Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

in Metabolic Engineering (2022)

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of ... [more ▼]

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis. [less ▲]

COBREXA.jl: constraint-based reconstruction and exascale analysis

in Bioinformatics (2021)

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of ... [more ▼]

COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.https://doi.org/10.17881/ZKCR-BT30.Supplementary data are available at Bioinformatics online. [less ▲]

GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets

in GigaScience (2020), 9(11),

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data ... [more ▼]

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies. [less ▲]

DAISY: A Data Information System for accountability under the General Data Protection Regulation

in GigaScience (2019), 8(12),

The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal ... [more ▼]

The new European legislation on data protection, namely, the General Data Protection Regulation (GDPR), has introduced comprehensive requirements for the documentation about the processing of personal data as well as informing the data subjects of its use. GDPR’s accountability principle requires institutions, projects, and data hubs to document their data processings and demonstrate compliance with the GDPR. In response to this requirement, we see the emergence of commercial data-mapping tools, and institutions creating GDPR data register with such tools. One shortcoming of this approach is the genericity of tools, and their process-based model not capturing the project-based, collaborative nature of data processing in biomedical research.We have developed a software tool to allow research institutions to comply with the GDPR accountability requirement and map the sometimes very complex data flows in biomedical research. By analysing the transparency and record-keeping obligations of each GDPR principle, we observe that our tool effectively meets the accountability requirement.The GDPR is bringing data protection to center stage in research data management, necessitating dedicated tools, personnel, and processes. Our tool, DAISY, is tailored specifically for biomedical research and can help institutions in tackling the documentation challenge brought about by the GDPR. DAISY is made available as a free and open source tool on Github. DAISY is actively being used at the Luxembourg Centre for Systems Biomedicine and the ELIXIR-Luxembourg data hub. [less ▲]

Characterization of Differentiated SH-SY5Y as Neuronal Screening Model Reveals Increased Oxidative Vulnerability

in Journal of Biomolecular Screening (2016)

Evaluation of Cell Line Suitability for Disease Specific Perturbation Experiments.

in Lausen, Berthold; Krolak-Schwerdt, Sabine; Böhmer, Matthias (Eds.) Data Science, Learning by Latent Structures, and Knowledge Discovery (2015, February 20)

Cell lines are widely used in translational biomedical research to study the genetic basis of diseases. A major approach for experimental disease modeling are genetic perturbation experiments that aim to ... [more ▼]

Cell lines are widely used in translational biomedical research to study the genetic basis of diseases. A major approach for experimental disease modeling are genetic perturbation experiments that aim to trigger selected cellular disease states. In this type of experiments it is crucial to ensure that the targeted disease- related genes and pathways are intact in the used cell line. In this work we are developing a framework which integrates genetic sequence information and disease- specific network analysis for evaluating disease-specific cell line suitability. [less ▲]

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson’s disease

in BMC Genomics (2014), 15(1154),

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often ... [more ▼]

Background: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson’s disease, the relevance of this cellular model in the context of Parkinson’s disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated. Results: We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD. Conclusions: The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson’s disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer’s disease but lower than for Huntington’s disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments. [less ▲]

Detection and characterization of critical transitions in mitochondrial activity via high content screening

Doctoral thesis (2014)

Critical transitions exist in many dynamical systems, ranging from the Earth’s cli- mate system to microcosm populations. During a critical transition, the state of a dynamical system abruptly changes ... [more ▼]

Critical transitions exist in many dynamical systems, ranging from the Earth’s cli- mate system to microcosm populations. During a critical transition, the state of a dynamical system abruptly changes from one stable state to another, typically without obvious prior warning. Preventing such abrupt changes remains a chal- lenge, however recently, several metrics were suggested as early warning signals. These indicators are thought to have predictive value for upcoming critical transi- tions. In Parkinson’s disease, there are no detectable motor symptoms in a patient until neuronal dopaminergic cell death exceeds 60–70%. Being able to define early warning signals in a disease context could open new avenues for both preventive and disease modifying treatments. We hypothesize that the dynamics of progression of some disorders including Parkinson’s disease could be manifested by critical tran- sitions. However, before rushing into medical applications, a thorough framework needs to be developed that aims to describe such nonlinear dynamics in cellular systems. In this thesis, we set out to study critical transitions in a simple cellular model using mitochondrial membrane potential ∆Ψ m as readout. To identify criti- cal transitions, we established a modular high-content screening platform allowing systematic perturbation of oxidative phosphorylation. To increase the probability for detecting a critical transition in ∆Ψ m , five inhibitory compounds were combined in multiple pairwise concentration landscapes. We show that critical transitions, de- tectable via ∆Ψ m , are an intrinsic property of the cellular system studied and that two-component Gaussian mixture models adequately capture the dynamics of the critical transition occurring for the combination of Oligomycin A and Antimycin A. Adding to that, we identified the coefficient of variation as a strong early warning signal for the upcoming of the critical transitions. This thesis should serve as a foundation for a broader application of critical transitions and early warning sig- nals in both cell culture systems and translational studies aiming to understand the nonlinear dynamics of biological systems. [less ▲]

Curation of complex molecular pathways of Parkinson's disease as a collaborative scientific community effort

Scientific Conference (2014, June 12)

Automated nuclei clump splitting by combining local concavity orientation and graph partitioning

in International Conference on Biomedical and Health Informatics (2014)

Automated clump decomposition is essential for single cell based analysis of fluorescent microscopy images. This paper presents a new method for automatically splitting clumps of cell nuclei in ... [more ▼]

Automated clump decomposition is essential for single cell based analysis of fluorescent microscopy images. This paper presents a new method for automatically splitting clumps of cell nuclei in fluorescence microscopy images. Nuclei are first segmented using histogram concavity analysis. Clumps of nuclei are detected by fitting an ellipse to the segmented objects and examining objects where the fitted ellipse does not overlap accurately with the segmented object. These clumps are then further processed to find concave points on the object boundaries. The orientation of the detected concavities is subsequently calculated based on the local shape of the object border. Finally, a graph segmentation based approach is used to pair concavities that represent best candidates for splitting touching nuclei based on properties derived from the local concavity properties. This approach was validated by manual inspection and has shown promising results in the high throughput analysis of HeLa cell images. [less ▲]