Systems biology of embryogenesis

Abstract

The development of a complete organism from a single cell involves extraordinarily complex orchestration of biological processes that vary intricately across space and time. Systems biology seeks to describe how all elements of a biological system interact in order to understand, model, and ultimately predict aspects of emergent biological processes. Embryogenesis represents an extraordinary opportunity -and challenge -for the application of systems biology. Systems approaches have already been used successfully to study various aspects of development, from complex intracellular networks to 4D models of organogenesis. Going forward, great advancements and discoveries can be expected from systems approaches applied to embryogenesis and developmental biology. KeywordsDevelopment; Regulatory Networks; Computational Models; Organogenesis; Complex Adaptive Systems The Systems Biology ParadigmSystems biology is an experimental and theoretical framework that treats biology as an informational science, and seeks to study the behavior of biological systems as a whole. In particular, the deep complexity of developmental processes motivates the use of systematic and integrative analyses to garner biological insights. In these approaches, biological information is represented as being transmitted, modulated and integrated by biological networks that are then executed by molecular 'machines' (Hood et al. 2004;Price et al. 2009). At its heart, systems biology seeks to understand the dynamic behavior of complex biological systems in sufficient detail to construct computational models that can predict how various perturbations will affect a living system. An iterative model-building process is often employed, wherein an in silico model evolves through various iterations and increases in complexity, completeness and predictive accuracy as it is informed by increasing experimental data. The constructed computational model thus serves as a large-scale hypothesis about how an integrated biological process works as a whole. That is, the model characterizes explicitly the relevant components, their relationships to one another, and the dynamics of the interacting system. For example, a systems analysis of a mouse embryo might involve as a first step the identification of all proteins and genes expressed using shot gun proteomics and transcriptomics. Then the systems biologist might try to identify how

Integrative Temporo-Spatial, Mineralogic, Spectroscopic, and Proteomic Analysis of Postnatal Enamel Development in Teeth with Limited Growth

“…Such multi-level and multi-scale data mining commonly requires a systems biology approach. Systems biology of development seeks to integrate bioinformatic data analysis with other molecular, cellular, and tissue-related information to reach a higher-level, multifaceted, and integrative understanding of developmental processes (Bard, 2007 ; Edelman et al, 2009 ). Integrative approaches toward biological problems have become possible as a result of recent advances in bioinformatics and omics technologies, including proteomics, transcriptomics, and metabolomics (Mochida and Shinozaki, 2011 ).…”

A model of stem cell population dynamics: in silico analysis and in vivo validation

“…This design specifies the cell as a basic information-processing unit that senses the extracellular space and acts on it over time. This design is an abstraction of the behavior of cells as the key unit within tissues (Cohen and Harel, 2007;Edelman et al, 2010).…”

In-silico models of stem cell and developmental systems

“…Models were constructed for the electrical function of the heart [ 14 ], diffusion in the early Drosophila embryo [ 15 ], pattern formation in Zebrafish [ 16 ], dual perturbation experiments [ 17 ], and more. The challenges and advances in this approach for modeling were recently reviewed in several publications [ 13 , 18 - 20 ]. Most studies, however, study specific models that are built to describe a specific biological system or process, often cell-intrinsic ones [ 21 - 24 ].…”

“…The in-silico models discussed here are different from the more traditional modeling efforts that often use toy-models to simplify complex process in order to seek hidden parameters or to test a specific hypothesis [ 18 ]. In particular, these models embody two key features: comprehensiveness and realism.…”