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bioRxiv2018

An In Silico Model of DNA Repair for Investigation of Mechanisms in Non-Homologous End Joining

In human cells, non-homologous end joining is the preferred process to repair radiation induced DNA double strand breaks. The complex nature of such biological systems involves many individual actions that combine to produce an overall behaviour. As such, experimentally determining the mechanisms involved, their individual roles, and how they interact is challenging. An in silico approach to radiobiology is uniquely suited for detailed exploration of these complex interactions and the unknown effects of specific mechanisms on overall behaviour. We detail the construction of a mechanistic model by combination of several, experimentally supported, hypothesised mechanisms. Compatibility of these mechanisms was tested by fitting to results reported in the literature. To avoid over fitting, individual mechanisms within this pathway were sequentially fitted. We demonstrate that using this approach the model is capable of reproducing published protein kinetics and overall repair trends. This process highlighted specific biological mechanisms which are not clearly defined experimentally, and showed that the assumed motion of individual double strand break ends plays a crucial role in determining overall system behaviour.

systems biology↗

bioRxiv2023

Machine Learning identifies conserved traits that influence lifespan and healthspan responses to dietary restriction

Dietary restriction (DR) is a potent method to enhance lifespan and healthspan, but individual responses are influenced by genetic variations. Understanding how metabolism-related genetic differences impact longevity and healthspan are unclear. To investigate this, we used metabolites as markers to reveal how different genotypes respond to diet to influence longevity and healthspan traits. We analyzed data from Drosophila Genetic Reference Panel strains raised under AL and DR conditions, combining metabolomic, phenotypic, and genome-wide information. Employing two computational methods across species--random forest modeling within the DGRP and Mendelian randomization in the UK Biobank--we pinpointed key traits with cross-species relevance that influence lifespan and healthspan. Notably, orotate was linked to parental age at death in humans and counteracted DR effects in flies, while threonine extended lifespan, in a strain- and sex-specific manner. Thus, utilizing natural genetic variation data from flies and humans, we employed a systems biology approach to elucidate potential therapeutic pathways and metabolomic targets for diet-dependent changes in lifespan and healthspan.

systems biology↗

bioRxiv2020

Overcoming sparseness of biomedical networks to identify drug repositioning candidates

Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing requires a thorough exploration of diverse biochemical concepts and their relationships, including drugs adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs the compressed sensing technique to overcome the sparseness of biomedical data and, in turn, to enrich the set of verified relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The data and programs are freely available at http://bioinfo.cs.uni.edu/AEONET.html.

systems biology↗

bioRxiv2025

Nanoscale protein clustering modulates the input-output response of cellular signalling pathways

A key function of the plasma membrane is regulating signal transduction. Proteins involved in the transduction process play a signal-processing role; mapping an input (e.g. number of engaged receptors) to an output (e.g. level of downstream phosphorylation). In many cases, a digital mapping is desirable, i.e. that a cell activates or responds fully once a set input threshold is surpassed. It is believed that the nanoscale organisation of proteins, such as their clustering, modulates this behaviour by altering the frequency of protein-protein interactions. Here, we develop a generalised simulator for dynamic molecular clustering built around agent-based modelling. We show that the clustering properties (e.g. size of clusters, percentage of monomers) tunes the cellular response. This work paves the way for designing potential therapeutic interventions that alter the nanoscale patterning of molecules on the cell surface in order to alter cell behaviour. AUTHOR SUMMARYSignal transduction is the process by which cells convert external signals into internal responses. This is essential for such functions as hormone signalling and immunity. Many membrane proteins form nanoscale clusters on the cell surface, and state-of-the-art imaging methods allow us to see these clusters in detail. We developed an agent-based simulator to explore how clustering of key signaling proteins (such as kinases and phosphatases) can influence cell signalling outputs. Our results suggest that protein clustering can "digitize" signals, pushing cells to respond in an "all-or-nothing" manner, rather than producing a smooth range of responses. This research contributes to a broader understanding of how dynamic reorganization of transmembrane proteins contributes to information processing. This is a central question in systems biology, with relevance to the fields of immunology, neuroscience, and cancer. By highlighting how the geometric properties of protein distributions can alter signalling responses, we may identify novel methods to manipulate cell behavior.

systems biology↗

bioRxiv2016

Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism

BackgroundThe human gut contains approximately 1014 bacteria, belonging to hundreds of different species. Together, these microbial species form a complex food web that can break down food sources that our own digestive enzymes cannot handle, including complex polysaccharides, producing short chain fatty acids and additional metabolites, e.g., vitamin K. The diversity of microbial diversity is important for colonic health: Changes in the composition of the microbiota have been associated with inflammatory bowel disease, diabetes, obestity and Crohns disease, and make the microbiota more vulnerable to infestation by harmful species, e.g., Clostridium difficile. To get a grip on the controlling factors of microbial diversity in the gut, we here propose a multi-scale, spatiotemporal dynamic flux-balance analysis model to study the emergence of metabolic diversity in a spatial gut-like, tubular environment. The model features genome-scale metabolic models of microbial populations, resource sharing via extracellular metabolites, and spatial population dynamics and evolution.\n\nResultsIn this model, cross-feeding interactions emerge readily, despite the species ability to metabolize sugars autonomously. Interestingly, the community requires cross-feeding for producing a realistic set of short-chain fatty acids from an input of glucose, If we let the composition of the microbial subpopulations change during invasion of adjacent space, a complex and stratifed microbiota evolves, with subspecies specializing on cross-feeding interactions via a mechanism of compensated trait loss. The microbial diversity and stratification collapse if the flux through the gut is enhanced to mimic diarrhea.\n\nConclusionsIn conclusion, this in silico model is a helpful tool in systems biology to predict and explain the controlling factors of microbial diversity in the gut. It can be extended to include, e.g., complex food source, and host-microbiota interactions via the gut wall.

Systems Biology↗

bioRxiv2022

Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes

Due to insufficient insulin secretion, patients with type 1 diabetes mellitus (T1DM) are prone to blood glucose fluctuations ranging from hypoglycemia to hyperglycemia. While dangerous hypoglycemia may lead to coma immediately, chronic hyperglycemia increases patients risks for cardiorenal and vascular diseases in the long run. In principle, an artificial pancreas - a closed-loop insulin delivery system requiring patients manually input insulin dosage according to the upcoming meals - could supply exogenous insulin to control the glucose levels and hence reduce the risks from hyperglycemia. However, insulin overdosing in some type 1 diabetic patients, who are physically active, can lead to unexpected hypoglycemia beyond the control of common artificial pancreas. Therefore, it is important to take into account the glucose decrease due to physical exercise when designing the next-generation artificial pancreas. In this work, we develop a deep reinforcement learning algorithm using a T1DM dataset, containing data from wearable devices, to automate insulin dosing for patients with T1DM. In particular, we build patient-specific computational models using systems biology informed neural networks (SBINN), to mimic the glucose-insulin dynamics for a few patients from the dataset, by simultaneously considering patient-specific carbohydrate intake and physical exercise intensity.

systems biology↗

bioRxiv2026

Net conversion calculations of catabolic pathways

Motivation: The stoichiometry (or net conversion) of a catabolic pathway is an often used principle of biochemistry. It expresses the molar yield of charged energy carriers (e.g. ATP) and catabolic products (e.g. lactate) on the energy source (e.g. glucose). Product yields are engineering targets of metabolic engineering and used in microbial ecology to assess energy metabolisms of microbial species. For a single species under a single condition, the catabolic pathway is frequently assumed fixed, while there might be multiple options encoded in its genome. To find these options, the manual (heuristic) methods that have been used for decades fall short. Results: In this paper, we explain how net conversions can be calculated from reaction stoichiometries of a metabolic network and evaluated using thermodynamic information. We start with an (old) heuristic method. Next, we explain we relate the net conversions of metabolic networks to their elementary flux modes (EFMs) and show that a single EFM gives rise to a single net conversion. EFMs are mathematical objects that are computable with existing software. We use these to illustrate how all net conversions of complex (pan-)metabolic networks can be computed. We consider examples from aerobic and anaerobic microbiology. Then, we introduce the parameter {Omega}, the driving force per unit flux, which allows for the thermodynamic comparison of pathways. To calculate {Omega}, only the standard Gibbs free energy potential, i.e., {Delta} G m' of the net conversion and its corresponding EFM are required. A generic workflow (and all underlying Python code) are provided, as well as applications to perform the workflow without coding. We also provide those software packages that automate our methods. Impact: This paper serves as an illustration of how modern computational systems biology can be used to automate the calculation of net conversions in microbial ecology and metabolic engineering. We hope that this paper inspires future metabolism research using quantitative, rigorous methods.

systems biology↗

bioRxiv2022

Sloppiness: fundamental study, new formalism and quantification

Computational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection. Author summary

systems biology↗

bioRxiv2024

Systems-level reconstruction of kinase phosphosignaling networks regulating endothelial barrier integrity using temporal data

Phosphosignaling networks control cellular processes. We built kinase-mediated regulatory networks elicited by thrombin stimulation of brain endothelial cells using two computational strategies: Temporal Pathway Synthesizer (TPS), which uses phosphoproteomics data as input, and Temporally REsolved KInase Network Generation (TREKING), which uses kinase inhibitor screens. TPS and TREKING predicted overlapping barrier-regulatory kinases connected with unique network topology. Each strategy effectively describes regulatory signaling networks and is broadly applicable across biological systems.

systems biology↗

bioRxiv2022

The slowdown of growth rate controls the single-cell distribution of biofilm matrix production via a SinI-SinR-SlrR network

In Bacillus subtilis, a master regulator Spo0A controls several cell-differentiation pathways. Under moderate starvation, phosphorylated Spo0A (Spo0A~P) induces biofilm formation by indirectly activating genes controlling matrix production in a subpopulation of cells via a SinI-SinR-SlrR network. Under severe starvation, Spo0A~P induces sporulation by directly and indirectly regulating sporulation gene expression. However, what determines the heterogeneity of individual cell fates is not fully understood. In particular, it is still unclear why, despite being controlled by a single master regulator, biofilm matrix production and sporulation are mutually exclusive on a single-cell level. In this work, with mathematical modeling, we showed that the fluctuations in the growth rate and the intrinsic noise amplified by the bistability in the SinI-SinR-SlrR network could explain the single-cell distribution of matrix production. Moreover, we predicted an incoherent feed-forward loop: the decrease in the cellular growth rate activates matrix production by increasing in Spo0A phosphorylation level but represses it via changing the relative concentrations of SinR and SlrR. Experimental data provide evidence to support model predictions. In particular, we demonstrate how the degree to which matrix production and sporulation appear mutually exclusive is affected by genetic perturbations. ImportanceThe mechanisms of cell-fate decisions are fundamental to our understanding of multicellular organisms and bacterial communities. However, even for the best-studied model systems we still lack a complete picture of how phenotypic heterogeneity of genetically identical cells is controlled. Here, using B. subtilis as a model system, we employ a combination of mathematical modeling and experiments to explain the population-level dynamics and single-cell level heterogeneity of biofilm gene expression. The results demonstrate how the two cell fates, biofilm matrix production and sporulation, can appear mutually exclusive without explicitly inhibiting one another. Such a mechanism could be used in a wide range of other biological systems.

systems biology↗

bioRxiv2026

Hierarchical organization in sparse gene regulatory networks shapes structural coherence and emergent regulatory coordination

How large-scale regulatory coordination in biological systems emerges from local signed and directed interactions in sparse gene regulatory networks (GRNs) remains an unanswered fundamental question. We introduce the coherence matrix, a topology-based framework that captures the consistency of regulatory influence between gene pairs by integrating information across all direct and indirect paths. Analysis of synthetic networks reveals that structural coherence - a metric derived from the coherence matrix - dictates global coordination: while highly coherent motifs maintain aligned regulatory coordination across widely varying network sparsity values, motifs with low coherence allow such coordination only at biologically unrealistic sparsity values. Our investigation of six whole-organism GRNs and further analysis of synthetic networks highlighted that hierarchical organization in GRNs a dense middle layer enriched in feedback loops that mediates coordination between input and output layers - serves as a structural buffer to allow regulatory coordination even for sparse networks. Finally, comparison with Escherichia coli transcriptomic modules further shows that the coherence matrix accurately predicts the sign of coordinated gene contribution, emphasizing its biological application, while also serving as a unifying descriptor integrating local interactions and global network architecture to explain the emergent regulatory coordination.

systems biology↗

bioRxiv2020

PathExt: a general framework for path-based mining of omics-integrated biological networks

MotivationLarge scale transcriptomic data are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. ResultsWe provide a computational tool - PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the Top-Net, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (a) Mycobacterium tuberculosis (M.tb) response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (b) tissue-relevant genes and processes using transcriptomic data from GTEx (Genotype-Tissue Expression) for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls. AvailabilityThe source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt. Contactnchandra@iisc.ac.in, sridhar.hannenhalli@nih.gov

systems biology↗

bioRxiv2020

Evaluating the reproducibility of single-cell gene regulatory network inference algorithms

Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth. Here, we benchmark four single-cell network inference methods based on their reproducibility, i.e. their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis. GENIE3 results to be the most reproducible algorithm, independently from the single-cell sequencing platform, the cell type annotation system, the number of cells constituting the dataset, or the thresholding applied to the links of the inferred networks. In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

systems biology↗

bioRxiv2023

Convergent Transcriptomic Evidence Reveals the Dysfunctional Quantitative Mechanism of Synaptic Plasticity Control in ASD

A prominent endophenotype in Autism Spectrum Disorder (ASD) is synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigated the postsynaptic signal transduction network in glutamatergic neurons and integrated transcriptomics to unveil the malfunction of translation control. We devised an innovative and highly dependable pipeline to transform our acquired signal transduction network into a mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employed Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD. The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncovered distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons. Importantly, our work has introduced a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.

systems biology↗

bioRxiv2025

Sensitivity Analysis to Isolate the Effects of Proteases and Protease Inhibitors on Extracellular Matrix Turnover

Matrix metalloproteinases (MMPs) are a family of proteases that drive degradation of extracellular matrix (ECM) across many tissues. MMP activity is antagonized by tissue inhibitors of metalloproteinases (TIMPs), resulting in a complex multivariate system with many MMP isoforms and TIMP isoforms interacting across a network of biochemical reactions - each with their own distinct kinetic rates. This system complexity makes it very difficult to identify which specific molecules are most responsible for driving ECM turnover in vivo and therefore the most promising therapeutic targets. To help elucidate the specific roles of various MMP and TIMP isoforms, we present a computational systems biology model of collagen turnover capturing all possible interactions between type I collagen, four different MMP isoforms (MMP-1, -2, -8, and -9), and three different TIMP isoforms (TIMP-1, -2, and -4). We used dye-quenched fluorescent collagen to monitor the degradation of collagen in the presence of various MMP+TIMP cocktails, and we then used these experimental data to fit hypothetical reaction system topologies in order to investigate their respective accuracies. We determined kinetic rate constants for this system and used post-myocardial infarct time courses of collagen, MMP, and TIMP levels to perform a parameter sensitivity analysis across the model reaction rates and predict which molecules and interactions are the important regulators of ECM in the infarcted heart. Notably, the model suggested that MMP degradation and inactivation terms were more important for driving collagen levels than TIMP interaction terms. In sum, this work highlights the need for systems-level analyses to distinguish the roles of various biomolecules operating with a complex system, prioritizes therapeutic targets for post-infarct cardiac remodeling, and presents a computational framework that can be applied to many other collagen-rich tissues.

systems biology↗

bioRxiv2018

Systems analysis by mass cytometry identifies susceptibility of latent HIV-infected T cells to targeting of p38 and mTOR pathways

Efforts to cure HIV are hindered by viral persistence in latently infected memory CD4+ T cells. Targeting T cell death pathways dysregulated by HIV infection offers a novel approach for eradication of the latent reservoir. To identify potential therapeutic targets, we compared signaling and apoptosis in uninfected and latently infected primary cultured CD4+ central memory T cells by mass cytometry following T cell receptor stimulation. We found that HIV-infected cells were sensitized to activation of pro-apoptotic p38 kinase signaling via p53, and to inhibition of anti-apoptotic mTOR kinase signaling, even without HIV protein expression. Simultaneous targeting of p38 and mTOR kinases in resting CD4+ T cells from virally-suppressed HIV+ patients ex vivo reduced cell-associated HIV RNA and DNA. Our results demonstrate how systems biology approaches are useful for identifying novel therapeutic approaches to treat HIV latency, and further suggest that it may be possible to deplete latent HIV-infected T cells without viral reactivation.

systems biology↗

bioRxiv2019

AligNet: Alignment of Protein-Protein Interaction Networks

One of the most difficult problems difficult problem in systems biology is to discover protein-protein interactions as well as their associated functions. The analysis and alignment of protein-protein interaction networks (PPIN), which are the standard model to describe protein-protein interactions, has become a key ingredient to obtain functional orthologs as well as evolutionary conserved pathways and protein complexes. Several methods have been proposed to solve the PPIN alignment problem, aimed to match conserved subnetworks or functionally related proteins. However, the right balance between considering network topology and biological information is one of the most difficult and key points in any PPIN alignment algorithm which, unfortunately, remains unsolved. Therefore, in this work, we propose AligNet, a new method and software tool for the pairwise global alignment of PPIN that produces biologically meaningful alignments and more efficient computations than state-of-the-art methods and tools, by achieving a good balance between structural matching and protein function conservation as well as reasonable running times.

systems biology↗

bioRxiv2023

Scaling, correlations, and artifacts in biological timeseries

SO_SCPLOWUMMARYC_SCPLOWTime is a key variable in biology. Conceptual and methodological advances focused recent studies of timing in biological systems on monitoring trait dynamics in singled individuals. Here we contribute to this effort by analyzing general properties of individual timeseries. We make four broad claims. First, we show that for traits that range from behavior in animals, to growth in plants, to division timing in cell lineages, faster timeseries tend to be comprised of consistently shorter sub-stages, not a few unusually fast sub-stages. We demonstrate that this property constitutes a particular type of scaling that can be readily detected by a straightforward comparison of absolute and relative variability within timeseries data for any trait. We show that correlations within timeseries are necessary and sufficient for the type of scaling we describe and infer that the ubiquitous occurrence of scaling results from natural correlations within the continuous processes that govern trait dynamics. Second, failing to observe scaling indicates a break in correlation and, we argue, a switch in the mechanisms underlying the rate of trait change. Third, we caution that analyses of individual timeseries are highly susceptible to batch effects, but contend that it is possible to detect signal despite the artifactual correlations introduced by batching. Finally, we show that the timing of biological process that scale is overdispersed compared to matched but uncorrelated comparisons; this is an advantage in variable environments. Our results advance understanding of common properties of biological timeseries and offer practical tools for their further study.

systems biology↗