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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2022

Generalizing the isothermal efficiency by using Gaussian distributions

Unlike the Carnot heat engine efficiency published in 1824, an isothermal efficiency derived from thermodynamics and information theory can be applied to biological systems. The original approach by Pierce and Cutler in 1959 to derive the isothermal efficiency equation came from Shannons channel capacity of 1949 and from Felkers 1952 determination of the minimum energy dissipation needed to gain a bit. In 1991 and 2010 Schneider showed how the isothermal efficiency equation can be applied to molecular machines and that this can be used to explain why several molecular machines are 70% efficient. Surprisingly, some macroscopic biological systems, such as whole ecosystems, are also 70% efficient but it is hard to see how this could be explained by a thermodynamic and molecular theory. The thesis of this paper is that the isothermal efficiency can be derived without using thermodynamics by starting from a set of independent Gaussian distributions. This novel derivation generalizes the isothermal efficiency equation for use at all levels of biology, from molecules to ecosystems.

bioinformatics↗

bioRxiv2024

Graph Contrastive Learning of Subcellular-resolution Spatial Transcriptomics Improves Cell Type Annotation and Reveals Critical Molecular Pathways

Imaging based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution. The subcellular localization of RNA molecules plays a crucial role in the compartmentalization-dependent regulation of genes within individual cells. Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type. However, current cell type annotation approaches of iST primarily utilize gene expression information while neglecting the spatial distribution of RNAs within cells. In this work, we introduce a semi-supervised graph contrastive learning method called Focus, the first method, to the best of our knowledge, that explicitly models RNAs subcellular distribution and community to improve cell type annotation. Focus first constructs gene neighborhood networks based on the subcellular colocalization relationship of RNA transcripts. Next, the subcellular graph of each cell can be augmented by adding important edges and nodes or removing trivial edges and nodes. Focus then aims to maximize the similarity between positive pairs from two augmented views of the same cell and minimize the similarity between negative pairs from different cells within a common batch. Guided by a limited amount of labeled data, Focus is capable of assigning cell type identities for the entire datasets at high accuracy. Extensive experiments demonstrate the effectiveness of Focus compared to existing state-of-the-art approaches across a range of spatial transcriptomics platforms and biological systems. Furthermore, Focus enjoys the advantages of revealing intricate cell type-specific subcellular spatial gene patterns and providing interpretable subcellular gene analysis, such as defining the gene importance score. Importantly, with the importance score, Focus identifies genes harboring strong relevance to cell type-specific pathways, indicating its potential in uncovering novel regulatory programs across numerous biological systems. Focus is freely accessible at https://github.com/OmicsML/focus.

bioinformatics↗

bioRxiv2025

Lipid Tail Length Determines Nano-Bio Interactions of Peptide Amphiphile Nanostructures

Understanding the interactions of nanomaterials with biological systems is essential to designing effective nanomedicines. However, most of our understanding originates from studies with solid nanoparticles, and nano-bio interactions of self-assembled nanomaterials have remained largely unexplored. To address this knowledge gap, we develop a series of self-assembling peptide amphiphiles (PAs) with different lipid modifications and investigate their interactions with biological systems. We find that PA nanostructures rapidly disassemble and reassemble with albumin and lipoproteins in blood plasma. While PAs with shorter lipid tails mainly assemble with albumin, increasing lipid length shifts binding to lipoproteins. All PAs show strong tumor accumulation in 4T1 tumor-bearing mice with tumor to liver ratios of [~]3-6. Overall, albumin-binding improves blood circulation and tumor accumulation compared to lipoprotein-binding, but also increases off-target accumulation. This study shows that the biointeractions of self-assembled nanomaterials can be controlled through molecular design, which may lead to the development of effective nanomedicines.

bioengineering↗

bioRxiv2016

Genetic heterogeneity in autism: from single gene to a pathway perspective

AbstractThe extreme genetic heterogeneity of autism spectrum disorder (ASD) represents a major challenge. Recent advances in genetic screening and systems biology approaches have extended our knowledge of the genetic etiology of ASD. In this review, we discuss the paradigm shift from a single gene causation model to pathway perturbation model as a guide to better understand the pathophysiology of ASD. We discuss recent genetic findings obtained through next-generation sequencing (NGS) and examine various integrative analyses using systems biology and complex networks approaches that identify convergent patterns of genetic elements associated with ASD. This review provides a summary of the genetic findings of family-based genome screening studies.

Genetics↗

bioRxiv2021

Polyelectrolyte brush bilayer under shear at linear and nonlinear response regimes: A combination of the density functional theory framework and the scaling theory

The density functional theory framework and the scaling theory are employed to approach the problem of the Polyelectrolyte brush bilayer under shear. It turns out that, the system at shear rates larger than a critical shear rate undergo a global restructuring during which chains stretch in the shear direction. In the absence of the electrostatic interactions as well as the hydrodynamic interactions, this global restructuring causes a sublinear scaling of the shear stress with the shear rate which makes the shear thinning effect. Nevertheless, in the presence of the hydrodynamic interactions, not only there is no sublinear regime but also a weak superlinear regime which makes a weak shear thickening effect. In the presence of the electrostatic interactions, the stress tensor components change by their second Virial coefficients, however, their shear rate power law are untouched. Nonetheless, the kinetic friction coefficient is independent of the electrostatic interactions. This suggests that the lubrication is not very much different than the neutral bilayers and the electrostatic interactions do not change that. The results of this study offers that maybe nature uses another mechanism to reduce friction coefficient in synovial joint and other biological systems. SIGNIFICANCEThe significance of this study is that it strongly criticizes the theoretical approach to the same system which is already published (T. Kreer, Soft Matter, 12, 3479 (2016)). Moreover, the results of this study may help our understanding from Biological systems and optimization of artificial synovial joint which is the core of this study.

biophysics↗

bioRxiv2021

A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each models components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models. Author summaryDetailed, multi-scale computational models bridging from biomolecular processes to entire organs and bodies have the potential to revolutionise medicine by enabling personalised treatments. One of the key challenges to achieving these models is connecting together the vast number of isolated biosimulation models into a coherent whole. Using recent advances in both modelling techniques and biological standards in the scientific community, we developed an approach to integrate and compose models in a physics-based environment. This provides significant advantages, including the automation of model composition and post-model-composition adjustments. We anticipate that our approach will enable the faster development of realistic and accurate models to understand complex biological systems.

bioengineering↗

bioRxiv2023

Anti-Windup Protection Circuits for Biomolecular Integral Controllers

Robust Perfect Adaptation (RPA) is a desired property of biological systems wherein a systems output perfectly adapts to a steady state, irrespective of a broad class of perturbations. Achieving RPA typically requires the deployment of integral controllers, which continually adjust the systems output based on the cumulative error over time. However, the action of these integral controllers can lead to a phenomenon known as "windup". Windup occurs when an actuator in the system is unable to respond to the controllers commands, often due to physical constraints, causing the integral error to accumulate significantly. In biomolecular control systems, this phenomenon is especially pronounced due to the positivity of molecular concentrations, inevitable promoter saturation and resource limitations. To protect against such performance deterioration or even instability, we present three biomolecular anti-windup topologies. The underlying architectures of these topologies are then linked to classical control-theoretic anti-windup strategies. This link is made possible due the development of a general model reduction result for chemical reaction networks with fast sequestration reactions that is valid in both the deterministic and stochastic settings. The topologies are realized as chemical reaction networks for which genetic designs, harnessing the flexibility of inteins, are proposed. To validate the efficacy of our designs in mitigating windup effects, we perform simulations across a range of biological systems, including a complex model of Type I diabetic patients and advanced biomolecular proportional-integral-derivative (PID) controllers. This work lays a foundation for developing robust and reliable biomolecular control systems, providing necessary safety and protection against windup-induced instability.

synthetic biology↗

bioRxiv2019

The temperature dependence of platelet metabolism

Temperature plays a fundamental role in biology, influencing cellular function, affecting chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is not well understood but remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we have used time-course metabolomics data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than is glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperature indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.\n\nStatement of SignificanceThe temperature dependence of cellular metabolism is difficult to study due to regulatory events that are activated upon deviation from the optimal temperature range. Platelets are blood components used in transfusion medicine but also serve as a model cell to study human energy metabolism in the absence of genetic regulation. We investigated changes in platelet metabolism at temperatures spanning from 4 {degrees}C-37 {degrees}C using a quantitative metabolic systems biology approach as opposed to assessing individual reactions. We found that energy producing metabolic pathways have different temperature sensitivities. The results define the metabolic response to temperature on the metabolic pathway level and are of importance for understanding the cryopreservation of human platelets and more complex human cells used in cellular therapy.

biochemistry↗

bioRxiv2023

DIRECTED GRAPH THEORY FOR THE ANALYSIS OF BIOLOGICAL REGULATORY NETWORKS

Regulated biological networks are commonly represented as logical diagrams, in which the exact interactions between the elements remain out of sight. Here we propose a new type of excitation-inhibition graph based on Boolean logic, which we name "logical directed graph or simply, logical digraph of the biological system". Such logical digraph allows the representation of every possible regulatory interaction among elements, based on Boolean interactions. The logical digraph contains information about the connectivity, dynamics, limit cycles, and attractors of the network. As proof of the application, the logical digraph was applied to analyze the functioning of the well-known neural network that produces oscillatory swimming in the mollusk Tritonia. Our method permits to transit from a regulatory network to its logical digraph and vice versa. In addition, we show that the spectral properties of the so-called state matrix provide mathematical evidence about why the elements in the attractors and limit cycles contain information about the dynamics of the biological system. Open software routines are provided for the calculations of the components of the network and the attractors and limit cycles. This approach offers new possibilities to visualize and analyze regulatory networks in biology.

neuroscience↗

bioRxiv2020

Developmental models reveal the role of phenotypic plasticity in explaining genetic evolvability

Biological evolution exhibits an extraordinary capability to adapt organisms to their environments. The explanation for this often takes for granted that random genetic variation produces at least some beneficial phenotypic variation for natural selection to act on. Such genetic evolvability could itself be a product of evolution, but it is widely acknowledged that the immediate selective gains of evolvability are small on short timescales. So how do biological systems come to exhibit such extraordinary capacity to evolve? One suggestion is that adaptive phenotypic plasticity makes genetic evolution find adaptations faster. However, the need to explain the origin of adaptive plasticity puts genetic evolution back in the driving seat, and genetic evolvability remains unexplained. To better understand the interaction between plasticity and genetic evolvability, we simulate the evolution of phenotypes produced by gene-regulation network-based models of development. First, we show that the phenotypic variation resulting from genetic and environmental change are highly concordant. This is because phenotypic variation, regardless of its cause, occurs within the relatively specific space of possibilities allowed by development. Second, we show that selection for genetic evolvability results in the evolution of adaptive plasticity and vice versa. This linkage is essentially symmetric but, unlike genetic evolvability, the selective gains of plasticity are often substantial on short, including within-lifetime, timescales. Accordingly, we show that selection for phenotypic plasticity is, in general, the most efficient and results in the rapid evolution of high genetic evolvability. Thus, without overlooking the fact that adaptive plasticity is itself a product of genetic evolution, this shows how plasticity can influence adaptive evolution and helps explain the genetic evolvability observed in biological systems.

evolutionary biology↗

bioRxiv2022

A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveals modulators of memory function

Genome-wide association studies (GWAS) have uncovered over 40 genomic loci associated with risk for late-onset Alzheimers Disease (LOAD), but identification of the underlying causal genes remains challenging. While the role of glial biology in the mediation of LOAD genetic risk has been increasingly recognized, recent studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects, absent interactions with other brain cell types or exposure to neurotoxic insults. Here, we searched for genetic contributions to neuronal dysfunction in LOAD pathobiology, using an integrative systems approach that incorporated multi-evidence-based gene-mapping and network analysis-based prioritization. We found widespread dysfunction in neuronal gene co-expression networks in the LOAD brain and identified synaptic and endolysosomal function as being specifically impacted by LOAD-associated genetic variation. A systematic perturbation screening of candidate risk genes in C. elegans revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) significantly alters short/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

genetics↗

bioRxiv2024

SonoNERFs: Neural Radiance Fields applied to Biological Echolocation Systems allow 3D Scene Reconstruction Through Perceptual Prediction

In this paper, we introduce SonoNERFs, a novel approach that adapts Neural Radiance Fields (NeRFs) to model and understand the echolocation process in bats, focusing on the challenges posed by acoustic data interpretation without phase information. Leveraging insights from the field of optical NeRFs, our model, termed SonoNERF, represents the acoustic environment through Neural Reflectivity Fields. This model allows us to reconstruct three-dimensional scenes from echolocation data, obtained by simulating how bats perceive their surroundings through sound. By integrating concepts from biological echolocation and modern computational models, we demonstrate the SonoNERFs ability to predict echo spectrograms for unseen echolocation poses and effectively reconstruct a mesh-based and energy-based representation of complex scenes. Our work bridges a gap in understanding biological echolocation and proposes a methodological framework that provides a first order model on how scene understanding might arise in echolocating animals. We demonstrate the efficacy of the SonoNERF model on three scenes of increasing complexity, including some biologically relevant prey-predator interactions.

animal behavior and cognition↗

bioRxiv2015

Selection on network dynamics drives differential rates of protein domain evolution

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

Evolutionary Biology↗

bioRxiv2024

Poincare and SimBio: a versatile and extensible Python ecosystem for modeling systems.

Chemical Reaction Networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level modelling of CRNs enables various simulation approaches, including deterministic and stochastic methods. However, existing Python tools for CRN modelling typically wrap external C/C++ libraries for modelling and simulation, limiting their extensibility and integration with the broader Python ecosystem. In response, we developed Poincare and SimBio, two novel Python packages for the definition and simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical system modelling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be Just-In-Time compiled with Numba. Our benchmark tests using curated models from the BioModels repository, demonstrate that these tools may provide a potentially superior performance advantage, compared to other existing tools. Additionally, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with Integrated Development Environments (IDEs). Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modelling community.

bioinformatics↗

bioRxiv2019

Design principles of selective transport through biopolymer barriers

In biological systems, polymeric materials block the movement of some macromolecules while allowing the selective passage of others. In some cases, binding enables selective transport, while in others the most inert particles appear to transit most rapidly. To study the general principles of filtering, we develop a model motivated by features of the nuclear pore complex (NPC) which are highly conserved and could potentially be applied to other biological systems. The NPC allows selective transport of proteins called transport factors which transiently bind to disordered, flexible proteins called FG Nups. While the NPC is tuned for transport factors and their cargo, we show that a single feature is sufficient for selective transport: the bound-state motion resulting from transient binding to flexible filaments. Interchain transfer without unbinding can further improve selectivity, especially for crosslinked chains. We generalize this observation to model nanoparticle transport through mucus and show that bound-state motion accelerates transport of transient nanoparticle application, even with clearance by mucus flow. Our model provides a framework to control binding-induced selective transport in bipolymeric materials.

biophysics↗

bioRxiv2026

Bio-CM{superscript 2}: Distributed computational optics for cortex-widecellular imaging

Understanding distributed biological systems, particularly neural circuits, requires simultaneous cellular-resolution imaging across millimeter-scale fields of view (FOV). Existing miniature microscopes remain fundamentally constrained by trade-offs among FOV, spatial resolution, and optical complexity, limiting their ability to bridge cellular microscopy with cortex-scale imaging. Here we introduce distributed computational optics, a framework that distributes image formation across coordinated optical modules and computationally integrates their measurements into a unified image. We realize this framework in Bio-CM2, a computational miniature mesoscope that partitions the imaging field across four optical modules while converging their measurements onto a common image sensor. This architecture overcomes the aberration-scaling limitations of conventional miniature optics while avoiding the hardware complexity of multi-camera systems and the contrast degradation associated with optical multiplexing. Bio-CM2 achieves a 7.5 x 10 mm2 FOV while enabling cellular-resolution in vivo imaging at video rates. We demonstrate its utility through two complementary imaging modalities in head-fixed mice: cortex-wide functional vascular imaging, enabling simultaneous quantification of pial arteriole vasomotion and mesoscale hemodynamic functional connectivity, and cellular-resolution calcium imaging, resolving the activity of over 3,000 neurons together with mesoscale neuronal functional connectivity. We further demonstrate the versatility of the platform through cellular-resolution imaging of entire coronal mouse brain sections, population-scale imaging of freely behaving Caenorhabditis elegans, and odor-evoked calcium imaging of the main olfactory bulb in head-fixed mice, highlighting its broad applicability across diverse biological systems and imaging modalities. By overcoming the conventional trade-off between FOV and spatial resolution in a compact miniature platform, Bio-CM2 establishes distributed computational optics as a scalable framework for multiscale biological imaging.

bioengineering↗

bioRxiv2023

Multifractal signatures in ossification reveal a disordered-to-ordered transition in mineral pattern

Developing biological systems can exhibit both dynamic pattern formation and cross-scale interactions. Multiscale relationships are critical in the establishment of these patterns but remain poorly understood. Classification of mineral pattern in bone is a quintessential example. One approach to quantifying these patterns relies upon statistical self-similarity and, in particular, monofractal analysis. However, simple monofractal characterisations fail to capture the complexity of multiscale interactions in developing biological systems. Here we show that multifractal techniques, effectively capture the complex patterns of self-similarity in a dimensionally reduced, usable way. Further, we show that a simple generative model of ossification in the mouse skull, coupled with multifractal methods indicates a primary role of collagen density in pattern establishment and predicts the existence of a sharp boundary in pattern complexity.

biophysics↗

bioRxiv2021

Model-checking ecological state-transition graphs

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, from state-and-transition models to assembly graphs. Thus, model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology. Here we promote the adoption of the model-checking toolbox in ecology through its application to an illustrative example. We assessed the dynamics of a vegetation model inspired from state-and-transition models by model-checking Computation Tree Logic formulas built from a proposed catalogue of patterns. Model-checking encompasses a wide range of concepts and available software, mentioned in discussion, thus its implementation can be fitted to the specific features of the described system. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarifying and comparing them. Author summaryEcologists have drawn state-transition graphs representing the dynamics of ecosystems for more than a century. Model-checking is an automated method for the analysis of such graphs developed in computer science and acknowledged by a Turing award in 2007. Ecologists appear to be mostly unaware of model-checking despite its successes in systems biology to assess the dynamics of biological networks. We promote model-checking of ecological state-transition graphs through its application to an illustrative vegetation model. We exemplify the insights provided by model-checking by assessing management policies aiming to tackle savanna encroachment. We also provide a catalogue of patterns to help ecologists with the difficulty of formally expressing dynamical properties. We also discuss the wide range of model-checking concepts and available software, enabling to fit the specific features of the studied system, such as durations or probabilities. Model-checking can be applied to both empirical data and theoretical models, as long as they sum up into state-transition graphs. It provides automated and accurate answers to complex questions that could barely be analysed through human examination, if not impossible to answer this way. In addition to the automated analysis of ecological state-transition graphs, we believe that formally defining ecological concepts within the model-checking framework could help in clarifying and comparing them.

bioinformatics↗