High-performance techniques in the study of diseases – informational impulses

High-performance techniques in the study of diseases - informational impulses

The advent and development of high-performance technologies opens a window to explore cellular activity from genome to genetic, protein and metabolic expression profiles on a large scale.

Interpretation of these data allows us to explore how living organisms control their responses at various biological scales and discover the principles that govern them.

Osbaldo Resendis-Antonio, researcher at the Research Support Network (RAIUNAM) of the National Institute of Genomic Medicine (INMEGEN) and member of the Center for Complexity Sciences (C3) of UNAM, explained and recognized the above:

“Despite the interest in the proposal, this objective is not easy. Along with the advancement of these technologies, there is an urgent need to develop new conceptual schemes capable of integrating data and generating hypotheses about the mechanisms that control living systems. This is where the sciences of complexity converge, that is, interdisciplinarity. The implementation of its methods undoubtedly contributes to projects with strategies aimed at understanding problems in the fields of biomedical sciences and the study of complex diseases.

In the Systems Biology Seminar of the Conference Cycle Contemporary Perspectives on Biology and Complexity Science Organized by C3, the expert addressed the topic “Cancer and metabolism in the microbiome: a systems biology perspective”.

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Resendis-Antonio explained that this discipline (systems biology) seeks to merge databases in biology with computational models, through classical fields (physics and bioinformatics) and new trends in information technologies such as machine learning. machine learning,

In general, systems biology paradigms aim to build mathematical/computational models that allow three objectives: to unify, explain, and integrate large amounts of biological data of an organism, such as its genome, transcriptome, (measurement of the expression levels of all genes in an organism’s genome), proteome and metabolome; Quantitatively model biological networks and generate hypotheses about their organizational principles, and finally evaluate these hypotheses experimentally.

From an organism’s genome and transcriptome we can discover how genes are activated (“turned on”) or repressed (“turned off”) when they encounter different stimuli. Along with large-scale measurements of genes and proteins, has been added the metabolome, a technology that captures “the dance of formation, transformation, and destruction of metabolites – any molecule involved (as a reactant or product) in metabolic pathways or transformations.” Provides an overview of. Biochemistry – which give rise to the life of a living organism. “All of this is subject to regulatory mechanisms and rules of impressive complexity.”

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Metabolic changes

In the lab, he said, “We are interested in designing strategies to elucidate changes in metabolic changes in diseases such as cancer and type 2 diabetes. We build mathematical and computational models that integrate transcriptome, proteome, and metabolomic data. With these models are able to in silico We generate hypotheses about the organizational mechanisms in the cell, and we subsequently evaluate them experimentally with the aim of understanding the changes that occur in a disease and potentially impacting the clinic,” said Resendis-Antonio.

The scientist and his team developed a model that, with gene expression data, is able to predict the proliferation speed of different cancer cell lines. “We reviewed databases of thousands of patients with a variety of diseases, from which we selected 33 different patients, a total of 12,000 patients.” They found that some types of cancer spread less and some more. But even within themselves there is heterogeneity in metabolic activity and expression profiles.

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The next thing, he said, “was to ask ourselves whether we could establish on the basis of metabolism what each individual depends on, because the skin is not the same as the brain. Many metabolic pathways were studied and this conclusion The conclusion was that, although it is the same tumor, the metabolism varies according to each tissue and patient. This is a kind of diversity that should be explored.”

But there is also intratumoral heterogeneity, because of the idea that cancer grows inconsistently; But no, there are different subpopulations within cancerous tissue that perform different functions.

For example, in a spheroid model of a breast cancer cell line (cell culture structures generated in 3D) “we found three sub-populations. The first is associated with malignant cells that are proliferating, the second refers to cells that are preparing to metastasize and encounter the immune system. Finally, a third population, which requires further study because it lacks a clear biological function. This intratumoral functional variability reflects the difficulty of preventing the condition and highlights the relevance of heterogeneity in the efficiency of treatment.


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