Systems biology for molecular life sciences and its impact in biomedicine

Abstract

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

Fig. 1

Some relevant scientists with outstanding systemic contributions to the understanding of complex biological problems before the emergence of modern systems biology

Fig. 2

The growing gap between the amount of available scientific data and the actual new knowledge generated from these data. Axis X time; axis Y relative accumulated quantity of scientific data (green), information (reddish) and knowledge (blue).

Fig. 3

a A general view of the human angiogenic network. This image is taken from Ref. [47], of which I am the corresponding author and that was published under the Creative-Commons license. b A zoom of part of the previous network. c A small subnetwork of the human angiogenic network. The subnetwork shows the interactions of the tyrosine kinase receptor VEGFR1 with other members of its family (VEGFR1 and VEGFR2), with their ligands (VEGFA, VEGFB, VEGFC, and VEGFD) and with other interactors named neuropilins (NRP1 and NRP2). Both B and C were selected from the original angiogenic network and represented with the viewer Cytoscape [45]

Fig. 4

Regulatory network around the seven signature genes predicted for pancreatic cancer by network-based ranking of marker genes in the study described in [122]. a Representation of all the direct neighbors for the seven candidates, namely, STAT3, FOS, JUN, SP1, CDX2, CDEBPA and BRCA1. b A subnetwork of genes regulated by FOS. c Physical protein-protein interactions between the transcription factor SP1, STAT3, JUN and FOS, and the transcription coactivator BRCA1. This figure is taken from Ref. [122], published under the Creative-Commons license

Fig. 5

A map of the major biosignaling pathways disrupted in cancer. This figure was obtained from Wikipedia Commons, originally derived from the review on the hallmarks of cancer [141]

Fig. 6

Different schedules of pharmacological treatment in time. a Conventional chemotherapy delivers the maximum tolerated dose in an only session or in short cycles of 2–5 days, followed by a long resting period (around 3 weeks) to allow the patient to recover. b Low-dose metronomic chemotherapy delivers low doses on an oscillatory or continuous schedule. Images a and b are taken from a previously published review from our group [83]. c Therapies can be based on delivering drugs with a single target (“single hit” treatment) or in combinations of drugs with different targets. Combinations can be delivered simultaneously or in time-staggered schedules. In this last case, the order of administration can be crucial. In the figure, the schedule “first drug B, then drug A” is not as effective as the schedule “first drug A, then drug B.” This is due to the fact that drug A induces a network re-wiring pushing tumor cells toward a state of increased sensitivity to drug B. This image is taken from the Preview commentary “Network medicine strikes a blow against breast cancer” [125] published in the journal Cell concerning the article published in the same journal describing that a sequential application of erlotinib followed by doxorubicin enhances triple-negative breast cancer cell death by rewiring apoptotic signaling networks [124]. The image is reproduced here with permission from Elsevier. d Other possible schedules are waiting to be tested. For instance, in the figure two different time-staggered combinations administered at two different metronomic frequencies are depicted

Fig. 7

Human disease-omes. The human disease network (a) and the disease gene network (b), taken from the original article by Goh et al. [137] published in the Proceedings of the National Academy of Sciences and reproduced here with permission

Fig. 8

The network of orphan diseases based on shared genes, taken from the original article by Zhnag et al. [140] published in the American Journal of Human Genetics and reproduced here with permission from Elsevier. a The loosely connected 184 subnetworks of the network of orphan diseases. b A zoom of the largest subnetworks showing the 76 modules within it