Chang F. Quo

Multiscale Integration of -Omic, Imaging, and Clinical Data in Biomedical Informatics

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

Development of a Laboratory Information System for Cancer Collaboration Projects

Technological advances increase the rate and quality of biomedical data collection. To exploit these advances to the fullest, laboratory information management systems (LIMS) have been developed to integrate laboratory equipment with software controls so as to achieve an automated and seamless workflow process. Ultimately, researchers and clinicians must collaborate closely to achieve a comprehensive interpretation of heterogeneous biomedical data, especially with respect to clinical diagnosis and treatment. We present eOncoLIMS, a modular data and process management system designed to provide the infrastructure and environment for a collaborative cancer research project. This system can be further extended to other collaboration projects to achieve a complete solution to research and clinical problems

Adaptive Control Model Reveals Systematic Feedback and Key Molecules in Metabolic Pathway Regulation

Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb .

Cardiovascular Genomics: A Biomarker Identification Pipeline

Genomic biomarkers are essential for understanding the underlying molecular basis of human diseases such as cardiovascular disease. In this review, we describe a biomarker identification pipeline for cardiovascular disease, which includes 1) high-throughput genomic data acquisition, 2) preprocessing and normalization of data, 3) exploratory analysis, 4) feature selection, 5) classification, and 6) interpretation and validation of candidate biomarkers. We review each step in the pipeline, presenting current and widely used bioinformatics methods. Furthermore, we analyze several publicly available cardiovascular genomics datasets to illustrate the pipeline. Finally, we summarize the current challenges and opportunities for further research.

Computational Modeling of A Metabolic Pathway in Ceramide de novo Synthesis

Studies have implicated ceramide as a key molecular agent in regulating programmed cell death, or apoptosis. Consequently, there is significant potential in targeting intracellular ceramide as a cancer therapeutic agent. The cells major ceramide source is the ceramide de novo synthesis pathway, which consists of a complex network of interdependent enzyme-catalyzed biochemical reactions. To understand how ceramide works, we have initiated the study of the ceramide de novo synthesis pathway using computational modeling based on fundamental principles of biochemical kinetics. Specifically, we designed and developed the model in MATLAB SIMULINK for the behavior of dihydroceramide desaturase. Dihydroceramide desaturase is one of three key enzymes in the ceramide de novo synthesis pathway, and it converts a relatively inert precursor molecule, dihydroceramide into biochemically reactive ceramide. A major issue in modeling is parameter estimation. We solved this problem by adopting a heuristic strategy based on a priori knowledge from literature and experimental data. We evaluated model accuracy by comparing the model prediction results with interpolated experimental data. Our future work includes more experimental validation of the model, dynamic rate constants assessment, and expansion of the model to include additional enzymes in the ceramide de novo synthesis pathway.

Development of a knowledge-based-multi-scheme cancer microarray data analysis system

Comparing genes expressed in normal and diseased states assists the understanding of cancer pathophysiology, detection, prognosis, and therapeutic target study. Many existing expression analysis papers show that microarray data are usually case dependent, have small sample (patients) sizes, and have large gene dimensions. Thus, we have been developing a robust multi-parameter, multi-scheme knowledge-based optimization system that integrates the strengths of statistics, pattern-recognition, and support vector machines (SVM). The optimization logic identifies optimal cancer signature genes by utilizing different analysis models based on unsupervised and supervised clustering. Our system is being finalized by testing over public and in-house datasets with the intention of validation through clinical knowledge feedback.

Quantitative metrics for bio-modeling algorithm selection

In this paper, we report our efforts in developing guidelines that are capable of helping researchers to select algorithms in systems biology modeling. We propose a set of metrics based on discrete observable units in terms of key bio-modeling considerations. We accomplish this by (i) reviewing classical metric definitions, (ii) implementing widely used modeling algorithms on a specific case study, and (iii) testing metrics that are a hybrid of classical metrics and key bio-modeling considerations. The modeling algorithms implemented are Michaelis-Menten kinetics, generalized mass action, flux balance analysis, and metabolic control analysis. This work extends our previous work in developing qualitative guidelines to select bio-modeling algorithms. Our results impact systems biology modeling specifically by increasing the level of confidence for users to select bio-modeling algorithms by using quantitative metrics appropriately.

An interactive visualization tool and data model for experimental design in systems biology

Experimental design is important, but is often under-supported, in systems biology research. To improve experimental design, we extend the visualization of complex sphingolipid pathways to study biosynthetic origin in SphinGOMAP. We use the ganglio-series sphingolipid dataset as a test bed and the Java Universal Network / Graph Framework (JUNG) visualization toolkit. The result is an interactive visualization tool and data model for experimental design in lipid systems biology research. We improve the current SphinGOMAP in terms of interactive visualization by allowing (i) choice of four different network layouts, (ii) dynamic addition / deletion of on-screen molecules and (iii) mouse-over to reveal detailed molecule data. Future work will focus on integrating various lipid-relevant data systematically i.e. SphinGOMAP biosynthetic data, Lipid Bank molecular data (Japan) and Lipid MAPS metabolic pathway data (USA). We aim to build a comprehensive and interactive communication platform to improve experimental design for scientists globally in high-throughput lipid systems biology research.

Quantitative comparison of numerical solvers for models of oscillatory biochemical systems

The goal of this work is to find optimal numerical solvers to study models of biological systems of interest. We report 2 simple rules to select the optimal numerical solver(s) for solving stiff, complex oscillatory systems. Numerical solvers currently used to solve mathematical models in systems biology can be ill-conditioned due to stiffness. As a case study, we choose the classic Belousov-Zhabotinskii (BZ) reaction, described by the Oregonator model. We determine the optimal numerical solver(s) to handle stiff initial-value problems that lead to limit cycle behavior, by systematically comparing qualitative and quantitative performance measures i.e. convergence, accuracy and computational cost of numerical solvers that are widely used by the engineering and modeling community. This is a cornerstone for our long-term research objective to systematically study a variety of molecular-level models for biomedical systems for human disease diagnosis and therapeutic treatment in order to understand and predict disease mechanism and progression.

Biological Interpretation of Model-Reference Adaptive Control in a Mass Action Kinetics Metabolic Pathway Model

The usefulness of control theory to model robustness in metabolic pathways is limited because controller properties and their implications on pathway regulation are unclear. Using sphingolipid biosynthesis in response to single-gene over expression as a case study, we apply model-reference adaptive control (MRAC) to model regulation in a mass action kinetics pathway model and report on its properties. Tracking error between treated cells (plant) and wild type (reference) is reduced in 9 of 10 system variables compared to using mass action kinetics only. This result is robust when system parameters are perturbed. Furthermore, we interpret control dynamics to infer potential regulatory interactions. Some observations are consistent with independent studies on the effects of the same experimental treatment, while others represent novel hypotheses that may be tested to yield additional biological insight. The usefulness and interpretation of MRAC to model metabolic pathway regulation is shown where plant dynamics approach the reference.