Systems Biology and Infectious Diseases

Systems Biology and Infectious Diseases

Alia Benkahla (Institut Pasteur de Tunis, Tunisia), Lamia Guizani-Tabbane (Institut Pasteur de Tunis, Tunisia), Ines Abdeljaoued-Tej (ESSAI-UR Algorithmes et Structures, Tunisia), Slimane Ben Miled (Institut Pasteur de Tunis, Tunisia and ENIT-LAMSIN, Tunisia) and Koussay Dellagi (Institut Pasteur de Tunis, Tunisia)
Copyright: © 2009 |Pages: 26
DOI: 10.4018/978-1-60566-076-9.ch023
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This chapter reports a variety of molecular biology informatics and mathematical methods that model the cell response to pathogens. The authors first outline the main steps of the immune response, then list the high throughput biotechnologies, generating a wealth of information on the infected cell and some of the immune-related databases; and finally explain how to extract meaningful information from these sources. The modelling aspect is divided into modelling molecular interaction and regulatory networks, through dynamic Boolean and Bayesian models, and modelling biochemical networks and regulatory networks, through Differential/Difference Equations. The interdisciplinary approach explains how to construct a model that mimics the cell’s dynamics and can predict the evolution and the outcome of infection.
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Systems biology is an emerging interdisciplinary field in life science research which purpose is to study the dynamic interactions and the network structure in cells or tissues or whole organisms. It is commonly accepted that the design of research in this field, encompasses the following six steps (Ng, Bursteinas, Gao, Mollison, & Zvelebil, 2006; Philippi & Kohler, 2006): (i) generation of suitable biological data sampled from a population; (ii) enrichment of the collected data by publicly available data; (iii) curation of this data; (iv) exhaustive integration of the curated data; (v) modelling of the mechanism(s) of interest; and (vi) validation of the predicted model. Knowing the complexity of systems biology considered at the tissue or organism level, we will focus in our review on systems biology at the cell level.

Systems biology research aims at understanding the mechanisms that shape the biological functions of a cell. It is achieved through the integration of biological information about the cell of interest (Figure 1), then the extraction of mathematical models that mimic the behaviour of this cell under different conditions. These models should then guide biologists to design the appropriate experiments to be tested on the bench, which hopefully will yield decisive results.

Figure 1.

The modelling of the mechanisms that shape the biological functions of an infected cell is achieved through the integration of information concerning the complexity of the system.


The biological function of the cell is determined by the dynamic interactions between different components or molecules involved in a given pathway. Pathways are not isolated but highly intricate and interconnected, and constitute a large, single, coherent but complex cellular network. The study of the cellular process modulated in response to an external stimulus is a phenomenon that depends not only on the number but also on the diversity and the dynamic of intracellular interactions.

The response of host cells to pathogens has been extensively investigated as a model of cellular response to external stimuli. Such model takes systems biology a step further as it integrates data that are generated from two interacting/conflicting living organisms: the host and the pathogen. The specificity of this system should be taken into account during the modelling process and advanced strategies that capture this cohabitation and its consequences are required for modelling (Forst, 2006). Numerous host’s and pathogen’s proteins are usually involved in these interactions and trigger various biochemical reactions regulated in cascade. The outcome of the infection will depend on this complex network of cellular interactions.

Another level of complexity is introduced by the interactions/exchanges between the host’s cells and the infectious agent (parasite, virus, bacteria). Especially as high throughput technologies, dedicated to the characterisation of these interactions/exchanges and their effects on the biological function of the two organisms, are still missing. However the use of organism’s oriented biotechnologies (i.e.: transcriptome) allowed a coherent and understandable view of the studied system. Even though these experiments fail to unveil the crosstalk between host and pathogen, they give separate insights into the host or pathogen responses. Developing tools that could integrate and process this data is a major task in infectious systems biology research.

Pathogens are recognised through the host’s cells receptors. The diversity of pathogens and the large array of cells they could infect add complexity to the system. One given pathogen may infect different cell types which may express a different set of receptors, resulting in the activation of different signalling cascades and thus transcription of different sets of genes. In a given cell several pathways could be activated by the pathogen depending on the specific receptor(s) they use to enter the cell and the duration of contact between the infectious agents and the cell. These activated pathways may be different depending on the tissue origin and the activated state of the cell.

Key Terms in this Chapter

Bayesian Network: Represent a probabilistic relationships between a set of nodes: it is a directed graph which vertexes encode conditional independencies between the nodes.

Boolean network: A set of nodes whose state is determined by other nodes in the network: it is a directed graph where nodes are either present (node of value=1) or absent (node of value=0).

Biochemical Reaction: Is a chemical reaction that involves biological molecules.

Gene Products: RNA, mRNA, protein and metabolites.

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