Integrative Systems Biology I—Biochemistry: Phase I Lead Discovery and Molecular Interactions

2012 ◽  
pp. 25-38
Author(s):  
Aleš Prokop ◽  
Seth Michelson
Keyword(s):  
Systems Biology ◽  
Phase I ◽  
Molecular Interactions ◽  
Lead Discovery ◽  
Biology I

Analysis of the Evolution and Structure of Systems Biology

2012 ◽  
Vol 488-489 ◽  
pp. 1006-1010
Author(s):  
Chao Liu ◽  
Lian Fen Liu ◽  
Fu Guo Li ◽  
Nai Hua Jiang ◽  
Wen Juan Guo ◽  
...  
Keyword(s):  
Systems Biology ◽  
Molecular Interactions ◽  
Close Association ◽  
Physiological Responses ◽  
Biological Molecules ◽  
System Level ◽  
Biological Research ◽  
Research Practice ◽  
Research Model ◽  
Fundamental Challenge

Systems biology is a term used to describe a number of trends in bioscience research, and a movement which draws on those trends. Systems biology aims to understand the biology from the system level. The fundamental challenge of systems biology is to establish a complete, detailed description of the link between biological molecules and to study molecular interactions and the close association between the physiological responses. Systems biology methods in the system under the guidance will enable us to break the shackles of the old research model to study life from the grasp of the whole phenomenon. We must effectively grasp and follow the systems biology approach to guide our biological research practice.

scholarly journals Causal Concepts Guiding Model Specification in Systems Biology

Disputatio ◽  
2017 ◽  
Vol 9 (47) ◽  
pp. 499-527
Author(s):  
Dana Matthiessen
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Causal Structure ◽  
Model Specification ◽  
Biology I ◽  
Adequate Representation ◽  
Gene Regulatory ◽  
High Throughput Experiments

Abstract In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

scholarly journals Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

2016 ◽  
Author(s):  
Artem V. Artemov ◽  
Evgeny Putin ◽  
Quentin Vanhaelen ◽  
Alexander Aliper ◽  
Ivan V. Ozerov ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Systems Biology ◽  
Phase I ◽  
Biological Data ◽  
Drug Induced ◽  
Multi Stage ◽  
Pathway Activation ◽  
Trial Outcomes ◽  
Analytical Tools

AbstractDespite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

scholarly journals A Customizable Analysis Flow in Integrative Multi-Omics

Biomolecules ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1606
Author(s):  
Samuel M. Lancaster ◽  
Akshay Sanghi ◽  
Si Wu ◽  
Michael P. Snyder
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Systems Biology ◽  
Nucleic Acid ◽  
Best Practices ◽  
Data Integration ◽  
Study Design ◽  
Molecular Interactions ◽  
Analysis Workflow ◽  
Integration Techniques

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

scholarly journals Cell-Based Systems Biology Analysis of Human AS03-Adjuvanted H5N1 Avian Influenza Vaccine Responses: A Phase I Randomized Controlled Trial

PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0167488 ◽  
Author(s):  
Leigh M. Howard ◽  
Kristen L. Hoek ◽  
Johannes B. Goll ◽  
Parimal Samir ◽  
Allison Galassie ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Systems Biology ◽  
Avian Influenza ◽  
Influenza Vaccine ◽  
Phase I ◽  
Controlled Trial ◽  
Vaccine Responses ◽  
Randomized Controlled ◽  
H5n1 Avian Influenza

scholarly journals Molecular Interaction Maps of Bioregulatory Networks: A General Rubric for Systems Biology

2006 ◽  
Vol 17 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Kurt W. Kohn ◽  
Mirit I. Aladjem ◽  
John N. Weinstein ◽  
Yves Pommier
Keyword(s):  
Computer Simulation ◽  
Systems Biology ◽  
Molecular Interactions ◽  
Molecular Interaction ◽  
Structure And Function ◽  
Graphical Notations ◽  
Interaction Map ◽  
And Function ◽  
Network Diagrams

A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. “Heuristic” MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. “Explicit” MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing a MIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one's understanding of the structure and function of a network.

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