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Results for “systems biology”

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bioRxiv2025

Toward Single-Cell Control: Noise-Robust Perfect Adaptation in Biomolecular Systems

Robust perfect adaptation (RPA), whereby a consistent output level is maintained even after a disturbance, is a highly desired feature in biological systems. This property can be achieved at the population average level by combining the well-known antithetic integral feedback (AIF) loop into the target network. However, the AIF controller amplifies the noise of the output level, disrupting the single-cell level regulation of the system output and compromising the conceptual goal of stable output level control. To address this, we introduce a new regulation motif, the noise controller, which is inspired by the AIF loop but differs by sensing the output levels through the dimerization of output species. Combining this noise controller with the AIF controller successfully maintained system output noise as well as mean at their original level, even after the perturbation, thereby achieving noise RPA. Furthermore, our noise controller could reduce the output noise to a desired target value, achieving a Fano factor as small as 1, the commonly recognized lower bound of intrinsic noise in biological systems. Notably, our controller remains effective as long as the combined system is ergodic, making it applicable to a broad range of networks. We demonstrate its utility by combining the noise controller with the DNA repair system of Escherichia coli, which reduced the proportion of cells failing to initiate the DNA damage response. These findings enhance the precision of existing biological controllers, marking a key step toward achieving single-cell level regulation.

systems biology↗

bioRxiv2018

Improvement of the memory function by negative autoregulation of a mutual repression network in a stochastic environment

BackgroundCellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological processes. However, our understanding of the origins of cellular memory remains incomplete. Stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic rate equations, stochastic simulations, and theoretical analyses of Fokker-Planck equations to investigate how intrinsic noise affects the memory function in a mutual repression network.\n\nResultsWe find that the addition of negative autoregulation improves the persistence of memory in a small gene regulatory network by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory.\n\nConclusionsOur work illuminates the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity to designing synthetic circuits with memory function.

systems biology↗

bioRxiv2024

The Radical Pair Mechanism Cannot Explain Telecommunication Frequency Effects on Reactive Oxygen Species

In order to investigate whether the radical pair mechanism (RPM) can explain the effects of telecommunication frequency radiation on reactive oxygen species production, we modelled the effects of oscillating magnetic fields on radical pair systems. Our analysis indicates that the RPM cannot account for the biological effects observed under exposure to telecommunication frequencies due to negligible effects under low-amplitude conditions used in experimental setups. Observable effects on radical pairs at these frequencies would require hyperfine coupling constants that are precisely fine-tuned to large values that far exceed those naturally occurring within biological systems. We conclude that some other mechanism must be responsible for the effects of telecommunication frequency fields in biological systems.

biophysics↗

bioRxiv2018

Optogenetics enables real-time spatiotemporal control over spiral wave dynamics in an excitable cardiac system

Propagation of non-linear waves is key to the functioning of diverse biological systems. Such waves can organize into spirals, rotating around a core, whose properties determine the overall wave dynamics. Theoretically, manipulation of a spiral wave core should lead to full spatiotemporal control over its dynamics. However, this theory lacks supportive evidence (even at a conceptual level), making it thus a long-standing hypothesis. Here, we propose a new phenomenological concept that involves artificially dragging spiral waves by their cores, to prove the afore-mentioned hypothesis in silico, with subsequent in vitro validation in optogenetically-modified monolayers of rat atrial cardiomyocytes. We thereby connect previously established, but unrelated concepts of spiral wave attraction, anchoring and unpinning to demonstrate that core manipulation, through controlled displacement of heterogeneities in excitable media, allows forced movement of spiral waves along pre-defined trajectories. Consequently, we impose real-time spatiotemporal control over spiral wave dynamics in a biological system.

bioinformatics↗

bioRxiv2021

Emergent properties of coupled bistable switches

Understanding the dynamical hallmarks of network motifs is one of the fundamental aspects of systems biology. Positive feedback loops constituting one or two nodes - self-activation, toggle switch, and double activation loops - are commonly observed motifs in regulatory networks underlying cell-fate decision systems. Their individual dynamics are well-studied; they are capable of exhibiting bistability. However, studies across various biological systems suggest that such positive feedback loops are interconnected with one another, and design principles of coupled bistable motifs remain unclear. We wanted to ask what happens to bistability or multistability traits and the phenotypic space (collection of phenotypes exhibited by a system) due to the couplings. In this study, we explore a set of such interactions using discrete and continuous simulation methods. Our results suggest that couplings that do not connect the bistable switches in a way that contradicts the connections within individual bistable switches lead to a steady state space that is strictly a subset of the set of possible combinations of steady states of bistable switches. Furthermore, adding direct and indirect self-activations to these coupled networks can increase the frequency of multistability. Thus, our observations reveal specific dynamical traits exhibited by various coupled bistable motifs.

systems biology↗

bioRxiv2015

Prediction of Fine-tuned Promoter Activity from DNA Sequence

The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.\n\nAuthor SummaryGene expression is the first step at which information encoded in DNA is transcribed into RNA. Predicting gene expression from DNA sequence can provide insights into the natural variation of gene expression underlying various phenotypes and direct the engineering of genes of desired activity, for example in industrial processes. While several studies show that gene expression is influenced by DNA sequence. its quantitative prediction from DNA sequence alone remains a challenging problem. Unfortunately, studies aimed at developing quantitative models for gene expression prediction are not directly comparable because most have used distinct data sets for training and evaluation. and many of the methods have not been independently verified. Open innovation challenges in which a problem is posed to a wide community provide a framework for independent verification of the performance of various computational methods using the same benchmark data sets and statistical procedures. Here. we describe the best performing computational model amongst those of 20 other teams in the DREAM6 Gene Expression Prediction challenge. We show that a highly predictive gene expression model can be obtained by an unbiased. data-driven approach that makes little assumption on the role of known mechanisms for gene regulation.

Genomics↗

bioRxiv2023

Uncovering specific mechanisms across cell types in dynamical models

Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements. Contactadrian.hauber@fdm.uni-freiburg.de Author SummaryMathematical models enable insights into biological systems beyond what is possible in the wet lab alone. However, constructing useful models can be challenging, since they both need a certain amount of complexity to adequately describe real-world observations, and simultaneously enough simplicity to enable understanding of these observations and precise predictions. Regularization techniques were suggested to tackle this challenge, especially when building models that describe two different types of cells, such as healthy and cancer cells. Typically, both cell types have a large portion of biological mechanisms in common, and the task is to identify the relevant differences that need to be included into the model. For more than two types of cells, the existing approaches are not readily applicable, because they require defining one of the cell types as reference, which potentially influences the results. In this work, we present a regularization method that is independent from the choice of a reference. We demonstrate its working principle and compare its performance to existing approaches. Since we implemented this method in a freely available software package, it is accessible to a broad range of researchers and will facilitate the construction of useful mathematical models for multiple types of cells.

systems biology↗

bioRxiv2024

Modeling Compound Lipid Homeostasis Using Stable Isotope Tracing

Lipids represent the most diverse pool of metabolites found in cells, facilitating compartmentation, signaling, and other functions. Dysregulation of lipid metabolism is linked to disease states such as cancer and neurodegeneration. However, limited tools are available for quantifying metabolic fluxes across the lipidome. To directly measure reaction fluxes encompassing compound lipid homeostasis, we applied stable isotope tracing, liquid chromatography-high-resolution mass spectrometry, and network-based isotopologue modeling to non-small cell lung cancer (NSCLC) models. Compound lipid metabolic flux analysis (CL-MFA) enables the concurrent quantitation of fatty acid synthesis, elongation, headgroup assembly, and salvage reactions within virtually any biological system. Here, we resolve liver kinase B1 (LKB1)-mediated regulation of sphingolipid recycling in NSCLC cells and precision-cut lung slice cultures. We also demonstrate that widely used tissue culture conditions drive cells to upregulate fatty acid synthase flux to supraphysiological levels. Finally, we identify previously uncharacterized isozyme specificity of ceramide synthase inhibitors. These results highlight the ability of CL-MFA to quantify lipid cycling in biological systems to discover biological function and elucidate molecular mechanisms in membrane lipid metabolism.

cell biology↗

bioRxiv2017

Conditional Robust Calibration (CRC): a new computational Bayesian methodology for model parameters estimation and identifiability analysis

Computational modeling is a remarkable and common tool to quantitatively describe a biological process. However, most model parameters, such as kinetics parameters, initial conditions and scale factors, are usually unknown because they cannot be directly measured.\n\nTherefore, key issues in Systems Biology are model calibration and identifiability analysis, i.e. estimate parameters from experimental data and assess how well those parameters are determined by the dimension and quality of the data.\n\nCurrently in the Systems Biology and Computational Biology communities, the existing methodologies for parameter estimation are divided in two classes: frequentist methods and Bayesian methods. The first ones are based on the optimization of a cost function while the second ones estimate the posterior distribution of model parameters through different sampling techniques.\n\nIn this work, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for model calibration and identifiability analysis. The algorithm is an iterative procedure based on parameter space sampling and on the definition of multiple objective functions related to each output variables. The method estimates step by step the probability density function (pdf) of parameters conditioned to the experimental measures and it returns as output a subset in the parameter space that best reproduce the dataset.\n\nWe apply CRC to six Ordinary Differential Equations (ODE) models with different characteristics and complexity to test its performances compared with profile likelihood (PL) and Approximate Bayesian Computation Sequential Montecarlo (ABC-SMC) approaches. The datasets selected for calibration are time course measurements of different nature: noisy or noiseless, real or in silico.\n\nCompared with PL, our approach finds a more robust solution because parameter identifiability is inferred by conditional pdfs of estimated parameters. Compared with ABC-SMC, we have found a more precise solution with a reduced computational cost.

systems biology↗

bioRxiv2015

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organisation and ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from - omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

Systems Biology↗

bioRxiv2024

Separating biological variance from noise by applying EM algorithm to modified General Linear Model

IntroductionThe General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. MethodsA modified GLM was proposed to explicitly model biological variance and non-biological noise. Employing the Expectation and Maximization (EM) scheme can distinguish biological variance from noise, termed EMSEV (EM for Separating Variances). The performance of EMSEV was evaluated by varying noise levels, dimensions of the design matrix, and covariance structures of the target variables. ResultsThe deviation between EMSEV outputs and the pre-defined distribution parameters increased with noise level. With a proper initial guess, when the noise magnitude and the variance of the target variables were similar, there were deviations of 3% and 10-16% in the estimated mean and covariance of the target variables, respectively, along with a 1.7% deviation in noise estimation. ConclusionEMSEV appears promising for distinguishing signal variance from noise in biological systems. The potential applications and implications in biological science and statistical inference are discussed.

bioinformatics↗

bioRxiv2025

A Quantitative Design Guideline for Biomolecular Positive Feedback Systems

Feedback is at the core of biological systems found in medicine and in biotechnology. While design metaphors from control engineering are widely used to understand negative feedback in such systems, they are relatively uncommon for positive feedback, especially for biomolecular circuits. Here, we extended a block diagram modelling framework for the design of positive feedback. We found a quantitative design guideline for the strength of the positive feedback, which when wrapped around a saturation function can give a threshold and a hysteretic response. The critical feedback strength was inversely proportional to the saturation value and directly proportional to the input scale where saturation starts. We found that this saturation-threshold-hysteresis hierarchy persisted in a realistic model of a positively autoregulated gene. We showed how this classical model fitted well in a block diagram framework with multiplicative feedback and derived expressions for the critical feedback strength in terms of the saturation parameters. The dependence of the critical feedback on the parameters matched with the obtained design guideline. To complete a rigorous workflow, we discussed how Groebner Bases computations and an Interval Newton algorithm can be used to provide validated numerical solutions in biological positive feedback systems. These results should be helpful in the analysis and design of biomolecular systems with applications to the control of biomedical systems and in biotechnology.

systems biology↗

bioRxiv2019

Community standards to facilitate development and address challenges in metabolic modeling

Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remains challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of metabolic modeling by conducting a community-wide survey. We used this feedback to (1) outline the major challenges that our field faces and to propose solutions and (2) identify a set of features that defines what a \"gold standard\" metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community-driven outline will help the long-term development of community-inspired resources as well as produce high-quality, accessible models. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure continued development of both practical, usable standards and reproducible, knowledge-rich models.

systems biology↗

bioRxiv2026

SHAPPEing agent-based models to spatial data: Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints

Agent-based models (ABMs) are widely used to study biological systems consisting of interacting species, such as tumour and immune cells, but their predictions depend on how agents are initially placed. High-resolution imaging techniques can capture the spatial configurations of various biological systems, but their use is costly. Furthermore, they only provide small regions of interest that may be too small to initialise most ABMs directly. Here, we present the Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints, or SHAPPE, a multitype point pattern reconstruction algorithm that generates synthetic reconstructions of arbitrary size and shape by moving points under spring-like forces with data-derived target distances. As it enforces a first-order structure through environmental constraints estimated with density grids, SHAPPE can be applied to both homogeneous and inhomogeneous point patterns. Applied to 87 glioblastoma IMC samples, we show that SHAPPE reduces dissimilarity in density distributions by a median of 53% and errors in inter-cell-type exposure levels by 34% relative to a random baseline. We then initialise a glioblastoma ABM with SHAPPE reconstructions instead of random configurations and find reduced tumour-CD8+ T cell interaction frequency with SHAPPE. This decrease translates into an over 20% decrease in the average number of tumour cells killed by CD8+ T cells over 50 days. Altogether, SHAPPE shows the potential to ease the integration of high-resolution imaging techniques into computational biology ABMs and ground initial conditions in data.

systems biology↗

bioRxiv2023

The Genetics of Aerotolerant Growth in a Naturally Reduced Genome Alphaproteobacterium

Reduced genome bacteria are genetically simplified systems that facilitate biological study and industrial use. The free-living Alphaproteobacterium, Zymomonas mobilis, has a naturally reduced genome containing fewer than 2000 protein coding genes. Despite its small genome, Z. mobilis thrives in diverse conditions including the presence or absence of atmospheric oxygen. However, insufficient characterization of essential and conditionally essential genes has limited broader adoption of Z. mobilis as a model Alphaproteobacterium. Here, we use genome-scale CRISPRi-seq to systematically identify and characterize Z. mobilis genes that are conditionally essential for aerotolerant or anaerobic growth, or are generally essential across both conditions. Comparative genomics revealed that the essentiality of most "generally essential" genes was shared between Z. mobilis and other Alphaproteobacteria, validating Z. mobilis as reduced genome model. Among conditionally essential genes, we found that the DNA repair gene, recJ, was critical only for aerobic growth but reduced the mutation rate under both conditions. Further, we show that genes encoding the F1FO ATP synthase and Rnf respiratory complex are required for anaerobic growth of Z. mobilis. Combining CRISPRi partial knockdowns with metabolomics and membrane potential measurements, we determined that the ATP synthase generates membrane potential that is consumed by Rnf to power downstream processes. Rnf knockdown strains accumulated isoprenoid biosynthesis intermediates, suggesting a key role for Rnf in powering essential biosynthetic reactions. Our work establishes Z. mobilis as a streamlined model for alphaproteobacterial genetics, has broad implications in bacterial energy coupling, and informs Z. mobilis genome manipulation for optimized production of valuable isoprenoid-based bioproducts. ImportanceThe inherent complexity of biological systems is a major barrier to our understanding of cellular physiology. Bacteria with markedly fewer genes than their close relatives, or reduced genome bacteria, are promising biological models with less complexity. Reduced genome bacteria can also have superior properties for industrial use, provided the reduction does not overly restrict strain robustness. Naturally reduced genome bacteria, such as the Alphaproteobacterium, Zymomonas mobilis, have fewer genes but remain environmentally robust. In this study, we show that Z. mobilis is a simplified genetic model for Alphaproteobacteria, a class with important impacts on the environment, human health, and industry. We also identify genes that are only required in the absence of atmospheric oxygen, uncovering players that maintain and utilize the cellular energy state. Our findings have broad implications for the genetics of Alphaproteobacteria and industrial use of Z. mobilis to create biofuels and bioproducts.

genetics↗

bioRxiv2022

A Quantitative Evaluation of Topological Motifs and Their Coupling in Gene Circuit State Distributions

One of the major challenges in biology is to understand how gene interactions collaborate to determine overall functions of biological systems. Here, we present a new computational framework that enables systematic, high-throughput, and quantitative evaluation of how small transcriptional regulatory circuit motifs, and their coupling, contribute to functions of a dynamical biological system. We illustrate how this approach can be applied to identify four- node gene circuits, circuit motifs, and motif coupling responsible for various gene expression state distributions, including those derived from single-cell RNA sequencing data. We also identify seven major classes of four-node circuits from clustering analysis of state distributions. The method is applied to establish phenomenological models of gene circuits driving human neuron differentiation, revealing important biologically relevant regulatory interactions. Our study will shed light on a better understanding of gene regulatory mechanisms in creating and maintaining cellular states.

systems biology↗

bioRxiv2018

Discrete Modeling Dynamical Systems That Determine the Role of Biodiversity in Different Regimes of Using Resources at Different Levels of Organization of Living Matter

The paper aims at mathematical modeling of dynamic systems that determine the role of biodiversity in different modes of resource usage at different levels of the organization of living matter. As a measure of biodiversity, the number of different strategies of the performance of a biological system is used, which differ in ways of usage of the resources by this system. This measure can be supplemented, and in some cases, replaced by the degree of evenness of the intensity of different ways of using resources. As a result of modeling with use of a new class of mathematical models called Discrete Models of Dynamical Systems, the appearance of matrices representing the sets of these strategies corresponding to combinations of values of the systems parameters in columns, strategies observed for the regimes of maximum and limited usage of resources was analyzed. Using the case study related to a local fish population in a pond, it was shown that, in comparison with the regimes of limited use of resources, the regime of maximum usage distinguishes by a larger degree of evenness of the intensities of different ways of using resources by the system. Different size-age groups of the population play the role of resources. A similar result was obtained earlier for different regimes of the performance of the human cardiovascular system; in this case, the heart rate and blood pressure played the role of different ways of resources usage. The similarity of the results obtained for such different biological systems allows us to hope for the prospects of developing universal system approaches to the use of generalized biodiversity parameters for the diagnosis of the state of bioobjects, in particular, with the help of aerospace methods. The necessity of such methods is significantly increasing in the context of global climate change.

systems biology↗

bioRxiv2020

Pseudomonas aeruginosa reverse diauxie is an optimized, resource utilization strategy

Pseudomonas aeruginosa is a globally-distributed bacterium often found in medical infections. The opportunistic pathogen uses a different, carbon catabolite repression (CCR) strategy than many, model microorganisms. It does not utilize a classic diauxie phenotype, nor does it follow common systems biology assumptions including preferential consumption of glucose with an overflow metabolism. Despite these contradictions, P. aeruginosa is competitive in many, disparate environments underscoring knowledge gaps in microbial ecology and systems biology. Physiological, omics, and in silico analyses were used to quantify the P. aeruginosa CCR strategy known as reverse diauxie. An ecological basis of reverse diauxie was identified using a genome-scale, metabolic model interrogated with in vitro omics data. Reverse diauxie preference for lower energy, nonfermentable carbon sources, such as acetate or succinate over glucose, was predicted using a multidimensional strategy which minimized resource investment into central metabolism while completely oxidizing substrates. Application of a common, in silico optimization criterion, which maximizes growth rate, did not predict the reverse diauxie phenotypes. This study quantifies P. aeruginosa metabolic strategies foundational to its wide distribution and virulence.

systems biology↗