Structural Biochemistry/Systems Biology

Overview

Systems biology, network biology, or integrated biology, is an emerging approach applied to biomedical and biological scientific research. Systems biology is a biology based inter-disciplinary field of study that focuses on complex interactions within biological systems, using a wider perspective (holism instead of the more traditional reductionism) approach to biological and biomedical research. In its view, gene (or any molecule of interest) and its product (or smaller molecules, like cofactors, messenger molecules, and metabolites) are seen as “nodes”. The link between the nodes represent a relationship that can be a physical interaction, enzymatic reaction, or a functional connection. This system assumes that perturbations, both internal and external, affect the phenotype. Mass spectrometry (MS) is used to identify the network wiring, such as protein-interaction networks. Particularly from year 2000 onwards, the concept has been used widely in the biosciences in a variety of contexts. One of the outreaching aims of systems biology is to model and discover emergent properties, properties of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques which fall under the remit of systems biology. These typically involve metabolic networks or cell signaling networks. Systems Biology has some important inferences: 1) requires a look at the biological processes as an integral; 2)provides new opportunities and depends on technology for advanced computational and experimental approaches.

Old. vs. New

In the past, studies have typically been carried out within the “one gene- one protein- one function” standard, called “molecular biology paradigm”. It is a mindset where it is assumes there is 1) a direct link between gene and protein function, where it is implied that genes and their translation products explain biological function, and 2) proteins are looked at individually and in a linear form (downstream and upstream). Although powerful technologies and remarkable technical advances have been made, a genotype-phenotype link still remains a challenge

Applying Mass Spectrometry

'Mass spectrometry (MS') is used for this proteomics study. Various strategies have been presented to address each biological need, where each strategy is only a portion of the “total proteome space”. The strategies are:

• Shotgun/ Discovery: can identify numerous proteins in a sample, but most likely to only detect most abundant
• Directed: specific, pre-determined set of proteins identified, and quantified at high level of reproducibility
• Targeted: high reproducibility, and accurate for small, pre-selected portion of proteins, high detection sensitivity and dynamic range
• Data dependent analysis (DDA): emerging, but attempt to identify all proteins in sample

Technologies to quantify and identify molecules are needed to quantify and identify the edges of networks, which has been address by 2 ways: a direct approach, where the edge is measured by catching an interacting molecule of the node by its higher affinity, and an indirect approach that uses an assay with a node between the molecule and node.

Networks

Two types of networks are distinguished in MS-based proteomics: protein interaction networks (PINs) which are undirected networks, so have no preference in directions, and protein signaling networks (PSNs), which are directed networks and have a preferred direction. PINs can be exemplified by the major MS-based proteomics interest, protein-protein interaction networks (PPINs), because the direct links between the nodes and edges in these networks are measurable. In order to study such networks, proteins are used to as a “bait molecule” to catch interacting proteins, then identify them by MS. To analyze PPINs, affinity purification mass spectrometry (AP MS) is preferred because it echoes the multi-directionality complexity of PPINs. Then, AP MS data are interpreted where two or more copurified nodes are connected to edges, forming a network, and can infer protein-signaling networks (PSNs).

Perturbation experiments are done to see the effects of changing an enzyme activity, like activation or repression in a cell that can be used to compare to in vitro experiments, which can give new insights of the rewiring network. There are two types of perturbations: the ones that affect mostly the edges, and those that effect the nodes, which consequently effect their edges. Phosphorylation networks have been heavily studied, where the downregulation of phosphopeptides with a kinase inhibitor lead to the question if it is kinase dependent, but perhaps not kinase mediated. The goal is to know how networks capture and take this information to produce a phenotype or to provoke a cellular response. Connecting network wiring to phenotypes is done by combining static PPINs, which is used to identify genes that have set network properties and can be correlated to a phenotype, with protein-DNA interaction data measured with microarrays. A search was then completed to identify genes with unusual numbers od edges, which could show a change in correlation in phenotypic subset of the samples. Then, an initial set of genes in a How reliable the conclusions from the data and network knowledge depend on multiple factors: coverage and correctness of the network, quality of the data, and method of correlation calculation. Also, the results do not automatically lead to causality.

General Roadmap

1) Define starting network of nodes from previous studies
2) Disrupt network components and present experimental data
3) Use information to improve network model and improve experimental data and phenotypes from network

references

Bu Z, Callaway DJ (2011). "Proteins MOVE! Protein dynamics and long-range allostery in cell signaling". Advances in Protein Chemistry and Structural Biology. Advances in Protein Chemistry and Structural Biology 83: 163–221

Bensimon A, Heck AJ, Aebersold R. Mass spectrometry-based proteomics and network biology. Annu Rev Biochem. 2012;81:379-405. Review. PubMed PMID: 22439968.