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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Ratiometric Distribution Of OCT4 During Stem Cell Division Controls The Balance Between Self-Renewal And Differentiation

Clonal cells can make different fate decisions, but it is often unclear to what extent these decisions are autonomous or predetermined. Here, we introduce a live-cell reporter for the pluripotency factor OCT4 into human embryonic stem cells to understand how they choose between self-renewal and differentiation. By tracing the histories of individual cells over multiple generations, we found that cells whose offspring were destined to differentiate showed decreased expression of OCT4 long before exposure to the differentiation stimulus. OCT4 levels were lineage-dependent; however, during cell division, mother cells distributed OCT4 asymmetrically to daughters. The resulting ratio of OCT4 between sister cells--established within minutes of mitosis--was predictive of downstream fates: cells receiving a greater ratio of maternal OCT4 showed sustained OCT4 levels and a reduced capacity to differentiate. Our observations imply that the choice between two developmental fates is almost entirely predetermined at the time of cell birth through inheritance of a pluripotency factor.

systems biology↗

bioRxiv2017

DNA Damage Checkpoint Dynamics Drive Cell Cycle Phase Transitions

DNA damage checkpoints are cellular mechanisms that protect the integrity of the genome during cell cycle progression. In response to genotoxic stress, these checkpoints halt cell cycle progression until the damage is repaired, allowing cells enough time to recover from damage before resuming normal proliferation. Here, we investigate the temporal dynamics of DNA damage checkpoints in individual proliferating cells by observing cell cycle phase transitions following acute DNA damage. We find that in gap phases (G1 and G2), DNA damage triggers an abrupt halt to cell cycle progression in which the duration of arrest correlates with the severity of damage. However, cells that have already progressed beyond a proposed \"commitment point\" within a given cell cycle phase readily transition to the next phase, revealing a relaxation of checkpoint stringency during later stages of certain cell cycle phases. In contrast to G1 and G2, cell cycle progression in S phase is significantly less sensitive to DNA damage. Instead of exhibiting a complete halt, we find that increasing DNA damage doses leads to decreased rates of S-phase progression followed by arrest in the subsequent G2. Moreover, these phase-specific differences in DNA damage checkpoint dynamics are associated with corresponding differences in the proportions of irreversibly arrested cells. Thus, the precise timing of DNA damage determines the sensitivity, rate of cell cycle progression, and functional outcomes for damaged cells. These findings should inform our understanding of cell fate decisions after treatment with common cancer therapeutics such as genotoxins or spindle poisons, which often target cells in a specific cell cycle phase.

systems biology↗

bioRxiv2017

Maximum Entropy Framework For Inference Of Cell Population Heterogeneity In Signaling Networks

Predictive models of signaling networks are essential tools for understanding cell population heterogeneity and designing rational interventions in disease. However, using network models to predict signaling dynamics heterogeneity is often challenging due to the extensive variability of signaling parameters across cell populations. Here, we describe a Maximum Entropy-based fRamework for Inference of heterogeneity in Dynamics of sIgAling Networks (MERIDIAN). MERIDIAN allows us to estimate the joint probability distribution over signaling parameters that is consistent with experimentally observed cell-to-cell variability in abundances of network species. We apply the developed approach to investigate the heterogeneity in the signaling network activated by the epidermal growth factor (EGF) and leading to phosphorylation of protein kinase B (Akt). Using the inferred parameter distribution, we also predict heterogeneity of phosphorylated Akt levels and the distribution of EGF receptor abundance hours after EGF stimulation. We discuss how MERIDIAN can be generalized and applied to problems beyond modeling of heterogeneous signaling dynamics.

systems biology↗

bioRxiv2017

A Model For Adult Organ Resizing Demonstrates Stem Cell Scaling Through A Tunable Commitment Rate

Many adult organs grow or shrink to accommodate different physiological demands. Often, as total cell number changes, stem cell number changes proportionally in a phenomenon called stem cell scaling. The cellular behaviors that give rise to scaling are unknown. Here we study two complementary theoretical models of the adult Drosophila midgut, a stem cell-based organ with known resizing dynamics. First, we derive a differential equations model of midgut resizing and show that the in vivo kinetics of growth can be recapitulated if the rate of fate commitment depends on the tissues stem cell proportion. Second, we develop a twodimensional simulation of the midgut and find that proportion-dependent commitment rate and stem cell scaling can arise phenomenologically from the stem cells exploration of physical tissue space during its lifetime. Together, these models provide a biophysical understanding of how stem cell scaling is maintained during organ growth and shrinkage.

systems biology↗

bioRxiv2017

Cellular Adaptation Through Fitness-Directed Transcriptional Tuning

Cells adapt to changes in their environment through transcriptional responses that are hard-coded in their regulatory networks. Such dedicated pathways, however, may be inadequate for adaptation to novel or extreme environments. We propose the existence of a fitness optimization mechanism that tunes the global transcriptional output of a genome to match arbitrary external conditions in the absence of dedicated gene-regulatory networks. We provide evidence for the proposed tuning mechanism in the adaptation of Saccharomyces cerevisiae to laboratory-engineered environments that are foreign to its native gene-regulatory network. We show that transcriptional tuning operates locally at individual gene promoters and its efficacy is modulated by genetic perturbations to chromatin modification machinery.

systems biology↗

bioRxiv2017

Integrating Planar Polarity And Tissue Mechanics In Computational Models Of Epithelial Morphogenesis

Cells in many epithelial tissues are polarised orthogonally to their apicobasal axis. Such planar polarity ensures that tissue shape and structure are properly organised. Disruption of planar polarity can result in developmental defects such as failed neural tube closure and cleft palette. Recent advances in molecular and live-imaging techniques have implicated both secreted morphogens and mechanical forces as orienting cues for planar polarisation. Components of planar polarity pathways act upstream of cytoskeletal effectors, which can alter cell mechanics in a polarised manner. The study of cell polarisation thus provides a system for dissecting the interplay between chemical and mechanical signals in development. Here, we discuss how different computational models have contributed to our understanding of the mechanisms underlying planar polarity in animal tissues, focusing on recent efforts to integrate cell signalling and tissue mechanics. We conclude by discussing ways in which computational models could be improved to further our understanding of how planar polarity and tissue mechanics are coordinated during development.

systems biology↗

bioRxiv2017

An Effective Model Of The Retinoic Acid Induced HL-60 Differentiation Program

In this study, we present an effective model All-Trans Retinoic Acid (ATRA)-induced differentiation of HL-60 cells. The model describes reinforcing feedback between an ATRA-inducible signalsome complex involving many proteins including Vav1, a guanine nucleotide exchange factor, and the activation of the mitogen activated protein kinase (MAPK) cascade. We decomposed the effective model into three modules; a signal initiation module that sensed and transformed an ATRA signal into program activation signals; a signal integration module that controlled the expression of upstream transcription factors; and a phenotype module which encoded the expression of functional differentiation markers from the ATRA-inducible transcription factors. We identified an ensemble of effective model parameters using measurements taken from ATRA-induced HL-60 cells. Using these parameters, model analysis predicted that MAPK activation was bistable as a function of ATRA exposure. Conformational experiments supported ATRA-induced bistability. Additionally, the model captured intermediate and phenotypic gene expression data. Knockout analysis suggested Gfi-1 and PPAR$[gamma] were critical to the ATRA-induced differentiation program. These findings, combined with other literature evidence, suggested that reinforcing feedback is central to hyperactive signaling in a diversity of cell fate programs.

systems biology↗

bioRxiv2017

Untargeted Metabolomic Analysis In Naturally Occurring Canine Diabetes Mellitus Identifies Similarities To Human Type 1 Diabetes

While predominant as a disease entity, knowledge voids exist regarding the pathogenesis of canine diabetes. To test the hypothesis that diabetic dogs have similar metabolomic perturbations to humans with type 1 diabetes (T1D), we analyzed serum metabolomic profiles of breed- and body weight-matched, diabetic (n=6) and healthy (n=6) dogs by liquid chromatography-mass spectrometry (LC-MS) profiling. We report distinct clustering of diabetic and control groups based on heat map analysis of known and unknown metabolites. Random forest classification identified 5/6 dogs per group correctly with overall out of bag error rate=16.7%. Diabetic dogs demonstrated significant upregulation of glycolysis/gluconeogenesis intermediates (e.g., glucose/fructose, C6H12O6, keto-hexose, deoxy-hexose, (P<0.01)), with significant downregulation of tryptophan metabolism metabolites (e.g., picolinic acid, indoxyl sulfate, anthranilate, (P<0.01)). Multiple amino acids (AA), AA metabolites, and bile acids were also significantly lower in diabetic versus healthy dogs (P<0.05) with the exception of the branched chain AA valine, which was elevated in diabetic animals (P<0.05). Metabolomic profiles in diabetic versus healthy dogs shared similarities with those reported in human T1D (e.g., alterations in glycolysis/gluconeogensis metabolites, bile acids, and elevated branched chain AA). Further studies are warranted to evaluate the utility of canine diabetes to provide novel mechanistic insights to the human disorder.\n\nAbbreviations

systems biology↗

bioRxiv2017

Expanding The Computable Reactome In Pseudomonas putida Reveals Metabolic Cycles Providing Robustness

Genome-scale network reconstructions are organism-specific representations of metabolism and powerful tools for analyzing systemic metabolic properties. The use of reconstructions is limited by the lack of coverage of the metabolic reactome. We present an exhaustive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440, greatly expanding its computable metabolic states. The reconstruction, iJN1411, represents a significant expansion over other reconstructed bacterial metabolic networks. Computations based on the reconstruction exhibit high accuracy in predicting nutrient sources, growth rates, carbon flux distributions, and gene essentiality, thus providing a deep understanding of Pseudomonas metabolism. iJN1411 was used for: i) the assessment of the metabolic capabilities of P. putida as a species through multi-strain modeling, ii) deciphering the molecular mechanisms underlying metabolic robustness, and iii) identification of metabolic \"capacitors\" based on ATP-fueled metabolic cycles. This study represents the most complete and comprehensive bacterial metabolic reconstruction built to date, while providing computational and experimental evidence about how bacteria increase metabolic robustness, paving the way for engineering more robust biocatalysts and searching for drug targets in robust pathogens.

systems biology↗

bioRxiv2017

A Systems-Level Framework For Drug Discovery Identifies Csf1R As A Novel Anti-Epileptic Drug Target

The identification of mechanistically novel drug targets is highly challenging, particularly for diseases of the central nervous system. To address this problem we developed and experimentally validated a new computational approach to drug target identification that combines gene-regulatory information with a causal reasoning framework (\"causal reasoning analytical framework for target discovery\" - CRAFT). Starting from gene expression data, CRAFT provides a predictive functional genomics framework for identifying membrane receptors with a direction-specified influence over network expression. As proof-of-concept we applied CRAFT to epilepsy, and predicted the tyrosine kinase receptor Csf1R as a novel therapeutic target for epilepsy. The predicted therapeutic effect of Csf1R blockade was validated in two pre-clinical models of epilepsy using a small molecule inhibitor of Csf1R. These results suggest Csf1R blockade as a novel therapeutic strategy in epilepsy, and highlight CRAFT as a systems-level framework for predicting mechanistically new drugs and targets. CRAFT is applicable to disease settings other than epilepsy.

systems biology↗

bioRxiv2017

Assigning Function To Natural Allelic Variation Via Dynamic Modeling Of Gene Network Induction

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be personalized according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

systems biology↗

bioRxiv2017

MitoCore: A Curated Constraint-Based Model For Simulating Human Central Metabolism

BackgroundThe complexity of metabolic networks can make the origin and impact of profound changes in central metabolism occurring during disease difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many current models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localization of reactions and transport steps, incorrect reaction parameters, and confounding of prosthetic groups and free metabolites in reactions. Other common drawbacks are due to their scale, such as being difficult to parameterise and simulation results being hard to interpret. Therefore, it remains important to develop smaller, manually curated models to represent central metabolism accurately.\n\nResultsWe present MitoCore, a manually curated constraint-based computer model of human metabolism that incorporates the complexity of central metabolism and simulates this metabolism successfully under normal and abnormal conditions, including hypoxia and mitochondrial diseases. MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy data interpretation. Its key innovations include accurate partitioning of metabolism between cytosol and mitochondrial matrix; correct modelling of connecting transport steps; proper differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCores default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG. These innovations--including over 100 reactions absent or modified from Recon 2--are essential to model central metabolism accurately.\n\nConclusionWe anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret data and generate further hypotheses, to experienced modellers predicting the consequences of disease or using computationally intensive methods that are infeasible with larger models, as well as a teaching tool for those new to modelling and needing a small manageable model on which to learn and experiment.

systems biology↗

bioRxiv2017

Sepsis Reconsidered: Identifying Novel Metrics For Behavioral Landscape Characterization With A High-Performance Computing Implementation Of An Agent-Based Model

Objectives: Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28-50m% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary conditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation. Design: Current predictive models for sepsis use correlative methods are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods. Results: The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days for the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic regions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis. Conclusions: HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish \"boundaries of futility\" with respect to existing investigatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. The issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the \"Crisis of Reproducibility\" at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time).

systems biology↗

bioRxiv2017

In Situ Genotyping Of A Pooled Strain Library After Characterizing Complex Phenotypes

So far, it has not been possible to perform advanced microscopy on pool generated strain libraries and at the same time know each strains genotype. We have overcome this barrier by identifying the genotypes for individual cells in situ after a detailed characterization of the phenotype. The principle is demonstrated by single molecule fluorescence imaging of E. coli strains harboring barcoded plasmids that express a sgRNA which suppress different genes through dCas9.

systems biology↗

bioRxiv2017

Predicting Ligand-Dependent Tumors From Multi-Dimensional Signaling Features

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using a novel approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.\n\nSummaryPrediction of ligand-induced growth of cancer cell lines, which correlates with ligand-blocking antibody efficacy, could be significantly improved by learning from features of a mechanistic signaling model, and was applied to reveal a correlation between growth factor expression and predicted response in patient samples.

systems biology↗

bioRxiv2017

Optimal Number Of Spacers In CRISPR Arrays

We estimate the number of spacers in a CRISPR array of a bacterium which maximizes its protection against a viral attack. The optimality follows from a competition between two trends: too few distinct spacers make the bacteria vulnerable to an attack by a virus with mutated corresponding protospacers, while an excessive variety of spacers dilutes the number of the CRISPR complexes armed with the most recent and thus most effective spacers. We first evaluate the optimal number of spacers in a simple scenario of an infection by a single viral species and later consider a more general case of multiple viral species. We find that depending on such parameters as the concentration of CRISPR-CAS interference complexes and its preference to arm with more recently acquired spacers, the rate of viral mutation, and the number of viral species, the predicted optimal array length lies within a range quite reasonable from the viewpoint of recent experiments.\n\nAuthor summaryCRISPR-Cas system is an adaptive immunity defense in bacteria and archaea against viruses. It works by accumulating in bacterial genome an array of spacers, or fragments of virus DNA from previous attacks. By matching spacers to corresponding parts of virus DNA called protospacers, CRISPR-Cas system identifies and destroys intruder DNA. Here we theoretically estimate the number of spacers that maximizes bacterial survival. This optimum emerges from a competition between two trends: More spacers allow a bacterium to hedge against mutations in viral protospacers. However, keeping too many spacers makes the older ones inefficient because of accumulation of mutations in corresponding protospacers in viruses. Thus, fewer CRISPR-Cas molecular machines are left armed with more efficient young spacers. We have shown that a higher efficiency of CRISPR-Cas system allows a bacterium to utilize more spacers, increasing the optimal array length. On contrary, a higher viral mutation rate makes older spacers useless and favors shorter arrays. A higher diversity in viral species reduces the efficiency of CRISPR-Cas but does not necessary lead to longer arrays. We think that our study provides a new viewpoint at a huge variety in the observed array lengths and adds relevance to evolutionary models of bacterial-phage coexistence.

systems biology↗

bioRxiv2017

Transcription Factors Recognize DNA Shape Without Nucleotide Recognition

We hypothesized that transcription factors (TFs) recognize DNA shape without nucleotide sequence recognition. Motivating an independent role for shape, many TF binding sites lack a sequence-motif, DNA shape adds specificity to sequence-motifs, and different sequences can encode similar shapes. We therefore asked if binding sites of a TF are enriched for specific patterns of DNA shape-features, e.g., helical twist. We developed ShapeMF, which discovers these shape-motifs de novo without taking sequence information into account. We find that most TFs assayed in ENCODE have shape-motifs and bind regulatory regions recognizing shape-motifs in the absence of sequence-motifs. When shape- and sequence-recognition co-occur, the two types of motifs can be overlapping, flanking, or separated by consistent spacing. Shape-motifs are prevalent in regions co-bound by multiple TFs. Finally, TFs with identical sequence motifs have different shape-motifs, explaining their binding at distinct locations. These results establish shape-motifs as drivers of TF-DNA recognition complementary to sequence-motifs.

systems biology↗

bioRxiv2018

Different alleles of the same gene vary in pleiotropy, often mediated through currency metabolite production

A major obstacle to the mapping of genotype-phenotype relationships is pleiotropy, the tendency of mutations to affect seemingly unrelated traits. Pleiotropy has major implications for evolution, development, ageing, and disease. Except for disease data, pleiotropy is almost exclusively estimated from full gene knockouts. However, most deleterious alleles segregating in natural populations do not fully abolish gene function, and the degree to which a polymorphism reduces protein function may influence the number of traits it affects. Utilizing genome-scale metabolic models for Escherichia coli and the bakers yeast Saccharomyces cerevisiae, we show that most fitness-reducing full gene knockouts of metabolic genes have pleiotropic effects, i.e., they compromise the production of multiple biomass components. Alleles of the same gene with increasingly reduced enzyme function typically affect an increasing number of biomass components. This increasing pleiotropy is often mediated through effects on the generation of currency metabolites such as ATP or NADPH. We conclude that the physiological effects observed in full gene knockouts will in most cases not be representative for alleles with only partially reduced enzyme capacity or expression level.

systems biology↗