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Systems Biology Understanding of the Effects of Lithium on Cancer.

Lithium has many widely varying biochemical and phenomenological effects, suggesting that a systems biology approach is required to understand its action. Multiple lines of evidence point to lithium as a significant factor in development of cancer, showing that understanding lithium action is of high importance. In this paper we undertake first steps towards a systems approach by analyzing mutual enrichment between the interactomes of lithium-sensitive enzymes and the pathways associated with cancer. This work integrates information from two important databases, STRING and KEGG pathways. We find that for the majority of cancer pathways the mutual enrichment is many times greater than chance, reinforcing previous lines of evidence that lithium is an important influence on cancer.

cancer biology

Formation Of Dominant Mode By Evolution In Biological Systems

A reduction in high-dimensional phenotypic states to a few degrees of freedom is essential to understand biological systems. One possible origin of such a reduction (as recently discussed) is the steady growth of cells that constrains each components replication rate. Here, in contrast, our aim is to investigate consequences of evolutionary robustness, which is shown to cause a stronger dimensional reduction in possible phenotypic changes in response to a variety of environmental conditions. First, we examined global protein expression changes in Escherichia coli after various environmental perturbations. Remarkably, they were proportional across components, across different types of environmental conditions, while the proportion coefficient corresponded to the change in growth rate. Because such global proportionality is not generic to all systems under a condition of steady growth, a new conceptual framework is then needed. We hypothesized that such proportionality is a result of evolution. To test this hypothesis, we analyzed a cell model--with a huge number of components, that reproduces itself via a catalytic reaction network--and confirmed that common proportionality in the concentrations of all components is shaped through evolutionary processes to maximize cell growth (and therefore fitness) under a given environmental condition. Furthermore, we found that the changes in concentration across all components in response to environmental and evolutionary changes are constrained to the changes along a one-dimensional major axis within a huge-dimensional state space. On the basis of these observations, we propose a theory in which high-dimensional phenotypic changes after evolution are constrained to the points near a one-dimensional major axis that correlates with the growth rate, to achieve both evolutionary robustness and plasticity. By formulating this proposition in terms of dynamical systems, broad experimental and numerical results on phenotypic changes caused by evolution and adaptation are coherently explained.\n\nSummaryCells generally consist of thousands of components whose abundance levels change through adaptation and evolution. Accordingly, each steady cell state can be represented as a point in a high-dimensional space of component concentrations. In the context of equilibrium statistical thermodynamics, even though the state space is high-dimensional, macroscopic description only by a few degrees of freedom is possible for equilibrium systems; however, such characterization by a few degrees of freedom has not yet been achieved for cell systems. Given that they are not in equilibrium, we need some other constraint to be imposed. Here, by restricting our focus to a cellular state with steady growth that is achieved after evolution,we examine how the expression levels of its several components change under different environmental conditions. On the basis of analysis of protein expression levels in recent bacterial experiments as well as the results of simulations using a toy cell model consisting of thousands of components that are reproduced by catalytic reactions, we found that adaptation and evolutionary paths in high-dimensional state space are constrained to changes along a one-dimensional curve, representing a major axis for all the observed changes. Moreover, this one-dimensional structure emerges only after evolution and is not applicable to any system showing steady growth. This curve is determined by the growth rate of a cell, and thus it is possible to describe an evolved system by means of a growth rate function. All the observed results are consistent with the hypothesis that changes in high-dimensional states are nearly confined to the major axis in response to environmental, evolutionary, and stochastic perturbations. This description opens up the possibility to characterize a cell state as a macroscopic growth rate, as is the case for the thermodynamic potential. This approach can provide estimates of which phenotypic changes are theoretically more evolvable, as predicted simply from their observed environmental responses.

biophysics

Systems Biology Analysis Using A Genome-Scale Metabolic Model Shows That Phosphine Triggers Global Metabolic Suppression In A Resistant Strain Of C. elegans

BackgroundPest insects are increasingly resistant to phosphine gas, which is used globally to protect grain reserves. The enzyme dihydrolipoamide dehydrogenase (DLD) is a phosphine resistance factor and participates in four key steps of core metabolism, making it a potential central metabolic regulator.\n\nResultsHere we used microarray data and NMR-based metabolomics to characterize the phosphine response of wild-type C. elegans and the phosphine-resistant strain dld-1(wr4) which has a partial loss-of-function mutation in the gene for DLD. In addition, we have constructed CeCon, a C. elegans genome-scale metabolic model to facilitate integration of gene expression and metabolomics data.\n\nConclusionsThe resulting systems biology analysis is consistent with the hypothesis that adaptation to a hypometabolic state is the most prominent mechanism of phosphine resistance in this nematode strain. The involvement of DLD in regulating and creating hypometabolic adaptation has implications for other biological phenomena involving hypometabolism, such as reperfusion injury and metabolic resistance.

systems biology

Protein-Protein Interaction Network Analysis and Identification of Key Players in N-hydroxy-nor-L-Arg (nor-NOHA) and N(omega)-hydroxy-L-arginine (NOHA) Mediated Pathways for Treatment of Cancer Through Arginase Inhibiton: Insights from Systems Biology

L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins-SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.

systems biology

Systems biology analysis of mitogen activated protein kinase inhibitor resistance in malignant melanoma

Kinase inhibition in the mitogen activated protein kinase (MAPK) pathway is a standard therapy for cancer patients with activating BRAF mutations. However, the anti-tumorigenic effect and clinical benefit are only transient, and tumors are prone to treatment resistance and relapse. To elucidate mechanistic insights into drug resistance, we have established an in vitro cellular model of MAPK inhibitor resistance in malignant melanoma. The cellular model evolved in response to clinical dosage of BRAF inhibitor, vemurafenib, PLX4032. We profiled transcriptomic changes using RNA-Seq and RT-qPCR arrays. Pathways of melanogenesis, MAPK signaling, cell cycle, and metabolism were significantly enriched among the set of differentially expressed genes of vemurafenib-resistant cells vs control. The transcriptomic changes were validated in two distinct melanoma models, SK-MEL-28 and A375. Both cell lines have activating BRAF mutations and display metastatic potential. Downregulation of tumor suppressors and negative MAPK regulators, dual specific phosphatases, reengages mitogenic signaling. Upregulation of growth factors or cytokine receptors triggers signaling pathways circumventing BRAF blockage. Changes in amino acid and one-carbon metabolism support cellular proliferation despite MAPK inhibitor treatment. In addition, an upregulation of pigmentation in inhibitor resistant melanoma cells was observed. Cellular pathways utilized during inhibitor resistance promoted melanogenesis, a pathway which partially overlaps with MAPK signaling. Upstream regulator analysis suggested gene expression changes of forkhead box and hypoxia inducible factor family transcription factors. The established cellular models offer mechanistic insight into cellular changes and therapeutic targets under inhibitor resistance in malignant melanoma. At a systems biology level, the MAPK pathway undergoes major rewiring while acquiring inhibitor resistance. The outcome of this transcriptional plasticity is selection for a set of transcriptional master regulators, which circumvent upstream targeted kinases and provide alternative routes of mitogenic activation. A fine-woven network of redundant signals maintains similar effector genes allowing for tumor cell survival and malignant progression in therapy resistant cancer.

systems biology

Tellurium: A Python Based Modeling and Reproducibility Platform for Systems Biology

In this article, we present Tellurium, a powerful Python-based integrated environment designed for model building, analysis, simulation and reproducibility in systems and synthetic biology. Tellurium is a modular, cross-platform, and open-source integrated development environment (IDE) composed of multiple libraries, plugins, and specialized modules and methods. Tellurium ensures exchangeability and reproducibility of computational models by supporting SBML (Systems Biology Markup Language), SED-ML (Simulation Experiment Description Markup Language), the COMBINE archive, and SBOL (Synthetic Biology Open Language). Tellurium is a self-contained modeling platform which comes with a fully configured Python distribution independent of other local Python installations on the target machine. The main interface is based on the Spyder IDE which has a highly accessible user interface akin to MATLAB (https://www.mathworks.com/). Tellurium uses libRoadRunner as the default SBML simulation engine due to its superior performance, scalability and ease of integration. libRoadRunner supports deterministic simulations, stochastic simulations and steady state analyses. Tellurium also includes Antimony, a human-readable model definition language which can be converted to and from SBML. Other standard Python scientific libraries such as NumPy, SciPy, and matplotlib are included by default. Additionally, we include several user-friendly plugins and advanced modules for a wide-variety of applications, ranging from visualization tools to complex algorithms for bifurcation analysis and multi-dimensional parameter scanning. By combining multiple libraries, plugins, and modules into a single package, Tellurium provides a unified but extensible solution for biological modeling and simulation.

Systems Biology

A Metabolic Labeling Strategy for Tracking Protein Synthesis in Complex Biological Systems

Protein synthesis supports most biological processes. In the brain in particular, protein synthesis plays a critical role in physiological and pathological states. Here, we describe Tellurophene-Alkyne Cycloaddition-mediated Amino acid Tagging (TeACAT), a versatile strategy for fast, facile, and flexible tagging of newly synthesized proteins in mice. TeACAT is based on metabolic incorporation of the non-canonical amino acid TePhe into proteins by the endogenous protein synthesis machinery. Due to their high similarity, TePhe can efficiently replace canonical Phe without dietary or genetic manipulation. The subsequent bio-orthogonal reaction of TePhe with either fluorescent dyes or affinity handles enables both visualization and affinity enrichment of proteins synthesized during TePhe exposure. TeACAT is compatible with immunofluorescence for cell-type specific visualization of protein synthesis with subcellular resolution and can be used in conjunction with routine proteomics to identify and quantify newly synthesized proteins. Robust incorporation into the mouse proteome was observed on the scale of hours to days, allowing the interrogation of various biological processes. In summary, TeACAT enables the visualization and quantification of protein synthesis with minimal perturbation for biological discoveries.

molecular biology

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

bioinformatics

Bridging the Genotype and the Phenotype: Towards An Epigenetic Landscape Approach to Evolutionary Systems Biology

Understanding the mapping of genotypes into phenotypes is a central challenge of current biological research. Such mapping, conceptually represents a developmental mechanism through which phenotypic variation can be generated. Given the nongenetic character of developmental dynamics, phenotypic variation to a great extent has been neglected in the study of evolution. What is the relevance of considering this generative process in the study of evolution? How can we study its evolutionary consequences? Despite an historical systematic bias towards linear causation schemes in biology; in the post-genomic era, a systems-view to biology based on nonlinear (network) thinking is increasingly being adopted. Within this view, evolutionary dynamics can be studied using simple dynamical models of gene regulatory networks (GRNs). Through the study of GRN dynamics, genotypes and phenotypes can be unambiguously defined. The orchestrating role of GRNs constitutes an operational non-linear genotype-phenotype map. Further extension of these GRN models in order to explore and characterize an associated Epigenetic Landscape enables the study of the evolutionary consequences of both genetic and non-genetic sources of phenotypic variation within the same coherent theoretical framework. The merging of conceptually clear theories, computational/mathematical tools, and molecular/genomic data into coherent frameworks could be the basis for a transformation of biological research from mainly a descriptive exercise into a truly mechanistic, explanatory endeavor.

Systems Biology

A systems biology approach uncovers the core gene regulatory network governing iridophore fate choice from the neural crest.

Multipotent neural crest (NC) progenitors generate an astonishing array of derivatives, including neuronal, skeletal components and pigment cells (chromatophores), but the molecular mechanisms allowing balanced selection of each fate remain unknown. In zebrafish, melanocytes, iridophores and xanthophores, the three chromatophore lineages, are thought to share progenitors and so lend themselves to investigating the complex gene regulatory networks (GRNs) underlying fate segregation of NC progenitors. Although the core GRN governing melanocyte specification has been previously established, those guiding iridophore and xanthophore development remain elusive. Here we focus on the iridophore GRN, where mutant phenotypes identify the transcription factors Sox10, Tfec and Mitfa and the receptor tyrosine kinase, Ltk, as key players. We present expression data, as well as loss and gain of function results, guiding the derivation of an initial iridophore specification GRN. Moreover, we use an iterative process of mathematical modelling, supplemented with a novel, Monte Carlo screening algorithm suited to the qualitative nature of the experimental data, to allow for rigorous predictive exploration of the GRN dynamics. Predictions were experimentally evaluated and testable hypotheses were derived to construct an improved version of the GRN, which we showed produced outputs consistent with experimentally observed gene expression dynamics. Our study reveals multiple important regulatory features, notably a sox10-dependent positive feedback loop between tfec and ltk driving iridophore specification; the molecular basis of sox10 maintenance throughout iridophore development; and the cooperation between sox10 and tfec in driving expression of pnp4a, a key differentiation gene. We also assess a candidate repressor of mitfa, a melanocyte-specific target of sox10. Surprisingly, our data challenge the reported role of Foxd3, an established mitfa repressor, in iridophore regulation. Our study builds upon our previous systems biology approach, by incorporating physiologically-relevant parameter values and rigorous evaluation of parameter values within a qualitative data framework, to establish for the first time the core GRN guiding specification of the iridophore lineage.\n\nAuthor SummaryMultipotent neural crest (NC) progenitors generate an astonishing array of derivatives, including neuronal, skeletal components and pigment cells, but the molecular mechanisms allowing balanced selection of each fate remain unknown. In zebrafish, melanocytes, iridophores and xanthophores, the three chromatophore lineages, are thought to share progenitors and so lend themselves to investigating the complex gene regulatory networks (GRNs) underlying fate segregation of NC progenitors. Although the core GRN governing melanocyte specification has been previously established, those guiding iridophore and xanthophore development remain elusive. Here we present expression data, as well as loss and gain of function results, guiding the derivation of a core iridophore specification GRN. Moreover, we use a process of mathematical modelling and rigorous computational exploration of the GRN to predict gene expression dynamics, assessing them by criteria suited to the qualitative nature of our current understanding of iridophore development. Predictions were experimentally evaluated and testable hypotheses were derived to construct an improved version of the GRN, which we showed produced outputs consistent with experimentally observed gene expression dynamics. The core iridophore GRN defined here is a key stepping stone towards exploring how chromatophores fate decisions are made in multipotent NC progenitors.

developmental biology

rxncon 2.0: a language for executable molecular systems biology

Large-scale knowledge bases and models become increasingly important to systematise and interpret empirical knowledge on cellular systems. In signalling networks, as opposed to metabolic networks, distinct modifications of and bonds between components combine into very large numbers of possible configurations, or microstates. These are essentially never measured in vivo, making explicit modelling strategies both impractical and problematic. Here, we present rxncon 2.0, the second generation rxncon language, as a tool to define signal transduction networks at the level of empirical data. By expressing both reactions and contingencies (contextual constraints on reactions) in terms of elemental states, both the combinatorial complexity and the discrepancy to empirical data can be minimised. It works as a higher-level language natural to biologists, which can be compiled into a range of graphical formats or executable models. Taken together, the rxncon language combines mechanistic precision with scalability in a composable and compilable language, that is designed for building executable knowledge bases on the molecular biology of signalling systems.

systems biology

Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model

Many biological processes can be described geometrically in a simple way: stem cell differentiation can be represented as a branching tree and cell division can be depicted as a cycle. In this paper we introduce the geometric hidden Markov model (GHMM), a dynamical model whose goal is to capture the low-dimensional characteristics of biological processes from multivariate time series data. The framework integrates a graph-theoretical algorithm for dimensionality reduction with a latent variable model for sequential data. We analyzed time series data generated by an in silico model of a biomolecular circuit, the represillator. The trained model has a simple structure: the latent Markov chain corresponds to a two-dimensional lattice. We show that the short-term and long-term predictions of the GHMM reflect the oscillatory behaviour of the genetic circuit. Analysis of the inferred model with a community detection methods leads to a coarse-grained representation of the process.

bioinformatics

Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

Systems Biology

Genome-wide characterization of Phytophthora infestansmetabolism: a systems biology approach

Genome-scale metabolic models (GEMs) provide a functional view of the complex network of biochemical reactions in the living cell. Initially mainly applied to reconstruct the metabolism of model organisms, the availability of increasingly sophisticated reconstruction methods and more extensive biochemical databases now make it possible to reconstruct GEMs for less characterized organisms as well, and have the potential to unravel the metabolism in pathogen-host systems. Here we present a GEM for the oomycete plant pathogen Phytophthora infestans as a first step towards an integrative model with its host. We predict the biochemical reactions in different cellular compartments and investigate the gene-protein-reaction associations in this model to get an impression of the biochemical capabilities of P. infestans. Furthermore, we generate life stage-specific models to place the transcriptomic changes of genes encoding metabolic enzymes into a functional context. In sporangia and zoospores there is an overall downregulation, most strikingly reflected in the fatty acid biosynthesis pathway. To investigate the robustness of the GEM, we simulate gene deletions to predict which enzymes are essential for in vitro growth. While there is room for improvement, this first model is an essential step towards an understanding of P. infestans and its interactions with plants as a system, which will help to formulate new hypotheses on infection mechanisms and disease prevention.

systems biology

ssbio: A Python Framework for Structural Systems Biology

SummaryWorking with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.\n\nAvailability and Implementationssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb.\n\nContactnmih@ucsd.edu\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

systems biology

Integrated systems biology and imaging of the smallest free-living eukaryote Ostreococcus tauri

Ostreococcus tauri is an ancient phototrophic microalgae that possesses favorable genetic and cellular characteristics for reductionist studies probing biosystem design and dynamics. Here multimodal bioimaging and multi-omics techniques were combined to interrogate O. tauri cellular changes in response to variations in bioavailable nitrogen and carbon ratios. Confocal microscopy, stimulated Raman scattering, and cryo-soft x-ray tomography revealed whole cell ultrastructural dynamics and composition while proteomic and lipidomic profiling captured changes at the molecular and macromolecular scale.\n\nDespite several energy dense long-chain triacylglycerol lipids showing more than 40-fold higher abundance under N deprivation, only a few proteins directly associated with lipid biogenesis showed significant expression changes. However, the entire pathway for starch granule biosynthesis was highly upregulated suggesting much of the cellular energy is preferentially directed towards starch over lipid accumulation. Additionally, three of the five most downregulated and five of the ten most upregulated proteins during severe nitrogen depletion were unnamed protein products that warrant additional biochemical analysis and functional annotation to control carbon transformation dynamics in this smallest eukaryote.

systems biology

System Biology Modeling with Compositional Microbiome Data Reveals Personalized Gut Microbial Dynamics and Keystone Species

A growing body of literature points to the important roles that different microbial communities play in diverse natural environments and the human body. The dynamics of these communities is driven by a range of microbial interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood, making it hard to predict the response of the community to different perturbations. With the increasing availability of high-throughput sequencing based community composition data, it is now conceivable to directly learn models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is however affected by several experimental limitations, particularly the compositional nature of sequencing data. We present a new computational approach (BEEM) that addresses this key limitation in the inference of generalised Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference in an expectation maximization like algorithm (BEEM). Surprisingly, BEEM outperforms state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEMs application to previously inaccessible public datasets (due to the lack of biomass data) allowed us for the first time to analyse microbial communities in the human gut on a per individual basis, revealing personalised dynamics and keystone species.

systems biology

A note on stochastic modeling of biological systems: automatic generation of an optimized Gillespie algorithm

Signaling pathways and gene regulatory networks (GRNs) play a central role in the signal trans-duction and regulation of biochemical processes occurring within the cellular environment. Under-standing their mechanisms and dynamics is of major interest in various areas of life sciences and biological sciences. For example controlling stem cell fate decision requires a comprehension of the dynamical behavior of the networks involved in stem cell differentiation and pluripotency mainte-nance. In addition to analytical mathematical methods which are applicable for small or medium sized systems, there are many computational approaches to model and analyze the behavior of larger systems. However, from a dynamical point of view, modeling a combination of signaling pathways and GRNs present several challenges. Indeed, in addition to being of large dimensionality, these systems have specific dynamical features. Among the most commonly encountered is that the signal transduction controlled by the signaling pathways occurs at a different time scale than the transcription and translation processes. Also, stochasticity is known to strongly impact the regulation of gene expression. In this paper, we describe a simple implementation of an optimized version of the Gille-spie algorithm for simulating relatively large biological networks which include delayed reactions. The implementation presented herein comes with a script for automatically generating the different data structures and source files of the algorithm using standardized input files.\n\nCode availabilityThe Fortran90 implementation of the code and the R script described here as well as the tutorial with practical instructions are stored on the following github repository qvhaelen/ typhon

systems biology