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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

Cumulative dose responses for adapting biological systems

This paper introduces the notion of cumulative dose response (cDR). The cDR is the area under the plot of a response variable, an integral taken over a fixed time interval and seen as a function of an input parameter. This work was motivated by the accumulation of cytokines resulting from T cell stimulation, where a non-monotonic cDR has been observed experimentally. However, the notion is of general applicability. A surprising conclusion is that incoherent feedforward loops studied in the systems biology literature, though capable of non-monotonic dose responses, can be mathematically shown to always result in monotonic cDR.

systems biology↗

bioRxiv2022

Bayesian Parameter Estimation for Dynamical Models in Systems Biology

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and -omics studies that have helped populate protein-protein interaction networks in great detail, systems biology modeling lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.

systems biology↗

bioRxiv2022

Rank Selection Genetic Algorithm optimises robust parameter estimation for systems biology models

The ability to reliably predict and infer cellular responses to environmental exposures would offer a major advance in the investigation of immune regulation in health and disease. One possible approach is the use of in silico modelling. Design of such a mathematical kinetic model would be based on existing knowledge of a biological system and utilise a partial data set to parameterise. However, the process of parameter estimation, key for the accuracy of the model, is difficult to conduct by hand, and thus a computational alternative is necessary. We report the utility of Genetic Algorithm with Rank Selection (GARS) as a parameter estimation tool on multiple biological models, including heat shock, signal transduction via ERK, circadian rhythm and NF{kappa}B systems, where it showed strong accuracy and superiority to the Extended Kalman Filter method, Algebraic Difference Equations, and MATLAB fminsearch approaches. GARS parameter estimation is a valuable tool for biological data because it reliably infers system behaviour from partial data sets, allowing for the prediction of cellular responses to environmental exposures.

systems biology↗

bioRxiv2018

A Bayesian framework for the analysis of systems biology models of the brain

Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain a more complete understanding of the parameter space as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a healthy and impaired state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space.\n\nAuthor summarySystems biology models are mathematical representations of biological processes that reproduce the overall behaviour of a biological system. They are comprised by a number of parameters representing biological information. We use them to understand the behaviour of biological systems, such as the brain. We do this by fitting the models parameter to observed or simulated data; and by looking at how these values change during the fitting process we investigate the mechanisms controlling the behaviour of our system. We are interested in understanding differences between a healthy and an injured brain. Here we outline a statistical framework that uses a Bayesian approach during the fitting process that can provide us with a distribution of parameters rather than single parameter number. We apply this method when simulating the physiological responses between a healthy and a vascular compromised brain to a drop in oxygenation. We then use experimental data that demonstrates the healthy brain response to an increase in arterial CO2 and fit our brain model predictions to the measurements. In both instances we show that our approach provides more information about the overlap between healthy and unhealthy brain states than a fitting process that provides a single value parameter estimate.

systems biology↗

bioRxiv2017

Building Applications For Interactive Data Exploration In Systems Biology

As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.\n\nOur approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.\n\nWe demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries.\n\nThe web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor.bci.mcgill.ca and open-sourced at github.com/fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

bioinformatics↗

bioRxiv2020

Confronting the COVID-19 Pandemic with Systems Biology

Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus. One Sentence SummaryA Systems Approach Indicates Non-respiratory Pathways of Infection as Key for the COVID-19 Pandemic

systems biology↗

bioRxiv2019

Multiscale mechanical model for cell division orientation in developing biological systems

Developing biological structures are highly complex systems, within which shape dynamics at different places is tightly coordinated. One key process at play during development is the regulation of cell division orientation. In this work, through a reformulation of cell division in terms of its energetic cost, we propose that oriented cell division is one mechanism by which cells can read and react to mechanical forces propagating in a tissue even in the absence of interphase cellular elongation in the direction of these forces. This view reproduces standard geometric division long-axis rules as a special case of a more general behaviour, in which systematic deviations from these rules can emerge. We show that states of anisotropic tension in multicellular systems can be the cause of these deviations, as often experimentally found in living tissues. Our results provide a unifying view on the different intracellular mechanisms at play in orienting cell division: they are processes which minimize energy loss, reflecting a trade-off between local and long-range mechanical signals. The consequences of this competition are explored in simulated tissues and confirmed in vivo during both the development of the pupal epithelium of dorsal thorax in D. melanogaster and the epidermal morphogenesis of ascidian embryos. Author summaryIn this work we reformulate the process of cell division orientation in development as a mechanical-energy optimization. We show that classical rules for division orientation naturally emerge when a cell minimizes the work performed against its local environment. Moreover, when multicellular stress profiles are taken into account, observed systematic violations of these rules are explained in correlation with states of anisotropic tension within the tissue. We confirm our findings experimentally on developing systems imaged with cellular resolution. Our results provide a new paradigm to understand cell division in multicellular contexts and contribute to building a physical view of biological phenomena.

biophysics↗

bioRxiv2021

NLoed: A Python package for nonlinear optimal experimental design in systems biology

MotivationModelling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data, and on how well-suited the available data is to a particular modelling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modelling objective. However, implementation of OED is limited by currently available software tools that are not well-suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. ResultsHere we present the NLoed software package. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models, and facilitates modelling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modelling workflow. NLoed offers an accessible, modular, and flexible OED tool-set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLOEDs capabilities by applying it to experimental design for characterization of a bacterial optogenetic system. AvailabilityNLoed is available via pip from the PyPi repository; https://pypi.org/project/nloed/. Source code, documentation and examples can be found on Github at https://github.com/ingallslab/NLoed. Contactbingalls@uwaterloo.ca Supplementary informationSupplementary materials are available online.

systems biology↗

bioRxiv2019

Metabolic Perceptrons for Neural Computing in Biological Systems

AbstractSynthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a new approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model trained on experimental data, we finally implement two four-input \"perceptrons\" for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.

synthetic biology↗

bioRxiv2024

Fundamental trade-offs in the robustness of biological systems with feedback regulation

Natural biological systems use a complex network of feedback regulation to effectively respond to their changing environment. Even though in engineered systems we understand how accurate feedback can be depending on the electronic or mechanical parts that it is implemented with, we largely lack a similar theoretical framework to study biological feedback. Specifically, it is not fully understood or quantified how accurate or robust the implementation of biological feedback actually is. In this paper, we study the sensitivity of biological feedback to variations in biochemical parameters using five example circuits: positive autoregulation, negative autoregulation, double-positive feedback, positive-negative feedback and double-negative feedback (the toggle switch). We find that of these examples of biological feedback are subjected to fundamental trade-offs, and we propose multi-objective optimisation as a framework to study them. The impact of this work is to improve robust circuit design for synthetic biology and to improve our understanding of systems biology.

synthetic biology↗

bioRxiv2025

Expert-guided multi-objective optimization: an efficient strategy for parameter estimation of biological systems with limited data.

Calibrating biological models is challenging due to high-dimensional parameter spaces and the limited availability of reliable experimental data. In this study, we propose a hybrid calibra- tion framework that integrates expert knowledge into a multi-objective optimization process using NSGA-II algorithm. Our approach combines hard constraints derived from biological measurements with soft constraints encoding qualitative domain expertise, such as expected curve shapes or event timing. This dual-constraint strategy guides the search toward biologi- cally plausible parameter sets while preserving flexibility and interpretability. We demonstrate the effectiveness of our method on a benchmark model of skin wound healing, comparing it to standard and unconstrained optimization strategies. Results show that incorporating expert guidance significantly improves the biological relevance of simulated dynamics and mitigates overfitting, especially in underdetermined or uncertain settings. The framework is flexible, it- erative, and generalizable, offering a principled way to leverage domain knowledge for model calibration in complex biological systems.

systems biology↗

bioRxiv2019

Systems biology informed deep learning for inferring parameters and hidden dynamics

Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems. Author summaryThe dynamics of systems biological processes are usually modeled using ordinary differential equations (ODEs), which introduce various unknown parameters that need to be estimated efficiently from noisy measurements of concentration for a few species only. In this work, we present a new "systems-informed neural network" to infer the dynamics of experimentally unobserved species as well as the unknown parameters in the system of equations. By incorporating the system of ODEs into the neural networks, we effectively add constraints to the optimization algorithm, which makes the method robust to noisy and sparse measurements.

bioinformatics↗

bioRxiv2018

Open source tools and standardized data in cancer systems biology

To reach the full potential of multicellular systems biology, mathematical and computational modelers must pool their efforts to share and curate biophysical measurements, create and combine mathematical models, analyze and visualize model predictions, and validate and refine against shared data. An ecosystem of open source software that reads standardized data is essential. We review the state-of-the-art in open source software and data standards in multicellular systems biology, and point out areas of needed growth to move beyond isolated models to community-driven frameworks that shed light on complex problems in multicellular systems biology.

systems biology↗

bioRxiv2021

System characterization of dynamic biological systems through improved data analysis.

Determining the functional development and dominant competitive strategy in microbial community enrichments is complicated by the extensive measurement campaigns required for off-line system analysis. This study demonstrates that detailed system characterization of aerobic pulse fed enrichments can be established using on-line measurements combined with automated data analysis. By incorporating the physicochemical processes in on-line data processing with a Particle Filter and kinetic process model, an accurate reconstruction of the dominant biological rates can be made. We hereby can differentiate between storage compound production and biomass growth in sequencing batch bioreactors. The method proposed allows for close monitoring of changes in functional behavior of long-running enrichment cultures, without the need for off-line samples, therewith enabling the identification of new insights in process dynamics with a minimal experimental effort. Even though a specific example application of the method proposed is described here, the approach can readily be extended to a wide range of dynamic experimental systems that can be characterized based on on-line measurements.

microbiology↗

bioRxiv2016

Petri Net-Based Graphical and Computational Modelling of Biological Systems

In silico modelling of biological pathways is a major endeavour of systems biology. Here we present a methodology for construction of pathway models from the literature and other sources using a biologist-friendly graphical modelling system. The pathway notation scheme, called mEPN, is based on the principles of the process diagrams and Petri nets, and facilitates both the graphical representation of complex systems as well as dynamic simulation of their activity. The protocol is divided into four sections: 1) assembly of the pathway in the yEd software package using the mEPN scheme, 2) conversion of the pathway into a computable format, 3) pathway visualisation and in silico simulation using the BioLayout Express3D software, 4) optimisation of model parameterisation. This method allows reconstruction of any metabolic, signalling and transcriptional pathway as a means of knowledge management, as well as supporting the systems level modelling of their dynamic activity.

Systems Biology↗

bioRxiv2020

BioKC: a collaborative platform for systems biology model curation and annotation

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).

systems biology↗

bioRxiv2022

Mathematical Modeling Of Systems Biology

A modeling is a mathematical tool, like a microscope, which allows consequences to logically follow from a set of assumptions by which a real world problem can be described by a mathematical formulation. It has become indispensable tools for integrating and interpreting heterogeneous biological data, validating hypothesis and identifying potential diagnostic markers. The modern molecular biology that is characterized by experiments that reveal the behaviours of entire molecular systems is called systems biology. A fundamental step in synthetic biology and systems biology is to derive appropriate mathematical model for the purposes of analysis and design. This manuscript has been engaged in the use of mathematical modeling in the Gene Regulatory System (GRN). Different mathematical models that are inspired in gene regulatory network such as Central dogma, Hill function, Gillespie algorithm, Oscillating gene network and Deterministic vs Stochastic modelings are discussed along with their codes that are programmed in Python using different modules. Here, we underlined that the model should describes the continuous nature of the biochemical processes and reflect the non-linearity. It is also found that the stochastic model is far better than deterministic model to calculate future event exactly with low chance of error.

systems biology↗

bioRxiv2021

SBMate: A Framework for Evaluating Quality of Annotations in Systems Biology Models

The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.

systems biology↗