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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

4D hybrid model interrogates agent-level rules and parameters driving hiPS cell colony dynamics

Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate cell shape and colony dynamics in human induced pluripotent stem cell (hiPS cell) colonies. This model aimed to first mimic and then explore the dynamics observed in real-world hiPS cell cultures. Initial outputs showed great potential, seeming to mimic small colony behaviors relatively well. However, longer simulations and quantitative comparisons revealed limitations, particularly with the CPM component, which lacked long-range interactions that might be necessary for accurate simulations. This challenge led us to thoroughly examine the hybrid models potential and limitations, providing insights and recommendations for systems where cell-wide mechanics play significant roles. The CPM supports 2D and 3D cell shapes using a Monte Carlo algorithm to prevent cell fragmentation. Basic "out of the box" CPM Hamiltonian terms of volume and adhesion were insufficient to match live cell imaging of hiPS cell cultures. Adding substrate adhesion resulted in flatter colonies, highlighting the need to consider environmental context in modeling. High-throughput parameter sweeps identified regimes that produced consistent simulated shapes and demonstrated the impact of specific model decisions on emergent dynamics. Full-scale simulations showed that while certain agent rules could form a hiPS cell monolayer in 3D, they could not maintain it over time. Our study underscores that "out of the box" 3D CPMs, which do not natively incorporate long-range cell mechanics like elasticity, may be insufficient for accurately simulating hiPS cell and colony dynamics. To address this limitation, future work could add mechanical constraints to the CPM Hamiltonian or integrate global agent rules. Alternatively, replacing the CPM with a methodology that directly represents cell mechanics might be necessary. Documenting and sharing our model development process fosters open team science and supports the broader research community in developing computational models of complex biological systems.

systems biology↗

bioRxiv2025

Neuronal subtype-specific metabolic changes in neurodegenerative and neuropsychiatric diseases predicted via a systems biology-based approach

Understanding how distinct neuronal subtypes contribute to Alzheimers disease (AD) pathology remains a major challenge. Patient-derived induced pluripotent stem cell (iPSC) studies have shown neuronal subtype-specific molecular and pathological signatures, yet the underlying metabolic shifts driving this selective vulnerability are not completely understood. Here we present iNeuron-GEM, the first manually curated, genome-scale metabolic network of human neurons that integrates transcriptomic and metabolic knowledge to resolve subtype-specific metabolic states. By coupling iNeuron-GEM with single nucleus RNA sequencing data from post-mortem human cohort studies, ROSMAP and SEA-AD, we capture neuronal subtype-specific metabolic features and fluxes and identify perturbations in lipid and energy metabolism across excitatory and inhibitory neurons. Integrative analysis with NPS-AD data shows overlapping metabolic disruptions in AD and schizophrenia (SCZ), suggesting shared molecular vulnerabilities between neurodegenerative and neuropsychiatric disorders. We also developed a computational pipeline to infer transcriptional regulation of metabolic pathways and identify NR6A1 and NR3C1 as important regulators of lipid dysregulation in AD neurons. Our study establishes iNeuron-GEM as a framework to identify neuronal subtype-specific metabolic vulnerabilities in complex brain disorders.

systems biology↗

bioRxiv2019

A Novel Optimization-Based Tool to Automate Infeasible Cycle-Free Gapfilling of Genome-Scale Metabolic Models

Stoichiometric metabolic modeling, particularly Genome-Scale Models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions, but cannot avoid Thermodynamically Infeasible Cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle free gapfilling solutions. Part of OptFill can, in addition, be adapted to automate inherent TICs identification in any GSM, such as iJR904. Overall, OptFill can address critical issues in automated development of high-quality GSMs.\n\nIn BriefStoichiometric models of metabolism are useful in studying metabolic interactions in biological systems, but are labor-intensive to create, particularly when addressing gaps or cycles in metabolic reconstruction process. Introduced here is a novel tool, OptFill, which can be used to address both gaps and cycles in model reconstruction, increasing automation.\n\nHighlightsO_LIThis work presents an alternative to state-of-the-art methods for gapfilling.\nC_LIO_LIUnlike current methods, this method is holistic and infeasible cycle free.\nC_LIO_LIThis method is applied to three test and one published model.\nC_LIO_LIThis method might also be used to address infeasible cycling.\nC_LI

systems biology↗

bioRxiv2022

Probabilistic Edge Weights Fine-tune Boolean Network Dynamics

Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this paper, we propose a method to explicitly encode edge-noise in Boolean dynamical systems by probabilistic edge-weight (PEW) operators. PEW operators have two important features: first, they introduce a form of edge-weight into Boolean models through the noise, second, the noise is dependent on the dynamical state of the system, which enables more biologically meaningful modeling choices. Moreover, we offer a simple-to-use implementation in the already well-established BooleanNet framework. In two application cases, we show how the introduction of just a few PEW operators in Boolean models can fine-tune the emergent dynamics and increase the accuracy of qualitative predictions. This includes fine-tuning interactions which cause non-biological behaviors when switching between asynchronous and synchronous update schemes in dynamical simulations. Moreover, PEW operators also open the way to encode more exotic cellular dynamics, such as cellular learning, and to implementing edge-weights for regulatory networks inferred from omics data.

systems biology↗

bioRxiv2024

A system biology-oriented investigation of Arabidopsis proteomes altered in chloroplast biogenesis and retrograde signaling reveals adaptive responses at whole cell level.

Communication across different plant cell compartments relies on an intricate network of molecular interactions, required for the orchestration of organelle development and adaptation to the environment. In this scenario, the Pentatricopeptide Repeat (PPR) Protein GENOMES UNCOUPLED1 (GUN1) plays a key role in transferring information from both developing and mature chloroplasts to the nucleus with the aim to coordinate gene expression between the two genomes. However, its role and the related signaling molecules are still under debate. To help shed light on this matter, we attempted the holistic description of Arabidopsis thaliana proteome upon perturbation of chloroplast biogenesis by lincomycin (Lin), in a genetic context devoid of GUN1-dependent plastid-to-nucleus signaling pathway. Furthermore, the topological analysis of protein-protein interaction (PPI) and protein co-expression networks allowed the identification of protein hubs/bottlenecks characterizing genotypes and conditions, such as proteases, HSPs/Chaperones and redox proteins. Taken together, our findings indicate that GUN1 is required to orchestrate a plastid-located response to plastid protein synthesis inhibition while, in its absence, the reorganization of the activities associated with extra-plastid compartments, such as cytosol, vacuole and mitochondria, prevails. From this landscape, we documented a new role of the Oxygen Evolving Complex subunit PsbO, which appears to be an unconventional photosynthetic protein, as it accumulates in non-photosynthetic plastids and plays a central role in promoting chloroplast breakdown when plastid functions are altered.

systems biology↗

bioRxiv2026

Inferring Cell-Cell Interaction Dynamics from Cell Trajectory Data Using Deep Attention Networks

Interactions between nearby cells are a key driver of cell movement in many biological systems, including collective cell migration and the immune response to cancer. However, inferring the interaction rules in a given system in a manner that is both accurate and biologically interpretable remains a challenge. A valuable experimental method for analysing cell-cell interaction dynamics is the tracking of individual cell locations over a series of time-lapse images, and in this work we present a model, based on the theory of deep attention networks, that learns how cell-cell interactions affect cell movement directly from cell trajectory data. Our approach requires no a priori assumptions about the mechanisms governing cell behaviour, enabling its application to cell trajectory data originating from a diverse range of biological systems. In addition to the model, we develop a suite of tools that exploit the models attention-based structure to present the learned interaction dynamics in an interpretable manner. Our model extends previous applications of deep attention networks to cell movement by providing deeper insights into cell-cell interaction dynamics, moving beyond inferring whether cells interact to inferring how these interactions affect cell movement, and providing the ability to infer type dependent cell-cell interaction dynamics in multi-type cell movement systems. By combining data-driven learning and structural interpretability, our approach represents a highly general methodology for linking cell trajectory data to mechanistic hypotheses, showing that deep attention networks constitute a powerful exploratory tool for characterising the effect of cell-cell interactions on cell movement in complex cellular systems.

cell biology↗

bioRxiv2019

Robustness and lethality in multilayer biological molecular networks

Robustness is a prominent feature of most biological systems. In a cell, the structure of the interactions between genes, proteins, and metabolites has a crucial role in maintaining the cells functionality and viability in presence of external perturbations and noise. Despite advances in characterizing the robustness of biological systems, most of the current efforts have been focused on studying homogeneous molecular networks in isolation, such as protein-protein or gene regulatory networks, neglecting the interactions among different molecular substrates. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, and protein-protein interaction layer and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall systems robustness, defined as its influence over the global network. We find that highly influential genes are enriched in essential and cancer genes, confirming the central role of these genes in critical cellular processes. Further, we determine that the metabolic layer is more vulnerable to perturbations involving genes associated to metabolic diseases. By comparing the robustness of the network to multiple randomized network models, we find that the real network is comparably or more robust than expected in the random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within or between layers. These results provide new insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

systems biology↗

bioRxiv2018

Predicting complex genetic phenotypes using error propagation in weighted networks

Network-biology view of biological systems is a ubiquitous abstraction that emerged in the last two decades to allow a high-level understanding of principles governing them. However, the principles according to which biological systems are organized are still unclear. Here, we investigate if biological networks could be approximated as overlapping, feed-forward networks where the nodes have non-linear activation functions. Such networks have been shown to be universal approximators and their stability has been explored in the context of artificial neural networks. Mathematical formalization of this model followed by numerical simulations based on genomic data allowed us to accurately predict the statistics of gene essentiality in yeast and hence indicate that biological networks might be better understood as a distributed system, comprising potentially unreliable components.

systems biology↗

bioRxiv2016

Massively parallel digital transcriptional profiling of single cells

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3' mRNA counting of up to tens of thousands of single cells per sample. Cell encapsulation in droplets takes place in [~]6 minutes, with [~]50% cell capture efficiency, up to 8 samples at a time. The speed and efficiency allow the processing of precious samples while minimizing stress to cells. To demonstrate the system's technical performance and its applications, we collected transcriptome data from [~][1/4] million single cells across 29 samples. First, we validate the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. Then, we profile 68k peripheral blood mononuclear cells (PBMCs) to demonstrate the system's ability to characterize large immune populations. Finally, we use sequence variation in the transcriptome data to determine host and donor chimerism at single cell resolution in bone marrow mononuclear cells (BMMCs) of transplant patients. This analysis enables characterization of the complex interplay between donor and host cells and monitoring of treatment response. This high-throughput system is robust and enables characterization of diverse biological systems with single cell mRNA analysis.

Genomics↗

bioRxiv2023

Top-down perceptual inference shaping the activity of early visual cortex

Deep discriminative models provide remarkable insights into hierarchical processing in the brain by predicting neural activity along the visual pathway. However, these models differ from biological systems in their computational and architectural properties. Unlike biological systems, they require teaching signals for supervised learning. Moreover, they rely on feed-forward processing of stimuli, which contrasts with the extensive top-down connections in the ventral pathway. Here, we address both issues by developing a hierarchical deep generative model and show that it predicts an extensive set of experimental results in the primary and secondary visual cortices (V1 and V2). We show that the widely documented sensitivity of V2 neurons to textures is a consequence of learning a hierarchical representation of natural images. Further, we show that top-down influences are inherent to hierarchical inference. Hierarchical inference explains neural signatures of top-down interactions and reveals how higher-level representation shapes low-level representations through modulation of response mean and noise correlations in V1.

neuroscience↗

bioRxiv2024

TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

RNA sequencing (RNA-seq) is widely used in biomedical research, advancing our understanding of gene expression across biological systems. Traditional methods require upstream RNA extraction from biological inputs, adding time and expense to workflows. We developed TIRE-seq (Turbocapture Integrated RNA Expression Sequencing) to address these challenges. TIRE-seq integrates mRNA purification directly into library preparation, eliminating the need for a separate extraction step. This streamlined approach reduces turnaround time, minimizes sample loss, and improves data quality. A comparative study with the widely used Prime-seq protocol demonstrates TIRE-seqs superior sequencing efficiency with crude cell lysates as inputs. TIRE-seqs utility was demonstrated across three biological applications. It captured transcriptional changes in stimulated human T cells, revealing activation-associated gene expression profiles. It also identified key genes driving murine dendritic cell differentiation, providing insights into lineage commitment. Lastly, TIRE-seq analyzed the dose-response and time-course effects of temozolomide on patient-derived neurospheres, identifying differentially expressed genes and enriched pathways linked to the drugs mechanism of action. With its simplified workflow and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies across diverse biological systems.

genomics↗

bioRxiv2024

Learning Biomolecular Models using Signal Temporal Logic

Modeling dynamical biological systems is key for understanding, predicting, and controlling complex biological behaviors. Traditional methods for identifying governing equations, such as ordinary differential equations (ODEs), typically require extensive quantitative data, which is often scarce in biological systems due to experimental limitations. To address this challenge, we introduce an approach that determines biomolecular models from qualitative system behaviors expressed as Signal Temporal Logic (STL) statements, which are naturally suited to translate expert knowledge into computationally tractable specifications. Our method represents the biological network as a graph, where edges represent interactions between species, and uses a genetic algorithm to identify the graph. To infer the parameters of the ODEs modeling the interactions, we propose a gradient-based algorithm. On a numerical example, we evaluate two loss functions using STL robustness and analyze different initialization techniques to improve the convergence of the approach.

bioengineering↗

bioRxiv2017

Tellurium Notebooks - An Environment for Dynamical Model Development, Reproducibility, and Reuse

The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system that facilitates building reproducible dynamical models and reusing models by 1) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 2) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python-based Jupyter-like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in-line, human-readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards-compliant models and simulations, run the simulations in-line, and re-export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.\n\nAuthor summaryThere is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human-readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human-readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard-encoded models and simulations, regardless of the users level of technical knowledge of said standards.

systems biology↗

bioRxiv2023

Label-free high-resolution infrared spectroscopy for spatiotemporal analysis of complex living systems

Label-free chemical and structural imaging of complex living tissue and biological systems is the holy grail of biomedical research and clinical diagnostics. The current analysis techniques are time-consuming and/or require extensive sample preparation, often due to the presence of interfering molecules such as water, making them unsuitable for the analysis of such systems. Here, we demonstrate a proof-of-principle study using label-free optical photothermal mid-infrared microspectroscopy (O-PTIR) for fast, direct spatiotemporal chemical analysis of complex living biological systems at submicron resolution. While other analytical methods can provide only static snapshots of molecular structures, our O-PTIR approach enables time-resolved and in situ investigation of chemical and structural changes of diverse biomolecules in their native conditions. This comprises a technological breakthrough in infrared spectroscopy to analyze biomolecules under native conditions over time: in fresh unprocessed biopsies, living brain tissue, and vertebrates without compromising their viability. One-Sentence SummaryProof-of-principle application of non-destructive O-PTIR for high-resolution spatiotemporal chemical and structural analysis of unprocessed biopsies, living brain tissue, and vertebrates.

molecular biology↗

bioRxiv2023

Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models

Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of the test statistics distribution. The lack of knowledge about this distribution for nonlinear ordinary differential equation (ODE) models requires an approximation which assumes the so-called asymptotic setting, i.e. a sufficiently large amount of data. Since the amount of data from quantitative molecular biology is typically limited in applications, this finite-sample case regularly occurs for mechanistic models of dynamical systems, e.g. biochemical reaction networks or infectious disease models. Thus, it is unclear whether the standard approach of using statistical thresholds derived for the asymptotic large-sample setting in realistic applications results in valid conclusions. In this study, empirical likelihood ratios for parameters from 19 published nonlinear ODE benchmark models are investigated using a resampling approach for the original data designs. Their distributions are compared to the asymptotic approximation and statistical thresholds are checked for conservativeness. It turns out, that corrections of the likelihood ratios in such finite-sample applications are required in order to avoid anti-conservative results. Author summaryStatistical methods based on the likelihood ratio are ubiquitous in mathematical modelling in systems biology. For example confidence intervals of estimated parameters rely on the statistical properties of the likelihood-ratio test. However, it is often overlooked that these intervals sizes rely on assumptions on the amounts of data, which are regularly violated in typical applications in systems biology. By checking the appropriateness of these assumptions in models from the literature, this study shows that in a surprisingly large fraction confidence intervals might be too small. Using a geometric interpretation of parameter estimation in the so-called data space, it is motivated why these issues appear and how they depend on the identifiability of the model parameters. In order to avoid such problematic situations, this work makes suggestions on how to adapt the statistical threshold values for likelihood-ratio test. By this, it can be assured that valid statistical conclusions are drawn from the analysis, also in situations where only smaller data sets are available. Such corrections yield for example more conservative confidence interval sizes and thus decrease a potential underestimation of the parameter uncertainty.

systems biology↗

bioRxiv2018

Intrinsic noise modulation in closed oligomerization-type systems

How random fluctuations impact on biological systems and what is their relationship with complexity and energetic cooperativity are challenging questions that are far from being elucidated. Using the stochastic differential equation formalism, we studied analytically the effect of fluctuations on a series of oligomerization processes, in which several molecules of the same or different species interact to form complexes, without interaction with the environment. The conservation of the total number of molecules within the systems imposes constraints on the stochastic quantities, among which the negativity of the covariances and the vanishing of the determinant of the covariance matrix. The intrinsic noise on the number of molecules of each species is represented by the Fano factor, defined as the variance to mean ratio. At the equilibrium steady states, the sum of the Fano factors of all molecular species is equal to the rank of the system, independently of the parameters. The Fano factors of the individual molecular species are, however, parameter dependent. We found that when the free energy cooperativity of the reactions increases, the intrinsic noise on the oligomeric product decreases, and is compensated by a higher noise on the monomeric reactants and/or intermediate states. The noise reduction is moreover more pronounced for higher complexity systems, involving oligomers of higher degrees.

systems biology↗

bioRxiv2024

Solving the "Blind men and the elephant problem": Additive deep learning of complex high dimensional models from partial faceted datasets

Biological systems are complex networks involving tens of thousands of interacting molecular components, and measurable biological functions are emerging properties of these complex networks. Many quantitative studies in biology attempt to connect biological function with molecular components and genes, in the process developing mechanistic understanding. However, it is challenging to quantify the contribution of all components to the biological function simultaneously, especially at the single cell level. Instead, in typical experiments, only a subset of the variables (or facet) is measured. This makes it difficult to obtain a complete and unbiased understanding of the network and how different components of the network cooperatively contribute to the biological function. In this paper, we explore a machine learning approach to combine different facets of data and obtain a complete picture of the biological system based on conditional distributions from faceted data subsets. Both a polynomial regression approach and a neural network approach are developed and examined with two set of concrete examples: A mechanical spring network system deforming under external forces and a small (8-dimensions) biological network including the cellular senescence marker P53. In the later example, single cell data is collected to validate the machine learning approach. We find that the full system is successfully reconstructed from faceted data in both examples. We further discuss the additive property of the model, where the model predictive accuracy increases with increasing number of simultaneously measured variables (dimension of subsets). Our model provides a systematic and novel approach to integrate different pieces of experimental information to reconstruct complex high dimensional systems, arriving at an unbiased and wholistic model of biological function.

bioinformatics↗

bioRxiv2023

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression. SignificanceA combined bioinformatics and systems-biology modeling approach is designed to model a transcriptional regulatory network for AML with IDH mutations. Network modeling identifies key regulators DNMT1 and E2F1, which is supported by patient survival data.

systems biology↗