High-throughput, genome-scale data present a unique opportunity to link host to pathogen on a molecular level. Forging such connections will help drive the development of mathematical models to better understand and predict both pathogen behavior and the epidemiology of infectious diseases, including malaria. However, the datasets that can aid in identifying these links and models are vast and not amenable to simple, reductionist, and univariate analyses. These datasets require data mining in order to identify the truly important measurements that best describe clinical and molecular observations. Moreover, these datasets typically have relatively few samples due to experimental limitations (particularly for human studies or in vivo animal experiments), making data mining extremely difficult. Here, after first providing a brief overview of common strategies for data reduction and identification of relationships between variables for inclusion in mathematical models, we present a new generalized strategy for performing these data reduction and relationship inference tasks. Our approach emphasizes the importance of robustness when using data to drive model development, particularly when using genome-scale, small-sample in vivo data. We identify the use of appropriate feature reduction combined with data permutations and subsampling strategies as being critical to enable increasingly robust results from network inference using high-dimensional, low-observation data.
BACKGROUND: Large amounts of metabolomics data have been accumulated in recent years and await analysis. Previously, we had developed a systems biology approach to infer biochemical mechanisms underlying metabolic alterations observed in cancers and other diseases. The method utilized the typical Euclidean distance for comparing metabolic profiles. Here, we ask whether any of the numerous alternative metrics might serve this purpose better. METHODS AND FINDINGS: We used enzymatic alterations in purine metabolism that were measured in human renal cell carcinoma to test various metrics with the goal of identifying the best metrics for discerning metabolic profiles of healthy and diseased individuals. The results showed that several metrics have similarly good performance, but that some are unsuited for comparisons of metabolic profiles. Furthermore, the results suggest that relative changes in metabolite levels, which reduce bias toward large metabolite concentrations, are better suited for comparisons of metabolic profiles than absolute changes. Finally, we demonstrate that a sequential search for enzymatic alterations, ranked by importance, is not always valid. CONCLUSIONS: We identified metrics that are appropriate for comparisons of metabolic profiles. In addition, we constructed strategic guidelines for the algorithmic identification of biochemical mechanisms from metabolomics data.
To conduct a patient-specific computational modeling of the aortic valve, 3-D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3-D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual finite element (FE) model generation, which is both time-consuming and prone to human errors. A seamless computational modeling framework, which can automate this process based on machine learning algorithms, is desirable, as it can not only eliminate human errors and ensure the consistency of the modeling results but also allow fast feedback to clinicians and permits a future population-based probabilistic analysis of large patient cohorts. In this study, we developed a novel computational modeling method to automatically reconstruct the 3-D geometries of the aortic valve from computed tomographic images. The reconstructed valve geometries have built-in mesh correspondence, which bridges harmonically for the consequent FE modeling. The proposed method was evaluated by comparing the reconstructed geometries from 10 patients with those manually created by human experts, and a mean discrepancy of 0.69 mm was obtained. Based on these reconstructed geometries, FE models of valve leaflets were developed, and aortic valve closure from end systole to middiastole was simulated for 7 patients and validated by comparing the deformed geometries with those manually created by human experts, and a mean discrepancy of 1.57 mm was obtained. The proposed method offers great potential to streamline the computational modeling process and enables the development of a preoperative planning system for aortic valve disease diagnosis and treatment.
This year we celebrate the 150th anniversary of the law of mass action. This law is often assumed to have been “there” forever, but it has its own history, background, and a definite starting point. The law has had an impact on chemistry, biochemistry, biomathematics, and systems biology that is difficult to overestimate. It is easily recognized that it is the direct basis for computational enzyme kinetics, ecological systems models, and models for the spread of diseases. The article reviews the explicit and implicit role of the law of mass action in systems biology and reveals how the original, more general formulation of the law emerged one hundred years later ab initio as a very general, canonical representation of biological processes.
Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500. ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1. s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets.