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.

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

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.

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 .

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 overexpression 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.

Quantitative analysis of numerical solvers for oscillatory biomolecular system models.

BACKGROUND: This article provides guidelines for selecting optimal numerical solvers for biomolecular system models. Because various parameters of the same system could have drastically different ranges from 10(-15) to 10(10), the ODEs can be stiff and ill-conditioned, resulting in non-unique, non-existing, or non-reproducible modeling solutions. Previous studies have not examined in depth how to best select numerical solvers for biomolecular system models, which makes it difficult to experimentally validate the modeling results. To address this problem, we have chosen one of the well-known stiff initial value problems with limit cycle behavior as a test-bed system model. Solving this model, we have illustrated that different answers may result from different numerical solvers. We use MATLAB numerical solvers because they are optimized and widely used by the modeling community. We have also conducted a systematic study of numerical solver performances by using qualitative and quantitative measures such as convergence, accuracy, and computational cost (i.e. in terms of function evaluation, partial derivative, LU decomposition, and "take-off" points). The results show that the modeling solutions can be drastically different using different numerical solvers. Thus, it is important to intelligently select numerical solvers when solving biomolecular system models. RESULTS: The classic Belousov-Zhabotinskii (BZ) reaction is described by the Oregonator model and is used as a case study. We report two guidelines in selecting optimal numerical solver(s) for stiff, complex oscillatory systems: (i) for problems with unknown parameters, ode45 is the optimal choice regardless of the relative error tolerance; (ii) for known stiff problems, both ode113 and ode15s are good choices under strict relative tolerance conditions. CONCLUSIONS: For any given biomolecular model, by building a library of numerical solvers with quantitative performance assessment metric, we show that it is possible to improve reliability of the analytical modeling, which in turn can improve the efficiency and effectiveness of experimental validations of these models. Also, our study can be extended to study a variety of molecular-level system models for human disease diagnosis and therapeutic treatment.

Exploration of quantitative scoring metrics to compare systems biology modeling approaches.

In this paper, we report a focused case study to assess whether quantitative metrics are useful to evaluate molecular-level system biology models on cellular metabolism. Ideally, the bio-modeling community shall be able assess systems biology models based on objective and quantitative metrics. This is because metric-based model design not only can accelerate the validation process, but also can improve the efficacy of model design. In addition, the metric will enable researchers to select models with any desired quality standards to study biological pathway. In this case study, we compare popular systems biology modeling approaches such as Michaelis-Menten kinetics and generalized mass action and flux balance analysis to examine the difficulties in developing quantitative metrics for bio-model assessment. We created a set of guidelines in evaluating the efficacy of various bio-modeling approaches and system analysis in several "bio-systems of interest". We found that quantitative scoring metrics are essential aids for (i) model adopters and users to determine fundamental distinctions among bio-models, and (ii) model developers to improve key areas in bio-modeling. Eventually, we want to extend this evaluation practice to broad systems biology modeling.

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 cell's 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.

Comparative study of microarray data for cancer research.

In comparison to traditional "single-gene" study method such as reverse transcriptase-polymerase chain reaction (RT-PCR), microarray technology can produce high-throughout gene expression data simultaneously. The advancement of this technology also presents a big challenge. In cancer research, the issue is how to identify the signature genes, or biomarkers associated with particular cancer to perform precise, objective and systematic cancer diagnosis and treatment. More specifically, the goal is how to accurately analyze and interpret the resulting large amount of gene expression data with relatively small patient sample size. As such, we have been developing a novel multischeme system that can derive optimal decision based on the best utilization of gene expression data features and clinical, and biological knowledge. In the paper, we are reporting the results of the first phase development of our novel system, to use unsupervised clustering methods to discover gene relationship and to use knowledge-based supervised classification to get highly accurate prediction in cancer diagnosis and prognosis study. This work sets up foundation for our next step drug target study.