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Results for “Synthetic 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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Characterizing Building Blocks of Resource Constrained Biological Networks

Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over-or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cells organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

bioinformatics↗

bioRxiv2018

A Patient-Derived Cellular Model for Huntington Disease Reveals Phenotypes at Clinically Relevant CAG Lengths

The huntingtin protein participates in several cellular processes that are disrupted when the polyglutamine tract is expanded beyond a threshold of 37 CAG DNA repeats in Huntingtons disease (HD). Cellular biology approaches to understand these functional disruptions in HD have primarily focused on cell lines with synthetically long CAG length alleles that clinically represent outliers in this disease and a more severe form of HD that lacks age-onset. Patient-derived fibroblasts are limited to a finite number of passages before succumbing to cellular senescence. We used human telomerase reverse transcriptase (hTERT) to immortalize fibroblasts taken from individuals of varying age, sex, disease onset and CAG repeat length, which we have termed TruHD cells. TruHD cells display classic HD phenotypes of altered morphology, size and growth rate, increased sensitivity to oxidative stress, aberrant ADP/ATP ratios and hypophosphorylated huntingtin protein. We additionally observed dysregulated ROS-dependent huntingtin localization to nuclear speckles in HD cells. We report the generation and characterization of a human, clinically relevant cellular model for investigating disease mechanisms in HD at the single cell level, which, unlike transformed cell lines, maintains TP53 function critical for huntingtin transcriptional regulation and genomic integrity.

cell biology↗

bioRxiv2016

Variation and evolution of the glutamine-rich repeat region of Drosophila Argonaute-2

RNA interference pathways mediate multiple biological processes through Argonaute-family proteins, which bind small RNAs as guides to silence complementary target nucleic acids. In insects and crustaceans, Argonaute-2 silences viral nucleic acids, and therefore acts as a primary effector of innate antiviral immunity. Although the function of the major of Argonaute-2 domains, which are conserved across most Argonaute-family proteins, are known, many invertebrate Argonaute-2 homologs contain a glutamine-rich repeat (GRR) region of unknown function at the N-terminus. Here we combine long-read amplicon sequencing of Drosophila Genetic Reference Panel (DGRP) lines with publicly available sequence data from many insect species to show that this region evolves extremely rapidly and is hypervariable within species. We identify distinct GRR haplotype groups in D. melanogaster, and suggest that one of these haplotype groups has recently risen to high frequency in North American populations. Finally, we use published data from genome-wide association studies of viral resistance in D. melanogaster to test whether GRR haplotypes are associated with survival after virus challenge. We find a marginally significant association with survival after challenge with Drosophila C Virus in the DGRP, but we were unable to replicate this finding using lines from the Drosophila Synthetic Population Resource panel.

Evolutionary Biology↗

bioRxiv2018

word2brain

Mapping brain functions to their underlying neural substrates is a central goal of cognitive neuroscience. Functional magnetic resonance imaging (fMRI) has proven indispensable in this endeavour. Recently, there has been growing interest in tackling this problem by mapping semantic concepts onto brain regions using repositories of images and text from the neuroimaging literature. However, no study has thus far approached this problem using (dense) vector representations of words. Using data from the Neurosynth database, we sought to develop a model that could (A) capture local correlations between words in text, as well as topics, (B) capture representation of distributed brain networks in relation to word embeddings, and (C) generate synthetic images given word inputs. We show that jointly embedding words and brain imaging data on a vector space can yield semantic representations that sensibly relate concepts across biological, psychological, and observational levels of analysis. Moreover, our proposed model makes no assumption about spatial orientation of fMRI voxels, which allows for embedding of distributed brain networks onto the semantic space. We demonstrate this capability by generating synthetic brain activation vectors from word inputs. Our model has the potential to advance neuroimaging meta-analysis as well as contextual word-embedding methods more broadly.

neuroscience↗

bioRxiv2017

Enzymes And Substrates Are Balanced At Minimal Combined Mass Concentration In Vivo

A fundamental problem in biology is how cells organize their resource investment. Cellular metabolism, for example, typically involves hundreds of enzymes and metabolites, but it is unclear according to which principles their concentrations are set. Reasoning that natural selection will drive cells towards achieving a given physiological state at minimal cost, we derive a general equation that predicts the concentration of a metabolite from the concentration of the most abundant and costly enzyme consuming it. Simulations of cellular growth as well as experimental data demonstrate that costs are approximately proportional to molecular masses. For effectively irreversible reactions, the cell maximizes its metabolic efficiency by investing equally into substrate and unbound enzyme molecules. Without fitting any free parameters, the resulting model predicts in vivo substrate concentrations from enzyme concentrations and substrate affinities with high accuracy across data from E. coli and diverse eukaryotes (R2=0.79, geometric mean fold-error 1.74). The corresponding organizing principle - the minimization of the summed mass concentrations of solutes - may facilitate reducing the complexity of kinetic models and will contribute to the design of more efficient synthetic cellular systems.

systems biology↗

bioRxiv2018

Backward evolution from gene network dynamics

Gene expression is controlled by regulator genes that together with effector genes form gene regulatory networks. How mutation in the genes comprising gene regulatory networks influences cell population dynamics has not been adequately investigated. In this study, we develop mathematical models to study how a mutation in a regulator gene that reaches the effector gene with a time delay affects short-term and long-term population growth. Using theory and experiment, we find a paradoxical outcome of evolution where a mutation in a regulator gene leads to an interaction between gene regulatory network and population dynamics, causing in certain cases a permanent decrease in population fitness in a constant environment.\n\nSignificance StatementThe properties of a cell are largely the products of its proteins, synthesized at rates depending on the regulation of protein coding genes. Single-cell measurements show that genetically identical cells can differ radically in their protein levels, partially due to the random production and degradation of proteins. It is currently unknown how mutants arise and spread in populations affected by such biological variability. We use computer simulations and evolution experiments to study how a mutant spreads in a population that carries a synthetic drug resistance gene network. Our results show for the first time a paradoxical outcome of evolution, where an initially beneficial mutation can interact with gene regulatory network dynamics and cause a permanent decrease in population fitness in the same environment.

evolutionary biology↗

bioRxiv2017

Transcription Factor Target Gene Network governs the Logical Abstraction Analysis of the Synthetic Circuit in Leishmaniasis

Stochastic variations in the transcription factor target gene network influences the dynamics of protein levels. The mathematical model built, here, is useful to study the cytokine response and the resulting dynamical patterns in leishmaniasis. The fluctuations produced affect the expression rate of its target in the regulatory synthetic circuit designed by means of a negative feedback loop insertion. Thus, the faster the response time, smaller is the fluctuation of the system observed and it can be justified that the TFTG network embedded can be understood with the recurring dynamics of the designed synthetic circuit.

systems biology↗

bioRxiv2018

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange can mediate beneficial interactions among microbes, helping explain diversity in microbial communities. These interactions are often assumed to involve a fitness cost, prompting questions on how cooperative phenotypes can be stable and withstand the emergence of cheaters. Here we use genome-scale models of metabolism to investigate whether a radically different scenario, the pervasive release of \"costless\" metabolites (i.e. those that cause no fitness cost to the producing organism), can serve as a prominent mechanism for inter-microbial interactions. By carrying out over 1 million pairwise growth simulations for 14 microbial species in a combinatorial assortment of environmental conditions, we find that there is indeed a large space of metabolites that can be secreted at no cost, which can generate ample cross-feeding opportunities. In addition to providing an atlas of putative costless interdependencies, our modeling also demonstrates that oxygen availability significantly enhances mutualistic interactions by providing more opportunities for metabolic exchange through costless metabolites, resulting in an over-representation of specific ecological network motifs. In addition to helping explain natural diversity, we show how the exchange of costless metabolites can facilitate the engineering of stable synthetic microbial consortia.

systems biology↗

bioRxiv2016

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage).\n\nPhysiCell--physics-based multicellular simulator--is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.\n\nWe demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.\n\nAuthor SummaryThis paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.\n\nPhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.\n\nWe developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.

systems biology↗

bioRxiv2018

Turing patterns are common but not robust

Turing patterns (TPs) underlie many fundamental developmental processes, but they operate over narrow parameter ranges, raising the conundrum of how evolution can ever discover them. Here we explore TP design space to address this question and to distill design rules. We exhaustively analyze 2- and 3-node biological candidate Turing systems: crucially, network structure alone neither determines nor guarantees emergent TPs. A surprisingly large fraction (>60%) of network design space can produce TPs, but these are sensitive to even subtle changes in parameters, network structure and regulatory mechanisms. This implies that TP networks are more common than previously thought, and evolution might regularly encounter prototypic solutions. Importantly, we deduce compositional rules for TP systems that are almost necessary and sufficient (96% of TP networks contain them, and 95% of networks implementing them produce TPs). This comprehensive network atlas provides the blueprints for identifying natural TPs, and for engineering synthetic systems.

developmental biology↗

bioRxiv2018

Nucleic acid extraction and sequencing from low-biomass synthetic Mars analog soils for in situ life detection

Recent studies regarding the origin of life and Mars-Earth meteorite transfer simulations suggest that biological informational polymers, such as nucleic acids (DNA and RNA), have the potential to provide unambiguous evidence of life on Mars. To this end, we are developing a metagenomics-based life-detection instrument which integrates nucleic acid extraction and nanopore sequencing: The Search for Extra-Terrestrial Genomes (SETG). Our goal is to isolate and sequence nucleic acids from extant or preserved life on Mars in order to determine if a particular genetic sequence (1) is distantly-related to life on Earth indicating a shared-ancestry due to lithological exchange, or (2) is unrelated to life on Earth suggesting a convergent origin of life on Mars. In this study, we validate prior work on nucleic acid extraction from cells deposited in Mars analog soils down to microbial concentrations observed in the driest and coldest regions on Earth. In addition, we report low-input nanopore sequencing results equivalent to 1 ppb life-detection sensitivity achieved by employing carrier sequencing, a method of sequencing sub-nanogram DNA in the background of a genomic carrier.

molecular biology↗

bioRxiv2016

MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections

The potential to understand fundamental biological processes from gene expression data has grown parallel with the recent explosion of the size of data collections. However, to exploit this potential, novel analytical methods are required, capable of handling massive data matrices. We found current methods limited in the size of correlated gene sets they could discover within biologically heterogeneous data collections, hampering the identification of multi-gene controlled fundamental cellular processes such as energy metabolism, organelle biogenesis and stress responses. Here we describe a novel biclustering algorithm called Massively Correlated Biclustering (MCbiclust) that selects samples and genes from large datasets with maximal correlated gene expression, allowing regulation of complex pathway to be examined. The method has been evaluated using synthetic data and applied to large bacterial and cancer cell datasets. We show that the large biclusters discovered, so far elusive to identification by existing techniques, are biologically relevant and thus MCbiclust has great potential use in the analysis of transcriptomics data to identify large scale unknown effects hidden within the data. The identified massive biclusters can be used to develop improved transcriptomics based diagnosis tools for diseases caused by altered gene expression, or used for further network analysis to understand genotype-phenotype correlations.

Bioinformatics↗

bioRxiv2017

Hyperactivation of extracellular signal-regulated kinase (ERK) by RAS-mediated signaling or inhibition of dual specificity phosphatase 6 (DUSP6) is associated with toxicity in lung adenocarcinoma cells with mutations in KRAS or EGFR

We recently described the synthetic lethality that results when mutant KRAS and mutant EGFR are coexpressed in human lung adenocarcinoma (LUAD) cells, revealing the biological basis for the mutual exclusivity of KRAS and EGFR mutations in lung cancers. We have now further defined the biochemical events responsible for the toxic effects of signaling through the RAS pathway. By combining pharmacological and genetic approaches, we have developed multiple lines of evidence that signaling through extracellular signal-regulated kinases (ERK1/2) mediates the toxicity. These findings imply that tumors with mutant oncogenes that drive signaling through the RAS pathway must restrain the activity of ERK1/2 to avoid cell toxicities and enable tumor growth. In particular, a dual specificity phosphatase, DUSP6, regulates phosphorylated (P)-ERK levels in lung adenocarcinoma cells, providing negative feedback to the RAS signaling pathway. Accordingly, inhibition of DUSP6 is cytotoxic in LUAD cells driven by either mutant KRAS or mutant EGFR, phenocopying the effects of co-expression of mutant KRAS and EGFR. Together, these data suggest that targeting DUSP6 or other feedback regulators of the EGFR-KRAS-ERK pathway may offer a strategy for treating certain cancers by exceeding an upper threshold of RAS-mediated signaling.

cancer biology↗

bioRxiv2017

A Multi-Species Functional Embedding Integrating Sequence and Network Structure

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment.\n\nIn this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HO_SCPLOWANDLC_SCPLOW (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space capture functional similarity across species, and the vectors themselves are useful for a variety of cross species tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HO_SCPLOWANDLC_SCPLOW-embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are co-located in HO_SCPLOWANDLC_SCPLOW-space both within and across species. Software for the HO_SCPLOWANDLC_SCPLOW algorithm is available at http://github.com/lrgr/HANDL.

bioinformatics↗

bioRxiv2018

Natural enemies of armored scales (Hemiptera: Diaspididae) and soft scales (Hemiptera: Coccoidae) in Chile: molecular and morphological identification

Scale insects (Hemiptera: Coccoidea) are key pests of agricultural crops and ornamental plants worldwide. Their populations are difficult to control, even with insecticides, due to their cryptic habits. Moreover, there is growing concern over the use of synthetic pesticides for their control, due to deleterious environmental effects and the emergence of resistant populations of target pests. In this context, biological control may be an effective and sustainable approach. Hymenoptera Chalcidoidea includes natural enemies of scale insects that have been successfully used in many biological control programs. However, the correct identification of pest scale species and their natural enemies is particularly challenging because these insects are very small and highly specialized. Integrative taxonomy, coupling DNA barcoding and morphological analysis, has been successfully used to characterize pests and natural enemy species. In this study, we performed a survey of parasitoids and predators of armored and soft scales in Chile, based on 28S and COI barcodes. Fifty-three populations of Diaspididae and 79 populations of Coccidae were sampled over the entire length of the country, from Arica (18{degrees}S) to Frutillar (41{degrees}S), between January 2015 and February 2016. The phylogenetic relationships obtained by Bayesian inference from multilocus haplotypes revealed 40 putative species of Chalcidoidea, five Coccinellidae and three Neuroptera. In Chalcidoidea, 22 species were identified morphologically, resulting in new COI barcodes for 12 species and new 28S barcodes for 14 species. Two predator species (Rhyzobius lophantae and Coccidophilus transandinus) were identified morphologically, and two parasitoid species, Chartocerus niger and Signiphora bifasciata, were recorded for the first time in Chile.

molecular biology↗

bioRxiv2016

Deep learning based root-soil segmentation from X-ray tomography

One of the most challenging computer vision problem in plant sciences is the segmentation of root and soil from X-ray tomography. So far, this has been addressed from classical image analysis methods. In this paper, we address this root/soil segmentation problem from X-ray tomography using a new deep learning classification technique. The robustness of this technique, tested for the first time on this plant science problem, is established with root/soil presenting a very low contrast in X-ray tomography. We also demonstrate the possibility to segment efficiently root from soil while learning on purely synthetic soil and root.

Plant Biology↗

bioRxiv2017

The fundamental principles of antibody repertoire architecture revealed by large-scale network analysis

The antibody repertoire is a vast and diverse collection of B-cell receptors and antibodies that confer protection against a plethora of pathogens. The architecture of the antibody repertoire, defined by the network similarity landscape of its sequences, is unknown. Here, we established a novel high-performance computing platform to construct large-scale networks from high-throughput sequencing data (>100000 unique antibodies), in order to uncover the architecture of antibody repertoires. We identified three fundamental principles of antibody repertoire architecture across B-cell development: reproducibility, robustness and redundancy. Reproducibility of network structure explains clonal expansion and selection. Robustness ensures a functional immune response even under extensive loss of clones (50%). Redundancy in mutational pathways suggests that there is a pre-programmed evolvability in antibody repertoires. Our analysis provides guidelines for a quantitative network analysis of antibody repertoires, which can be applied to other facets of adaptive immunity (e.g., T cell receptors), and may direct the construction of synthetic repertoires for biomedical applications.

systems biology↗

bioRxiv2017

Revealing The Cell-Material Interface With Nanometer Resolution By FIB-SEM

The interface between biological cells and non-biological surfaces profoundly influences cellular activities, chronic tissue responses, and ultimately the success of medical implants. Materials in contact with cells can be plastics, metal, ceramics or other synthetic materials, and their surfaces vary widely in chemical compositions, stiffness, topography and levels of roughness. To understand the molecular mechanism of how cells and tissues respond to different materials, it is of critical importance to directly visualize the cell-material interface at the relevant length scale of nanometers. Conventional ultrastructural analysis by transmission electron microscopy (TEM) often requires substrate removal before microtome sectioning, which is not only challenging for most substrates but also can cause structural distortions of the interface. Here, we present a new method for in situ examination of the cell-to-material interface at any desired cellular location, based on focused-ion beam milling and scanning electron microscopy imaging (FIB-SEM). This method involves a thin-layer plastification procedure that preserves adherent cells as well as enhances the contrast of biological specimen. We demonstrate that this unique procedure allows the visualization of cell-to-material interface and intracellular structures with 10nm resolution, compatible with a variety of materials and surface topographies, and capable of volume and multi-directional imaging. We expect that this method will be very useful for studies of cell-to-material interactions and also suitable for in vivo studies such as examining osteoblast adhesion and new bone formation in response to titanium implants.

biophysics↗