Differential Drug Target Selection in Blood Coagulation: What can we get from Computational Systems Biology Models?

Discovery and selection of the potential targets are some of the important issues in pharmacology. Even when all the reactions and the proteins in a biological network are known, how does one choose the optimal target? Here, we review and discuss the application of the computational methods to address this problem using the blood coagulation cascade as an example. The problem of correct antithrombotic targeting is critical for this system because, although several anticoagulants are currently available, all of them are associated with bleeding risks. The advantages and the drawbacks of different sensitivity analysis strategies are considered, focusing on the approaches that emphasize: 1) the functional modularity and the multi-tasking nature of this biological network; and 2) the need to normalize hemostasis during the anticoagulation therapy rather than completely suppress it. To illustrate this effect, we show the possibility of the differential regulation of lag time and endogenous thrombin potential in the thrombin generation. These methods allow to identify the elements in the blood coagulation cascade that may serve as the targets for the differential regulation of this system.

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Faculty Opinions recommendation of Computational systems biology and in silico modeling of the human microbiome.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717697961.793153071 ◽

2012 ◽

Author(s):

Nicola Mulder

Keyword(s):

Systems Biology ◽

In Silico ◽

Human Microbiome ◽

Computational Systems Biology ◽

In Silico Modeling ◽

Computational Systems

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Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches

Cancers ◽

10.3390/cancers13010035 ◽

2020 ◽

Vol 13 (1) ◽

pp. 35

Author(s):

Sahar Aghakhani ◽

Naouel Zerrouk ◽

Anna Niarakis

Keyword(s):

Rheumatoid Arthritis ◽

Extracellular Matrix ◽

Systems Biology ◽

Healing Process ◽

Metabolic Reprogramming ◽

Critical Event ◽

Wound Healing Process ◽

Computational Systems Biology ◽

Structural Framework ◽

Computational Systems

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

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