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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Landscape structure drives eco-evolution in host-parasite systems.

Spatial network structure of biological systems drives ecology and evolution by distributing organisms and their genes. The ubiquitous host-parasite systems are no exception. However, past work has largely ignored relevant spatial complexity, hampering the translation of theoretical predictions to real ecosystems. Thus, we develop an eco-evolutionary metapopulation model of host-parasite dynamics where hosts and parasites disperse through realistically complex spatial networks representing major biomes: riverine aquatic and terrestrial. We generate the testable prediction that parasite virulence, or how parasites harm their hosts, is unimodal with dispersal and can reach greater values in aquatic landscapes but saturates to lower values in terrestrial systems. Moreover, we show that kin selection drives virulence evolution. Spatial networks generate characteristic patterns of parasite relatedness which drive differential virulence evolution. Finally, we show that accounting for virulence evolution allows us to predict the distribution of key epidemiological variables (e.g., parasite extinction risks) within spatial networks.

evolutionary biology↗

bioRxiv2017

Insights into the relation between noise and biological complexity

Understanding under which conditions the increase of systems complexity is evolutionary advantageous, and how this trend is related to the modulation of the intrinsic noise, are fascinating issues of utmost importance for synthetic and systems biology. To get insights into these matters, we analyzed chemical reaction networks with different topologies and degrees of complexity, interacting or not with the environment. We showed that the global level of fluctuations at the steady state, as measured by the sum of the Fano factors of the number of molecules of all species, is directly related to the topology of the network. For systems with zero deficiency, this sum is constant and equal to the rank of the network. For higher deficiencies, we observed an increase or decrease of the fluctuation levels according to the values of the reaction fluxes that link internal species, multiplied by the associated stoichiometry. We showed that the noise is reduced when the fluxes all flow towards the species of higher complexity, whereas it is amplified when the fluxes are directed towards lower complexity species.\n\nPACS numbers: 02.50.Ey, 05.10.Gg, 05.40.Ca, 87.18.-h

systems biology↗

bioRxiv2019

Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues

The past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.

genomics↗

bioRxiv2018

Computational modelling of atherosclerosis: developing a community resource

RationaleAtherosclerosis is a dynamical process that emerges from the interplay between lipid metabolism, inflammation and innate immunity. The arterial location of atherosclerosis makes it logistically and ethically difficult to study in vivo. To improve our understanding of the disease, we must find alternative ways to investigate its progression. There is currently no computational model of atherosclerosis openly available to the research community for use in future studies and for refinement and development.\n\nObjectiveHere we develop the first predictive computational model to be made openly available and demonstrate its use for therapeutic hypothesis generation.\n\nMethods and ResultsWe compiled a dataset of relevant interactions from the literature along with available parameters. These were used to build a network model describing atherosclerotic plaque development. A visual map of the network model was produced using the Systems Biology Graphical Notation (SBGN) and a dynamic mathematical description of the network model that enables us to simulate plaque growth was developed and is made available using the Systems Biology Markup Language (SBML). We used this model to investigate whether multi-drug therapeutic interventions could be identified that stimulate plaque regression. The model produced comprised 20 cell types and 41 proteins with 89 species in total. The visual map is available for reuse and refinement using the SBGN Markup Language standard format and the mathematical model is available using the SBML standard format. We used a genetic algorithm to identify a multi-drug intervention hypothesis comprising five drugs that comprehensively reverse plaque growth within the model.\n\nConclusionsWe have produced the first predictive mathematical and computational model of atherosclerosis that can be reused and refined by the cardiovascular research community. We demonstrated its potential as a tool for future studies of cardiovascular disease by using it to identify multi-drug intervention hypotheses.\n\nSubject CodesAtherosclerosis, Computational Biology, Lipids and Cholesterol, Cell Signaling/Signal Transduction, Cardiovascular Disease

physiology↗

bioRxiv2026

IMPAIRED BRIDGING OF TEMPORAL DISCONTINUITIES IN OLDER ADULT HIV-1 TG RATS

The advent and widespread uptake of combination antiretroviral therapy dramatically changed the epidemiological features of human immunodeficiency virus type 1 (HIV-1), whereby older individuals (>50 years of age) account for approximately 50% of HIV-1 seropositive individuals in the United States. Nevertheless, to date, there is no extant in vivo biological system to model the unique age-related neurocognitive impairments observed in HIV-1 seropositive individuals. Herein, the utility of the HIV-1 transgenic (Tg) rat as a biological system to model age-related neurocognitive impairments and neuroanatomical alterations was evaluated. Older adult HIV-1 Tg rodents (i.e., >12 months of age upon testing initiation), relative to their control counterparts, exhibited profound neurocognitive alterations characterized by impairments in stimulus-reinforcement learning, sustained attention, and selective attention; neurocognitive deficits which support a fundamental distortion of temporal processing. Neuronal dysfunction in older adult HIV-1 Tg animals was characterized by structural alterations in pyramidal neurons, and their associated dendritic spines, in the medial prefrontal cortex and abnormal accumulation of amyloid beta (A{beta}). Interestingly, the abnormal accumulation of A{beta} mechanistically underlies, at least in part, the profound dendritic spine dysmorphology in male, but not female, HIV-1 Tg rats. More critically, however, neuronal dysfunction mechanistically underlies neurocognitive impairments in both male and female HIV-1 Tg rodents, whereby neuronal dysfunction accounts for 65.4% and 60.8% of the variance in neurocognitive function, respectively. Establishing the utility of the HIV-1 Tg rat for age-related neurocognitive impairments is fundamental to disentangling the role of HIV-1 viral proteins and comorbidities in neurocognitive function.

neuroscience↗

bioRxiv2023

The Kinetic and Energetic Pull of Chemical Entropy On Small Ensembles of Proteins

Biological systems are fundamentally containers of thermally fluctuating atoms that through unknown mechanisms are structurally layered across many thermal scales from atoms to amino acids to primary, secondary, and tertiary structures to functional proteins to functional macromolecular assemblies and up. Understanding how the irreversible kinetics (i.e., the arrow of time) of biological systems emerge from the equilibrium kinetics of constituent structures defined on smaller thermal scales is central to describing biological function. Muscles irreversible power stroke - with its mechanochemistry defined on both the thermal scale of muscle and the thermal scale of myosin motors - provides a clear solution to this problem. Individual myosin motors function as reversible force-generating switches induced by actin binding and gated by the release of inorganic phosphate, Pi. As shown in a companion article, when N individual switches thermally scale up to an ensemble of N switches in muscle, the entropy of a binary system of switches is created. We have shown in muscle that a change in state of this binary system of switches entropically drives actin-myosin binding (the switch) and muscles irreversible power stroke, and that this simple two-state model accurately accounts for most key aspects of muscle contraction. Extending this observation beyond muscle, here I show that the chemical kinetics of an ensemble of N molecules differs fundamentally from a conventional chemical analysis of N individual molecules, describing irreversible chemical reactions as being pulled into the future by the a priori defined entropy of a binary system rather than being pushed forward by the physical occupancy of chemical states (e.g., mass action).

biophysics↗

bioRxiv2025

Characterization and orthogonality assessment of two quorum sensing systems for synthetic biology applications

Quorum sensing systems have a broad range of applications within the field of synthetic biology. However, a bottleneck is the optimization and tuning of these systems due to the lack of standardization and complete characterization. In this research, two quorum sensing systems, namely the LasI/LasR and the EsaI/EsaR system, were fully characterized in the model host organism Escherichia coli. Furthermore, insight was gained in the interplay between the various parts of these systems. To further expand the range of possibilities with these quorum sensing systems, the orthogonality of the two systems was assessed to allow simultaneous use within the same cell without interfering crosstalk. This assessment was performed on three levels: promoter, signal and synthase crosstalk. It was demonstrated that LasR is able to interact with the promoter of the EsaI/EsaR system, albeit to a low extent. Additionally, LasR was able to respond to the autoinducers produced by EsaI. To solve the promoter crosstalk, a nucleotide change was introduced into the binding site of EsaR within the promoter region. Additionally, LasR mutants were created rationally and screened for decreased response to EsaI while retaining functionality. The best performing mutant, LasR(P117S), was further characterized. In conclusion, we have further unlocked the potential of quorum sensing systems for synthetic biology applications by obtaining two functional, characterized and orthogonal quorum sensing systems. Highlights- Characterization of two LuxR-type quorum sensing systems - Assessing the orthogonality of the EsaI/EsaR and LasI/LasR quorum sensing system - Eliminating the crosstalk between the EsaI/EsaR and LasI/LasR quorum sensing system

synthetic biology↗

bioRxiv2026

BoolDog: integrated Boolean and semi-quantitative network modelling in Python

SummaryBoolean and logic-based modeling approaches are well suited for the analysis of complex biological systems, particularly when detailed biochemical and kinetic information is unavailable. In such settings, biological pathways are represented as networks capturing system components and their interactions, providing a simplified yet informative abstraction of system behavior. While the structural topology of these networks is often well characterized, the absence of mechanistic detail limits the applicability of parameter-dependent modeling frameworks. To address this, we present BoolDog, a Python package for the construction, simulation, and analysis of Boolean and semi-quantitative Boolean networks. BoolDog supports synchronous simulation with events, attractor and steady-state identification, network visualization, and the systematic transformation of logic-based models into continuous ordinary differential equation (ODE) systems -- enabling the seamless integration of discrete and continuous modeling paradigms. Networks can be imported and exported across standard formats, and BoolDog integrates natively with established Python libraries for network analysis and visualisation, including NetworkX, igraph, and py4Cytoscape. Together, these capabilities provide a flexible, accessible, and interoperable platform for logic-based modeling of complex biological systems. Availability and implementationBoolDog is implemented in Python and available at https://github.com/NIB-SI/BoolDog/.

systems biology↗

bioRxiv2025

Paradoxical gene regulation explained by competition for genomic sites

Understanding how opposing regulatory factors shape gene expression is essential for understanding complex biological systems. A motivating observation, drawn from cancer epigenetics, is that removing an activating factor can sometimes lead to higher, not lower, expression of a gene that is also subject to a repressing factor. Prior theoretical work explained this counterintuitive behavior by competition of repressors and activators for genomic binding sites. However, it has been difficult to test this directly in natural systems, where layers of regulation obscure causal relationships. This paper introduces a fully synthetic, tunable genetic platform in a prokaryotic model system that reconstitutes this competition mechanism in a controlled and isolated setting. The genetic platform contains a target gene with binding sites for both an activator and a repressor, together with separate overlapping decoy binding sites for the same regulators. Activator and repressor functions are implemented using CRISPRa and CRISPRi, which permit independent control of regulator expression levels, design of the binding sites, and modulation of the binding affinities. Using this minimal system, we demonstrate that increasing activator expression level can reduce expression of the target gene when both regulators are present, consistent with the hypothesis that additional activator molecules displace the repressor from decoy sites, which becomes available to repress the target. By demonstrating how competition for genomic binding sites can invert expected regulatory responses, this synthetic framework provides a system for understanding similar paradoxical behaviors in natural regulatory networks and establishes a foundation for future studies in more complex mammalian contexts. Significance StatementGene regulation is often described in terms of activators that increase expression and repressors that decrease it, yet biological systems frequently display counterintuitive behaviors. Here we show that competition between regulators for shared genomic binding sites can invert expected responses, so that increasing an activator can reduce target gene expression. Using a minimal, fully controllable synthetic system based on CRISPR activation and interference, we isolate and experimentally validate this mechanism. Our results demonstrate that such paradoxical effects arise not from changes in intrinsic regulatory roles but from redistribution of regulators across competing sites. This work provides a general, mechanistic framework for understanding nonintuitive gene-expression patterns observed in complex systems, including those relevant to disease.

synthetic biology↗

bioRxiv2024

Effects of multistability, absorbing boundaries and growth on Turing pattern formation

Turing patterns are a fundamental concept in developmental biology, describing how homogeneous tissues develop into self-organized spatial patterns. However, the classical Turing mechanism, which relies on linear stability analysis, often fails to capture the complexities of real biological systems, such as multistability, non-linearities, growth, and boundary conditions. Here, we explore the impact of these factors on Turing pattern formation, contrasting linear stability analysis with numerical simulations based on a simple reaction-diffusion model, motivated by synthetic gene-regulatory pathways. We demonstrate how non-linearities introduce multistability, leading to unexpected pattern outcomes not predicted by the traditional Turing theory. The study also examines how growth and realistic boundary conditions influence pattern robustness, revealing that different growth regimes and boundary conditions can disrupt or stabilize pattern formation. Our findings are critical for understanding pattern formation in both natural and synthetic biological systems, providing insights into engineering robust patterns for applications in synthetic biology. Author summaryDuring development, tissues self-organize to go from a single cell to a structured organism. In this process, simple chemical reactions lead to the emergence of the intricate designs we see in nature, like the stripes on a zebra or the labyrinths on a brain cortex. Although multiple theories have been proposed to model this phenomenon, one of the most simple and popular ones was introduced in the 1950s by the mathematician Alan Turing. However, his theory oversimplifies the biological conditions and ignores properties such as non-linearities, boundary effects, or growth in the tissue. In this work, we used a combination of mathematical models and computer simulations to investigate how these real-world factors influence pattern formation. Our findings show that when we account for these realistic effects, the patterns that emerge can be very different from what Turings theory would predict. Thus, this work may help us better understand the laws behind pattern formation and could have practical applications in tissue engineering for medical or environmental applications.

biophysics↗

bioRxiv2026

scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq

BackgroundPseudotime inference has become a standard approach for reconstructing dynamic biological processes from single-cell transcriptomic data. However, after a pseudotemporal ordering has been established, systematically identifying and interpreting gene expression trends along pseudotime remains challenging. Existing approaches often rely on clustering-based heuristics or subjective parameter choices, which can compromise interpretability, reproducibility, and scalability in large datasets. ResultsWe present scTrends, an automated and interpretable framework for gene-level trend classification and strength quantification along a given pseudotime trajectory. Importantly, scTrends does not perform pseudotime inference; instead, it operates downstream of established pseudotime methods to characterize expression dynamics once a temporal ordering is available. scTrends models pseudotime-binned gene expression profiles using generalized additive models and assigns genes to predefined temporal trend categories through a hierarchical, rule-based procedure combined with empirical significance testing, data-adaptive parameter selection, and quantitative assessment of trend strength. This enables simultaneous identification of the direction, shape, and magnitude of gene expression changes along pseudotime. We applied scTrends tothree distinct datasets: human PBMC, human brain, and mouse pancreas, using three different pseudotime inference methods (CytoTRACE v2, Monocle3, and scVelo, respectively). scTrends systematically characterized gene expression dynamics during T cell differentiation, oligodendrocyte precursor differentiation, and pancreatic endocrine cell maturation. The analysis revealed diverse monotonic, non-monotonic, and complex expression patterns, with varying strengths, consistent with known biological processes. Benchmarking analyses further demonstrate that scTrends is computationally efficient and scalable to large single-cell datasets, with modest memory requirements, making it suitable for diverse applications across a range of biological systems. ConclusionsscTrends provides a systematic, automated, and resource-efficient solution for gene-level trend analysis in single-cell pseudotime studies, enabling reproducible characterization of dynamic expression patterns across diverse biological systems.

bioinformatics↗

bioRxiv2024

Optimisation strategies for directed evolution without sequencing

Directed evolution can enable engineering of biological systems with minimal knowledge of their underlying sequence-to-function relationships. A typical directed evolution process consists of iterative rounds of mutagenesis and selection that are designed to steer changes in a biological system (e.g. a protein) towards some functional goal. Much work has been done, particularly leveraging advancements in machine learning, to optimise the process of directed evolution. Many of these methods, however, require DNA sequencing and synthesis, making them resource-intensive and incompatible with developments in targeted in vivo mutagenesis. Operating within the experimental constraints of established sorting-based directed evolution techniques (e.g. Fluorescence-Activated Cell Sorting, FACS), we explore approaches for optimisation of directed evolution that do not require sequencing information. We then expand our methods to the context of emerging experimental techniques in directed evolution, which allow for single-cell selection based on fitness objectives defined from any combination measurable traits. Finally, we validate the developed selection strategies on the GB1 empirical landscape, demonstrating that they can lead to up to a 7.5 fold increase in the probability of attaining the global fitness peak. Author summaryThe standard approach to sorting-based selection in directed evolution is to take forward only the top-performing variants from each generation of a single population. In this work, we begin to explore alternative selection strategies within a simulated directed evolution framework. We propose "selection functions", which allow us to tune the balance of exploration and exploitation of a fitness landscape, and we demonstrate that splitting a population into sub-populations can improve both the likelihood and magnitude of a successful outcome. We also propose strategies to leverage emerging selection methods that can implement single-cell selection based on any combination of measurable traits. We validate our optimised directed evolution approaches on the empirical fitness landscape of the GB1 immunoglobulin protein.

bioengineering↗

bioRxiv2018

Dynamic Network Analysis of the 4D Nucleome

MotivationFor many biological systems, it is essential to capture simultaneously the function, structure, and dynamics in order to form a comprehensive understanding of underlying phenomena. The dynamical interaction between 3D genome spatial structure and transcriptional activity creates a genomic signature that we refer to as the four-dimensional organization of the nucleus, or 4D Nucleome (4DN). The study of 4DN requires assessment of genome-wide structure and gene expression as well as development of new approaches for data analysis.\n\nResultsWe propose a dynamic multilayer network approach to study the co-evolution of form and function in the 4D Nucleome. We model the dynamic biological system as a temporal network with node dynamics, where the network topology is captured by chromosome conformation (Hi-C), and the function of a node is measured by RNA sequencing (RNA-seq). Network-based approaches such as von Neumann graph entropy, network centrality, and multilayer network theory are applied to reveal universal patterns of the dynamic genome. Our model integrates knowledge of genome structure and gene expression along with temporal evolution and leads to a description of genome behavior on a system wide level. We illustrate the benefits of our model via a real biological dataset on MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage. We show that our methods enable better predictions on form-function relationships and refine our understanding on how cell dynamics change during cellular reprogramming.\n\nAvailability: The software is available upon request.\n\nContactindikar@umich.edu\n\nSupplementary informationSee Supplementary Material.

bioinformatics↗

bioRxiv2019

Compartmental Modeling Software: a fast, discrete stochastic framework for biochemical and epidemiological simulation

The compartmental modeling software (CMS) is an open source computational framework that can simulate discrete, stochastic reaction models which are often utilized to describe complex systems from epidemiology and systems biology. In this article, we report the computational requirements, the novel input model language, the available numerical solvers, and the output file format for CMS. In addition, the CMS code repository also includes a library of example model files, unit and regression tests, and documentation. Two examples, one from systems biology and the other from computational epidemiology, are included that highlight the functionality of CMS. We believe the creation of computational frameworks such as CMS will advance our scientific understanding of complex systems as well as encourage collaborative efforts for code development and knowledge sharing.

epidemiology↗

bioRxiv2018

Identifying protein subsets and features responsible for improved drug repurposing accuracies using the CANDO platform

Drug repurposing is a valuable tool for combating the slowing rates of novel therapeutic discovery. The Computational Analysis of Novel Drug Opportunities (CANDO) platform performs shotgun repurposing of 3,733 drugs/compounds that map to 2,030 indications/diseases by predicting their interactions with 46,784 protein structures and relating them via proteomic interaction signatures. The accuracy of the CANDO platform is evaluated using our benchmarking protocol that assesses indication accuracies based on whether or not pairs of drugs associated with the same indication can be captured within a certain cutoff, which is a measure of the drug repurposing recovery rate. To identify subsets of proteins that exhibit the same therapeutic effectiveness as the full set, groups of 8 proteins were randomly selected and subsequently benchmarked 50 times. The resulting protein sets were ranked according to average indication accuracy, pairwise accuracy, and coverage (count of indications with non-zero accuracy). The best 50 subsets of 8 according to each metric were progressively combined into supersets after each iteration and benchmarked. These supersets yield up to 14% improvement in benchmarking accuracy, and represent a 100-1,000 fold reduction in the number of proteins relative to the full set. Protein supersets optimized using independent compound libraries derived from the full library were cross-tested and were shown to reproduce the performance relative to using all 46,784 proteins, indicating that these reduced size supersets are broadly applicable for characterizing drug behavior. Further analysis revealed that sets comprised of proteins with more equitably diverse ligand interactions are important for describing drug behavior. Our work elucidates the role of particular protein subsets and corresponding ligand interactions that play a role in computational drug repurposing, and paves the way for the use of machine learning approaches to further improve the accuracy of the CANDO platform and its repurposing potential.\n\nAuthor summaryDrug repurposing is a valuable approach for ameliorating the current problems plaguing drug discovery. We introduce a novel protein subset analysis pipeline that allows us to elucidate features important for drug repurposing accuracies using the Computational Analysis of Novel Drug Opportunities (CANDO) platform. Our platform relates drugs based on the similarity of their interactions with a diverse library of proteins. We subjected all proteins in the platform to a splitting and ranking protocol that ranked protein subsets based on their benchmarking performance. Further analysis of the best performing protein subsets revealed that the most useful proteins for describing how small molecule compounds behave in biological systems are those that are predicted to interact with a structurally diverse range of ligands. We hypothesize that this is a consequence of the multitarget nature of drugs and, conversely, the implied promiscuity of proteins in biological systems. These results may be used to make drug discovery more accurate and efficient by alleviating some of its bottlenecks, bringing us one step further in better understanding how drugs behave in the context of their environments.

bioinformatics↗

bioRxiv2017

SuperDCA for genome-wide epistasis analysis

The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 104-105 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 105 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.\n\nAuthor SummaryRecent work has demonstrated the emerging potential in statistical genome-wide modeling to uncover co-selection and epistatic interactions between polymorphisms in bacterial chromosomes from densely sampled population data. Here we develop the Potts model based approach further into a fully mature computational method which can be applied to most existing bacterial population genomic data sets in a straightforward manner. Our advances are relying on more efficient parameter scoring, highly optimized and parallelized open source C++ code, which does not rely on the computation-intensive polymorphism subsampling approximations used earlier. By analyzing the two largest available population samples of Streptococcus pneumoniae (the pneumococcus), we highlight several biological discoveries related to the survival of the pneumococcus and co-evolution of penicillin-binding loci, which were not uncovered by the earlier analyses. Our method holds considerable potential for building understanding about numerous organisms at a systems biological level.

genomics↗

bioRxiv2023

Biosys-LiDeOGraM: A visual analytics framework for interactive modelling of multiscale biosystems

In this paper, we present a test of an interactive modelling scheme in real conditions. The aim is to use this scheme to identify the physiological responses of microorganisms at different scales in a real industrial application context. The originality of the proposed tool, Biosys-LiDeOGraM, is to generate through a human-machine cooperation a consistent and concise model from molecules to microbial population scales: If multi-omics measurements can be connected relatively easily to the response of the biological system at the molecular scale, connecting them to the macroscopic level of the biosystem remains a difficult task, where human knowledge plays a crucial role. The use-case considered here pertains to an engineering process of freeze-drying and storage of Lactic Acid Bacteria. Producing a satisfying model of this process is a challenge due to (i) the scarcity and variability of the experimental dataset, (ii) the complexity and multi-scale nature of biological phenomena, and (iii) the wide knowledge about the biological mechanisms involved in this process. The Biosys-LiDeOGraM tool has two main components that can have to be utilized in an iterative manner: the Genomic Interactive Clustering (GIC) module and the Interactive Multi-Scale modellIng Exploration (IMSIE) module, both involve users in their learning loops. Applying our approach to a dataset of 2,741 genes, an initial model, as a graph involving 33 variables and 165 equations, was first built. Then the system was able to interactively improve a synthetic version of this model using only 27 variables and 16 equations. The final graph providing a consistent and explainable biological model. This graphical representation allows various user interpretations at local and global scales, an easy confrontation with data, and an exploration of various assumptions. Finally Biosys-LiDeOGraM is easily transferable to other use-cases of multi-scale modelling using functional graphs. Author summaryThe use of "omics" data for understanding biological systems has become prevalent in several research domains. However, the data generated from diverse macroscopic scales used for this purpose is highly heterogeneous and challenging to integrate. Yet, it is crucial to incorporate this information to gain a comprehensive understanding of the underlying biological system. Although various integrative analysis methods that have been developed provide predictive molecular-scale models, they only offer a mechanistic view of the biological system at the cellular level. In addition, they often focus on specific biological hypotheses through dedicated case studies, making it difficult to apply their results to other scientific problems. To address these issues, we propose an interactive multi-scale modelling approach to integrate cross-scale relationships providing predictive and potentially explanatory models. A proof-of-concept tool has been developed and was validated in the context of the bioproduction of Lactococcus lactis, a bacterial species of high economic interest in the food industry and for which the control of the bioprocess is essential to guarantee its viability and functionality. Our approach can be applied to any biological system that can be defined through a set of variables, constraints and scales.

bioengineering↗

bioRxiv2022

BlotIt - Optimal alignment of western blot and qPCR experiments

Biological systems are frequently analyzed by means of mechanistic mathematical models. In order to infer model parameters and provide a useful model that can be employed for systems understanding and hypothesis testing, the model is often calibrated on quantitative, time-resolved data. To do so, it is typically important to compare experimental measurements over broad time ranges and various experimental conditions, e.g. perturbations of the biological system. However, most of the established experimental techniques such as Western blot, or quantitative real-time polymerase chain reaction only provide measurements on a relative scale, since different sample volumes, experimental adjustments or varying development times of a gel lead to systematic shifts in the data. In turn, the number of measurements corresponding to the same scale enabling comparability is limited. Here, we present a new flexible method to align measurement data that obeys different scaling factors. We propose an alignment model to estimate these scaling factors and provide the possibility to adapt this model depending on the measurement technique of interest. In addition, an error model can be specified to adequately weight the different data points and obtain scaling-model based confidence intervals of the finally scaled data points. Our approach is applicable to all sorts of relative measurements and does not need a particular experimental condition that has been measured over all available scales. An implementation of the method is provided with the R package blotIt including refined ways of visualization.

bioinformatics↗