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bioRxiv2024

Markov Field network integration of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques

Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems, but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that BCG vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, cytometry) of vaccinated macaques, we applied Markov Fields (MF), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.e. macaques) relative to multivariate features. Furthermore, we find that integrating multiple data modes with MFs helps to remove spurious connections. Finally, we used the MF to predict outcomes of perturbations at various immune nodes, including a B-cell depletion that induced network-wide shifts without reducing vaccine protection, which we validated experimentally.

systems biology↗

bioRxiv2025

Statistical inference of the cellular origin of chronic myeloid leukemia using a discrete-parameter ABC-PMC framework

Chronic myeloid leukemia (CML) arises from the BCR::ABL1 fusion gene, but the exact stage of cellular differentiation at which the first leukemic cell emerges remains uncertain. We develop a stochastic 27-compartment model of hematopoiesis (blood cell development) using a continuous-time multitype branching process to capture the dynamics of both healthy and cancer cells. To infer the origin of CML, we develop a discrete-parameter Approximate Bayesian Computation - Population Monte Carlo (ABC-PMC) algorithm, tailored to estimate the posterior distribution for the stage of differentiation at which the first cancer cell appeared. Applied to patient data, our method consistently identifies the stem cell compartment as the most likely source of CML. These findings improve understanding of disease initiation and demonstrate the power of discrete-parameter ABC-PMC for statistical inference in complex biological systems. Author summaryChronic myeloid leukemia is a blood cancer that begins when a genetic change called the BCR::ABL1 fusion gene appears in one cell. Although this disease has been widely studied, some questions remain, particularly about the exact stage of blood cell development at which the first cancer cell arises. In our study, we build a stochastic model based on a biological hematopoiesis model that represents how blood cells grow and mature through many stages, from stem cells to fully developed white blood cells. Using this mathematical model, we develop a statistical approach that can infer from patient data where in this hierarchy the disease most likely began. When we apply the method to clinical data from patients with chronic myeloid leukemia, it consistently points to the stem cell stage as the most probable origin. By linking biological data with mathematical modelling, our work offers new insight into how this cancer starts and shows how quantitative approaches can help answer questions that are difficult to test experimentally.

systems biology↗

bioRxiv2022

Diverse photoperiodic gene expression patterns are likely mediated by distinct transcriptional systems in Arabidopsis

Photoperiod is an annual cue measured by biological systems to align growth and reproduction with the seasons. In plants, photoperiodic flowering has been intensively studied for over 100 years, but we lack a complete picture of the transcriptional networks and cellular processes that are photoperiodic. We performed a transcriptomics experiment on Arabidopsis plants grown in 3 different photoperiods, and find that nearly one-third of the known genes show photoperiodic alteration in gene expression. Gene clustering, daily expression integral calculations and cis-element analysis then separate photoperiodic genes into co-expression subgroups that display 19 diverse seasonal expression patterns, opening the possibility that many photoperiod measurement systems work in parallel in Arabidopsis. Then, functional enrichment analysis predicts co-expression of important cellular pathways. To test these predictions, we generated a comprehensive catalog of genes in the phenylpropanoid biosynthesis pathway, overlaid gene expression data and demonstrated that photoperiod intersects with the two major phenylpropanoid pathways differentially, controlling flavonoids but not lignin. Finally, we describe the development of a new app that visualizes photoperiod transcriptomic data for the wider community.

plant biology↗

bioRxiv2020

hu.MAP 2.0: Integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies

A general principle of biology is the self-assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack a comprehensive set of protein complexes for human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 259 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface (http://humap2.proteincomplexes.org/), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease.

systems biology↗

bioRxiv2019

FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

SummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.\n\nMotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.\n\nResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.\n\nAvailability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT License.\n\nContactnielsenj@chalmers.se\n\nSupplementary informationSupplementary data are available online.

systems biology↗

bioRxiv2020

Interpretation of network-based integration from multi-omics longitudinal data

Cost reduction of high-throughput technologies has enabled the monitoring of the same biological sample across multiple omics studies and multiple timepoints. The goal is to combine longitudinal multi-omics data to detect temporal relationships between molecules and interactions between omics layers. This can finally lead to uncover new regulation mechanisms and interactions that could be responsible for causing complex phenotype or disease. However multi-omics integration of diverse omics data is still challenging due to heterogeneous data and designs. Moreover, interpretation of multi-omics models is the key to understand biological systems. We propose a generic analytic and integration framework for multi-omics longitudinal datasets that consists of multi-omics kinetic clustering and multi-layer network-based analysis. This frame-work was successfully applied to two case studies with different experimental designs and omics data collected. The first case studied transcriptomic and proteomic changes during cell cycle in human HeLa cells, while the second focused on maize transcriptomic and metabolomic response to aphid feeding. Propagation analysis on multi-layer networks identifies regulatory mechanisms and function prediction for both case studies. Our framework has led to the identification of new multi-layer interactions involved in key biological functions that cannot be revealed with single omics analysis and interplay in the kinetics that could help identify novel biological mechanisms.

systems biology↗

bioRxiv2022

Data-Driven Flow-Map Models for Data-Efficient Discovery of Dynamics and Fast Uncertainty Quantification of Biological and Biochemical Systems

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-toevaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a co-culture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.

systems biology↗

bioRxiv2019

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line

Data-driven mathematical modeling of biological systems has enormous potential to understand and predict the interplay between molecular and phenotypic response to perturbation, and provides a rational approach to the nomination of therapies in complex diseases such as cancer. Melanoma is a particularly debilitating disease for which most therapies eventually fail as resistance to chemotherapy and targeted drugs develop. We have previously applied an iterative experimental-computational modeling approach, termed perturbation biology, to predict and test effective drug combinations in melanoma cell lines [1].\n\nIn this work, we extend our analysis framework to derive models of temporally-acquired perturbation data that do not require prior knowledge and explicit specification of the targets of individual drugs. Specifically, we characterize the response of the melanoma cell line A2058 to 54 cancer drug combinations at 8 logarithmically spaced time points from 10 minutes to 67 hours. At each time point, 124 antibodies of proteins and phospho-proteins with broad coverage of cancer-related pathways and two phenotypes (cell number and apoptosis) were measured. These data are used to infer interactions in ordinary differential equation-based models that capture temporal aspects of the drug perturbation data. This network representation of drug-protein, protein-protein, and protein-phenotype interactions can be used to identify new logical (not necessarily direct biochemical) interactions. The agreement between the predicted phenotypic response and corresponding data for unseen drug perturbations has a Pearsons correlation coefficient of 0.79. We further use model predictions to nominate effective combination therapies and perform experimental validation of the highest ranked combinations.\n\nThis new data-driven modeling framework is a step forward in perturbation biology as it incorporates the temporal aspect of data. This work therefore opens the door to a new understanding of dynamic drug responses at a molecular level.

systems biology↗

bioRxiv2020

Chameleon microRNAs in breast cancer: their elusive role as regulatory factors in cancer progression

Breast cancer is one of the most frequent cancers affecting women. Non-coding micro RNAs (miRNAs) seem to play an important role in the regulation of pathways involved in tumor occurrence and progression. Extending on the research in Haakensen et al., where significant miRNAs were selected as being associated with the progression from normal breast tissue to breast cancer, in this work we put forward 112 sets of miRNA combinations, each including at most 5 expressions with high accuracy in discriminating healthy breast tissue from breast carcinoma. Our results are based on a recently developed machine learning technique which, instead of selecting a single model (or combination of features), delivers a set of models with equivalent predictive capabilities that allow to interpret and visualize the interaction of these features. These results shed new light on the biological action of the selected miRNAs which can behave in different ways according to the miRNA network with which they interact. Indeed, these revealed connections may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA. It is therefore possible to understand how the role of a genomic variable may change when considered in interaction with other sets of variables, as opposed to only considering its effect when it is evaluated within a unique combination of features. The approach proposed in this work provides a statistical basis for the notion of chameleon miRNAs and is inspired by the emerging field of systems biology. Author SummaryO_LIThe notion of a single predictive genomic (statistical) model is replaced by that of a set of models that can be considered as exchangeable due to their indistinguishable (optimal) predictive abilities; C_LIO_LIOur results indicate that the role of miRNAs cannot be interpreted independently from the combination of features with which they interact and can therefore vary considerably when considered in a network of different combinations. Some miRNAs may act as chameleons and behave in opposite manners thereby showing some kind of antagonistic duality; C_LIO_LISome miRNAs are exchangeable inside models with equivalent predictive ability and seem to point to latent biological functions. C_LI

systems biology↗

bioRxiv2020

Synthetic data generation with probabilistic Bayesian Networks

Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The latter is arguably the most comprehensive approach; however, existing implementations are typically limited by their reliance on the SEM (structural equation modeling) framework, which includes many explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that more appropriately fits with real biological data and biological network models. In conjunction, we also expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.

systems biology↗

bioRxiv2022

Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo Tree Search

1MotivationMany important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search (MCTS), an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models. ResultsWe demonstrate that MC-Boomer works well at reconstructing randomly generated models. Then, using single time point measurements and reasonable biological constraints, our method generates hundreds of thousands of candidate models that match experimentally validated in-vivo behaviors of the Drosophila segment polarity network. Finally we outline how our multimodel analysis procedures elucidate potentially novel biological mechanisms and provide opportunities for model-driven experimental validation. AvailabilityCode is available at: www.github.com/bglazer/mcboomer

systems biology↗

bioRxiv2026

Serial enrichment based quantitative proteomics enables deep and multiplexed profiling of post-translational modifications

Post-translational modifications (PTMs) play central roles in regulating protein function, localization, stability, and signaling networks. Comprehensive characterization of multiple PTMs, however, remains technically challenging due to limited sample availability, enrichment, incompatibilities, and analytical complexity. Here, we present an integrated serial enrichment workflow that enables quantitative profiling of multiple PTMs from a single biological sample. Using a streamlined strategy that combines StageTip-based fractionation with sequential immunoaffinity and metal oxide affinity enrichment, we achieved robust identification and quantification of lysine acetylation, lysine succinylation, phosphotyrosine, and global phosphorylation within the same sample. Application of this workflow resulted in deep proteome coverage alongside high-confidence PTM site identification with minimal sample loss and high reproducibility. Importantly, serial enrichment preserved PTM specificity and enabled comparative quantitative analysis across modification types. This approach provides a practical and scalable solution for integrated multi-PTM analysis, facilitating comprehensive interrogation of proteome regulation under limited sample conditions and offering broad applicability to systems biology, disease profiling, and translational proteomics studies.

systems biology↗

bioRxiv2020

Implementation of a Computing Pipeline for Evaluating the Extensibility of Boolean Networks' Structure and Function

Formal interaction networks are well suited for representing complex biological systems and have been used to model signalling pathways, gene regulatory networks, interaction within ecosystems, etc. In this paper, we introduce Sign Boolean Networks (SBNs), which are a uniform variant of Threshold Boolean Networks (TBFs). We continue the study of the complexity of SBNs and build a new framework for evaluating their ability to extend, i.e. the potential to gain new functions by addition of nodes, while also maintaining the original functions. We describe our software implementation of this framework and show some first results. These results seem to confirm the conjecture that networks of moderate complexity are the most able to grow, because they are not too simple, but also not too constrained, like the highly complex ones. Biological Regulation, Biological Networks, Sign Boolean Networks, Complexity, Extensibility, Network Growth

systems biology↗

bioRxiv2021

Effect of aqueous extract of Antiaris africana stem bark on phagocytic activities of human neutrophils ex-vivo.

The immune system is one of the most complex biological systems in the body. During infection, the immune system is under attack by a large number of viruses, bacteria, fungi and parasites. Immune response firstly involves, recognition of the pathogen or foreign object and secondly, a reaction to eliminate it. Aqueous extract of Antiaris africana is used to study their immune modulator activity. This plant has its various parts used in folk medicines. The mechanism of action has not been fully elucidated. Therefore, this research work studies the effect of aqueous extract of Antiaris Africana stem bark on phagocytic activities of neutrophil isolated from apparently healthy individual using a non-subjective commercial colourimetric assay kit obtained from Cell-Biolab Inc., USA. The purity and viability of isolated neutrophils were >90% and >95% respectively. The extract enhances neutrophil phagocytosis at 1.0, 5.0, 10.0, 15.0, 20.0 and 25 g/ml by 2.5%, 11.6%, 18.4%, 24.4%, 31.2% and 38.2% respectively, compared to the control (100%). Hence, it was observed that neutrophil phagocytosis increases with increased extract concentrations. It can be concluded from the study that enhancement of phagocytosis may be the possible mechanism of action of the plant as an immune modulator.

immunology↗

bioRxiv2022

Quadratic computations maintain neural specificity to natural stimuli across stages of visual processing

Despite recent successes in machine vision, artificial recognition systems continue to be less robust than biological systems. The brittleness of artificial recognition system has been attributed to the linearity of the core operation that matches inputs to target patterns at each stage of the system. Here we analyze responses of neurons from the visual areas V1, V2, and V4 of the brain using the framework that incorporates quadratic computations into multi-stage models. These quadratic computations make it possible to capture local recurrent computation, and in particular, nonlinear suppressive interactions between visual features. We find that incorporating quadratic computation not only strongly improved predictive power of the resulting model, but also revealed several computation motifs that increased the selectivity of neural responses to natural stimuli. These motifs included the organization of excitatory and suppressive features along mutually exclusive hypotheses about incoming stimuli, such as orthogonal orientations or opposing motion directions. The balance between excitatory and suppressive features was largely maintained across brain regions. These results emphasize the importance and properties of quadratic computations that are necessary for achieving robust object recognition.

neuroscience↗

bioRxiv2023

Emerging dynamic regimes and tipping points from finite empirical principles

The dynamics of biogeochemical, ecological, and astronomical systems are transient. Yet, predicting the occurrence of dynamical shifts remains a challenge due to inferential uncertainties from datasets and the limitations of asymptotic-dependent theories. To address this problem, we developed a theoretical framework that builds on the finite nature of observations. This framework assesses the relative importance of processes, defined as the mechanisms that contribute to the rate of change of the systems dynamic variables, and it predicts the critical values that would trigger a shift into a new regime. The number of observable dynamic regimes within the framework increases exponentially with the number of processes. Observers, however, only experience dynamic regimes associated with relevant processes-- those exceeding a tipping point--within their reference framework. A case study of the framework was tested for a classic predator-prey system with four processes parameterized for bacteria (prey) and lytic bacteriophages (predator). The analysis recovered the sixteen dynamic regimes predicted by the framework, including two non-trivial quasi-equilibrium dynamics. An adaptive Boolean model, which used only relevant observable processes, validated the accuracy of the framework, recovering the dynamics of the full model. The observational framework introduced here provides a strategy for identifying the processes and conditions that lead to tipping points, representing a conceptual paradigm shift in transient dynamics, placing the focus on the specific, finite context of the observer, rather than the intrinsic, asymptotic states of the system. SIGNIFICANCESudden shifts in ecological, climate, and biological systems--so-called tipping points or critical transitions--are notoriously difficult to predict. This study introduces a mathematical framework that redefines these transitions as outcomes shaped by the observers empirical limits. By accounting for finite observation time and resolution, the framework uncovers a rich spectrum of dynamic regimes that classical theories overlook. Its conceptual rigor and practical value are demonstrated in a predator-prey system. This new approach reframes how to forecast regime shifts in complex systems and offers a tool with broad relevance, from microbial ecosystems to planetary climate.

ecology↗

bioRxiv2024

Super-Resolution Simplified: Sub-10nm Imaging Over Large Areas and Deep Penetration via SDC-OPR and DNA-PAINT

Single Molecule Localization Microscopy (SMLM) has traditionally faced challenges to optimize signal-to-noise ratio, penetration depth, field-of-view (FOV), and spatial resolution simultaneously. Here, we show that DNA-PAINT imaging on a Spinning Disk Confocal with Optical Photon Reassignment (SDC-OPR) system overcomes these trade-offs, enabling high-resolution imaging across multiple cellular layers and large FOVs. We demonstrate the systems capability with DNA origami constructs and biological samples, including nuclear pore complexes, mitochondria, and microtubules, achieving a spatial resolution of 6 nm in the basal plane and sub-10 nm localization precision at depths of 9 {micro}m within a 53 x 53 {micro}m{superscript 2} FOV. Additionally, imaging of the developing Drosophila eye epithelium at depths up to 9 {micro}m with sub-13 nm average localization precision, reveals distinct E-cadherin populations in adherens junctions. Quantitative analysis of Collagen IV deposition in this epithelium indicated an average of 46 {+/-} 27 molecules per secretory vesicle. These results underscore the versatility of DNA-PAINT on an SDC-OPR for advancing super-resolution imaging in complex biological systems.

biophysics↗

bioRxiv2024

Curating models from BioModels: Developing a workflow for creating OMEX files

The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the reproducibility procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.

systems biology↗