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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Estimation of the cancer risk induced by rejuvenation therapy with young blood and treatment recommendations

In recent years the transfer of blood from young to old individuals was shown to bear the potential of rejuvenation of stem cell activity. While this process might increase life expectancy by prolonging functionality of organs, higher cell replication rates bear also the risk of cancer. The extent of this risk is not known.\n\nWhile it is difficult to evaluate this cancer risk in experiments, this is possible with a mathematical model for tissue homeostasis by stem cell replication and associated cancer risk. The model suggests that young blood treatments can induce a substantial delay of organ failure with only minor increase in cancer risk. The benefit of rejuvenation therapy as well as the impact on cancer risk depend on the biological age at the time of treatment and on the overall cell turnover rate of the organs. Different organs have to be considered separately in the planning of the systemic treatment. In particular, the model predicts that the treatment schedules successfully applied in mice are not directly transferable to humans and guidelines for successful protocols are proposed. The model presented here may be used as a guidance for the development of treatment protocols.\n\nAdditional informationThere is NO competing interests.

systems biology↗

bioRxiv2015

Design and strain selection criteria for bacterial communication networks

In this paper we discuss data and methodological challenges for building bacterial communication networks using two examples: Escherichia coli as a flagellate bacterium and of Geobacter sulfurreducens as a biofilm forming bacterium. We first highlight the link between the bacterial network communication design with respect to metabolic information processing design. The potentialities of designing routing network schemes described previously in literature and based on bacteria motility and genetic message exchanges will depend on the genes coding for the intracellular and intercellular signalling pathways. In bacteria, the \"mobilome\" is related to horizontal gene transfer. Bacteria trade off the acquisition of new genes which could improve their survival (and often their communication bandwidth), keeping their genome enough small to assure quick DNA replication and increase fast the biomass to speed up cell division. First, by using a multi-objective optimisation procedure, we search for the optimal trade off between energy production, which is a requirement for the motility, and the biomass growth, which is related to the overall survival and fitness of the bacterium. We use flux balance analysis of genome-scale biochemical network of Escherichia coli k-13 MG1655. Then, as a second case study we analyze the electric properties and biomass trade-off of the bacterium Geobacter sulfurreducens which constructs an electric biofilm where electrons move across the nanowires. Here we discuss the potentialities of optimisation methodologies to design and select bacterial strains with desiderata properties. The optimisation methodologies establish also a relation between metabolism, network communication and computing. Moreover, we point to genetic design and synthetic biology as key areas to develop bacterial nano communication networks.

Systems Biology↗

bioRxiv2015

A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data

We propose an integrative framework to select important genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a logistic Bayesian network model based on The Cancer Genome Atlas data. The constructed Bayesian network has identified four gene clusters of distinct cellular functions, 13 driver genes, as well as some new biological pathways which may shed new light into the molecular mechanisms of ovarian cancer.

Systems Biology↗

bioRxiv2015

Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - Part A

Ever since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway at work and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript is an endeavour to * explore the strength of contributing factors in the signaling pathway, * analyze the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and * investigate the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the superiority of the density based indices in comparison to the use of variance based indices mainly due to the former's employment of distance measures using the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis and representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs and reveal hidden biological information within colorectal cancer samples. Code has been made available at Google drive on https://drive.google.com/folderview?id=0B7Kkv8wlhPU-Q2NBZGt1ZERrSVE&usp=sharing

Systems Biology↗

bioRxiv2016

Micro-Magellan: A flexible, open source acquisition software for high throughput biological light microscopy

We demonstrate the capabilities of Magellan: a flexible, open source microscopy software for reproducible high throughput imaging of biological samples across heterogeneous scales of space and time. Magellan provides a simple user interface for exploration and automated imaging of non-cuboidal regions. By utilizing the hardware abstraction layer of Magellan, Magellan provides a powerful and extensible platform for imaging heterogeneous biological samples on a wide range of existing microscopes.

Systems Biology↗

bioRxiv2017

Maintaining maximal metabolic flux by gene expression control

Many evolutionarily successful bacteria attain high growth rates across growth-permissive conditions. They express metabolic networks that synthesise all cellular components at a high rate. Metabolic reaction rates are bounded by the concentration of the catalysing enzymes and cells have finite resources available for enzyme synthesis. Therefore, bacteria that grow fast should express needed metabolic enzymes at precisely tuned concentrations. To maintain fast growth in a dynamic environment, cells should adjust gene expression of metabolic enzymes. The activity of many of the associated transcription factors is regulated by their binding to intracellular metabolites. We study optimal metabolite-mediated regulation of metabolic-gene expression that preserves maximisation of metabolic fluxes across varying conditions. We logically derive the underlying control logic of this type of optimal regulation, which we term Specific Flux (q) Optimization by Robust Adaptive Control (qORAC), and illustrate it with several examples. We show that optimal metabolic flux can be maintained in the face of K changing parameters only if the number of transcription-factor-binding metabolites is at least equal to K. qORAC-regulation of metabolism can generally be achieved with basic biochemical interactions, indicating that metabolism can operate close to optimality. The theory that we present is directly applicable to synthetic biology, biotechnology and fundamental studies of the regulation of metabolism.

systems biology↗

bioRxiv2018

GEESE: Metabolically driven latent space learning for gene expression data

Gene expression microarrays provide a characterisation of the transcriptional activity of a particular biological sample. Their high dimensionality hampers the process of pattern recognition and extraction. Several approaches have been proposed for gleaning information about the hidden structure of the data. Among these approaches, deep generative models provide a powerful way for approximating the manifold on which the data reside.\n\nHere we develop GEESE, a deep learning based framework that provides novel insight into the manifold learning for gene expression data, employing a metabolic model to constrain the learned representation. We evaluated the proposed framework, showing its ability to capture biologically relevant features, and encoding that features in a much simpler latent space. We showed how using a metabolic model to drive the autoencoder learning process helps in achieving better generalisation to unseen data. GEESE provides a novel perspective on the problem of unsupervised learning for biological data.\n\nAvailabilitySource code of GEESE is available at https://bitbucket.org/mbarsacchi/geese/.

systems biology↗

bioRxiv2017

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

BackgroundThe advent of \"omics\" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability.\n\nResultsHere we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB.\n\nConclusionsCellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

systems biology↗

bioRxiv2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patients immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.

systems biology↗

bioRxiv2018

A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis

The high-throughput data generated by new biotechnologies used in biological studies require specific and adapted statistical treatments. In this work, we propose a novel and powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We illustrate it using the package mixOmics for the R software as it specifically addresses data integration issues. Our work also aims at confronting the most recent functionalities of mixOmics to real data sets because, even if multi-block integrative methodologies exist, they still have to be used to enlarge our know-how and to provide an operational framework to biologists. Natural populations of the model plant Arabidopsis thaliana are employed in this work but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question. Four omics data sets (phenomics, metabolomics, cell wall proteomics and transcriptomics) have been collected, analysed and integrated in order to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented start from basic univariate statistics and lead to multi-block integration analysis, and we highlight the fact that each method is associated to one biological issue. Using this powerful framework led us to novel biological conclusions that could not have been reached using standard statistical approaches.

systems biology↗

bioRxiv2017

Lessons in effector and NLR biology of plant-microbe systems

A diversity of plant-associated organisms secrete effectors--proteins and metabolites that modulate plant physiology to favor host infection and colonization. However, effectors can also activate plant immune receptors, notably nucleotide-binding domain and leucine-rich repeat-containing (NLR) proteins, enabling plants to fight off invading organisms. This interplay between effectors, their host targets, and the matching immune receptors is shaped by intricate molecular mechanisms and exceptionally dynamic coevolution. In this article, we focus on three effectors, AVR-Pik, AVR-Pia, and AVR-Pii, from the rice blast fungus Magnaporthe oryzae (syn. Pyricularia oryzae), and their corresponding rice NLR immune receptors, Pik, Pia, and Pii, to highlight general concepts of plant-microbe interactions. We draw 12 lessons in effector and NLR biology that have emerged from studying these three little effectors and are broadly applicable to other plant-microbe systems.

plant biology↗

bioRxiv2017

A CRISPR-Cas9 Genome Engineering Platform in Primary CD4+ T Cells for the Interrogation of HIV Host Factors

CRISPR-Cas9 gene editing strategies have revolutionized our ability to engineer the human genome for robust functional interrogation of complex biological processes. We have recently adapted this technology to primary human T cells to generate a high-throughput platform for analyzing the role of host factors in pathogen infection and lifecycle. Here, we describe applications of this system to investigate HIV pathogenesis in CD4+ T cells. Briefly, CRISPR-Cas9 ribonucleoproteins (crRNPs) are synthesized in vitro and delivered to activated primary human CD4+ T cells by nucleofection. These edited cells are then validated and expanded for use in downstream cellular, genetic, or protein-based assays. Our platform supports the arrayed generation of several gene manipulations in only a few hours time and is widely adaptable across culture conditions, infection protocols, and downstream applications. We present detailed protocols for crRNP synthesis, primary T cell culture, 96-well nucleofection, molecular validation, and HIV infection with additional considerations for guide and screen design as well as crRNP multiplexing.

systems biology↗

bioRxiv2018

DevoWorm: data-theoretical synthesis of C. elegansdevelopment.

Biological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model system Caenorhabditis elegans, we examine time-dependent properties of the embryonic phenotype and utilize the unique life-history properties to demonstrate how these emergent properties can be linked together by data analysis and theory-building. We also focus on embryogenetic differentiation processes, and how terminally-differentiated cells contribute to structure and function of the adult phenotype. Examining embryogenetic dynamics from 200 to 400 minutes post-fertilization provides basic quantitative information on developmental tempo and process. To summarize, theory construction techniques are summarized and proposed as a way to rigorously interpret our data. Our proposed approach to a formal data representation that can provide critical links across life-history, anatomy and function.

systems biology↗

bioRxiv2016

Modeling Chromosome Maintenance as a Property of Cell Cycle in Saccharomyces cerevisiae

Defects in DNA repair, synthesis, and chromosome transmission can often cause chromosome instability, which are understood with respect to molecular-genetic mechanisms. However, transition from descriptive models to quantitative ones is generally difficult. Here we use a computationally intensive numerical technique based on linear programming to analyze the processes of chromosome maintenance during the cell cycle in yeast, Saccharomyces cerevisiae. We first experimentally identify 19 genes that when ectopically expressed cause chromosome instability. We then build an 18 x 19 matrix by assaying the genetic interactions of pairs of genes that each normally functions to maintain chromosomes, including the 19 genes discovered here. We then use a \"seriation\" algorithm based on linear optimization to find an optimal arrangement of rows and columns to confirm an optimum temporal arrangement of gene influence during cell cycle phases. We experimentally demonstrate that the method yields new biological insights, which we test and validate.

Systems Biology↗

bioRxiv2018

Formal Analysis of Network Motifs

A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.

systems biology↗

bioRxiv2018

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

BackgroundAdvances in large scale tumor sequencing have lead to an understanding that there are combinations of genomic and transcriptomic alterations speciflc to tumor types, shared across many patients. Unfortunately, computational identiflcation of functionally meaningful shared alteration patterns, impacting gene/protein interaction subnetworks, has proven to be challenging. FindingsWe introduce a novel combinatorial method, cd-CAP, for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not), and simultaneously detects multiple alteration proflle conserved subnetworks. ConclusionsIn a number of The Cancer Genome Atlas (TCGA) data sets, cd-CAP identifled large biologically signiflcant subnetworks with conserved alteration patterns, shared across many tumor samples.

systems biology↗

bioRxiv2026

The trade-off between parsimony and model complexity for understanding biomedical mechanisms from mathematical models

Mechanistic mathematical models have been used extensively to provide a deeper understanding of biological mechanisms, including unveiling the regulation of tumour growth and its response to various treatments. However, given the breadth of biological regulatory mechanisms, these models are frequently large and thus prone to potential issues with parameter identifiability. Statistical metrics like the Akaike and Bayesian information criteria can help identify a parsimonious model by balancing goodness of fit against model complexity. Yet simple models may fail to provide sufficient biological insight if they do not adequately capture known physiological processes or mechanisms. A modeller must therefore balance hypothesis generation and biological learning with model tractability. Here, we illustrate this balance using models of ovarian cancer growth and treatment response to cisplatin and immune checkpoint blockade in homologous recombination (HR)-deficient and HR-proficient immunocompetent mouse models. We develop a hierarchy of mathematical models of increasing complexity to describe tumour growth, treatment response, and immune dynamics. Our results highlight the limits of relying purely on statistical metrics for model selection, particularly when the goal is to obtain biological insight and underscore the importance of balancing model complexity to avoid overfitting and parameter unidentifiability.

systems biology↗

bioRxiv2018

Development of a novel protein identification approach to define mitochondrial proteomic signatures in glioblastoma oncogenesis: T98G vs U87MG cell lines model.

Glioblastoma Multiforme is a cancer type with an important mitochondrial component. Here was used mitochondrial proteome Random Sampling in 2D gels from T98G (oxidative metabolism) and U87MG (glycolytic metabolism) cell lines to obtain and analyze representative spots (regardless of their intensity, size, or difference in abundance between cell lines) by Principal Component Analysis for protein identification. Identified proteins were ordered into specific Protein-Protein Interaction networks, to each cell line, showing mitochondrial processes related to metabolic change, invasion, and metastasis; and other nonmitochondrial processes such as DNA translation, chaperone response, and autophagy in gliomas. T98G and U87MG cell lines were used as glioblastoma transition model; representative proteomic signatures, with the most important biological processes in each cell line, were defined. This pipeline analysis describes the metabolic status of each line and defines clear mitochondria performance differences for distinct glioblastoma stages, introducing a new useful strategy for the understanding of glioblastoma carcinogenesis formation.\n\nBiological significanceThis study defines the mitochondria as an organelle that follows and senses the carcinogenesis process by an original proteomic approach, a random sampling in 2DE gels to obtain a representative spots sample and analyzing their relative abundance by Principal Components Analysis; to faithfully describe glioblastoma cells biology.

systems biology↗