Physiological Network From Anthropometric and Blood Test Biomarkers
Por:
Barajas-Martinez, Antonio, Ibarra-Coronado, Elizabeth, Sierra-Vargas, Martha Patricia, Cruz-Bautista, Ivette, Almeda-Valdes, Paloma, Aguilar-Salinas, Carlos A., Fossion, Ruben, Stephens, Christopher R., Vargas-Dominguez, Claudia, Atzatzi-Aguilar, Octavio Gamaliel, Debray-Garcia, Yazmin, Garcia-Torrentera, Rogelio, Bobadilla, Karen, Naranjo Meneses, Maria Augusta, Mena Orozco, Dulce Abril, Lam-Chung, Cesar Ernesto, Martinez Garces, Vania, Lecona, Octavio A., Marin-Garcia, Arlex O., FRANK, ALEJANDRO, RIVERA, ANA LEONOR
Publicada:
12 ene 2021
Resumen:
Currently, research in physiology focuses on molecular mechanisms
underlying the functioning of living organisms. Reductionist strategies
are used to decompose systems into their components and to measure
changes of physiological variables between experimental conditions.
However, how these isolated physiological variables translate into the
emergence -and collapse- of biological functions of the organism as a
whole is often a less tractable question. To generate a useful
representation of physiology as a system, known and unknown interactions
between heterogeneous physiological components must be taken into
account. In this work we use a Complex Inference Networks approach to
build physiological networks from biomarkers. We employ two unrelated
databases to generate Spearman correlation matrices of 81 and 54
physiological variables, respectively, including endocrine, mechanic,
biochemical, anthropometric, physiological, and cellular variables. From
these correlation matrices we generated physiological networks by
selecting a p-value threshold indicating statistically significant
links. We compared the networks from both samples to show which features
are robust and representative for physiology in health. We found that
although network topology is sensitive to the p-value threshold, an
optimal value may be defined by combining criteria of stability of
topological features and network connectedness. Unsupervised community
detection algorithms allowed to obtain functional clusters that
correlate well with current medical knowledge. Finally, we describe the
topology of the physiological networks, which lie between random and
ordered structural features, and may reflect system robustness and
adaptability. Modularity of physiological networks allows to explore
functional clusters that are consistent even when considering different
physiological variables. Altogether Complex Inference Networks from
biomarkers provide an efficient implementation of a systems biology
approach that is visually understandable and robust. We hypothesize that
physiological networks allow to translate concepts such as homeostasis
into quantifiable properties of biological systems useful for
determination and quantification of health and disease.
Filiaciones:
Barajas-Martinez, Antonio:
Univ Nacl Autonoma Mexico, Fac Med, Posgrad Ciencias Biomed, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Ibarra-Coronado, Elizabeth:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Inst Ciencias Nucl, Ciudad De Mexico, Mexico
Sierra-Vargas, Martha Patricia:
Inst Nacl Enfermedades Resp, Subdirecc Invest Clin, Ciudad De Mexico, Mexico
Cruz-Bautista, Ivette:
Univ La Salle, Fac Mexicana Med, Ciudad De Mexico, Mexico
Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metabol, Ciudad De Mexico, Mexico
Almeda-Valdes, Paloma:
Univ La Salle, Fac Mexicana Med, Ciudad De Mexico, Mexico
Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metabol, Ciudad De Mexico, Mexico
Aguilar-Salinas, Carlos A.:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Univ La Salle, Fac Mexicana Med, Ciudad De Mexico, Mexico
Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metabol, Ciudad De Mexico, Mexico
Tecnol Monterrey, Escuela Med & Ciencias Salud, Monterrey, Mexico
Fossion, Ruben:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Inst Ciencias Nucl, Ciudad De Mexico, Mexico
Stephens, Christopher R.:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Inst Ciencias Nucl, Ciudad De Mexico, Mexico
Vargas-Dominguez, Claudia:
Inst Nacl Enfermedades Resp, Dept Invest Inmunol & Med Ambiental, Ciudad De Mexico, Mexico
Atzatzi-Aguilar, Octavio Gamaliel:
Inst Nacl Enfermedades Resp, Dept Invest Inmunol & Med Ambiental, Ciudad De Mexico, Mexico
Cátedras CONACYT, Ciudad de México, Mexico
Debray-Garcia, Yazmin:
Inst Nacl Enfermedades Resp, Dept Invest Inmunol & Med Ambiental, Ciudad De Mexico, Mexico
Garcia-Torrentera, Rogelio:
Inst Nacl Enfermedades Resp, Unidad Urgencias Resp, Ciudad De Mexico, Mexico
Bobadilla, Karen:
Inst Nacl Enfermedades Resp, Dept Invest Inmunol & Med Ambiental, Ciudad De Mexico, Mexico
Naranjo Meneses, Maria Augusta:
Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metabol, Ciudad De Mexico, Mexico
Mena Orozco, Dulce Abril:
Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metabol, Ciudad De Mexico, Mexico
Lam-Chung, Cesar Ernesto:
Inst Nacl Ciencias Med & Nutr Salvador Zubiran, Unidad Invest Enfermedades Metabol, Ciudad De Mexico, Mexico
Martinez Garces, Vania:
Univ Nacl Autonoma Mexico, Fac Med, Programa Estudios Combinados Med, Ciudad De Mexico, Mexico
Lecona, Octavio A.:
Univ Nacl Autonoma Mexico, Fac Med, Posgrad Ciencias Biomed, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Marin-Garcia, Arlex O.:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
FRANK, ALEJANDRO:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Inst Ciencias Nucl, Ciudad De Mexico, Mexico
El Colegio Nacl, Ciudad De Mexico, Mexico
RIVERA, ANA LEONOR:
Univ Nacl Autonoma Mexico, Ctr Ciencias Complejidad, Ciudad De Mexico, Mexico
Univ Nacl Autonoma Mexico, Inst Ciencias Nucl, Ciudad De Mexico, Mexico
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