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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Kernel Filter-Based Adaptive Controllers For Cybergenetics Applications

Cybergenetics is an advancing field that seeks to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in tune with the current behaviour of the biological system. Controllers are often constrained by their sampling rate, which is usually relatively low when using microfluidics/microscopy platforms. Current adaptive filters can lead to an inaccurate predictive model when operating with a low sampling rate, leading to sub-optimal control. Here, we propose a kernel filter that can fit model parameters online to produce a more accurate predictive model that can be included within an adaptive model predictive control scheme. The use of the kernel filter is demonstrated in in silico and in vitro experiments, where we control a synthetic gene oscillator and a P53 oscillator, and observe a synthetic toggle switch. Our results show that the kernel filter outperforms a particle filter when used for parameter estimation in both the predictive model accuracy and when included within an adaptive model-based controller.

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↗

bioRxiv2020

Symmetry structures in dynamic models of biochemical systems

Symmetries provide a powerful concept for the development of mechanistic models by describing structures corresponding to the underlying dynamics of biological systems. In this paper, we consider symmetries of the non-linear Hill model describing enzymatic reaction kinetics, and derive a class of symmetry transformations for each order n of the model. We consider a minimal example consisting in the application of symmetry based methods to a model selection problem, where we are able to demonstrate superior performance compared to ordinary residual-based model selection. Finally, we discuss the role of symmetries in systematic model building in systems biology.

systems biology↗

bioRxiv2026

DBSOMA: A Machine Learning Method that Identifies Chemical Modulators of Transcriptional States Uncovers Effectors of Beta-Cell Maturation

The effects of perturbation on a biological system can be readily measured in terms of transcriptional changes. However, despite a wealth of transcriptional perturbation response data, there are currently few methods to draw equivalence between the many biological systems used to generate that data and a specific system of interest. Here we use density analysis of transcriptional correlations to computationally predict whether a given perturbation readout is relevant to Stem Cell derived islet (SC-Islet) maturation. The approach, Density Based Self-Organizing Map Analysis (DBSOMA), first learns patterns of gene expression represented in scRNA-seq sets by clustering genes with the Self-Organizing-Map (SOM) algorithm. Perturbation expression profiles and other gene lists are then projected onto the SOM grid, where the degree of clustering is determined by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. We applied DBSOMA to SC-Islet maturation and identified known and novel regulators of {beta}-cell maturation. This workflow can be applied broadly to biological systems where single-cell RNA-sequencing data is available, and a desired outcome can be represented in transcriptional changes.

bioinformatics↗

bioRxiv2021

Co-transcriptional RNA strand displacement circuits

Engineered molecular circuits that process information in biological systems could address emerging human health and biomanufacturing needs. However, such circuits can be difficult to rationally design and scale. DNA-based strand displacement reactions have demonstrated the largest and most computationally powerful molecular circuits to date but are limited in biological systems due to the difficulty in genetically encoding components. Here, we develop scalable co-transcriptional RNA strand displacement (ctRSD) circuits that are rationally programmed via base pairing interactions. ctRSD addresses the limitations of DNA-based strand displacement circuits by isothermally producing circuit components via transcription. We demonstrate the programmability of ctRSD in vitro by implementing logic and amplification elements, and multi-layer signaling cascades. Further, we show ctRSD kinetics are accurately predicted by a simple model of coupled transcription and strand displacement, enabling model-driven design. We envision ctRSD will enable rational design of powerful molecular circuits that operate in biological systems, including living cells.

synthetic biology↗

bioRxiv2023

More is Different: Constructing the Most Comprehensive Human Protein High-Order Interaction Dataset

In biological systems, protein-protein interactions (PPI) weave intricate network patterns that are fundamental to the structural and functional integrity of organisms. While the majority of existing research has been anchored in the study of pairwise PPIs, the realm of high-order interactions remains relatively untapped. This oversight could potentially obscure the deeper intricacies embedded within biological networks. To address this gap, this study formulates a scientific task aimed at predicting high-order protein-protein interactions and introduces a multi-level comprehensive dataset focused on triadic high-order interactions within PPI networks. This dataset incorporates more than 80% of the known human protein interaction relationships and partitions into 60 subsets across a diverse range of functional contexts and confidence. Through meticulous evaluation using cutting-edge high-order network prediction tools and benchmark PPI prediction methodologies, our findings resonate with the principle that "more is different". Triadic high-order interactions offer a more enriched and detailed informational canvas than their pairwise counterparts, paving the way for a deeper comprehension of the intricate dynamics at play in biological systems. In summary, this research accentuates the critical importance of high-order PPI interactions in biological systems and furnishes invaluable resources for subsequent scholarly investigations. The dataset is poised to catalyze future research endeavors in protein-protein interaction networks, elucidating their pivotal roles in both health and disease states.

genomics↗

bioRxiv2020

Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

High-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2023

Localgini: A method for harnessing inequality in geneexpression to improve the quality of context-specific models

Genome-scale metabolic models (GEMs) are valuable tools for investigating normal and disease phenotypes of biological systems through the prediction of fluxes in biochemical reactions. However, in specific contexts such as different cell lines, tissues, or diseases, only a subset of reactions is active. To address this, several model extraction methods (MeMs) have been developed to filter the reactions in GEMs and extract context-specific models. These methods utilize gene expression data as a source of context-specific information. To construct context-specific models, MeMs require core reactions specific to the given context as input. Typically, core reactions are derived using a single threshold applied to gene expression data. Reactions associated with genes whose expression values exceed the threshold are considered as core reactions. However, it is important to note that enzyme activity is not solely determined by gene expression levels. This approach based on a single threshold may inadvertently exclude reactions that require enzymes in smaller quantities. In this study, we propose a novel thresholding algorithm called Localgini, which leverages the Gini coefficient and transcriptomics data to derive gene-specific thresholds. Localgini is implemented as a pre-processing step to obtain core reactions for MeMs. To demonstrate the effectiveness of Localgini, we construct context-specific models for NCI-60 cancer cell lines and human tissues using different MeMs. We compare the performance of Localgini with existing thresholding methods, namely LocalT2 and StanDep. The results show that the models derived using Localgini recover a greater number of housekeeping functionalities compared to the other thresholding methods. Moreover, the Localgini-derived core reactions exhibit increased self-consistency and display enhanced consensus among models built using different MeMs. By incorporating transcriptomic support, Localgini includes low-expression reactions in the core reaction list, enhancing the comprehensiveness of the resulting models. Codes used in this study, compatible with COBRA toolbox are available at https://github.com/NiravBhattLab/Localgini Author summaryGenome-scale models are becoming a desirable tool to understand the metabolism of a biological system and hence find applications in the fields of systems and synthetic biology. These models are often integrated with transcriptomics data to improve prediction accuracy. Algorithms developed to integrate transcriptomics data with genome-scale models require core reactions to be derived from omics data using a threshold. In this work, we propose a thresholding method that uses an inequality-based metric to derive thresholds. We implied the proposed method and other existing methods to datasets of cancer cell lines and human tissue. We showed that our method improves the inclusion of reactions required for basic cellular maintenance. Furthermore, we validated the built models for the reduction in variance owing to the model-extraction algorithms. Overall, the proposed method improves the quality of metabolic models by inferring inequality in the distribution of gene expression levels across samples/contexts.

systems biology↗

bioRxiv2022

NETISCE: A Network-Based Tool for Cell Fate Reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using Signal Flow Analysis and Feedback Vertex Set Control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.

systems biology↗

bioRxiv2021

Computational modeling of cell signaling and mutations in pancreatic cancer

Published research articles are rich sources of data when the knowledge is incorporated into models. Complex biological systems benefit from computational modelings ability to elucidate dynamics, explain data and address hypotheses. Modeling of pancreatic cancer could guide treatment of this devastating disease that has a known mutational profile disrupting signaling pathways but no reliable therapies. The approach described here is to utilize discrete modeling of the major signaling pathways, metabolism and the tumor microenvironment including macrophages. This modeling approach allows for abstraction in order to assemble large networks to capture numerous facets of the biological system under investigation. The Hallmarks of Cancer are represented as the processes of apoptosis, autophagy, cell cycle progression, inflammation, immune response, oxidative phosphorylation and proliferation. The model is initialized with pancreatic cancer receptors and mutations and simulated in time. The model portrays the hallmarks of cancer and suggests combinations of inhibitors as therapies.

systems biology↗

bioRxiv2025

Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks

Comprehensive analysis of the dynamics of Boolean models of biological systems is hampered by the exponentially large state space. Here we introduce the succession-diagram-based Markov chain (SD Markov chain), a coarse-grained representation that uses trap spaces (unescapable state subspaces) of the Boolean model as the states of a Markov chain. These trap spaces and their succession diagram can be efficiently identified, and constitute a dramatic reduction compared to the full state space. The SD Markov chain preserves the decisions that trap the systems dynamics while making the state space computationally tractable. Using an ensemble of random Boolean networks with known state transition matrices, we show that the SD Markov chain accurately reproduces attractors, basins of attraction, convergence probabilities, decision transitions, and sequences of events. By combining the interpretability of the succession diagram with the probabilistic rigor of Markov analysis, the SD Markov chain offers a compact quantitative description of the attractor landscape and provides a new avenue for studying control and stability in complex biological systems.

systems biology↗

bioRxiv2021

Biological regulatory networks are less nonlinear than expected by chance

The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed regulatory nonlinearity, we analyzed a suite of 137 published Boolean network models, containing a variety of complex nonlinear regulatory interactions, using a probabilistic generalization of Boolean logic that George Boole himself had proposed. Leveraging the continuous-nature of this formulation, we used Taylor decomposition to approximate the models with various levels of regulatory nonlinearity. A comparison of the resulting series of approximations of the biological models with appropriate random ensembles revealed that biological regulation tends to be less nonlinear than expected, meaning that higher-order interactions among the regulatory inputs tend to be less pronounced. A further categorical analysis of the biological models revealed that the regulatory nonlinearity of cancer and disease networks could not only be sometimes higher than expected but also relatively more variable. We show that this variation is caused by differences in the apportioning of information among the various orders of regulatory nonlinearity. Our results suggest that there may have been a weak but discernible selection pressure for biological systems to evolve linear regulation on average, but for certain systems such as cancer, on the other hand, to also evolve more nonlinear rules.

systems biology↗

bioRxiv2023

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathways activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

systems biology↗

bioRxiv2025

A Universal Method for Quantifying Elemental Sulfur and Selenium in Biological Matrices

Elemental sulfur occupies a central position in biological sulfur chemistry as a thermodynamically stable endpoint of reactive sulfur species (RSS) interconversion, yet its role in biology has remained poorly defined due to the lack of robust analytical methods for its direct measurement. Here, we describe a selective, accessible chemical workflow for the detection and quantification of zero-valent elemental sulfur (S0) and elemental selenium (Se0) in complex biological systems, enabling systematic interrogation of these species as biological redox metabolites. Elemental sulfur is isolated from biological samples as the cyclo-octasulfur (S8) allotrope via phase-selective organic solvent extraction and structurally confirmed by Raman spectroscopy. Extracted S8 is subsequently derivatized with triphenylphosphine (TPP) to form triphenylphosphine sulfide (TPP=S), which is quantified using complementary 31P-nuclear magnetic resonance (NMR) spectroscopy and GC-MS, providing sensitivity across nanomolar to millimolar concentration ranges. The method exhibits strong chemical selectivity against competing sulfur species and is compatible with a wide range of biological matrices, including conditioned media, cultured cells, tissues, and solid biological materials. Extension of the workflow to selenium chemistry enables selective detection and quantification of elemental selenium (Se0) following chemical or cellular reduction of selenite. Collectively, these results establish a practical and transferable analytical platform that renders zero-valent chalcogen species experimentally accessible in biological contexts. By enabling direct measurement of elemental sulfur across diverse biological systems, this method reveals S0 as a previously inaccessible, redox-active sulfur metabolite and provides a foundation for defining its formation, distribution, and function in cellular and tissue redox biology. Highlights{circ} S0 and Se0 are predicted redox endpoints, yet selective detection is limited. {circ}A broadly accessible chemical method is introduced to quantify S0 and Se0. {circ}Quantification spans nM-mM ranges in diverse matrices, e.g., cells and media. {circ}Reveals S0 as a redox-active metabolite present in multiple biological matrices.

biochemistry↗

bioRxiv2026

Large-scale Perturbation of Systems Biology-Derived Genes Reveals Modifiers of HD-associated Transcriptomic Networks and Pathology

In Huntingtons disease (HD), disease-driver genes are broadly expressed, but pathogenic players specific to vulnerable neurons remain poorly defined. Leveraging a previously defined mutant huntingtin (mHtt) CAG-length-associated gene network, we perturbed 115 module hub genes with heterozygous knockout (KO-het) to assess their striatal transcriptomic modifier effects in wildtype and Q140 HD mice. We generated 3,592 striatal RNA-seq datasets, mapped 6,517 perturbagen-responder gene pairs, and uncovered regulators of medium spiny neuron (MSN) identity gene expression and DNA- methylase/demethylase-sensitive genes. We developed a bioinformatic pipeline to rank the perturbations with significant impacts on striatal transcriptome and HD-associated gene networks in wildtype or Q140 mice. KO-het for FoxP1 and Scn4b (two MSN-selective genes) exacerbated, whereas Pdp1 KO-het ameliorated, Q140 striatal pathology. Importantly, knockdown of functionally opposing ion channels, SCN4B and KCNH4, dichotomously affected aggregation and neurodegeneration in reprogrammed HD patient MSNs. Together, our study rigorously evaluated systems biology-derived candidates to identify modifiers of HD-associated molecular networks and pathology, providing an in vivo perturbation- transcriptome resource and highlighting genes involved in MSN excitability, transcription, calcium signaling, and mitochondrial metabolism in HD pathogenesis.

genetics↗

bioRxiv2020

HIV-1 and microglia: EcoHIV and HIV-1 transgenic rats

The persistence of HIV-1 viral reservoirs in the brain, despite treatment with combination antiretroviral therapy (cART), remains a critical roadblock for the development of a novel cure strategy for HIV-1. To enhance our understanding of viral reservoirs, two complementary studies were conducted to 1) evaluate the HIV-1 mRNA neuroanatomical distribution pattern and major cell type expressing HIV-1 mRNA in the HIV-1 transgenic (Tg) rat (i.e., under conditions of latent infection), and 2) to validate our findings by developing and critically testing a novel biological system to model active HIV-1 infection in the rat. First, a restricted, region-specific HIV-1 mRNA distribution pattern was observed in the HIV-1 Tg rat. Microglia were the predominant cell type expressing HIV-1 mRNA in the HIV-1 Tg rat. Second, we developed and critically tested a novel biological system to model key aspects of HIV-1 by infusing F344/N control rats with chimeric HIV (EcoHIV). In vitro, primary cultured microglia were treated with EcoHIV revealing prominent expression within 24 hours of infection. In vivo, EcoHIV expression was observed seven days after stereotaxic injections. Following EcoHIV infection, microglia were the major cell type expressing HIV-1 mRNA, results which are consistent with observations in the HIV-1 Tg rat. Within eight weeks of infection, EcoHIV rats exhibited neurocognitive impairments, synaptic dysfunction, which may result from activation of the NogoA-NgR3/PirB-RhoA signaling pathway, and neuroinflammation. Collectively, these studies enhance our understanding of HIV-1 viral reservoirs in the brain and offer a novel biological system to model HIV-associated neurocognitive disorders and associated comorbidities (i.e., drug abuse) in rats.

neuroscience↗

bioRxiv2023

Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses

Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinsons disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlight the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.

systems biology↗

bioRxiv2026

Evolution on degenerate fitness landscapes is not neutral: curvature drives directional bias

Degeneracy - the multiplicity of phenotypes with equivalent fitness - is a prevalent feature of biological systems. Such degeneracy is often associated with neutral evolution, under the assumption that adaptive dynamics on degenerate fitness manifolds lacks direction. Here we show that this is not generally the case. Using a minimal model of evolutionary dynamics on smooth degenerate fitness landscapes, we demonstrate that stochastic mutation-selection dynamics induce a directional drift on manifolds of optimal fitness toward regions of reduced curvature. This drift arises from an interaction between population variability and landscape curvature: curvature shapes phenotypic variation, which in turn biases evolutionary exploration even when fitness gradients vanish. As a result evolution exhibits an implicit bias, preferentially selecting flat and robust regions of the degenerate fitness manifolds, without explicit optimization for these properties. Interestingly, similar flatness-seeking implicit biases have been discovered in other stochastic optimization algorithms; here we reveal their different underlying mechanisms despite similar outcome, highlighting the unique properties of evolutionary dynamics. Our results highlight a general mechanism by which degeneracy shapes long-term evolutionary outcomes, affecting our interpretation of phenotypic variability, robustness, and neutrality in high-dimensional biological systems.

systems biology↗