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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Non-ergodicity in ecology and evolution

Stochasticity plays an important role in all biological systems. The standard way to deal with stochasticity involves averaging over an ensemble of independent realizations. However, such mean statistics need not accurately reflect the typical outcomes in any finite sample unless the system satisfies the property of ergodicity, which guarantees that each trajectory will over time experience the same statistics as the entire ensemble. Here, we argue that, in contrast, non-ergodicity might instead be the rule rather than exception in real biological systems and investigate its implications for eco-evolutionary dynamics through three case-studies. First, we show how demographic stochasticity leads to ergodicity breaking where the asymptotic growth rate carries a signature of the initial condition. This motivates us to define a mutant establishment threshold, which quantifies a critical population size above which the typical mutant population starts to grow. Second, we consider environmental stochasticity and demonstrate that autocorrelated environmental variation does not average out in time, which has the important consequence that the fitness of a genotype cannot be simply averaged over the environments. Finally, we show how in a metapopulation structure the evolutionary dynamics within a typical subpopulation can deviate from the ensemble dynamics in the entire metapopulation, which is sufficient to explain the evolution and persistence of cooperation despite a fitness cost.

evolutionary biology↗

bioRxiv2021

Supervised Learning Model Predicts Protein Adsorption to Nanotubes

Engineered nanoparticles are advantageous for numerous biotechnology applications, including biomolecular sensing and delivery. However, testing the compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable biofouling via protein corona formation often prevents effective implementation. Moreover, rational design of biomolecule-nanoparticle conjugates requires prior knowledge of such interactions or extensive experimental testing. Toward better applying engineered nanoparticles in biological systems, herein, we develop a random forest classifier (RFC) trained with proteomic mass spectrometry data that identifies proteins that adsorb to nanoparticles, based solely on the proteins amino acid sequence. We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)-based optical nanosensor and study whether there is a relationship between the proteins amino acid-based properties and the proteins adsorption to SWCNTs. We optimize the classifier and characterize the classifier performance against other models. To evaluate the predictive power of our model, we apply the classifier to rapidly identify proteins with high binding affinity to SWCNTs, followed by experimental validation. We further determine protein features associated with increased likelihood of SWCNT binding: high content of solvent-exposed glycine residues and non-secondary structure-associated amino acids. Conversely, proteins with high content of leucine residues and beta-sheet-associated amino acids are less likely to form the SWCNT protein corona. The classifier presented herein provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions, which is needed for more rapid and effective translation of nanobiotechnologies from in vitro synthesis to in vivo use. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/449132v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@bbc175org.highwire.dtl.DTLVardef@9a0b94org.highwire.dtl.DTLVardef@16e22e1org.highwire.dtl.DTLVardef@1b40a84_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

Controlling semiconductor growth with structured de novo protein interfaces

Protein design now enables the precise arrangement of atoms on the nanometer length scales of inorganic crystal nuclei, opening up the possibility of templating the growth of metal oxides including semiconductors. We designed proteins presenting regularly repeating interfaces containing functional groups that organize ions and water molecules, and characterized their ability to bind to and template metal oxides. Two interfaces promoted the growth of hematite under conditions that otherwise resulted in the formation of magnetite. Three interfaces promoted ZnO nucleation under conditions where traditional ZnO-binding peptides and control proteins were ineffective. Designed cyclic assemblies with these ZnO nucleating interfaces lining interior cavities promoted ZnO growth within the cavity. CryoEM analysis of a designed octahedral nanocage revealed atomic density likely corresponding to the growing ZnO directly adjacent to the designed nucleation promoting interfaces. These findings demonstrate that designed proteins can direct the formation of metal oxides not observed in biological systems, opening the door to protein-semiconductor hybrid materials. One Sentence SummaryWe describe the design of structured protein interfaces that bind to, promote, and localize the growth of zinc oxide and hematite, inorganic materials which are not found in biological systems.

bioengineering↗

bioRxiv2025

A Co-Evolutionary Multi-Objective Framework for Joint Topology and Parameter Discovery in Gene Regulatory Networks

Inferring gene regulatory networks (GRN) poses a complex discrete-continuous, multi-objective optimization problem that requires the simultaneous discovery of both network topology and kinetic parameters. Current methods are often limited because they address these two challenges separately. We propose a novel co-evolutionary multi-objective framework that evolves topology and parameter populations in tandem to optimize three competing objectives: dynamic fidelity, network simplicity, and robustness. This algorithm utilizes biologically-inspired operators and is grounded in theoretical results on Pareto optimality for mixed discrete-continuous spaces. In computational experiments on synthetic benchmarks, our approach showed superior performance, attaining the lowest mean squared error in three of four test cases against single-objective and topology-fixed methods. The framework successfully discovers multiple functionally equivalent network configurations, a finding that highlights the degeneracy inherent in biological systems and offers practical insights for synthetic biology, where function is often prioritized over precise structural accuracy.

systems biology↗

bioRxiv2021

Workflows for rapid functional annotation of diverse arthropod genomes

Genome sequencing of a diverse array of arthropod genomes is already underway and these genomes will be used to study human health, agriculture, biodiversity and ecology. These new genomes are intended to serve as community resources and provide the foundational information that is required to apply omics technologies to a more diverse set of species. However, biologists require genome annotation to use these genomes and derive a better understanding of complex biological systems. Genome annotation incorporates two related but distinct processes: demarcating genes and other elements present in genome sequences (structural annotation); and associating function with genetic elements (functional annotation). While there are well established and freely available workflows for structural annotation of gene identification in newly assembled genomes, workflows for providing the functional annotation required to support functional genomics studies are less well understood. Genome-scale functional annotation is required for functional modeling (enrichment, networks, etc.) and a first-pass genome-wide functional annotation effort can rapidly identify under-represented gene sets for focused community annotation efforts. We present an open source, open access and containerized pipeline for genome-scale functional annotation of insect proteomes and apply it to a diverse range of arthropod species. We show that the performance of the predictions is consistent across a set of arthropod genomes with varying assembly and annotation quality.Complete instructions for running each component of the functional annotation pipeline on the command line, a high performance computing cluster and the CyVerse Discovery Environment can be found at the readthedocs site (https://agbase-docs.readthedocs.io/en/latest/agbase/workflow.html). Simple summaryGenomic technologies are accumulating information about genes at a faster rate than ever before, and sequencing initiatives like the Earth Biogenome Project, i5k and Ag100Pest are expected to increase this rate of acquisition. However, if genomic sequencing is to be used for improvement of human health, agriculture and our understanding of biological systems, it is necessary to identify genes and understand how they contribute to biological outcomes. While there are several well-established workflows for assembling genomic sequences and identifying genes, understanding gene function is essential to create actionable knowledge. Moreover this functional annotation process must be easily accessible and provide information at a genomic scale to keep up with new sequence data. We report a well defined workflow for rapid functional annotation of whole proteomes to produce Gene Ontology and pathways information. We test this workflow on a diverse set of arthropod genomes and compare it to common arthropod reference genomes. The workflow we described is freely and publicly available via a web interface on CyVerse or as biocontainers that can be deployed scalably on local computing systems.

bioinformatics↗

bioRxiv2019

A deep learning framework for improving protein interaction prediction using sequence properties

MotivationAlmost all critical functions and processes in cells are sustained by the cellular networks of protein-protein interactions (PPIs), understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack high-quality PPI data for constructing the networks, which makes it challenging to study the functions of association of proteins. High-throughput experimental techniques have produced abundant data for systematically studying the cellular networks of a biological system and the development of computational method for PPI identification. ResultsWe have developed a deep learning-based framework, named iPPI, for accurately predicting PPI on a proteome-wide scale depended only on sequence information. iPPI integrates the amino acid properties and compositions of protein sequence into a unified prediction framework using a hybrid deep neural network. Extensive tests demonstrated that iPPI can greatly outperform the state-of-the-art prediction methods in identifying PPIs. In addition, the iPPI prediction score can be related to the strength of protein-protein binding affinity and further showed the biological relevance of our deep learning framework to identify PPIs. Availability and ImplementationiPPI is available as an open-source software and can be downloaded from https://github.com/model-lab/deeplearning.ppi Contactxiang-chen@zju.edu.cn

bioinformatics↗

bioRxiv2023

Whole mouse body histology using standard IgG antibodies

Most diseases involve multiple interconnected physiological systems, but histological evaluation of their pathology is currently limited to small tissue samples. Here, we present wildDISCO, a technology that uses cholesterol extraction to enable deep tissue penetration of standard 150 kDa IgG antibodies in chemically fixed whole mice. Combining wildDISCO with whole mouse clearing, we generate whole-body maps of the nervous, immune, and lymphatic systems and show their close interactions throughout the mouse body. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/528921v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@f0de23org.highwire.dtl.DTLVardef@187fe94org.highwire.dtl.DTLVardef@15ac0faorg.highwire.dtl.DTLVardef@133703e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWildDISCO uses new tissue chemistry based on {beta}-cyclodextrin to enable histology in whole mouse bodies using full-size antibodies C_LIO_LIWildDISCO generates the first whole mouse body atlases for neurons, immune cells, blood and lymph vessels C_LIO_LIThe whole mouse atlases are available online to study the biological systems in health and disease C_LIO_LIVirtual Reality (VR) exploration of these atlases disentangles complex anatomical structures between organs and biological systems C_LI Supplementary Videos can be seen athttp://discotechnologies.org/wildDISCO/

biochemistry↗

bioRxiv2024

Replacing Normalizations with Interval AssumptionsImproves the Rigor and Robustness of DifferentialExpression and Differential Abundance Analyses

Standard methods for differential expression and differential abundance analysis rely on normalization to address sample-to-sample variation in sequencing depth. However, normalizations imply strict, unrealistic assumptions about the unmeasured scale of biological systems (e.g., microbial load or total cellular transcription). This introduces bias that can lead to false positives and false negatives. To overcome these limitations, we suggest replacing normalizations with interval assumptions. This approach allows researchers to explicitly define plausible lower and upper bounds on the unmeasured biological systems scale, making these assumptions more realistic, transparent, and flexible than those imposed by traditional normalizations. Compared to recent alternatives like scale models and sensitivity analyses, interval assumptions are easier to use, resulting in potentially reduced false positives and false negatives, and have stronger guarantees of Type-I error control. We make interval assumptions accessible by introducing a modified version of ALDEx2 as a publicly available software package. Through simulations and real data studies, we show these methods can reduce false positives and false negatives compared to normalization-based tools.

bioinformatics↗

bioRxiv2023

Systematic Characterization of LSD metabolites in C. elegans by ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry

Psychedelic compounds have gained renewed interest for their potential therapeutic applications, but their metabolism and effects on complex biological systems remain poorly understood. Here, we present a systematic characterization of LSD metabolites in the model organism Caenorhabditis elegans using state-of-the-art analytical techniques. By employing ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS), we identified and quantified a range of LSD metabolites, shedding light on their metabolic pathways and offering insights into their pharmacokinetics. Our study demonstrates the suitability of C. elegans as a valuable model system for investigating the metabolism of psychedelic compounds and provides a foundation for further research on the therapeutic potential of LSD. These findings contribute to the growing body of knowledge in the field and highlight the importance of advanced analytical methodologies in elucidating the effects of psychedelic substances on biological systems.

pharmacology and toxicology↗

bioRxiv2025

Active and Probe-Free Intracellular Rheology via Phase-Sensitive Thermoviscous Flows

Determination of the rheological properties of cells is known to require active measurements, which largely depend on the internalisation of mechanical probes. Here we circumvent this problem via the introduction of Rheo-FLUCS: an active, yet probe-free approach that leverages light-induced flows to access the mechanics of complex systems. While Rheo-FLUCS is facilitated by thermoviscous expansion phenomena rather than external forces, we here show equivalence in its ability to measure canonical viscoelastic properties. Specifically, we demonstrate a phase-lag equivalence with probe-dependent active microrheology in a wide range of physically different, yet chemically identical materials. We exemplify the utility of Rheo-FLUCS in three distinctly different biological systems: compound-treated mouse fibroblasts (NIH-3T3), genetically modified human osteoblasts (U2OS) to elucidate the role of myosins in cytoplasmic mechanics, and early ascidian oocytes of Phallusia mammillata at fertilisation stage. This probe-free methodology represents a transformative advance in cellular rheology, enabling non-invasive and precise mechanical measurements across diverse biological systems without perturbing their natural state through probe internalisation.

biophysics↗

bioRxiv2016

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

bioinformatics↗

bioRxiv2020

DReSS: A difference measurement based on reachability between state spaces of Boolean networks

Researches on dynamical features of biological systems are mostly based on fixed network structure. However, both biological factors and data factors can cause structural perturbations to biological regulatory networks. There are researches focus on the influence of such structural perturbations to the systems dynamical features. Reachability is one of the most important dynamical features, which describe whether a state can automatically evolve into another state. However, there is still no method can quantitively describe the reachability differences of two state spaces caused by structural perturbations. DReSS, Difference based on Reachability between State Spaces, is proposed in this research to solve this problem. First, basic properties of DReSS such as non-negativity, symmetry and subadditivity are proved based on the definition. And two more indexes, diagDReSS and iDReSS are proposed based on the definition of DReSS. Second, typical examples like DReSS = 0 or 1 are shown to explain the meaning of DReSS family, and the differences between DReSS and traditional graph distance are shown based on the calculation steps of DReSS. Finally, differences of DReSS distribution between real biological regulatory network and random networks are compared. Multiple interaction positions in real biological regulatory network show significant different DReSS value with those in random networks while none of them show significant different diagDReSS value, which illustrates that the structural perturbations tend to affect reachability inside and between attractor basins rather than to affect attractor set itself. Author summaryBoolean network is a kind of networks which is widely used to model biological regulatory systems. There are structural perturbations in biological systems based on both biological factors and data-related factors. We propose a measurement called DReSS to describe the difference between state spaces of Boolean networks, which can be used to evaluate the influence of specific structural perturbations of a network to its state space quantitively. We can use DReSS to detect the sensitive interactions in a regulatory network, where structural perturbations can influence its state space significantly. We proved properties of DReSS including non-negativity, symmetry and subadditivity, and gave examples to explain the meaning of some special DReSS values. Finally, we present an example of using DReSS to detect sensitive vertexes in yeast cell cycle regulatory network. DReSS can provide a new perspective on how different interactions affect the state space of a specific regulatory network differently.

bioinformatics↗

bioRxiv2025

Degeneracy and bifurcation diversity at the single neuron level

Degeneracy--the capacity of structurally distinct systems to achieve similar functions--is a fundamental property of living organisms, enabling adaptability and resilience. Neurons can maintain stable activity patterns despite wide variability in ion channel expression, highlighting how distinct internal configurations can yield equivalent electrophysiological behaviors. However, it remains unclear how degeneracy manifests in the context of bifurcations, the critical transitions between activity regimes that underlie phenomena such as different firing patterns, seizures and depolarization block. Here, we investigate how different biophysical parameter sets can lead to equivalent bifurcation sequences. Using a minimal neuron model with three variable conductances, we systematically explore how changes in extracellular potassium--mimicking physiological and pathological conditions--affect neuronal dynamics. Our results reveal that neurons with distinct intrinsic properties can traverse the same bifurcation pathways, entering regimes of bursting, seizure-like activity, and depolarization block. Yet, the specific parameter set determines the sensitivity and thresholds for these transitions. This work clarifies how degeneracy extends to the dynamical landscape of neurons, with implications for understanding resilience and vulnerability in neural circuits. 1 Author SummaryBiophysical models expressed through differential equations can reproduce the dynamics of biological systems with varying levels of detail. By changing model parameters, simulations can capture the natural variability that allows biological systems to achieve the same behavior through different mechanisms. This property, called degeneracy, underlies the robustness of living systems to internal and external perturbations. In this work, we identify dis-tinct behaviors in single-neuron models and link them to electrophysiological patterns observed under elevated extracellular potassium during epileptic events. These patterns correspond to specific classes of spontaneous bursting activity within the framework of nonlinear dynamics. We show that different combinations of conductance parameters, at a fixed extracellular potassium level, can generate the same class of patterns, defining degeneracy groups. Finally, we assess the robustness of these groups by analyzing how they respond to changes in extracellular potassium. Our findings provide a basis for studying variability and resilience in the dynamics of more complex neuronal systems.

neuroscience↗

bioRxiv2020

Delayed global feedback in the genesis and stability of spatiotemporal patterns in paced biological excitable media

A multi-scale approach was used to investigate the roles of delayed global feedback (DGF) in the genesis and stability of spatiotemporal patterns in periodically-paced excitable media. Patterns that are temporal period-2 (P2) and spatially concordant (in-phase) or discordant (out-of-phase) were investigated. First, simulations were carried out using a generic spatiotemporal model composed of coupled FitzHugh-Nagumo units with DGF. When DGF is absent, concordant and discordant P2 patterns occur depending on initial conditions. The discordant P2 patterns are spatially random. When the DGF is negative, only concordant P2 patterns exist. When the DGF is positive, both concordant and discordant P2 patterns can occur. The discordant P2 patterns are still spatially random, but they satisfy that the global signal exhibits a temporal period-1 behavior. Second, to validate the spatiotemporal dynamics in a biological system, simulations were carried out using a 3-dimensional physiologically detailed ventricular myocyte model. This model can well capture the intracellular calcium release patterns widely observed in experiments. The properties of DGF were altered by changing ionic currents or clamping voltage. The spatiotemporal pattern dynamics of calcium release in this model match precisely with those of the generic model. Finally, theoretical analyses were carried out using a coupled map lattice model with DGF, which reveals the instabilities and bifurcations leading to the spatiotemporal dynamics and provides a general mechanistic understanding of the role of DGF in the genesis, selection, and stability of spatiotemporal patterns in paced excitable media. Author SummaryUnderstanding the mechanisms of pattern formation in biological systems is of great importance. Here we investigate the dynamical mechanisms by which delayed global feedback affects pattern formation and stability in periodically-paced biological excitable media, such as cardiac or neural cells and tissue. We focus on the formation and stability of the temporal period-2 and spatially in-phase and out-of-phase patterns. Using a multi-scale modeling approach, we show that when the delayed global feedback is negative, only the spatially in-phase patterns are stable; when the feedback is positive, both spatially in-phase and out-of-phase patterns are stable. Also, under the positive feedback, the out-of-phase patterns are spatially random but satisfy that the global signals are temporal period-1 solutions.

biophysics↗

bioRxiv2023

Demographic Consequences of Damage Dynamics in Single-Cell Ageing

Ageing is driven by damage accumulation leading to a decline in function over time. In single-cell systems, in addition to this damage accumulation within individuals, asymmetric damage partitioning at cell division can play a crucial role in shaping demographic ageing patterns. Despite empirical single-cell studies providing quantitative data at the molecular and demographic level, a comprehensive theory of how cellular damage production and asymmetric partitioning propagate and influence demographic patterns is still lacking. Here, we present a generic and flexible damage model using a stochastic differential equation approach that incorporates stochastic damage accumulation and asymmetric damage partitioning at cell divisions. We formulate an analytical approximation linking cellular and damage parameters to demographic ageing patterns. Interestingly, the lifespan of cells follows an inverse-gaussian distribution whose underlying properties derive from cellular and damage parameters. We demonstrate how stochasticity (noise) in damage production, asymmetry in damage partitioning, and division frequency shape lifespan distribution. Confronting the model to various empirical E.coli data reveals non-exponential scaling in mortality rates, a scaling that cannot be captured by classical Gompertz-Makeham models. Our findings provide a deep understanding of how fundamental processes contribute to cellular damage dynamics and generate demographic patterns. Our damage models generic nature offers a valuable framework for investigating ageing in diverse biological systems. SignificanceAsymmetries and randomness in cellular events play important roles in establishing the diversity at evolutionary and demographic scales. Looking at single-cells, ageing processes are influenced by stochastic damage accumulation and asymmetric damage partitioning at cell divisions. Utilising stochastic differential equations, we develop a cellular damage model that encapsulate both noisy damage accumulation within cells and asymmetric damage partitioning among cells when they divide. In doing so we bridge molecular stochastic processes, demographic fates of individuals, and population level demographic distributions. Our model aligns with empirical data from E.coli single-cell studies and advances our understanding compared to traditional demographic models. The generic and adaptable nature of our model paves the way for broader applications in ageing research across biological systems, highlighting the influences of stochasticity and asymmetry on cellular biology.

evolutionary biology↗

bioRxiv2025

Endophytes induce systemic spatial reprogramming of metabolism in poplar roots under drought

Beneficial endophytes help plants thrive in challenging environments by altering their hosts metabolism, but how these cellular scale metabolic changes propagate to the systems biology scale is unknown. In this work, we employed a high-resolution chemical imaging approach to map metabolic changes at the root zone and cell type levels and found that a 9-strain consortium of beneficial endophytes differentially altered the metabolome of droughted root tissues according to cell types and locations along the root system architecture. Using machine learning (ML) models, we identified root metabolites and exudates that have predictive power over treatment class and could therefore be used as systems biology indicators of drought and endophyte inoculation status. We calculated the correlation between each endophyte and metabolite and found that this relationship shifts under drought conditions, indicating the dynamic role endophytes play in a plants microbiome and metabolism in response to environmental changes.

plant biology↗

bioRxiv2025

SwitchClass: dissecting attenuated and escalated molecular features via a label-switch classification framework

Biological systems exhibit complex molecular trajectories in response to perturbations, ranging from changes that revert or attenuate towards homeostasis to alterations that persist or escalate. Capturing these complex patterns is essential for understanding molecular resilience and maladaptive persistence. Here, we introduce SwitchClass, a label-switch classification framework that distinguishes attenuated and escalated molecular features across biological states. By training dual classifiers with inverted outcome labels, SwitchClass computes a differential feature importance score ({delta}) that quantifies directional change in high-dimensional data. Applied to colorectal cancer proteomics spanning healthy, pre-treatment, and post-treatment samples, Switch-Class reveals proteins that normalise after therapy and those remaining dysregulated, uncovering partial molecular recovery. In phosphoproteomics of dietary perturbation and reversal, it uncovers phosphorylation sites linked to incomplete restoration of insulin signalling. In single-cell transcriptomes from COVID-19 patients with varying severities, it identifies cell-type-specific transcripts that mark either the resolution or persistence of inflammatory activity. Together, these analyses establish SwitchClass as a generalisable and interpretable framework for mapping directional molecular changes underlying adaptation, divergence, and disease severity across biological systems. SwitchClass is freely available from https://github.com/PYangLab/SwitchClass.

bioinformatics↗

bioRxiv2021

Macroscopic dynamics of gene regulatory networks revealed by individual entropy

Complex systems are usually high-dimensional with intricate interactions among internal components, and may display complicated dynamics under different conditions. While it is difficult to measure detail dynamics of each component, proper macroscopic description of a complex system is crucial for quantitative studies. In biological systems, each cell is a complex system containing a huge number of molecular components that are interconnected with each other through intricate molecular interaction networks. Here, we consider gene regulatory networks in a cell, and introduce individual entropy as a macroscopic variable to quantify the transcriptional dynamics in response to changes in random perturbations and/or network structures. The proposed individual entropy measures the information entropy of a system at each instant with respect to a basal reference state, and may provide temporal dynamics to characterize switches of system states. Individual entropy provides a method to quantify the stationary macroscopic dynamics of a gene set that is dependent on the gene regulation connections, and can be served as an indicator for the evolution of network structure variation. Moreover, the individual entropy with reference to a preceding state enable us to characterize different dynamic patterns generated from varying network structures. Our results show that the proposed individual entropy can be a valuable macroscopic variable of complex systems in characterizing the transition processes from order to disorder dynamics, and to identify the critical events during the transition process.

systems biology↗