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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 703 records · Page 39Linked to original sources

bioRxiv2015

Micro-Pharmacodynamics: Bridging In Vitro and In Vivo Experimental Scales in Testing Drug Efficacy and Resistance

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue that can impede the transport of therapeutic agents to tumor cells in sufficient quantities. As a result, drugs that work well in vitro often fail at clinical trials when confronted with the complexities of interstitial transport within the tumor microenvironment. The microPD model that we developed is used to investigate the penetration of drug molecules through the tumor tissue and influenced by the physical and metabolic properties of tumor microenvironment, and how it affects drug efficacy and the emergence of drug resistance.

Systems Biology↗

bioRxiv2015

Mesoscopic oscillations in a single-gene circuit without delay

It is well known that single-gene circuits with negative feedback loop can lead to oscillatory gene expression when they operate with time delay. In order to generate these oscillations many processes can contribute to properly timing such delay. Here we show that the time delay coming from the transitions between internal states of the cis-regulatory system (CRS) can drive sustained oscillations in an auto-repressive single-gene circuit operating in a small volume like a cell. We found that the cooperative binding of repressor molecules is not mandatory for a oscillatory behavior if there are enough binding sites in the CRS. These oscillations depend on an adequate balance between the CRS kinetic, and the synthesis/degradation rates of repressor molecules. This finding suggest that the multi-site CRS architecture plays a key role for oscillatory behavior of gene expression.\n\nPACS numbers: 82.40.Bj,87.10.Mn,87.16.-b,87.16.Yc,87.18.Tt,87.18.Vf

Systems Biology↗

bioRxiv2015

Vectorization techniques for efficient agent-based model simulations of tumor growth

AbstractMulti-scale agent-based models are increasingly used to simulate tumor growth dynamics. Simulating such complex systems is often a great challenge despite large computational power of modern computers and, thus, implementation techniques are becoming as important as the models themselves. Here we show, using a simple agent-based model of tumor growth, how the computational time required for simulation can be decreased by using vectorization techniques. In numerical examples we observed up to 30-fold increases in computation performance when standard approaches were, at least in part, replaced with vectorized routines in MATLAB.

Systems Biology↗

bioRxiv2015

Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress

Standfirst textUsing a new statistical tool to analyze time-series protein and matching mRNA concentration data, this study deconvoluted the contributions of mRNA and protein level regulation in the response of mammalian cells to stress of the endoplasmatic reticulum.\n\n- We quantified protein and mRNA concentrations for 3,235 genes across two replicates and time points, with a high-confidence dataset of 1,237 genes/mRNAs.\n- We use a new statistical tool to quantify the contribution of regulatory processes, and we find that mRNA and protein level regulation play similarly important roles.\n- mRNA and protein level regulation have different dynamics: mRNA concentrations spike in their change and return to pre-perturbation levels, while protein concentrations switch in their behavior and reach a new steady-state.\n- We generated hypotheses on modes of regulation for several groups of genes.\n\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC=\"FIGDIR/small/032797_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@97d00corg.highwire.dtl.DTLVardef@dbe5fborg.highwire.dtl.DTLVardef@d801c7org.highwire.dtl.DTLVardef@15cd52d_HPS_FORMAT_FIGEXP M_FIG C_FIG AbstractThe relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped the proteomics and transcriptomics changes in mammalian cells responding to stress induced by dithiothreitol over 30 hours. Specifically, we estimated the kinetic parameters for synthesis and degradation of RNA and proteins, and deconvoluted response patterns common and unique to each regulatory level using a new statistical tool. Overall, both regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. Further, mRNA concentrations were regulated in a transient, spike-like pattern and returned to values close to pre-treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for example groups of genes.

Systems Biology↗

bioRxiv2015

BAGEL: A computational framework for identifying essential genes from pooled library screens.

BackgroundThe adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed.\n\nResultsWe offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude.\n\nConclusionsUsing BAGEL, we identify ~2,000 fitness genes in pooled library knockout screens in human cell lines at 5% FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens with highly variable reagent quality.

Systems Biology↗

bioRxiv2015

Competition between binding sites determines gene expression at low transcription factor concentrations

The response of gene expression to intra-and extra-cellular cues is largely mediated through changes in the activity of transcription factors (TFs), whose sequence specificities are largely known. However, the rules by which promoters decode the amount of active TF into gene expression are not well understood. Here, we measure the activity of 6500 designed promoters at six different levels of TF activity in budding yeast. We observe that maximum promoter activity is determined by TF activity and not by the number of sites. Surprisingly, the addition of an activator-binding site often reduces expression. A thermodynamic model that incorporates competition between neighboring binding sites for a local pool of TF molecules explains this behavior and accurately predicts both absolute expression and the amount by which addition of a site increases or reduces expression. Taken together, our findings support a model in which neighboring binding sites interact competitively when TF is limiting but otherwise act additively\n\nSignificance StatementIn response to intracellular and extracellular signals organisms alter the concentration and activity of transcription factors (TFs), proteins that regulate gene expression. However, the molecular mechanisms that determine the response of a target promoter to changes in the number of active TF molecules are not well understood. By combining mathematical modeling with measurements of TF dose-response curves for thousands of designed promoters, we show that competition for active TF molecules is a major factor in determining gene expression. At low TF concentrations additional activator-binding sites within a promoter can actually reduce expression. Thermodynamic modeling suggests that steric hindrance between neighboring binding sites cannot explain this behavior, but that competition for limiting TF molecules can.

Systems Biology↗

bioRxiv2015

Emergent Patterns from Probabilistic Generalisations of Lateral Activation and Inhibition

The combination of laterally activating and inhibiting feedbacks is well known to spontaneously generate spatial organisation. It was introduced by Gierer and Meinhardt as an extension of Turings great insight, that two reacting and diffusing chemicals can spontaneously drive spatial morphogenesis per se. In this study, we develop an accessible nonlinear and discrete probabilistic model to study simple generalisations of lateral activation and inhibition. By doing so, we identify novel modes of morphogenesis beyond the familiar Turing-type modes; notably, beyond stripes, hexagonal nets, pores, and labyrinths, we identify labyrinthine highways, Kagome lattices, gyrating labyrinths, and multi-colour travelling waves and spirals. The results are discussed within the context of Turings original motivating interest: the mechanisms which underpin the morphogenesis of living organisms.

Systems Biology↗

bioRxiv2015

Multiscale Modeling of Cancer

Breast cancer remains the second leading cause of cancer death in women, exceeded only by lung cancer. Specifically, triple-negative breast cancer (TNBC) has the worst prognosis, as it is more invasive and lacks estrogen, progesterone, and HER2 receptors that can be targeted with therapies. Due to the need for effective therapies for this type of breast cancer, it is critical to develop methods to (1) understand how TNBC progresses and (2) facilitate development of effective therapies. Here, we describe a multiscale model focusing on tumor formation. Our approach uses multiple scales to investigate the progression and possible treatments of tumors.

Systems Biology↗

bioRxiv2015

Ewing sarcoma breakpoint region 1 prevents transcription-associated genome instability

Ewing Sarcoma break point region 1 (EWSR1) is a multi-functional RNA-binding protein that is involved in many cellular processes, from gene expression to RNA processing and transport. Translocations into its locus lead to chimeric proteins with tumorigenic activity, that lack the RNA binding domain. With crosslinking and immunoprecipitation we have found that EWSR1 binds to intronic regions that are present in polyadenylated nuclear RNAs, which include the translocation-prone region of its own locus. Reduced EWSR1 expression leads to gene expression changes that indicate reduced proliferation. By fluorescence in situ hybridization (FISH) with break-apart probes that flanked the translocation-prone region within the EWSR1 locus we found that reduced EWSR1 expression increases the frequency of split signals, indicative of DNA double strand breaks (DSB). The response in phosphorylated histone H2AX and p53-binding protein 1 (53BP1) double-stained foci to the topoisomerase poison camptothecin in cells treated with a control shRNA and with sh-EWSR1 further suggests that EWSR1 functions in the prevention of DNA DSBs. Our data reveal a new function of the EWSR1 member of the FET family and suggest a connection between the RNA-binding activity of EWSR1 and the instability of its own locus that may play a role in malignancy-associated translocations.

Systems Biology↗

bioRxiv2015

On Miller’s rule for the brain working memory, or why human memory is so short

Working memory is a cognitive construct that describes how information can be maintained in brain for a limited period of time, while concurrent processing is also performed. We present a simple model that accounts for working memory span and explains the origin of the cognitive Millers rule (Magical Number Seven).

Systems Biology↗

bioRxiv2015

Exploiting variability of single cells to uncover the in vivo hierarchy of miRNA targets

MiRNAs are post-transcriptional repressors of gene expression that may additionally reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels and provide a layer through which targets can influence each others expression as competing RNAs (ceRNAs). Here we combined single cell sequencing of human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, with mathematical modeling, to estimate Michaelis-Menten (KM)-type constants for hundreds of evolutionarily conserved miRNA targets. These parameters, which we inferred here for the first time in the context of the entire network of endogenous miRNA targets, vary over ~2 orders of magnitude. They reveal an in vivo hierarchy of miRNA targets, defined by the concentration of miRNA-Argonaute complexes at which the targets are most sensitively down-regulated. The data further reveals miRNA-induced correlations in target expression at the single cell level, as well as the response of target noise to the miRNA concentration. The approach is generalizable to other miRNAs and post-transcriptional regulators and provides a deeper understanding of gene expression dynamics.

Systems Biology↗

bioRxiv2015

Agent-based modeling of cancer stem cell driven solid tumor growth

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

Systems Biology↗

bioRxiv2015

Tunable protein synthesis by transcript isoforms in human cells

Eukaryotic genes generate multiple mRNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex and not well understood at a global level. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and the resulting translational output of each transcript isoform. We extracted a panel of 5' and 3' untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5' untranslated regions exert robust translational control between cell lines, while 3' untranslated regions can confer cell-type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels.

Systems Biology↗

bioRxiv2015

A mechanistic first-passage time framework for bacterial cell-division timing

How exponentially growing cells maintain size homeostasis is an important fundamental problem. Recent single-cell studies in prokaryotes have uncovered the adder principle, where cells on average, add a fixed size (volume) from birth to division. Interestingly, this added volume differs considerably among genetically-identical newborn cells with similar sizes suggesting a stochastic component in the timing of cell-division. To mechanistically explain the adder principle, we consider a time-keeper protein that begins to get stochastically expressed after cell birth at a rate proportional to the volume. Cell-division time is formulated as the first-passage time for protein copy numbers to hit a fixed threshold. Consistent with data, the model predicts that while the mean cell-division time decreases with increasing size of newborns, the noise in timing increases with size at birth. Intriguingly, our results show that the distribution of the volume added between successive cell-division events is independent of the newborn cell size. This was dramatically seen in experimental studies, where histograms of the added volume corresponding to different newborn sizes collapsed on top of each other. The model provides further insights consistent with experimental observations: the distributions of the added volume and the cell-division time when scaled by their respective means become invariant of the growth rate. Finally, we discuss various modifications to the proposed model that lead to deviations from the adder principle. In summary, our simple yet elegant model explains key experimental findings and suggests a mechanism for regulating both the mean and fluctuations in cell-division timing for size control.

Systems Biology↗

bioRxiv2015

Incoherent feedforward motifs as immune change detectors

We speculate that incoherent feedforward loops may be phenomenologically involved in self/nonself discrimination in immune-infection and immune-tumor interactions, acting as \"change detectors\". In turn, this may result in logarithmic sensing (Weber phenomenon) and even scale invariance (fold-change detection).

Systems Biology↗

bioRxiv2016

A portable library to support the SBML Layout Extension

1The SBML layout extension enables SBML models to encode layout information which describes the graphical depiction of model elements. In this application note, we describe libSBNW, a portable library that supports the SBML layout extension and can automatically generate layout for SBML models. The library can be used to automatically generate layout information for SBML models lacking it, or to edit coordinate information already encoded in a model. We provide C and Python APIs to allow other applications to host the library or to use it directly from the Python console. We show that the library is sufficient for creating a graphical application for displaying and editing layout information. The library is open-source and licensed under the BSD 3-clause license. Project source code, downloads, documentation and binaries for Windows and Mac OS X are available at https://github.com/sys-bio/sbnw. The library is also included in Tellurium, available at http://tellurium.analogmachine.org/. Video tutorials are available at http://0u812.github.io/sbnw/tutorials/.

Systems Biology↗

bioRxiv2016

Revised estimates for the number of human and bacteria cells in the body

We critically revisit the \"common knowledge\" that bacteria outnumber human cells by a ratio of at least 10:1 in the human body. We found the total number of bacteria in the \"reference man\" to be 3.9{middle dot}1013, with an uncertainty (SEM) of 25%, and a variation over the population (CV) of 52%. For human cells we identify the dominant role of the hematopoietic lineage to the total count of body cells ({approx}90%), and revise past estimates to reach a total of 3.0{middle dot}1013 human cells in the 70 kg \"reference man\" with 2% uncertainty and 14% CV. Our analysis updates the widely-cited 10:1 ratio, showing that the number of bacteria in our bodies is actually of the same order as the number of human cells. Indeed, the numbers are similar enough that each defecation event may flip the ratio to favor human cells over bacteria.

Systems Biology↗

bioRxiv2016

Phenotypic spandrel: absolute discrimination and ligand antagonism

We consider the general problem of absolute discrimination between categories of ligands irrespective of their concentration. An instance of this problem is immune discrimination between self and not-self. We connect this problem to biochemical adaptation, and establish that ligand antagonism - the ability of sub threshold ligands to negatively impact response - is a necessary consequence of absolute discrimination.Thus antagonism constitutes a \"phenotypic spandrel\": a phenotype existing as a necessary by-product of another phenotype. We exhibit a simple analytic model of absolute discrimination displaying ligand antagonism, where antagonism strength is linear in distance from threshold. This contrasts with proofreading based models, where antagonism vanishes far from threshold and thus displays an inverted hierarchy of antagonism compared to simple model. The phenotypic spandrel studied here is expected to structure many decision pathways such as immune detection mediated by TCRs and FceRIs.

Systems Biology↗