Biology subjects

Kadelka, C.

Publications and source records attributed to Kadelka, C..

10 recordsLinked to original sources

bioRxiv2026

CLEAR: Concise List Enrichment Analysis Reducing Redundancy

MotivationHigh-throughput experiments generate genome-wide measurements for thousands of genes, which are often tested marginally. Biological processes are driven by coordinated groups of genes rather than individual genes, making gene set enrichment analysis an essential post hoc interpretation tool. Traditional approaches such as Over-Representation Analysis and Gene Set Enrichment Analysis test gene sets independently, which ignores the hierarchical and overlapping structure of gene set collections such as the Gene Ontology, and often leads to redundant enrichment results. Set-based approaches such as MGSA address this issue by modeling multiple gene sets simultaneously, but they rely on binary gene activation states derived from arbitrary thresholds on gene-level statistics. ResultsWe introduce Concise List Enrichment Analysis Reducing Redundancy (CLEAR), a Bayesian gene set enrichment framework that jointly models gene sets while incorporating continuous gene-level statistics such as test statistics or p-values. CLEAR extends model-based gene set analysis by replacing threshold-based gene activation with a probabilistic model for continuous gene-level statistics. This approach preserves the redundancy-reduction advantages of set-based enrichment methods while avoiding the information loss introduced by binarization. Using both simulated datasets and human gene expression data, we show that CLEAR improves sensitivity compared with existing enrichment approaches while producing a more concise and interpretable set of enriched gene sets. Availability and implementationThe source code, data, and a brief tutorial are freely available at https://github.com/jiatuya/CLEAR

bioinformatics↗

bioRxiv2026

Hierarchical organization in sparse gene regulatory networks shapes structural coherence and emergent regulatory coordination

How large-scale regulatory coordination in biological systems emerges from local signed and directed interactions in sparse gene regulatory networks (GRNs) remains an unanswered fundamental question. We introduce the coherence matrix, a topology-based framework that captures the consistency of regulatory influence between gene pairs by integrating information across all direct and indirect paths. Analysis of synthetic networks reveals that structural coherence - a metric derived from the coherence matrix - dictates global coordination: while highly coherent motifs maintain aligned regulatory coordination across widely varying network sparsity values, motifs with low coherence allow such coordination only at biologically unrealistic sparsity values. Our investigation of six whole-organism GRNs and further analysis of synthetic networks highlighted that hierarchical organization in GRNs a dense middle layer enriched in feedback loops that mediates coordination between input and output layers - serves as a structural buffer to allow regulatory coordination even for sparse networks. Finally, comparison with Escherichia coli transcriptomic modules further shows that the coherence matrix accurately predicts the sign of coordinated gene contribution, emphasizing its biological application, while also serving as a unifying descriptor integrating local interactions and global network architecture to explain the emergent regulatory coordination.

systems biology↗

bioRxiv2025

Attractors are less stable than their basins: Canalization creates a coherence gap in gene regulatory networks

Waddingtons epigenetic landscape has served as biologys central metaphor for cellular differentiation for over half a century, depicting mature cell types as balls resting in stable valley floors. Boolean networks - introduced by Kauffman in 1969 to model gene regulatory dynamics - provide a mathematical formalization of this landscape, where attractors represent phenotypes and basins of attraction correspond to developmental valleys. Traditional stability measures quantify robustness by perturbing arbitrary states, yet biological systems typically reside at attractors rather than in transient states. Here we formalize and systematically analyze attractor coherence - a stability measure Kauffman originally envisioned but never rigorously developed - which quantifies how likely a perturbation of an attractor state causes phenotype switching. Analyzing 122 expertcurated biological Boolean models, we reveal a striking paradox: attractors representing mature cell types are consistently less stable than the developmental trajectories approaching them. Largescale simulations of random networks demonstrate that this coherence gap arises from canalization - a hallmark of biological regulation where individual genes can override others. While canalization increases overall network stability, it disproportionately stabilizes transient states, positioning attractors near basin boundaries. The gaps magnitude is almost perfectly predicted by network bias (Spearmans{rho} = -0.997), itself modulated by canalization. These findings revise Waddingtons landscape: canalization carves deep protective valleys ensuring developmental robustness, yet simultaneously flattens ridges near valley floors, facilitating phenotypic plasticity when multiple fates coexist. This explains how biological systems achieve both reliable development and plasticity, with implications for understanding development, disease-related transitions, and designing robust yet controllable synthetic gene circuits.

systems biology↗

bioRxiv2025

Impacts of competition and phenotypic plasticity on the viability of adaptive therapy

Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging modality of cancer treatment that leverages this drug resistance heterogeneity to improve therapeutic outcomes. Current standard treatments typically eliminate a large fraction of drug-sensitive cells, leading to drug-resistant relapse due to competitive release. Adaptive therapy aims to retain some drug-sensitive cells, thereby limiting resistant cell growth by ecological competition. While early clinical trials of such a strategy have shown promise, optimisation of adaptive therapy is a subject of active study. Current methods largely assume cell phenotypes to remain constant, even though cell-state transitions could permit drug-sensitive and -resistant phenotypes to interchange and thus escape therapy. We address this gap using a deterministic model of population growth, in which sensitive and resistant cells grow under competition and undergo cell-state transitions. Based on the models steady-state behaviour and temporal dynamics, we identify distinct balances of competition and phenotypic transitions that are suitable for effective adaptive versus constant dose therapy. Our data indicate that under adaptive therapy, models with cell-state transitions show a higher frequency of fluctuations than those without, suggesting that the balance between ecological competition and phenotypic transitions could determine population-level dynamical properties. Our analyses also identify key limitations of applying phenomenological models in clinical practice for therapy design and implementation, particularly when cell-state transitions are involved. These findings provide an overall perspective on the relevance of phenotypic plasticity for emerging cancer treatment strategies using population dynamics as an investigation framework. Significance StatementDrug-sensitive and -resistant cancer cells can compete with each other within the same tumour, and adaptive therapy exploits this competition to control overall tumour growth. The fact that sensitive and resistant cell types can switch phenotypes complicates the implementation of adaptive therapy. Our ODE-based theoretical ecology framework shows that asymmetric competition favouring sensitive cells generally benefits therapy outcomes, while phenotypic plasticity is usually detrimental. Our model also provides cell population-level indicators that can help predict the underlying balance between competition and plasticity. Phenomenological models are limited by whether their parameters can be reliably identified given scarce data, and our study illuminates the scope of such models in understanding cancer population dynamics and the need for mechanistic modelling.

systems biology↗

bioRxiv2025

Critical assessment of the ability of Boolean threshold models to describe gene regulatory network dynamics.

The inference of gene regulatory networks (GRNs) from high-throughput data constitutes a fundamental and challenging task in systems biology. Boolean networks are a popular modeling framework to understand the dynamic nature of GRNs. In the absence of reliable methods to infer the regulatory logic of Boolean GRN models, researchers frequently assume threshold logic as a default. Using the largest repository of published expert-curated Boolean GRN models as best proxy of reality, we systematically compare the ability of two popular threshold formalisms, the Ising and the 01 formalism, to truthfully recover biological functions and biological system dynamics. While Ising rules match fewer biological functions exactly than 01 rules, they yield a better average agreement. In general, more complex regulatory logic proves harder to be represented by either threshold formalism. Informed by these results and a meta-analysis of regulatory logic, we propose modified versions for both formalisms, which provide a better function-level and dynamic agreement with biological GRN models than the usual threshold formalisms. For small biological GRN models with low connectivity, corresponding threshold networks exhibit similar dynamics. However, they generally fail to recover the dynamics of large networks or highly-connected networks. In conclusion, this study provides new insights into an important question in computational systems biology: how truthfully do Boolean threshold networks capture the dynamics of GRNs? Significance statementGene regulatory networks (GRNs) describe the complex interactions between genes. GRNs control biological processes, respond to environmental changes, and contribute to diseases. An accurate understanding of the dynamics of GRNs is therefore crucial. Given insufficient data, researchers frequently employ Boolean network models with default threshold logic to study the dynamics of GRNs. We systematically assess the ability of two commonly-used types of threshold networks to recover the true regulatory logic and the dynamics of published expert-curated Boolean GRN models. Inspired by biological insights, we further propose modifications to each threshold formalism that improve their match with biological networks.

systems biology↗

bioRxiv2024

A mathematical model informs optimal fungicide use against Sclerotinia stem rot to maximize profits in soybean production

Sclerotinia sclerotiorum, the causative agent of stem rot (SR), is a significant yield-limiting disease affecting soybean crops in the temperate climates around the globe. Effective disease management practices rely on fungicides to mitigate the growth and spread of the disease. To infer optimal, profit-maximizing fungicide application rates, this study develops a mathematical model of mold and soybean growth with a requisite profit function. Sensitivity of the optimal fungicide application rate was computed against profit parameters (fungicide cost and soybean bushel price), and model parameters (mold growth rate, maximal SR damage to crops and fungicide efficiency). Expectantly, higher soybean bushel prices, rates of mold growth, and maximal mold damage to crops return elevated optimal fungicide rates. In contrast, higher levels of fungicide efficiency motivate lower optimal fungicide rates. The model also reveals a discontinuity in the optimal fungicide application rates for elevated fungicide costs; in this economic context, it becomes more profitable to apply no fungicide rather than low, ineffectual amounts that still allow mold to reach near-maximal outbreak levels in a finite time period. Future refinements of the model will incorporate variable mold growth rates modeled on annual weather patterns, crop rotation practices, and further exploring the relationships that soybean densities and row spacing have on mold growth, in order to build a more robust system to analyze the long-term effect of disease behavior on soybean crop yield.

plant biology↗

bioRxiv2024

A Longitudinal Analysis of Function Annotations of the Human Proteome Reveals Consistently High Biases

The resources required to study gene function are limited, especially when considering the number of genes in the human genome and the complexity of their function. Therefore, genes are prioritized for experimental studies based on many different considerations, including, but not limited to, perceived biomedical importance, such as disease-associated genes, or the understanding of biological processes, such as cell signaling pathways. At the same time, most genes are not studied or are under-characterized, which hampers our understanding of their function and potential effects on human health and wellness. Understanding function annotation disparity is a necessary first step toward understanding how much functional knowledge is gained from the human genome, and toward guidelines for better targeting future studies of the genes in the human genome effectively. Here, we present a comprehensive longitudinal analysis of the human proteome utilizing data analysis tools from economics and information theory. Specifically, we view the human proteome as a population of proteins within a knowledge economy: we treat the quantified knowledge of the proteins function as the analog of wealth and examine the distribution of information in a population of proteins in the proteome in the same manner distribution of wealth is studied in societies. Our results show a highly skewed distribution of information about human proteins over the last decade, in which the inequality in the annotations given to the proteins remains high. Additionally, we examine the correlation between the knowledge about protein function as captured in databases and the interest in proteins as reflected by mentions in the scientific literature. We show a large gap between knowledge and interest and dissect the factors leading to this gap. In conclusion, our study shows that research efforts should be redirected to less studied proteins to mitigate the disparity among human proteins both in databases and literature.

bioinformatics↗

bioRxiv2023

Modularity of biological systems: a link between structure and function

This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.

systems biology↗

bioRxiv2023

Concise functional enrichment of ranked gene lists

Genome-wide expression data has become ubiquitous within the last two decades. Given such data, functional enrichment methods identify functional categories (e.g., biological processes) that preferentially annotate differentially expressed genes. However, many existing methods operate in a binary manner, disregarding valuable information contained in the gene ranking. The few methods that consider the ranking often return redundant or non-specific functional categories. To address these limitations, we developed a novel method called Concise Ranked Functional Enrichment (CRFE), which effectively leverages the ranking information in gene expression data to compute a non-redundant set of specific functional categories that are notably enriched for highly ranked genes. A particularly useful feature of CRFE is a tunable parameter that defines how much focus should be given to the most highly ranked genes. Using four treatment-control RNA-seq datasets, we compared the performance of CRFE with the two most widely used types of functional enrichment methods, Gene Set Enrichment Analysis and over-representation analysis. We evaluated the methods based on their ability to utilize ranking information, generate non-redundant results, and return functional categories with high information content. CRFE excelled in all evaluated criteria, outperforming the existing methods, each of which exhibits deficiencies in at least one aspect. Using lung adenocarcinoma data, we further showed that the functional categories identified by CRFE are biologically meaningful. In conclusion, CRFE computes an informative set of functional categories that summarizes genome-wide expression data. With its superior performance over existing methods, CRFE harbors great promise to become a widely used functional enrichment method. Author summaryGiven a list of differentially expressed genes as input, functional enrichment methods reveal which functional categories (e.g., biological processes) were likely activated by the cell and are responsible for the differential expression. We developed a new such method, called Concise Ranked Functional Enrichment (CRFE), which addresses the limitations of current approaches by incorporating gene ranking information to compute a concise and specific set of enriched functional categories. Using four treatment-control RNA-seq datasets, we evaluate how well CRFE and the two currently most widely used methods perform in three criteria. We find that CRFE outperforms each of the alternative methods in at least one of the evaluated criteria, demonstrating its superiority. A high-level interpretation of the functional categories identified by CRFE for lung adenocarcinoma datasets highlights its usefulness for experimentalists. Overall, CRFE harnesses the power of ranked gene lists to generate a focused and non-redundant set of enriched functional categories. Our study positions CRFE as a promising method for functional enrichment analysis, with the potential to advance research in this field.

bioinformatics↗

bioRxiv2017

Development of a high-throughput bead based assay system to measure HIV-1 specific immune signatures in clinical samples

The monitoring and assessment of a broadly neutralizing antibody (bnAb) based HIV-1 vaccine require detailed measurements of HIV-1 binding antibody responses to support the detection of correlates of protection. Here we describe the development of a flexible, high-throughput microsphere based multiplex assay system that allows monitoring complex binding antibody signatures. Studying a panel of 13 HIV-1 antigens in a parallel assessment of different IgG subclasses (IgG1, IgG2 and IgG3) we demonstrate the potential of our strategy. The technical advances we describe include means to improve antigen reactivity using directed neutravidin-biotin immobilization of antigens and biotin saturation to reduce background. A particular emphasis of our study was to provide tools for the assessment of reproducibility and stability of the assay system and strategies to control for variations allowing the application in high-throughput assays, where reliability of single measurements needs to be guaranteed.

immunology↗