Geoffrey Koh

A decompositional approach to parameter estimation in pathway modeling

Parameter estimation is a critical problem in modeling biological pathways. It is difficult because of the large number of parameters to be estimated and the limited experimental data available. In this paper, we propose a decompositional approach to parameter estimation. It exploits the structure of a large pathway model to break it into smaller components, whose parameters can then be estimated independently. This leads to significant improvements in computational efficiency. We present our approach in the context of Hybrid Functional Petri Net modeling and evolutionary search for parameter value estimation. However, the approach can be easily extended to other modeling frameworks and is independent of the search method used. We have tested our approach on a detailed model of the Akt and MAPK pathways with two known and one hypothesized crosstalk mechanisms. The entire model contains 84 unknown parameters. Our simulation results exhibit good correlation with experimental data, and they yield positive evidence in support of the hypothesized crosstalk between the two pathways.

Software applications for flux balance analysis

Flux balance analysis (FBA) is a widely used computational method for characterizing and engineering intrinsic cellular metabolism. The increasing number of its successful applications and growing popularity are possibly attributable to the availability of specific software tools for FBA. Each tool has its unique features and limitations with respect to operational environment, user-interface and supported analysis algorithms. Presented herein is an in-depth evaluation of currently available FBA applications, focusing mainly on usability, functionality, graphical representation and inter-operability. Overall, most of the applications are able to perform basic features of model creation and FBA simulation. COBRA toolbox, OptFlux and FASIMU are versatile to support advanced in silico algorithms to identify environmental and genetic targets for strain design. SurreyFBA, WEbcoli, Acorn, FAME, GEMSiRV and MetaFluxNet are the distinct tools which provide the user friendly interfaces in model handling. In terms of software architecture, FBA-SimVis and OptFlux have the flexible environments as they enable the plug-in/add-on feature to aid prospective functional extensions. Notably, an increasing trend towards the implementation of more tailored e-services such as central model repository and assistance to collaborative efforts was observed among the web-based applications with the help of advanced web-technologies. Furthermore, most recent applications such as the Model SEED, FAME, MetaFlux and MicrobesFlux have even included several routines to facilitate the reconstruction of genome-scale metabolic models. Finally, a brief discussion on the future directions of FBA applications was made for the benefit of potential tool developers.

Tyrosine Phosphorylation Profiling in FGF-2 Stimulated Human Embryonic Stem Cells

The role of fibroblast growth factor-2 (FGF-2) in maintaining undifferentiated human embryonic stem cells (hESC) was investigated using a targeted phosphoproteomics approach to specifically profile tyrosine phosphorylation events following FGF-2 stimulation. A cumulative total number of 735 unique tyrosine phosphorylation sites on 430 proteins were identified, by far the largest inventory to date for hESC. Early signaling events in FGF-2 stimulated hESC were quantitatively monitored using stable isotope dimethyl labeling, resulting in temporal tyrosine phosphorylation profiles of 316 unique phosphotyrosine peptides originating from 188 proteins. Apart from the rapid activation of all four FGF receptors, trans-activation of several other receptor tyrosine kinases (RTKs) was observed as well as induced tyrosine phosphorylation of downstream proteins such as PI3-K, MAPK and several Src family members. Both PI3-K and MAPK have been linked to hESC maintenance through FGF-2 mediated signaling. The observed activation of the Src kinase family members by FGF-2 and loss of pluripotent marker expression post Src kinase inhibition may point to the regulation of cytoskeletal and actin depending processes to maintain undifferentiated hESC.

Translatome analysis of CHO cells to identify key growth genes

We report the first investigation of translational efficiency on a global scale, also known as translatome, of a Chinese hamster ovary (CHO) DG44 cell line producing monoclonal antibodies (mAb). The translatome data was generated via combined use of high resolution and streamlined polysome profiling technology and proprietary Nimblegen microarrays probing for more than 13K annotated CHO-specific genes. The distribution of ribosome loading during the exponential growth phase revealed the translational activity corresponding to the maximal growth rate, thus allowing us to identify stably and highly translated genes encoding heterogeneous nuclear ribonucleoproteins (Hnrnpc and Hnrnpa2b1), protein regulator of cytokinesis 1 (Prc1), glucose-6-phosphate dehydrogenase (G6pdh), UTP6 small subunit processome (Utp6) and RuvB-like protein 1 (Ruvbl1) as potential key players for cellular growth. Moreover, correlation analysis between transcriptome and translatome data sets showed that transcript level and translation efficiency were uncoupled for 95% of investigated genes, suggesting the implication of translational control mechanisms such as the mTOR pathway. Thus, the current translatome analysis platform offers new insights into gene expression in CHO cell cultures by bridging the gap between transcriptome and proteome data, which will enable researchers of the bioprocessing field to prioritize in high-potential candidate genes and to devise optimal strategies for cell engineering toward improving culture performance.

Integrated Transcriptome and Binding Sites Analysis Implicates E2F in the Regulation of Self-Renewal in Human Pluripotent Stem Cells

Rapid cellular growth and multiplication, limited replicative senescence, calibrated sensitivity to apoptosis, and a capacity to differentiate into almost any cell type are major properties that underline the self-renewal capabilities of human pluripotent stem cells (hPSCs). We developed an integrated bioinformatics pipeline to understand the gene regulation and functions involved in maintaining such self-renewal properties of hPSCs compared to matched fibroblasts. An initial genome-wide screening of transcription factor activity using in silico binding-site and gene expression microarray data newly identified E2F as one of major candidate factors, revealing their significant regulation of the transcriptome. This is underscored by an elevated level of its transcription factor activity and expression in all tested pluripotent stem cell lines. Subsequent analysis of functional gene groups demonstrated the importance of the TFs to self-renewal in the pluripotency-coupled context; E2F directly targets the global signaling (e.g. self-renewal associated WNT and FGF pathways) and metabolic network (e.g. energy generation pathways, molecular transports and fatty acid metabolism) to promote its canonical functions that are driving the self-renewal of hPSCs. In addition, we proposed a core self-renewal module of regulatory interplay between E2F and, WNT and FGF pathways in these cells. Thus, we conclude that E2F plays a significant role in influencing the self-renewal capabilities of hPSCs.

Integrative analysis workflow for the structural and functional classification of C-type lectins

BackgroundIt is important to understand the roles of C-type lectins in the immune system due to their ubiquity and diverse range of functions in animal cells. It has been observed that currently confirmed C-type lectins share a highly conserved domain known as the C-type carbohydrate recognition domain (CRD). Using the sequence profile of the CRD, an increasing number of putative C-type lectins have been identified. Hence, it is highly needed to develop a systematic framework that enables us to elucidate their carbohydrate (glycan) recognition function, and discover their physiological and pathological roles.ResultsPresented herein is an integrated workflow for characterizing the sequence and structural features of novel C-type lectins. Our workflow utilizes web-based queries and available software suites to annotate features that can be found on the C-type lectin, given its amino acid sequence. At the same time, it incorporates modeling and analysis of glycans - a major class of ligands that interact with C-type lectins. Thereafter, the results are analyzed together with context-specific knowledge to filter off unlikely predictions. This allows researchers to design their subsequent experiments to confirm the functions of the C-type lectins in a systematic manner.ConclusionsThe efficacy and usefulness of our proposed immunoinformatics workflow was demonstrated by applying our integrated workflow to a novel C-type lectin -CLEC17A - and we report some of its possible functions that warrants further validation through wet-lab experiments.

Mathematical modeling and sensitivity analysis of the integrated TNFα-mediated apoptotic pathway for identifying key regulators

TNFα-mediated apoptosis is one of the complex and tightly regulated cellular processes as it involves the activation of both pro- and anti-apoptotic signaling pathways. Thus, it is important to elucidate the molecular players of this process and their dynamics in order to gain an in-depth understanding of the mechanisms underlying apoptosis. To this end, we proposed an integrated model of TNFα-mediated apoptosis pathway in Type I cells, formulated based on the principles of mass action kinetics. The model includes major apoptotic modules-the extrinsic and intrinsic pathways, the NFκB survival signaling and various regulatory mechanisms. We performed simulations and sensitivity analyses to study the role of NFκB pathway in regulating apoptosis, and identified IAP as one of the more potent regulators of apoptosis.

Component-based construction of bio-pathway models: The parameter estimation problem

Constructing and analyzing large biological pathway models is a significant challenge. We propose a general approach that exploits the structure of a pathway to identify pathway components, constructs the component models, and finally assembles the component models into a global pathway model. Specifically, we apply this approach to pathway parameter estimation, a main step in pathway model construction. A large biological pathway often involves many unknown parameters and the resulting high-dimensional search space poses a major computational difficulty. By exploiting the structure of a pathway and the distribution of available experimental data over the pathway, we decompose a pathway into components and perform parameter estimation for each component. However, some parameters may belong to multiple components. Independent parameter estimates from different components may be in conflict for such parameters. To reconcile these conflicts, we represent each component as a factor graph, a standard probabilistic graphical model. We then combine the resulting factor graphs and use a probabilistic inference technique called belief propagation to obtain the maximally likely parameter values that are globally consistent. We validate our approach on a synthetic pathway model based on the Akt-MAPK signaling pathways. The results indicate that the approach can potentially scale up to large pathway models.

Incremental signaling pathway modeling by data integration

Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology Pathway model construction is often an inherently incremental process, with new pathway players and interactions continuously being discovered and additional experimental data being generated Here we focus on the problem of performing model parameter estimation incrementally by integrating new experimental data into an existing model A probabilistic graphical model known as the factor graph is used to represent pathway parameter estimates By exploiting the network structure of a pathway, a factor graph compactly encodes many parameter estimates of varying quality as a probability distribution When new data arrives, the parameter estimates are refined efficiently by applying a probabilistic inference algorithm known as belief propagation to the factor graph A key advantage of our approach is that the factor graph model contains enough information about the old data, and uses only new data to refine the parameter estimates without requiring explicit access to the old data To test this approach, we applied it to the Akt-MAPK pathways, which regulate the apoptotic process and are among the most actively studied signaling pathways The results show that our new approach can obtain parameter estimates that fit the data well and refine them incrementally when new data arrives.

Genome-Wide Transcriptome and Binding Sites Analyses Identify Early FOX Expressions for Enhancing Cardiomyogenesis Efficiency of hESC Cultures

The differentiation efficiency of human embryonic stem cells (hESCs) into heart muscle cells (cardiomyocytes) is highly sensitive to culture conditions. To elucidate the regulatory mechanisms involved, we investigated hESCs grown on three distinct culture platforms: feeder-free Matrigel, mouse embryonic fibroblast feeders, and Matrigel replated on feeders. At the outset, we profiled and quantified their differentiation efficiency, transcriptome, transcription factor binding sites and DNA-methylation. Subsequent genome-wide analyses allowed us to reconstruct the relevant interactome, thereby forming the regulatory basis for implicating the contrasting differentiation efficiency of the culture conditions. We hypothesized that the parental expressions of FOXC1, FOXD1 and FOXQ1 transcription factors (TFs) are correlative with eventual cardiomyogenic outcome. Through WNT induction of the FOX TFs, we observed the co-activation of WNT3 and EOMES which are potent inducers of mesoderm differentiation. The result strengthened our hypothesis on the regulatory role of the FOX TFs in enhancing mesoderm differentiation capacity of hESCs. Importantly, the final proportions of cells expressing cardiac markers were directly correlated to the strength of FOX inductions within 72 hours after initiation of differentiation across different cell lines and protocols. Thus, we affirmed the relationship between early FOX TF expressions and cardiomyogenesis efficiency.