimal design framework, and a bandit-like exploration-exploitation scenario where the goal is to recommend movies. Beyond that, we describe a situation modelling risk-averse exploration in which an additional constraint (not to sample below a certain threshold) needs to be accounted for. Lastly, we summarize recent psychological experiments utilizing Gaussian processes. Software and literature pointers are also provided.

animal behavior and cognition↗