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Results for “systems biology”

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bioRxiv2023

ISRES+: An improved evolutionary strategy for function minimization to estimate the free parameters of Systems Biology models

Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a measure of the error between model predictions and experimental data. In this work, we build on an algorithm for function minimization proposed by Runnarson and Yao, named Improved Evolutionary Strategy by Stochastic Ranking (ISRES), that finds a best-fit individual by evolving a population in the direction of minimizing error by using information at most from a pair of individuals in any generation to create a new population. Our algorithm, named ISRES+, builds on it by combining information from all individuals across the population and across all generations to gain a better sense of direction to evolve the population. ISRES+ makes use of the additional information generated by the creation of a large population in the evolutionary methods to approximate the local neighborhood around the best-fit individual using linear least squares fit in one and two dimensions. We compared the performance of the two algorithms on three systems biology models with varying complexities and found that not only does the ISRES+ lead to fitter individuals, but it also leads to a tighter distribution of fittest individuals over successive runs.

systems biology↗

bioRxiv2024

Increasing certainty in systems biology models using Bayesian multimodel inference

Mathematical models are indispensable to the system biology toolkit for studying the structure and behavior of intracellular signaling networks. A common approach to modeling is to develop a system of equations that encode the known biology using approximations and simplifying assumptions. As a result, the same signaling pathway can be represented by multiple models, each with its set of underlying assumptions, which opens up challenges for model selection and decreases certainty in model predictions. Here, we use Bayesian multimodel inference to develop a framework to increase certainty in systems biology models. Using models of the extracellular regulated kinase (ERK) pathway, we first show that multimodel inference increases predictive certainty and yields predictors that are robust to changes in the set of available models. We then show that predictions made with multi-model inference are robust to data uncertainties introduced by decreasing the measurement duration and reducing the sample size. Finally, we use multimodel inference to identify a new model to explain experimentally measured sub-cellular location-specific ERK activity dynamics. In summary, our framework highlights multimodel inference as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.

systems biology↗

bioRxiv2017

A Metabolic Reaction Balancing Web Service for Computational Systems Biology

BackgroundIn metabolic network reconstruction the stoichiometric balancing of reactions is essential to create realistic constraint-based models. At the genome scale, balancing is a repetitive task that consumes valuable curator resource that could be deployed elsewhere. Automatic reaction balancing is possible and could be useful across computational systems biology, but widespread use of the appropriate code has been limited by the diversity of non-interoperable programming languages used in the field. RESTful web services offer a language-agnostic way of binding services together.\n\nResultsReaction balancing can be posed as a mixed integer linear programming problem to identify stoichiometric coefficients and infer commonly missing components. This functionality has been exposed as a web service that consumes a list of reactions as JSON or SBML. The reaction balancing web service has been deployed at http://www.nactem.ac.uk/balancer. Code is available via Github. By way of demonstration the service has been applied to a Chinese hamster ovary cell metabolic reconstruction to bring a further 219 reactions into balance.\n\nConclusionsThe majority of systems biology software cannot access existing automatic reaction balancing tools due to a lack of language-specific bindings. Web services bridge different languages by using widely-spoken web communication protocols, meaning that one binding works for almost all languages. Automatic reaction balancing can now be consumed by any systems biology software via a RESTful web service.

systems biology↗

bioRxiv2022

Stable Potassium Isotopes (41K/39K) Track Transcellular and Paracellular Potassium Transport in Biological Systems

As the most abundant cation in archaeal, bacterial, and eukaryotic cells, potassium (K+) is an essential element for life. While much is known about the machinery of transcellular and paracellular K transport - channels, pumps, co-transporters, and tight-junction proteins - many quantitative aspects of K homeostasis in biological systems remain poorly constrained. Here we present measurements of the stable isotope ratios of potassium (41K/39K) in three biological systems (algae, fish, and mammals). When considered in the context of our current understanding of potential mechanisms of K isotope fractionation and K+ transport in these biological systems, our results provide evidence that the fractionation of K isotopes depends on transport pathway and transmembrane transport machinery. Specifically, we find that passive transport of K+ down its electrochemical potential through channels and pores in tight-junctions at favors 39K, a result which we attribute to a kinetic isotope effect associated with dehydration and/or size selectivity at the channel/pore entrance. In contrast, we find that transport of K+ against its electrochemical gradient via pumps and co-transporters is associated with less/no isotopic fractionation, a result that we attribute to small equilibrium isotope effects that are expressed in pumps/co-transporters due to their slower turnover rate and the relatively long residence time of K+ in the ion pocket. These results indicate that stable K isotopes may be able to provide quantitative constraints on transporter-specific K+ fluxes (e.g. the fraction of K efflux from a tissue by channels vs. co-transporters) and how these fluxes change under different physiological states. In addition, precise determination of K isotope effects associated with K+ transport through channels, pumps, and co-transporters may provide unique constraints on the mechanisms of K transport that could be tested with steered molecular dynamic simulations.

physiology↗

bioRxiv2020

Comparative single-cell trajectory network enrichment identifies pseudo-temporal systems biology patterns in hematopoiesis and CD8 T-cell development

Single cell transcriptomics (scRNA-seq) technologies allow for investigating cellular processes on an unprecedented resolution. While software packages for scRNA-seq raw data analysis exist, no method for the extraction of systems biology signatures that drive different pseudo-time trajectories exists. Hence, pseudo-temporal molecular sub-network expression profiles remain undetermined, thus, hampering our understanding of the molecular control of cellular development on a single cell resolution. We have developed Scellnetor, the first network-constraint time-series clustering algorithm implemented as interactive webtool to identify modules of genes connected in a molecular interaction network that show differentiating temporal expression patterns. Scellnetor allows selecting two differentiation courses or two developmental trajectories for comparison on a systems biology level. Scellnetor identifies mechanisms driving hematopoiesis in mouse and mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Scellnetor is the first method to allow for single cell trajectory network enrichment for systems level hypotheses generation, thus lifting scRNA-seq data analysis to a systems biology level. It is available as an interactive online tool at https://exbio.wzw.tum.de/scellnetor/.

bioinformatics↗

bioRxiv2025

Generating Bounded Linear Temporal Logic in Systems Biology with Large Language Models

Motivation: Translating natural language biological discoveries into formal temporal logic specifications for model verification demands expertise most experimentalists lack. Bounded Linear Temporal Logic (BLTL), which attaches explicit time bounds to temporal operators, is well suited for capturing biological dynamics. However, no automated solution exists for this translation, limiting the broader adoption of statistical model checking in systems biology. Results: We present NL2BLTL, the first framework to automate natural language to BLTL translation for systems biology. NL2BLTL combines a synthetic dataset of 5,000 NL-BLTL pairs built via grammar-guided generation, Chain-of-Thought preprocessing to resolve linguistic ambiguity in biological hypotheses, and grammar-constrained decoding to enforce syntactic validity and prevent hallucinated variables or time bounds. Evaluated on a newly curated biomedical NL-BLTL dataset drawn from published T cell and pancreatic cancer models, NL2BLTL framework achieves 84.62% exact match and 100% syntactic validity, outperforming GPT-4 by over 16 points and improving 14 points over the base fine-tuned model.

systems biology↗

bioRxiv2019

It’s about time: Analysing an alternative approach for reductionist modelling of linear pathways in systems biology

Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions or it can prevent an otherwise accurate model from describing experimentally observed dynamics.\n\nHere, we perform a computational investigation of linear pathway models that contain fewer pathway steps than the system they are designed to emulate. We demonstrate when such models will fail to reproduce data and how detrimental truncation of a linear pathway leads to detectable signatures in model dynamics and its optimised parameters.\n\nAn alternative assumption is suggested for simplifying linear pathways. Rather than assuming a truncated number of pathway steps, we propose to use the assumption that the rates of information propagation along the pathway is homogeneous and instead letting the length of the pathway be a free parameter. This results in a three-parameter representation of arbitrary linear pathways which consistently outperforms its truncated rival and a delay differential equation alternative in recapitulating observed dynamics.\n\nOur results provide a foundation for well-informed decision making during model simplifications.\n\n1 Author summaryMathematical modelling can be a highly effective way of condensing our understanding of biological processes and highlight the most important aspects of them. Effective models are based on simplifying assumptions that reduce complexity while still retaining the core dynamics of the original problem. Finding such assumptions is, however, not trivial.\n\nIn this paper, we explore ways in which one can simplify long chains of simple reactions wherein each step is linearly dependent on its predecessor. After generating synthetic data from models that describe such chains in explicit detail, we compare how well different simplifications retain the original dynamics. We show that the most common such simplification, which is to ignore parts of the chain, often renders models unable to account for time delays. However, we also show that when such a simplification has had a detrimental effect, it leaves a detectable signature in its optimal parameter values. We also propose an alternative assumption which leads to a highly effective model with only three parameters. By comparing the effects of these simplifying assumptions in thousands of different cases and for different conditions we are able to clearly show when and why one is preferred over the other.

systems biology↗

bioRxiv2017

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

neuroscience↗

bioRxiv2021

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

systems biology↗

bioRxiv2021

Integrative system biology analysis of barley transcriptome - hormonal signaling against biotic stress

Biotic stresses are environmental factors that cause a variety of crop diseases and damages. In contrast, crops trigger specific transduction signaling pathways that the hormones are the central players. Integrative OMICS for systems genetic engineering approach contributes in the understanding of molecular mechanisms. In this research, the system biology approaches were applied to discover particular molecular interactions between biotic stresses and hormonal signaling in barley. The meta-analysis of the data identified a total of 1232 and 304 differentially expressed genes (DEGs) respectively so that were significantly involved in defense processes and hormone signaling. A total of 24 TFs belonged to 15 conserved families and 6 TFs belonged to 6 conserved families were identified for biotic and hormonal data respectively, whereas NF-YC, GNAT, and whirly families were the most abundant groups. The functional analysis of the upstream regions for over-represented cis-acting elements revealed that were involved activation of transcription factors in response to pathogens and hormones. Based on the co-expression analysis, 6 and 7 distinct co-expression modules related to biotic stresses and hormonal signaling were respectively uncovered. The gene network analysis also identified novel hub genes such as TIM10, DRT101, ADG1, and TRA2 which may be involved in regulating defense responses to biotic stresses. In addition, many new genes with unknown function were obtained. Since this study represents a first preliminary curated system biology analysis of barley transcriptomic responses to biotic stresses and hormone treatments, introduces important candidate genes that may be beneficial to crop biotechnologists to accelerate genetic engineering programs.

systems biology↗

bioRxiv2021

Novel antiviral interferon sensitive genes unveiled by correlation-driven gene selection and integrated systems biology approaches.

Interferons (IFNs) are key cytokines involved in alerting the immune system to viral infection. After IFN stimulation, cellular transcriptional profile critically changes, leading to the expression of several IFN stimulated genes (ISGs) that exert a wide variety of antiviral activities. Despite many ISGs have been already identified, a comprehensive network of coding and non-coding genes with a central role in IFN-response still needs to be elucidated. We performed a global RNA-Seq transcriptome profile of the HCV permissive human hepatoma cell line Huh7.5 and its parental cell line Huh7, upon IFN treatment, to define a network of genes whose coordinated modulation plays a central role in IFN-response. Our study adds molecular actors, coding and non-coding genes, to the complex molecular network underlying IFN-response and shows how systems biology approaches, such as correlation networks, networks topology and gene ontology analyses can be leveraged to this aim.

systems biology↗

bioRxiv2024

Optimizing ODE-derived Synthetic Data for Transfer Learning in Dynamical Biological Systems

BackgroundAccurate prediction of the temporal dynamics of biological systems is crucial for informing timely and effective interventions, e.g., in ecological or epidemiological contexts, or for treatment adjustments in therapy. While machine learning has proven its capabilities in generalizing the underlying non-linear dynamics of such systems, unlocking its predictive power is often restrained by the limited availability of large, curated datasets. To supplement real-world data, informing machine learning by transfer learning with synthetic data derived from simulations using ordinary differential equations has emerged as a promising solution. However, the success of this approach highly depends on the designed characteristics of the synthetic data. ResultsWe suggest scrutinizing these characteristics, such as size, diversity, and noise, of ordinary differential equation-based synthetic time series datasets. Here, we demonstrate how to systematically evaluate the influence of such design choices on transfer learning performance. We conduct a proof-of-concept study on three simple, but widely used systems and four real-world datasets. We find a strong interdependency between synthetic dataset size and diversity effects. Good transfer learning settings heavily rely on real-world data characteristics as well as the datas coherence with the dynamics of the model underlying the synthetic data. We achieve a performance improvement of up to 92% in mean absolute error for simulation-based transfer learning compared to non-informed deep learning. ConclusionsOur work emphasizes the relevance of carefully selecting properties of synthetic data for leveraging the valuable domain knowledge contained in ordinary differential equation models for machine-learning based predictions. The code is available at https://github.com/DILiS-lab/opt-synthdata-4tl.

bioinformatics↗

bioRxiv2024

A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Whole-genome sequencing has revealed that TP53, NOTCH1, ATM, SF3B1, BIRC3, ABL, NXF1, BCR, ZAP70 are often mutated in CLL, but not consistently across all CLL patients. This paper employs a statistical thermo-dynamics approach in combination with the systems biology of the CLL protein-protein interaction networks to identify the most significant participant proteins in the cancerous transformation. Betti number (a topology of complexity) estimates highlight a protein hierarchy, primarily in the Wnt pathway known for aberrant CLL activation. These individually identified proteins suggest a network-targeted strategy over single-target drug development. The findings advocate for a multi-target inhibition approach, limited to several key proteins to minimize side effects, thereby providing a foundation for designing therapies. This study emphasizes a shift towards a comprehensive, multi-scale analysis to enhance personalized treatment strategies for CLL, which could be experimentally validated using siRNA or small molecule inhibitors. The result is not just the identification of these proteins but their rank-order, offering a potent signal amplification in the context of the 20,000 proteins produced by the human body, thus providing a strategic basis for therapeutic intervention in CLL, underscoring the necessity for a more holistic, cellular, chromosomal, and genome-wide study to develop tailored treatments for CLL patients. Author SummaryChronic Lymphocytic Leukemia (CLL) is a unique and slowly progressing cancer affecting white blood cells, and research on CLL has highlighted the inconsistency of gene mutations across patients. Using a novel approach that merges statistical thermodynamics and systems biology, this research examines the CLL protein-protein interaction networks to pinpoint proteins integral to the onset of the disease. Betti number (a topology of complexity) estimates, which measure the importance of individual proteins when removed from the network, helped identify numerous potential therapeutic targets, notably within the Wnt signaling pathway, a pathway implicated in various cellular processes and known for its defective expression in CLL. The finding advocates for a multi-target inhibition approach, focusing on several key proteins to minimize side effects, thereby laying a foundation for designing more effective therapies for CLL. This paper emphasizes the potential benefits of a comprehensive study, spanning cellular to genome-wide scales, to design personalized treatments for CLL patients.

systems biology↗

bioRxiv2025

DeepCEF: A Deep Causal Estimation Framework for Complex Biological Systems Integrating Local Scores, Independence Tests, and Relation Attributes

Causal relationship identification is a fundamental and complex research challenge that spans multiple disciplines, including biology, epidemiology, economics, and philosophy. Various scoring techniques and independence tests, such as local scores (e.g., Degenerate Gaussian (DG) and Bayesian Information Criterion (BIC)) and independence tests (e.g., Fishers Z), have been employed in causality estimation. However, these local scores often excel in specific data types or application areas but falter in others, limiting their ability to capture the complexity and heterogeneity of underlying causal mechanisms. For instance, a method may perform well on linear relationships or continuous variables but struggle with discrete variables or non-linear relationships. Real-world observational datasets, particularly those generated in complex biological systems, often contain diverse data types and relationships, making it essential to develop a more comprehensive approach. To address this challenge, we propose a novel causal estimation framework that leverages the powerful classification capabilities of deep neural networks (DNNs) to identify causal patterns in pairwise relationships. Our framework integrates multiple local causality estimation scores, independence tests, and variable attributes, allowing it to capture a wide range of causal mechanisms. To ensure the frameworks robustness and generalizability, we incorporate a diverse range of simulation data and 10 curated real-world datasets into the training procedure. Furthermore, our framework is designed to be extensible, enabling users to easily integrate their own data and additional scores and tests. Our validation results demonstrate that our framework outperforms existing methods in terms of estimation accuracy and precision on both simulation data and real-world biological datasets. By providing a more comprehensive and adaptable approach to causal relationship identification, our framework has the potential to advance research in various fields and improve our understanding of complex biological systems.

bioinformatics↗

bioRxiv2015

A cell-free framework for biological systems engineering

SUMMARYWhile complex dynamic biological networks control gene expression and metabolism in all living organisms, engineering comparable synthetic networks remains challenging1,2. Conducting extensive, quantitative and rapid characterization during the design and implementation process of synthetic networks is currently severely limited due to cumbersome molecular cloning and the difficulties associated with measuring parts, components and systems in cellular hosts. Engineering gene networks in a cell-free environment promises to be an efficient and effective approach to rapidly develop novel biological systems and understand their operating regimes3-5. However, it remains questionable whether complex synthetic networks behave similarly in cells and a cell-free environment, which is critical for in vitro approaches to be of significance to biological engineering. Here we show that synthetic dynamic networks can be readily implemented, characterized, and engineered in a cell-free framework and consequently transferred to cellular hosts. We implemented and characterized the \"repressilator\"6, a three-node negative feedback oscillator in vitro. We then used our cell-free framework to engineer novel three-node, four-node, and five-node negative feedback architectures going from the characterization of circuit components to the rapid analysis of complete networks. We validated our cell-free approach by transferring these novel three-node and five-node oscillators to Escherichia coli, resulting in robust and synchronized oscillations reflecting the in vitro observation. We demonstrate that comprehensive circuit engineering can be performed in a cell-free system and that the in vitro results have direct applicability in vivo. Cell-free synthetic biology thus has the potential to drastically speed up design-build-test cycles in biological engineering and enable the quantitative characterization of synthetic and natural networks.

Synthetic Biology↗

bioRxiv2018

Systems Biology Theory Clarification of a Controversy in Pancreatic Beta Cell Regeneration

Whether new pancreatic beta-cells arise via pre-existing beta-cells or from differentiation of precursor cells - a question of fundamental importance for diabetic therapy - has long been debated. Recent experiments suggest that multipotent precursors from adult mouse pancreas, that give rise to beta-cells, do exist. However, such a finding is at odds with prior evidence that beta-cell expansions occurs exclusively through self-replication. Here we show that these two observations can be partially compatible. We use a systems biology approach to analyze the dynamics of the endogenous molecular-cellular network in the pancreas. Our results show that self-replicating beta-cells can themselves be multipotent precursors. In addition, our model predicts heterogeneity in beta-cell regeneration and suggests various differentiation paths of precursors. This work therefore provides a means of reconciling an apparent contradiction in the field, but also sheds light on possible paths of beta-cell regeneration from a systems biology perspective.

systems biology↗

bioRxiv2018

Building mathematical models of biological systems with modelbase, a Python package for semi-automatic ODE assembly and construction of isotope-specific models

The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods to define reactions and their rates. Based on the rates and stoichiometries, the system of differential equations is assembled automatically. modelbase minimises the constraints imposed on the user, allowing for easy and dynamic access to all variables, including derived ones, in a convenient manner. A simple incorporation of algebraic equations is, for example, convenient to study systems with rapid equilibrium or quasi steady-state approximations. Moreover, modelbase provides construction methods that automatically build all isotope-specific versions of a particular reaction, making it a convenient tool to analyse non-steady state isotope-labelling experiments.

systems biology↗

bioRxiv2024

Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.

BackgroundRice is a critical global food source, but it faces challenges due to nutritional deficiencies and the pressures of a growing population. Understanding the molecular mechanisms and protein functions in rice seed development is essential to improve yield and grain quality. However, there is still a significant knowledge gap regarding the key proteins and their interactions that govern rice seed development. Protein-protein interaction (PPI) analysis is a powerful tool for studying developmental processes like seed development, though its potential in rice research is yet to be fully realized. With the aim of unravelling the protein interaction landscape associated with rice seed development, this systems biology study conducted a PPI network-based analysis. Using a list of known seed development proteins from the Gene Ontology (GO) knowledgebase and literature, novel candidate proteins for seed development were predicted using an ensemble of network-based algorithms, including majority voting (MV), Hishigaki, Functional flow, and Random Walk with Restart (RWR). The predictions were validated using enrichment analysis, and the rice seed development subnetwork was further analyzed for community and hub detection. ResultsThe study predicted 196 new proteins linked to rice seed development and identified 14 sub-modules within the network, each representing different developmental pathways such as endosperm development and seed growth regulation. Of these, 17 proteins were identified as intra-modular hubs and 6 as inter-modular hubs. Notably, the protein SDH1 emerged as a dual hub, acting as both an intra-modular and inter-modular hub, highlighting its crucial role in coordinating interactions during rice seed development. ConclusionsThese findings, including the identified hub proteins and sub-modules, provide a better understanding of the PPI interaction landscape governing seed development in rice. This information is useful for improving rice cultivars for better yield and quality through genetic engineering and breeding. This study implements an ensemble of algorithms for the analysis and showcases how systems biology techniques can be applied in developmental biology.

systems biology↗