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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

Exploring Mechanisms of Glucose Uptake Regulation and Dilution Resistance in Growing Cancer Cells

Most cancer cells rely on aerobic glycolysis and increased glucose uptake for the production of biosynthetic precursors needed to support rapid proliferation. Increased glucose uptake and glycolytic activity may result in intracellular acidosis and increase of osmotically active substances, leading to cell swelling. This causes dilution of cellular constituents, which can markedly influence cellular reactions and the function of proteins, and hence, control mechanisms used by cancer cells to maintain a highly glycolytic phenotype must be robust to dilution. In this paper, we review the literature on cancer cell metabolism and glucose uptake, and employ mathematical modeling to examine control mechanisms in cancer cell metabolism that show robust homeostatic control in the presence of dilution. Using differential gene expression data from the Expression Atlas database, we identify the key components of glucose uptake in cancer, in order to guide the construction of a mathematical model. By simulations of this model we show that while negative feedback from downstream glycolytic metabolites to glucose transporters is sufficient for homeostatic control of glycolysis in a constant cellular volume, it is necessary to control intermediate glycolytic enzymes in order to achieve homeostatic control during growth. With a focus on glucose uptake in cancer, we demonstrate a systems biology approach to the identification, reduction, and analysis of complex regulatory systems. SIGNIFICANCERapid proliferation and increased glycolytic activity in cancer cells lead to dilution of cellular constituents, which can markedly influence cellular reactions and the function of proteins. Therefore, control mechanisms used by cancer cells to maintain a highly glycolytic phenotype must be robust to dilution. We construct a mathematical model of glucose uptake in cancer, and using a systems biology approach to the analysis of regulatory networks, identify the presence of integral control motifs as a means for achieving dilution resistance. Furthermore, we show that while negative feedback from downstream glycolytic metabolites to glucose transporters is sufficient for homeostatic control of glycolysis in a constant cellular volume, it is necessary to control intermediate glycolytic enzymes to achieve homeostatic control during growth.

cancer biology↗

bioRxiv2021

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

MotivationThe analysis and comparison of compounds transcriptomic signatures can help elucidate a compounds Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compounds effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs. ResultsWe created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA. Availability and ImplementationThe source code and pre-trained deepSNEM models are available at https://github.com/BioSysLab/deepSNEM. ContactEmail for correspondence: leo@mail.ntua.gr. Supplementary informationAccompanying supplementary material are available online.

bioinformatics↗

bioRxiv2023

Transkingdom Network Analysis (TkNA): a systems approach inferring causal factors underlying host-microbiota interactions.

Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the networks local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment.

systems biology↗

bioRxiv2020

Evolution leads to emergence: An analysis of protein interactomes across the tree of life

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8,782,166 protein-protein interactions, at different scales. We demonstrate the emergence of higher order macroscales in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared to nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of Eukaryota, as compared to Prokaryota. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being certain at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

systems biology↗

bioRxiv2019

Synthetic Genetic Codes Designed to Hinder Evolution

One challenge in engineering organisms is guaranteeing system behavior over many generations. Spontaneous mutations that arise before or during use can impact heterologous genetic functions, disrupt system integration, or change organism phenotype. Here, we propose restructuring the genetic code itself such that all point mutations in protein-coding sequences are selected against. Synthetic genetic systems so-encoded should \"fail safely\" in response to many individual spontaneous mutations. We designed a family of such fail-safe codes and analyzed their expected effect on the evolution of engineered organisms via simulation. We predict that fail-safe codes supporting expression of 20 or 15 amino acids could slow the evolution of proteins in so-encoded organisms to 30% or 0% the rate of standard-code organisms, respectively. We also designed quadruplet-codon codes that should be capable of encoding at least 20 amino acids while ensuring all single point mutations in protein-coding sequences are selected against. We show by in vitro experiments that a reduced set of 21 tRNA is capable of expressing a protein whose coding sequence is recoded to use a fail-safe code, whereas a standard-code encoding is not expressed. Our work suggests that a rationally depleted but otherwise natural translation system should yield biological systems with intrinsically reduced evolutionary capacity, and that so-encoded hypoevolvable organisms might be less likely to invade new niches or outcompete native populations.

synthetic biology↗

bioRxiv2024

3D Bioprinting of Collagen-based Microfluidics for Engineering Fully-biologic Tissue Systems

Microfluidic and organ-on-a-chip devices have improved the physiologic and translational relevance of in vitro systems in applications ranging from disease modeling to drug discovery and pharmacology. However, current manufacturing approaches have limitations in terms of materials used, non-native mechanical properties, patterning of extracellular matrix (ECM) and cells in 3D, and remodeling by cells into more complex tissues. We present a method to 3D bioprint ECM and cells into microfluidic collagen-based high-resolution internally perfusable scaffolds (CHIPS) that address these limitations, expand design complexity, and simplify fabrication. Additionally, CHIPS enable size-dependent diffusion of molecules out of perfusable channels into the surrounding device to support cell migration and remodeling, formation of capillary-like networks, and integration of secretory cell types to form a glucose-responsive, insulin-secreting pancreatic-like microphysiological system. One-Sentence SummaryMulti-material FRESH 3D bioprinting of microfluidic CHIPS to generate fully biologic centimeter-scale and vascularized pancreatic-like tissue systems.

bioengineering↗

bioRxiv2023

Distilling identifiable and interpretable dynamic models from biological data

Mechanistic dynamical models allow us to study the behavior of complex biological systems. They can provide an objective and quantitative understanding that would be difficult to achieve through other means. However, the systematic development of these models is a non-trivial exercise and an open problem in computational biology. Currently, many research efforts are focused on model discovery, i.e. automating the development of interpretable models from data. One of the main frameworks is sparse regression, where the sparse identification of nonlinear dynamics (SINDy) algorithm and its variants have enjoyed great success. SINDy-PI is an extension which allows the discovery of rational nonlinear terms, thus enabling the identification of kinetic functions common in biochemical networks, such as Michaelis-Menten. SINDy-PI also pays special attention to the recovery of parsimonious models (Occams razor). Here we focus on biological models composed of sets of deterministic nonlinear ordinary differential equations. We present a methodology that, combined with SINDy-PI, allows the automatic discovery of structurally identifiable and observable models which are also mechanistically interpretable. The lack of structural identifiability and observability makes it impossible to uniquely infer parameter and state variables, which can compromise the usefulness of a model by distorting its mechanistic significance and hampering its ability to produce biological insights. We illustrate the performance of our method with six case studies. We find that, despite enforcing sparsity, SINDy-PI sometimes yields models that are unidentifiable. In these cases we show how our method transforms their equations in order to obtain a structurally identifiable and observable model which is also interpretable. Author summaryDynamical models provide a quantitative understanding of complex biological systems. Since their development is far from trivial, in recent years many research efforts focus on obtaining these models automatically from data. One of the most effective approaches is based on implicit sparse regression. This technique is able to infer biochemical networks with kinetic functions containing rational nonlinear terms. However, as we show here, one limitation is that it may yield models that are unidentifiable. These features may lead to inaccurate mechanistic interpretations and wrong biological insights. To overcome this limitation, we propose an integrated methodology that applies additional procedures in order to ensure that the discovered models are structurally identifiable, observable, and interpretable. We demonstrate our method with six challenging case studies of increasing model complexity.

systems biology↗

bioRxiv2021

Ligand geometry dictates cellular and in vivo uptake of 3D DNA nanostructures

Fabrication of nanoscale DNA devices to generate 3D nano-objects with precise control of shape, size, and presentation of ligands has shown tremendous potential for therapeutic applications. The interactions between different topologies of 3D DNA nanostructures and the cell membranes are crucial for designing efficient tools for interfacing DNA devices with biological systems. The practical applications of these DNA nanocages are still limited in cellular and biological systems owing to the limited understanding of interactions of different surface topologies of DNA nanodevices with cell membranes. The correlation between the geometry of DNA nanostructures and their internalization efficiency remains elusive. We investigated the influence of the shape and size of 3D DNA nanostructure on their cellular internalization efficiency. We found that of different geometries designed, one particular geometry, i.e., the tetrahedral shape, is more favoured over other geometries for their cellular uptake in 2D and 3D cell models. This is also replicable for cellular processes like 3D cell invasion assays in 3D spheroid models and passing the epithelial barriers in in-vivo zebrafish model systems. Our work establishes ground rules for the rational designing of DNA nanodevices for their upcoming biological and biomedical applications.

bioengineering↗

bioRxiv2025

Higher-Throughput Proteome Profiling Enabled by Parallelized Pre-Accumulation and Optimized Ion Processing in the Orbitrap Astral Zoom Mass Spectrometer

High-throughput proteomics is critical for understanding biological processes, enabling large-scale studies such as biomarker discovery and systems biology. However, current mass spectrometry technologies face limitations in speed, sensitivity, and scalability for analyzing large sample cohorts. The Thermo Scientific Orbitrap Astral Zoom mass spectrometer (MS) was developed to address these limitations by improving acquisition speed, ion utilization, and spectral processing, which are all essential for advancing proteome depth in high-throughput proteomics. The Orbitrap Astral Zoom MS achieves ultra-fast MS/MS scan rates of up to 270 Hz with enhanced ion utilization through pre-accumulation, enabling the identification of [~]100,000 unique peptides and >8,400 proteins in a single 300 samples-per-day (SPD) analysis of human cell lysate. The optimized system reduces analysis time by 40%, achieves near-complete proteome coverage (>12,000 proteins) in 2.7 hours, and enables ultra-high-throughput workflows, identifying >7,000 proteins in a 500 SPD method with exceptional reproducibility (Pairwise Pearson correlations >0.99). These advancements establish the Orbitrap Astral Zoom MS as a new benchmark in proteomics, significantly enhancing speed, sensitivity, and scalability, paving the way for routine large-scale proteome studies with applications in clinical research and systems biology. TeaserHigh-Speed Human Proteome Analysis using the Orbitrap Astral Zoom Mass Spectrometer

biochemistry↗

bioRxiv2022

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolic networks relate metabolic fluxes, metabolite concentrations, and enzyme levels through well-defined mechanistic relations rendering them an essential tool for systems biology studies aiming to capture and understand the behavior of living organisms. However, due to the lack of information about the kinetic properties of enzymes and the uncertainties associated with available experimental data, traditional kinetic modeling approaches often yield only a few or no kinetic models with desirable dynamical properties making the computational analysis unreliable and computationally inefficient. We present REKINDLE (REconstruction of KINetic models using Deep LEarning), a deep-learning-based framework for efficiently generating large-scale kinetic models with dynamic properties matching the ones observed in living organisms. We showcase REKINDLEs efficiency and capabilities through three studies where we: (i) generate large populations of kinetic models that allow reliable in silico testing of hypotheses and systems biology designs, (ii) navigate the phenotypic space by leveraging the transfer learning capability of generative adversarial networks, demonstrating that the generators trained for one physiology can be fine-tuned for another physiology using a low amount of data, and (iii) expand upon existing datasets, making them amenable to thorough computational biology and data-science analyses. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate novel kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in health, biotechnology, and systems and synthetic biology. REKINDLE is available as an open-access tool.

systems biology↗

bioRxiv2022

Laboratory evolution of synthetic electron transport system variants reveals a larger metabolic respiratory system and its plasticity

Respiration requires organisms to have an electron transport system (ETS) for the generation of proton motive force across the membrane that drives ATP synthase. Although the molecular details of the ETS are well studied and constitute textbook material, few studies have appeared to elucidate its systems biology. The most thermodynamically efficient ETS consists of two enzymes, an NADH: quinone oxidoreductase (NqRED) and a dioxygen reductase (O2RED), which facilitate the shuttling of electrons from NADH to oxygen. However, evolution has produced variations within ETS which modulate the overall energy efficiency of the system even within the same organism 1-3. The system-level impact of these variations and their individual physiological optimality remain poorly determined. To mimic varying ETS efficiency we generated four Escherichia coli deletion strains (named ETS-1H, 2H, 3H, and 4H) harboring unbranched ETS variants that pump 1, 2, 3, or 4 proton(s) per electron respectively. We then used a combination of synergistic methods (laboratory evolution, multi-omic analyses, and computation of proteome allocation) to characterize these ETS variants. We found that: (a) all four ETS variants evolved to a similar optimized growth rate, (b) the evolution of ETS variants was enabled by specific rewiring of major energy-generating pathways that couple to the ETS to optimize their ATP production capability, (c) proteome allocation per ATP generated was the same for all the variants, (d) the aero-type, that designates the overall ATP generation strategy 4 of a variant, remained conserved during its laboratory evolution, with the exception of the ETS-4H variant, and (e) integrated computational analysis of then data supported a proton-to-ATP ratio of 10 protons per 3 ATP for ATP synthase for all four ETS variants. We thus have defined the Aero-Type System (ATS) as a generalization of the aerobic bioenergetics, which is descriptive of the metabolic systems biology of respiration and demonstrates its plasticity.

systems biology↗

bioRxiv2017

Positive Feedback Between Contractile Ring Myosin and Ring-Directed Cortical Flow Drives Cytokinesis

During cytokinesis, an equatorial actomyosin contractile ring constricts at a relatively constant overall rate despite its progressively decreasing size. Thus, the per-unit-length rate of ring closure increases as ring perimeter decreases. To understand this acceleration, we monitored cortical surface and ring component dynamics during the first division of the C. elegans embryo. We show that the polar cortex expands during ring constriction to provide the cortical surface area required for division. Polar expansion also allows ring myosin to compress cortical surface along the pole-to-pole axis, leading to a continuous flow of cortical surface into the ring. We propose that feedback between ring myosin and compression-driven cortical flow drives an exponential increase in the amount of ring myosin that maintains the high overall closure rate as ring perimeter decreases. We further show that an analytical mathematical formulation of the proposed feedback, called the Compression Feedback model, recapitulates the experimental observations.\n\nIMPACT STATEMENTDuring cytokinesis, positive feedback between myosin motors in the contractile ring and compression-driven cortical flow along the axis perpendicular to the ring drives constriction rate acceleration to ensure timely cell separation.\n\nMAJOR SUBJECT AREASCell biology, Computational and Systems Biology

cell biology↗

bioRxiv2020

Designer protein assemblies with tunable phase diagrams in living cells

The self-organization of proteins into specific assemblies is a hallmark of biological systems. Principles governing protein-protein interactions have long been known. However, principles by which such nanoscale interactions generate diverse phenotypes of mesoscale assemblies, including phase-separated compartments, remains challenging to characterize and understand. To illuminate such principles, we create a system of two proteins designed to interact and form mesh-like assemblies in living cells. We devise a novel strategy to map high-resolution phase diagrams in vivo, which provide mesoscale self-assembly signatures of our system. The structural modularity of the two protein components allows straightforward modification of their molecular properties, enabling us to characterize how point mutations that change their interaction affinity impact the phase diagram and material state of the assemblies in vivo. Both, the phase diagrams and their dependence on interaction affinity were captured by theory and simulations, including out-of-equilibrium effects seen in growing cells. Applying our system to interrogate biological mechanisms of self-assembly, we find that co-translational protein binding suffices to recruit an mRNA to the designed micron-scale structures.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2020

Parallelized manipulation of adherent living cells by magnetic nanoparticles-mediated forces

The remote actuation of cellular processes such as migration or neuronal outgrowth is a challenge for future therapeutic applications in regenerative medicine. Among the different methods that have been proposed, the use of magnetic nanoparticles appears to be promising since magnetic fields can act at a distance without interactions with the surrounding biological system. To control biological processes at a subcellular spatial resolution, magnetic nanoparticles can be used either to induce biochemical reactions locally or to apply forces on different elements of the cell. Here, we show that cell migration and neurite outgrowth can be directed by the forces produced by a switchable parallelized array of micro-magnetic pillars, following passive uptake of nanoparticles. Using live cell imaging, we first demonstrate that adherent cell migration can be biased toward magnetic pillars and that cells can be reversibly trapped onto these pillars. Second, using differentiated neuronal cells we were able to induce events of neurite outgrowth in the direction of the pillars without impending cell viability. Our results show that the range of forces applied needs to be adapted precisely to the cellular process under consideration. We propose that cellular actuation is the result of the force on the plasma membrane caused by magnetically filled endo-compartments, which exert a pulling force on the cell periphery.

bioengineering↗

bioRxiv2024

Insights into Yeast Response to Chemotherapeutic Agent through Time Series Genome-Scale Metabolic Models

BackgroundOrganism-specific genome-scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time-series transcriptomics in GSMM simulations. Yeast is an easy-to-manipulate model organism for tumor research; MethodsHere, a novel approach (TS-GSMM) was proposed to integrate time-series transcriptomics with GSMMs to narrow down the feasible solution space of all possible flux distributions and attain time-series flux samples. The flux samples were clustered using machine learning techniques, and the clusters functional analysis was performed using reaction set enrichment analysis; ResultsA time series transcriptomics response of Yeast cells to a chemotherapeutic reagent - doxorubicin - was mapped onto a Yeast GSMM. Eleven flux clusters were obtained with our approach, and pathway dynamics were displayed. Induction of fluxes related to bicarbonate formation and transport, ergosterol and spermidine transport, and ATP production were captured; ConclusionsIntegrating time-series transcriptomics data with GSMMs is a promising approach to reveal pathway dynamics without any kinetic modeling and detects pathways that cannot be identified through transcriptomics-only analysis. The codes are available at https://github.com/karabekmez/TS-GSMM.

bioengineering↗

bioRxiv2019

Chi.Bio: An open-source automated experimental platform for biological science research

The precise characterisation and manipulation of in vivo biological systems is critical to their study.1 However, in many experimental frameworks this is made challenging by non-static environments during cell growth,2, 3 as well as variability introduced by manual sampling and measurement protocols.4 To address these challenges we present Chi.Bio, a parallelised open-source platform that offers a new experimental paradigm in which all measurement and control actions can be applied to a bulk culture in situ. In addition to continuous-culturing capabilities (turbidostat functionality, heating, stirring) it incorporates tunable light outputs of varying wavelengths and spectrometry. We demonstrate its application to studies of cell growth and biofilm formation, automated in silico control of optogenetic systems, and readout of multiple orthogonal fluorescent proteins. By combining capabilities from many laboratory tools into a single low-cost platform, Chi.Bio facilitates novel studies in synthetic, systems, and evolutionary biology, and broadens access to cutting-edge research capabilities.

bioengineering↗

bioRxiv2023

Multiscale networks in multiple sclerosis

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information, and Boolean simulations identified paths within and among all layers. The path more commonly found from the boolean simulations connects MP2K, with Th17 cells, the retinal nerve fiber layer (RNFL) thickness and the age related MS severity score (ARMSS). Combinations of several proteins (HSPB1, MP2K1, SR6, KS6B1, SRC, MK03, LCK and STAT6)) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/530153v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@7dd26org.highwire.dtl.DTLVardef@482239org.highwire.dtl.DTLVardef@1bef03borg.highwire.dtl.DTLVardef@8dc6e6_HPS_FORMAT_FIGEXP M_FIG C_FIG Author SummaryComplex diseases such as Multiple Sclerosis (MS) involve the contribution of a wide range of biological processes. We conducted a systems biology study of MS based on network analysis and deep phenotyping in a prospective cohort of patients with clinical, imaging, genetics, and omics assessments. The gene, proteins and cell paths explained variation in central nervous system damage, and in metrics of disease severity. Such multilayer paths explain the different phenotypes of the disease and can be developed as biomarkers of MS.

systems biology↗

bioRxiv2024

Polygenic prediction and gene regulation networks

Exploring the degree to which phenotypic variation, influenced by intrinsic nonlinear biological mechanisms, can be accurately captured using statistical methods is essential for advancing our comprehension of complex biological systems and predicting their functionality. Here, we examine this issue by combining a computational model of gene regulation networks with a linear additive prediction model, akin to polygenic scores utilized in genetic analyses. Inspired by the variational framework of quantitative genetics, we create a population of individual networks possessing identical topology yet showcasing diversity in regulatory strengths. By discerning which regulatory connections determine the prediction of phenotypes, we contextualize our findings within the framework of core and peripheral causal determinants, as proposed by the omnigenic model of complex traits. We establish connections between our results and concepts such as global sensitivity and local stability in dynamical systems, alongside the notion of sloppy parameters in biological models. Furthermore, we explore the implications of our investigation for the broader discourse surrounding the role of epistatic interactions in the prediction of complex phenotypes. Author SummaryThis research delves into how well statistical methods can capture phenotypic variation influenced by nonlinear biological mechanisms. The study combines a computational model of gene regulation networks with a linear additive prediction model, similar to polygenic scores used in genetic analysis. By creating a population of individual networks with identical topology but varying regulatory strengths, the research identifies key regulatory connections that predict phenotypes. The findings are framed within the omnigenic model of complex traits, distinguishing core and peripheral causal determinants. The study also links its results to concepts like global sensitivity and local stability in dynamical systems, as well as sloppy parameters in biological models. Additionally, it examines the implications for understanding the role of epistatic interactions in predicting complex phenotypes. This work enhances our understanding of complex biological systems and their functionality.

systems biology↗