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Protein networks as logic functions in development and cancer.

ABSTRACT: Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic.

SUBMITTER: Dutkowski J

PROVIDER: S-EPMC3182870 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Protein networks as logic functions in development and cancer.

Dutkowski Janusz J Ideker Trey T

PLoS computational biology 20110929 9

Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify pred ...[more]

PMID: 21980275

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Project description:BackgroundA large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts.ResultsTo overcome the limitations of methods that rely solely on explicitly mentioned terms in texts to predict protein functions, we propose in this paper an Information Extraction system called PL-PPF. The proposed system employs techniques for predicting the functions of proteins based on their co-occurrences with explicitly and implicitly mentioned biological molecule terms that pertain functional categories in biomedical literature. That is, PL-PPF employs a combination of statistical-based explicit term extraction techniques and logic-based implicit term extraction techniques. The statistical component of PL-PPF predicts some of the functions of a protein by extracting the explicitly mentioned functional terms that directly describe the functions of the protein from the biomedical texts associated with the protein. The logic-based component of PL-PPF predicts additional functions of the protein by inferring the functional terms that co-occur implicitly with the protein in the biomedical texts associated with it. First, the system employs its statistical-based component to extract the explicitly mentioned functional terms. Then, it employs its logic-based component to infer additional functions of the protein. Our hypothesis is that important biological molecule terms pertaining functional categories of proteins are likely to co-occur implicitly with the proteins in biomedical texts. We evaluated PL-PPF experimentally and compared it with five systems. Results revealed better prediction performance.ConclusionsThe experimental results showed that PL-PPF outperformed the other five systems. This is an indication of the effectiveness and practical viability of PL-PPF's combination of explicit and implicit techniques. We also evaluated two versions of PL-PPF: one adopting the complete techniques (i.e., adopting both the implicit and explicit techniques) and the other adopting only the explicit terms co-occurrence extraction techniques (i.e., without the inference rules for predicate logic). The experimental results showed that the complete version outperformed significantly the other version. This is attributed to the effectiveness of the rules of predicate logic to infer functional terms that co-occur implicitly with proteins in biomedical texts. A demo application of PL-PPF can be accessed through the following link: http://ecesrvr.kustar.ac.ae:8080/plppf/.

Project description:Tumor microenvironment is considered nowadays as one of the main players in cancer development and progression. Tumor microenvironment is highly complex and consists of non-tumor cells (i.e., cancer-associated fibroblast, endothelial cells, or infiltrating leukocytes) and a large list of extracellular matrix proteins and soluble factors. The way that microenvironment components interact among them and with the tumor cells is very complex and only partially understood. However, it is now clear that these interactions govern and modulate many of the cancer hallmarks such as cell proliferation, the resistance to death, the differentiation state of tumor cells, their ability to migrate and metastasize, and the immune response against tumor cells. One of the microenvironment components that have emerged in the last years with strength is a heterogeneous group of multifaceted proteins grouped under the name of matricellular proteins. Matricellular proteins are a family of non-structural matrix proteins that regulate a variety of biological processes in normal and pathological situations. Many components of this family such as periostin (POSTN), osteopontin (SPP1), or the CNN family of proteins have been shown to regulate key aspect of tumor biology, including proliferation, invasion, matrix remodeling, and dissemination to pre-metastatic niches in distant organs. Matricellular proteins can be produced by tumor cells themselves or by tumor-associated cells, and their synthesis can be affected by intrinsic and/or extrinsic tumor cell factors. In this review, we will focus on the role of POSTN in the development and progression of cancer. We will describe their functions in normal tissues and the mechanisms involved in their regulation. We will analyze the tumors in which their expression is altered and their usefulness as a biomarker of tumor progression. Finally, we will speculate about future directions for research and therapeutic approaches targeting POSTN.

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Protein networks as logic functions in development and cancer.

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Protein networks as logic functions in development and cancer.

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