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bioRxiv2017

TRuML: A Translator for Rule-Based Modeling Languages

Rule-based modeling languages, such as the Kappa and BioNetGen languages (BNGL), are powerful frameworks for modeling the dynamics of complex biochemical reaction networks. Each language is distributed with a distinct software suite and modelers may wish to take advantage of both toolsets. This paper introduces a practical application called TRuML that translates models written in either Kappa or BNGL into the other language. While similar in many respects, key differences between the two languages makes translation sufficiently complex that automation becomes a useful tool. TRuML accommodates the languages complexities and produces a semantically equivalent model in the alternate language of the input model when possible and an approximate model in certain other cases. Here, we discuss a number of these complexities and provide examples of equivalent models in both Kappa and BNGL.\n\nCCS CONCEPTS* Applied computing [->] Systems biology; * Computing methodologies [->] Simulation languages;

systems biology↗

bioRxiv2016

Development of prokaryotic cell-free systems for synthetic biology

Prokaryotic cell-free systems are currently heavily used for the production of protein that can be otherwise challenging to produce in cells. However, historically cell-free systems were used to explore natural phenomena before the advent of genetic modification and transformation technology. Recently, synthetic biology has seen a resurgence of this historical use of cell-free systems as a prototyping tool of synthetic and natural genetic circuits. For these cell-free systems to be effective prototyping tools, an understanding of cell-free system mechanics must be established that is not purely protein-expression driven. Here we discuss the development of E. coli-based cell-free systems, with an emphasis on documenting published extract and energy preparation methods into a uniform format. We also discuss additional considerations when applying cell-free systems to synthetic biology.

Synthetic Biology↗

bioRxiv2018

Enhanced Waddington Landscape Model with Cell-CellCommunication Can Explain Molecular Mechanisms ofSelf-Organization

The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured on particular micropatterns, can self-organize and mimic early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential. Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation. To prevent overfitting of the model, there is a minimal number of parameters in the model, which are sufficient to address different experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters, and unexpected spotted forms of the germ layers under certain conditions. This model provides a basis for in-silico modeling of self-organization.

systems biology↗

bioRxiv2018

CausalTab: PSI-MITAB 2.8 updated format for signaling data representation and dissemination

Combining multiple layers of information underlying biological complexity into a structured framework, and in particular deciphering the molecular mechanisms behind cellular phenotypes, represent two challenges in systems biology. A key task is the formalisation of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signaling information, such as SIGNOR, SignaLink and IntAct, focus on capturing, organising and displaying signaling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources, through the development of a standardized exchange format, ontologies and controlled vocabularies supporting the domain of causal interactions. Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signaling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated MITAB2.8. We define the new term \"causal interaction\", and related child terms, which are children of the PSI-MI \"molecular interaction\" term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signaling networks more precisely and with higher coverage than before.

systems biology↗

bioRxiv2015

An Effective Model of HL-60 Differentiation

We present an effective model All-Trans Retinoic Acid (ATRA)-induced differentiation of HL-60 cells. The model describes a key architectural feature of ATRA-induced differentiation, positive feedback between an ATRA-inducible signalsome complex involving many proteins including Vav1, a guanine nucleotide exchange factor, and the activation of the mitogen activated protein kinase (MAPK) cascade. The model, which was developed by integrating logical rules with kinetic modeling, was significantly smaller than previous models. However, despite its simplicity, it captured key features of ATRA induced differentiation of HL-60 cells. We identified an ensemble of effective model parameters using measurements taken from ATRA-induced HL-60 cells. Using these parameters, model analysis predicted that MAPK activation was bistable as a function of ATRA exposure. Conformational experiments supported ATRA-induced bistability. These findings, combined with other literature evidence, suggest that positive feedback is central to a diversity of cell fate programs.\n\nIndex Terms--Mathematical modeling, systems biology

Systems Biology↗

bioRxiv2017

Fundamental limits on dynamic inference from single cell snapshots

Single cell expression profiling reveals the molecular states of individual cells with unprecedented detail. However, because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. But some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, Population Balance Analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.\n\nSignificanceSeeing a snapshot of individuals at different stages of a process can reveal what the process would look like for a single individual over time. Biologists apply this principle to infer temporal sequences of gene expression states in cells from measurements made at a single moment in time. However, these inferences are fundamentally under-determined. Using a conservation law, we enumerate reasons that there is no unique dynamics associated with a single snapshot, limiting our ability to infer gene regulatory mechanisms. We then propose a method for dynamic inference that provides a unique dynamic solution under defined approximations and apply it to data from bone marrow stem cells. Overall, this study introduces formal biophysical approaches to single cell bioinformatics.\n\nClassificationBIOLOGICAL SCIENCES / Systems Biology

systems biology↗

bioRxiv2017

Mechanistic hierarchical population model identifies latent causes of cell-to-cell variability

All biological systems exhibit cell-to-cell variability, and this variability often has functional implications. To gain a thorough understanding of biological processes, the latent causes and underlying mechanisms of this variability must be elucidated. Cell populations comprising multiple distinct subpopulations are commonplace in biology, yet no current methods allow the sources of variability between and within individual subpopulations to be identified. This limits the analysis of single-cell data, for example provided by flow cytometry and microscopy. In this study, we present a data-driven modeling framework for the analysis of populations comprising heterogeneous subpopulations. Our approach combines mixture modeling with frameworks for distribution approximation, facilitating the integration of multiple single-cell datasets and the detection of causal differences between and within subpopulations. The computational efficiency of our framework allows hundreds of competing hypotheses to be compared, giving unprecedented depth of a study. We demonstrated the ability of our method to capture multiple levels of heterogeneity in the analyzes of simulated data and data from highly heterogeneous sensory neurons involved in pain initiation. Our approach identified the sources of cell-to-cell variability and revealed mechanisms that underlie the modulation of nerve growth factor-induced Erk1/2 signaling by extracellular scaffolds.

systems biology↗

bioRxiv2017

Inferring propensity amongst lung and breast carcinomas via overlapped gene expression profiles

Reconstruction of biological networks for topological analyses helps in correlation identification between various types of biomarkers. These networks have been vital components of System Biology in present era. Genes are the basic physical and structural unit of heredity. Genes act as instructions to make molecules called proteins. Alterations in the normal sequence of these genes are the root cause of various diseases and cancer is the prominent example disease caused by gene alteration or mutation. These slight alterations can be detected by microarray analysis. The high throughput data obtained by microarray experiments aid scientists in reconstructing cancer specific gene regulatory networks. The purpose of experiment performed is to find out the overlapping of the gene expression profiles of breast and lung cancer data, so that the common hub genes can be sifted and utilized as drug targets which could be used for the treatment of diseased conditions. In this study, first the differentially expressed genes have been identified (lung cancer and breast cancer), followed by a filtration approach and most significant genes are chosen using paired t-test and gene regulatory network construction. The obtained result has been checked and validated with the available databases and literature.

systems biology↗

bioRxiv2017

Probabilistic Graphical Models Relate Immune Status With Response To Neoadjuvant Chemo-Therapy In Breast Cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patients immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

cancer biology↗

bioRxiv2016

Network Identification Methods

Recently, network inference algorithms have grown tremendously in the field of systems biology because network identification is essential for understanding relationships between regulation mechanisms for genes, elucidating functional mechanisms underlying cellular processes, as well as identifying molecular targets for discoveries in medicines. This article provides a brief overview of different approaches used to identify biological networks and reviews recent advances in network identification.

Systems Biology↗

bioRxiv2018

Using maximum entropy production to describe microbial biogeochemistry over time and space in a meromictic pond

The maximum entropy production (MEP) conjecture posits that systems with many degrees of freedom will likely organize to maximize the rate of free energy dissipation. Previous work indicates that biological systems can outcompete abiotic systems by maximizing free energy dissipation over time by utilizing temporal strategies acquired and refined by evolution, and over space via cooperation. In this study, we develop an MEP model to describe biogeochemistry observed in Siders Pond, a phosphate limited meromictic system located in Falmouth, MA that exhibits steep chemical gradients due to density-driven stratification that supports anaerobic photosynthesis as well as microbial communities that catalyze redox cycles involving O, N, S, Fe and Mn. The MEP model uses a metabolic network to represent microbial redox reactions, where biomass allocation and reaction rates are determined by solving an optimization problem that maximizes entropy production over time and a 1D vertical profile constrained by an advection-dispersion-reaction model. We introduce a new approach for modeling phototrophy and explicitly represent aerobic photoautotrophs, anoxygenic photoheterotrophs and anaerobic photoautotrophs. The metabolic network also includes reactions for heterotrophic bacteria, sulfate reducing bacteria, sulfide oxidizing bacteria and aerobic and anaerobic grazers. Model results were compared to observations of biogeochemical constituents collected over a 24 hour period at 8 depths at a single 15 m deep station in Siders Pond. Maximizing entropy production over long (3 d) intervals produced results more similar to field observations than short (0.25 d) interval optimizations, which support the importance of temporal strategies for maximizing entropy production over time. Furthermore, we found that entropy production must be maximized locally instead of globally where energy potentials are degraded quickly by abiotic processes, such as light absorption by water. This combination of field observations with modeling results show that microbial systems in nature can be accurately described by the maximum entropy production conjecture applied over time and space.

systems biology↗

bioRxiv2017

The latent geometry of the human protein interaction network

To mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

systems biology↗

bioRxiv2015

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed Virtual Patients. In order to reproduce the statistics of a clinical population, Virtual Patients are often weighted to form a Virtual Population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate Virtual Patients and, from this ensemble, demonstrate how to select a Virtual Population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious Virtual Patients become over-represented in Virtual Populations.

Pharmacology and Toxicology↗

bioRxiv2015

Role of autoregulation and relative synthesis of operon partners in alternative sigma factor networks

Despite the central role of alternative sigma factors in bacterial stress response and virulence their regulation remains incompletely understood. Here we investigate one of the best-studied examples of alternative sigma factors: the {sigma}B network that controls the general stress response of Bacillus subtilis to uncover widely relevant general design principles that describe the structure-function relationship of alternative sigma factor regulatory networks. We show that the relative stoichiometry of the synthesis rates of {sigma}B, its anti-sigma factor RsbW and the anti-anti-sigma factor RsbV plays a critical role in shaping the network behavior by forcing the {sigma}B network to function as an ultrasensitive negative feedback loop. We further demonstrate how this negative feedback regulation insulates alternative sigma factor activity from competition with the housekeeping sigma factor for RNA polymerase and allows multiple stress sigma factors to function simultaneously with little competitive interference.\n\nMajor Subject Areas: Computational and systems biology, Microbiology & Infectious disease\n\nResearch Organism: Bacillus subtilis

Systems Biology↗

bioRxiv2016

GeNNet: An Integrated Platform for Unifying Scientific Workflow Management and Graph Databases for Transcriptome Data Analysis

BackgroundThere are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced may additionally be integrated with other biological databases, such as Protein-Protein Interactions and annotations. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managingthe resulting data and its respective metadata are challenging tasks. Running in-silico experiments to structure and compose the information as needed for analysis is a daunting task. Different programsmay need to be applied and different files are produced during the experiment cycle. In this context,the availability of a platform supporting experiment execution is paramount.\n\nResultsWe present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. GeNNet includes pre-loaded biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and geneset enrichment analysis. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships.The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene regulatory networks.\n\nConclusionsGeNNet is the first platform to integrate the analytical process of transcriptome data with graph database. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers as well can add new functionality to each component of GeNNet. The resulting data allows for testing previous hypotheses about an experiment as well as exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms.

bioinformatics↗

bioRxiv2016

System-wide quantitative proteomics of the metabolic syndrome in mice: genotypic and dietary effects

Advances in mass spectrometry have made the quantitative measurement of proteins across multiple samples a reality, allowing for the study of complex biological systems such as the metabolic syndrome. Although deregulation of lipid metabolism and increased hepatic storage of triacylglycerides are known to play a part in the onset of the metabolic syndrome, its molecular basis and dependency on dietary and genotypic factors are poorly characterized. Here, we used a rich experimental design with two different mouse strains, dietary and metabolic perturbations to generate a compendium of quantitative proteome data, using three mass spectrometry strategies. The data recapitulates known properties of the metabolic system and indicate differential molecular adaptation of the two mouse strains to perturbations, contributing to a better understanding of the metabolic syndrome. We show that high-quality, high-throughput proteomic datasets provide an unbiased broad overview of complex systems upon perturbation.\n\nHIGHLIGHTSO_LIRich experimental design with two mouse strains, dietary and metabolic perturbations\nC_LIO_LIThree mass spectrometry proteomic data collection strategies in one data compendium\nC_LIO_LISWATH-MS data with systematic perturbation provide a broad unbiased view of the system\nC_LIO_LIThe data reveals differential adaptation of molecular systems in two mouse strains\nC_LI

Systems Biology↗

bioRxiv2017

A quantitative systems pharmacology analysis of KRAS G12C covalent inhibitors

The KRAS oncogene is the most common, activating, oncogenic mutation in human cancer. KRAS has proven difficult to target effectively. Two different strategies have recently been described for covalently targeting the most common activating KRAS mutant in lung cancer, KRAS G12C. Previously, we have developed a computational model of the processes that regulate Ras activation and this model has proven useful for understanding the complex behaviors of Ras signaling. Here, we use this model to perform a computational systems pharmacology analysis of KRAS G12C targeted covalent inhibitors. After updating our model to include Ras protein turnover, we verified the validity of our model for problems in this domain by comparing model behaviors with experimental behaviors. The model naturally reproduces previous experimental data, including several experimental observations that were interpreted as being contrary to conventional wisdom. Overall, this suggests that our model describes the Ras system well, including those areas where conventional wisdom struggles. We then used the model to investigate possible strategies to improve the ability of KRAS G12C inhibitors to inhibit Ras pathway signaling. We identify one, as of yet unexplored mechanism, that, if optimized, could improve the effectiveness of one class of KRAS inhibitor. We also simulated resistance to targeted therapies and found that resistance promoting mutations may reverse which class of KRAS G12C inhibitor inhibits the system better, suggesting that there may be value to pursuing both types of KRAS G12C inhibitors. Overall, this work demonstrates that systems biology approaches can provide insights that inform the drug development process.

systems biology↗

bioRxiv2016

Control mechanisms for stochastic biochemical systems via computation of reachable sets

Controlling the behaviour of cells by rationally guiding molecular processes is an overarching aim of much of synthetic biology. Molecular processes, however, are notoriously noisy and frequently non-linear. We present an approach to studying the impact of control measures on motifs of molecular interactions, that addresses the problems faced in biological systems: stochasticity, parameter uncertainty, and non-linearity. We show that our reachability analysis formalism can describe the potential behaviour of biological (naturally evolved as well as engineered) systems, and provides a set of bounds on their dynamics at the level of population statistics: for example, we can obtain the possible ranges of means and variances of mRNA and protein expression levels, even in the presence of uncertainty about model parameters.

Systems Biology↗