Development of constrained fuzzy logic for modeling biological regulatory networks and predicting contextual therapeutic effects

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 199-213).Upon exposure to environmental cues, protein modifications form a complex signaling network that dictates cellular response. In this thesis, we develop methods for using continuous logic-based models to aide our understanding of these signaling networks and facilitate data interpretation. We present a novel modeling framework called constrained fuzzy logic (cFL) that maintains a simple logic-based description of interactions with AND, OR, and NOT gates, but allows for intermediate species activities with mathematical functions relating input and output values (transfer functions). We first train a prior knowledge network (PKN) to data with cFL, which reveals what aspects of the dataset agree or disagree with prior knowledge. The cFL models are trained to a dataset describing signaling proteins in a hepatocellular carcinoma cell line after exposure to ligand cues in the presence or absence of small molecule inhibitors. We find that multiple models with differing topology and parameters explain the data equally well, and it is crucial to consider this non-identifiability during model training and subsequence analysis. Our trained models generate new biological understanding of network crosstalk as well as quantitative predictions of signaling protein activation. In our next applications of cFL, we explore the ability of models either constructed based solely on prior knowledge or trained to dedicated biochemical data to make predictions that answer the following questions: 1) What perturbations to species in the system are effective at accomplishing a clinical goal? and 2) In what environmental conditions are these perturbations effective? We find that we are able to make accurate predictions in both cases. Thus, we offer cFL as a flexible modeling methodology to assist data interpretation and hypothesis generation for choice of therapeutic targets.by Melody K. Morris.Ph.D

Logic-Based Models for the Analysis of Cell Signaling Networks

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.National Institutes of Health (U.S.) (Grant P50- GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

Comparing Signaling Networks between Normal and Transformed Hepatocytes Using Discrete Logical Models

Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of “omic” data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.National Institutes of Health (U.S.) (Grant GM68762)National Institutes of Health (U.S.) (Grant CA112967

Normalization and Statistical Analysis of Multiplexed Bead-Based Immunoassay Data Using Mixed-Effects Modeling

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.United States. Army Research Office (Contract W911NF-09-D-0001)National Institutes of Health (U.S.) (NIH P50-GM68762

Convective and Wave Signatures in Ozone Profiles Over the Equatorial Americas: Views from TC4 (2007) and SHADOZ

During the months of July-August 2007 NASA conducted a research campaign called the Tropical Composition, Clouds and Climate Coupling (TC4) experiment. Vertical profiles of ozone were measured daily using an instrument known as an ozonesonde, which is attached to a weather balloon and launch to altitudes in excess of 30 km. These ozone profiles were measured over coastal Las Tablas, Panama (7.8N, 80W) and several times per week at Alajuela, Costa Rica (ION, 84W). Meteorological systems in the form of waves, detected most prominently in 100- 300 in thick ozone layer in the tropical tropopause layer, occurred in 50% (Las Tablas) and 40% (Alajuela) of the soundings. These layers, associated with vertical displacements and classified as gravity waves ("GW," possibly Kelvin waves), occur with similar stricture and frequency over the Paramaribo (5.8N, 55W) and San Cristobal (0.925, 90W) sites of the Southern Hemisphere Additional Ozonesondes (SHADOZ) network. The gravity wave labeled layers in individual soundings correspond to cloud outflow as indicated by the tracers measured from the NASA DC-8 and other aircraft data, confirming convective initiation of equatorial waves. Layers representing quasi-horizontal displacements, referred to as Rossby waves, are robust features in soundings from 23 July to 5 August. The features associated with Rossby waves correspond to extra-tropical influence, possibly stratospheric, and sometimes to pollution transport. Comparison of Las Tablas and Alajuela ozone budgets with 1999-2007 Paramaribo and San Cristobal soundings shows that TC4 is typical of climatology for the equatorial Americas. Overall during TC4, convection and associated meteorological waves appear to dominate ozone transport in the tropical tropopause layer

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.National Institutes of Health (U.S.) (Grant P50-GM068762)National Institutes of Health (U.S.) (Grant R24-DK090963)United States. Army Research Office (Grant W911NF-09-0001)German Research Foundation (Grant GSC 111

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.National Institutes of Health (U.S.) (NIH grant P50-GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

The expression level of HJURP has an independent prognostic impact and predicts the sensitivity to radiotherapy in breast cancer

INTRODUCTION. HJURP (Holliday Junction Recognition Protein) is a newly discovered gene reported to function at centromeres and to interact with CENPA. However its role in tumor development remains largely unknown. The goal of this study was to investigate the clinical significance of HJURP in breast cancer and its correlation with radiotherapeutic outcome. METHODS. We measured HJURP expression level in human breast cancer cell lines and primary breast cancers by Western blot and/or by Affymetrix Microarray; and determined its associations with clinical variables using standard statistical methods. Validation was performed with the use of published microarray data. We assessed cell growth and apoptosis of breast cancer cells after radiation using high-content image analysis. RESULTS. HJURP was expressed at higher level in breast cancer than in normal breast tissue. HJURP mRNA levels were significantly associated with estrogen receptor (ER), progesterone receptor (PR), Scarff-Bloom-Richardson (SBR) grade, age and Ki67 proliferation indices, but not with pathologic stage, ERBB2, tumor size, or lymph node status. Higher HJURP mRNA levels significantly decreased disease-free and overall survival. HJURP mRNA levels predicted the prognosis better than Ki67 proliferation indices. In a multivariate Cox proportional-hazard regression, including clinical variables as covariates, HJURP mRNA levels remained an independent prognostic factor for disease-free and overall survival. In addition HJURP mRNA levels were an independent prognostic factor over molecular subtypes (normal like, luminal, Erbb2 and basal). Poor clinical outcomes among patients with high HJURP expression were validated in five additional breast cancer cohorts. Furthermore, the patients with high HJURP levels were much more sensitive to radiotherapy. In vitro studies in breast cancer cell lines showed that cells with high HJURP levels were more sensitive to radiation treatment and had a higher rate of apoptosis than those with low levels. Knock down of HJURP in human breast cancer cells using shRNA reduced the sensitivity to radiation treatment. HJURP mRNA levels were significantly correlated with CENPA mRNA levels. CONCLUSIONS. HJURP mRNA level is a prognostic factor for disease-free and overall survival in patients with breast cancer and is a predictive biomarker for sensitivity to radiotherapy.National Institutes of Health, National Cancer Institute (R01 CA116481, P50 CA 5820, P30 CA 82103, U54 CA 112970); Office of Science; U.S. Department of Energy Office of Science, Office of Biological & Environmental Research (DE-AC02-05CH11231

Deciphering mollusc shell production: the roles of genetic mechanisms through to ecology, aquaculture and biomimetics

Most molluscs possess shells, constructed from a vast array of microstructures and architectures. The fully formed shell is composed of calcite or aragonite. These CaCO3 crystals form complex biocomposites with proteins, which although typically less than 5% of total shell mass, play significant roles in determining shell microstructure. Despite much research effort, large knowledge gaps remain in how molluscs construct and maintain their shells, and how they produce such a great diversity of forms. Here we synthesize results on how shell shape, microstructure, composition and organic content vary among, and within, species in response to numerous biotic and abiotic factors. At the local level, temperature, food supply and predation cues significantly affect shell morphology, whilst salinity has a much stronger influence across latitudes. Moreover, we emphasize how advances in genomic technologies [e.g. restriction site-associated DNA sequencing (RAD-Seq) and epigenetics] allow detailed examinations of whether morphological changes result from phenotypic plasticity or genetic adaptation, or a combination of these. RAD-Seq has already identified single nucleotide polymorphisms associated with temperature and aquaculture practices, whilst epigenetic processes have been shown significantly to modify shell construction to local conditions in, for example, Antarctica and New Zealand. We also synthesize results on the costs of shell construction and explore how these affect energetic trade-offs in animal metabolism. The cellular costs are still debated, with CaCO3 precipitation estimates ranging from 1-2 J/mg to 17-55 J/mg depending on experimental and environmental conditions. However, organic components are more expensive (~29 J/mg) and recent data indicate transmembrane calcium ion transporters can involve considerable costs. This review emphasizes the role that molecular analyses have played in demonstrating multiple evolutionary origins of biomineralization genes. Although these are characterized by lineage-specific proteins and unique combinations of co-opted genes, a small set of protein domains have been identified as a conserved biomineralization tool box. We further highlight the use of sequence data sets in providing candidate genes for in situ localization and protein function studies. The former has elucidated gene expression modularity in mantle tissue, improving understanding of the diversity of shell morphology synthesis. RNA interference (RNAi) and clustered regularly interspersed short palindromic repeats - CRISPR-associated protein 9 (CRISPR-Cas9) experiments have provided proof of concept for use in the functional investigation of mollusc gene sequences, showing for example that Pif (aragonite-binding) protein plays a significant role in structured nacre crystal growth and that the Lsdia1 gene sets shell chirality in Lymnaea stagnalis. Much research has focused on the impacts of ocean acidification on molluscs. Initial studies were predominantly pessimistic for future molluscan biodiversity. However, more sophisticated experiments incorporating selective breeding and multiple generations are identifying subtle effects and that variability within mollusc genomes has potential for adaption to future conditions. Furthermore, we highlight recent historical studies based on museum collections that demonstrate a greater resilience of molluscs to climate change compared with experimental data. The future of mollusc research lies not solely with ecological investigations into biodiversity, and this review synthesizes knowledge across disciplines to understand biomineralization. It spans research ranging from evolution and development, through predictions of biodiversity prospects and future-proofing of aquaculture to identifying new biomimetic opportunities and societal benefits from recycling shell products.FCT: UID/Multi/04326/2019; European Marine Biological Research Infrastructure Cluster-EMBRIC (EU H2020 research and innovation program) 654008; European Union Seventh Framework Programme [FP7] ITN project 'CACHE: Calcium in a Changing Environment' under REA 60505; NERC Natural Environment Research Council NE/J500173/1info:eu-repo/semantics/publishedVersio

Adjunctive rifampicin for Staphylococcus aureus bacteraemia (ARREST): a multicentre, randomised, double-blind, placebo-controlled trial.

BACKGROUND: Staphylococcus aureus bacteraemia is a common cause of severe community-acquired and hospital-acquired infection worldwide. We tested the hypothesis that adjunctive rifampicin would reduce bacteriologically confirmed treatment failure or disease recurrence, or death, by enhancing early S aureus killing, sterilising infected foci and blood faster, and reducing risks of dissemination and metastatic infection. METHODS: In this multicentre, randomised, double-blind, placebo-controlled trial, adults (≥18 years) with S aureus bacteraemia who had received ≤96 h of active antibiotic therapy were recruited from 29 UK hospitals. Patients were randomly assigned (1:1) via a computer-generated sequential randomisation list to receive 2 weeks of adjunctive rifampicin (600 mg or 900 mg per day according to weight, oral or intravenous) versus identical placebo, together with standard antibiotic therapy. Randomisation was stratified by centre. Patients, investigators, and those caring for the patients were masked to group allocation. The primary outcome was time to bacteriologically confirmed treatment failure or disease recurrence, or death (all-cause), from randomisation to 12 weeks, adjudicated by an independent review committee masked to the treatment. Analysis was intention to treat. This trial was registered, number ISRCTN37666216, and is closed to new participants. FINDINGS: Between Dec 10, 2012, and Oct 25, 2016, 758 eligible participants were randomly assigned: 370 to rifampicin and 388 to placebo. 485 (64%) participants had community-acquired S aureus infections, and 132 (17%) had nosocomial S aureus infections. 47 (6%) had meticillin-resistant infections. 301 (40%) participants had an initial deep infection focus. Standard antibiotics were given for 29 (IQR 18-45) days; 619 (82%) participants received flucloxacillin. By week 12, 62 (17%) of participants who received rifampicin versus 71 (18%) who received placebo experienced treatment failure or disease recurrence, or died (absolute risk difference -1·4%, 95% CI -7·0 to 4·3; hazard ratio 0·96, 0·68-1·35, p=0·81). From randomisation to 12 weeks, no evidence of differences in serious (p=0·17) or grade 3-4 (p=0·36) adverse events were observed; however, 63 (17%) participants in the rifampicin group versus 39 (10%) in the placebo group had antibiotic or trial drug-modifying adverse events (p=0·004), and 24 (6%) versus six (2%) had drug interactions (p=0·0005). INTERPRETATION: Adjunctive rifampicin provided no overall benefit over standard antibiotic therapy in adults with S aureus bacteraemia. FUNDING: UK National Institute for Health Research Health Technology Assessment