Systems modeling: a pathway to drug discovery

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.     

Systems biology model repository for macrophage pathway simulation

SUMMARY: The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis. AVAILABILITY: MACPAK can be accessed via an interactive web site at http://macpak.csml.org. The CSML pathway models are available under the Creative Commons license.     

Prospects for systems biology and modeling of the gut microbiome

Abundant microorganisms that inhabit the human intestine are implicated in health and disease. The gut microbiome has been studied with metagenomic tools, and over 3 million genes have been discovered, constituting a 'parts list' of this ecosystem; further understanding requires studies of the interacting parts. Mouse models have provided a glimpse into the microbiota and host interactions at metabolic and immunologic levels; however, to provide more insight, there is a need to generate mathematical models that can reveal genotype-phenotype relationships and provide scaffolds for integrated analyses. To this end, we propose the use of genome-scale metabolic models that have successfully been used in studying interactions between human hosts and microbes, as well as microbes in isolation and in communities.     

Systems biology of the metabolic network regulated by the Akt pathway

Cancer has been proposed as an example of systems biology disease or network disease. Accordingly, tumor cells differ from their normal counterparts more in terms of intracellular network dynamics than single markers. Here we shall focus on a recently recognized hallmark of cancer, the deregulation of cellular energetics. The constitutive activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway has been confirmed as an essential step toward cell transformation. We will consider how the effects of Akt activation are connected with cell metabolism; more precisely, we will review existing metabolic models and discuss the current knowledge available to construct a kinetic model of the most relevant metabolic processes regulated by the PI3K/Akt pathway. The model will enable a systems biology approach to predict the metabolic targets that may inhibit cell growth under hyper activation of Akt.     

Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology.Proteomics technologies have evolved to produce larg...     

Computational modeling of the EGF-receptor system: a paradigm for systems biology

Computational models have rarely been used as tools by biologists but, when models provide experimentally testable predictions, they can be extremely useful. The epidermal growth factor receptor (EGFR) is probably the best-understood receptor system, and computational models have played a significant part in its elucidation. For many years, models have been used to analyze EGFR dynamics and to interpret mutational studies, and are now being used to understand processes including signal transduction, autocrine loops and developmental patterning. The success of EGFR modeling can be a guide to combining models and experiments productively to understand complex biological processes as integrated systems.     

Biological complexity and drug discovery: a practical systems biology approach

Drugs fail in clinical studies most often from lack of efficacy or unexpected toxicities. These failures result from an inadequate understanding of drug action and follow, in part, from our dependence on drug discovery technologies that do not take into account the complexity of human disease biology. Biological systems exhibit many features of complex engineering systems, including modularity, redundancy, robustness, and emergent properties. Addressing these features has contributed to the successful design of an improved biological assay technology for inflammation drug discovery. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), involves the statistical analysis of protein datasets generated from novel complex primary human cell-based assay systems. Compound profiling in these systems has revealed that a surprisingly large number of biological mechanisms can be detected and distinguished. Features of these assays relevant to the behaviour of complex systems are described.     

A hybrid agent-based approach for modeling microbiological systems

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10³ cells and 1.2×10⁶ molecules. The model produces cell migration patterns that are comparable to laboratory observations.     

系统生物学与细胞发生系统动力学

系统生物学是系统理论和实验生物技术、计算机数学模型等方法整合的生物系统研究,系统遗传学研究基因组的稳态与进化、功能基因组和生物性状等复杂系统的结构、动态与发生演变等。合成生物学是系统生物学的工程应用,采用工程学方法、基因工程和计算机辅助设计等研究人工生物系统的生物技术。系统与合成生物学的结构理论,序列标志片段显示分析与微流控生物芯片,广泛用于研究细胞代谢、繁殖和应激的自组织进化、生物体形态发生等细胞分子生物系统原理等。     

Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery

Mass Spectrometry-based proteomics is now considered a relatively established strategy for protein analysis, ranging from global expression profiling to the identification of protein complexes and specific post-translational modifications. Recently, Selected Reaction Monitoring Mass Spectrometry (SRM-MS) has become increasingly popular in proteome research for the targeted quantification of proteins and post-translational modifications. Using triple quadrupole instrumentation (QqQ), specific analyte molecules are targeted in a data-directed mode. Used routinely for the quantitative analysis of small molecular compounds for at least three decades, the technology is now experiencing broadened application in the proteomics community. In the current review, we will provide a detailed summary of current developments in targeted proteomics, including some of the recent applications to biological research and biomarker discovery.     

Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data

Reconstructing biological networks, such as metabolic and signaling networks, is at the heart of systems biology. Although many approaches exist for reconstructing network structure, few approaches recover the full dynamic behavior of a network. We survey such approaches that originate from computational scientific discovery, a subfield of machine learning. These take as input measured time course data, as well as existing domain knowledge, such as partial knowledge of the network structure. We demonstrate the use of these approaches on illustrative tasks of finding the complete dynamics of biological networks, which include examples of rediscovering known networks and their dynamics, as well as examples of proposing models for unknown networks.     

Targeting the human genome-microbiome axis for drug discovery: inspirations from global systems biology and traditional Chinese medicine

Most chronic diseases impairing current human public health involve not only the human genome but also gene-environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug-receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern-Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted "druggable genome" and shed light on current high interest issues in Western medicine such as drug-drug and drug-diet-gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient.     

Social pathway annotation: extensions of the systems biology metabolic modelling assistant

High-throughput experiments have produced large amounts of heterogeneous data in the life sciences. These data are usually represented in different formats (and sometimes in technical documents) on the Web. Inevitably, life science researchers have to deal with all these data and different formats to perform their daily research, but it is simply not possible for a single human mind to analyse all these data. The integration of data in the life sciences is a key component in the analysis of biological processes. These data may contain errors, but the curation of the vast amount of data generated in the 'omic' era cannot be done by individual researchers. To address this problem, community-driven tools could be used to assist with data analysis. In this article, we focus on a tool with social networking capabilities built on top of the SBMM (Systems Biology Metabolic Modelling) Assistant to enable the collaborative improvement of metabolic pathway models (the application is freely available at http://sbmm.uma.es/SPA).