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Citi, L.

Publications and source records attributed to Citi, L..

3 recordsLinked to original sources

The Binary Cellular Biology of Human Growth I Quantifying the Creation and Growth of the Body and its Parts

Assessing and understanding the Growth and Development of animals generally, and of human fetuses specifically, provides essential information for the management of the health of the newborn and its mother. Here we show that a consideration of the formation of the body, in units of numbers of cells, N, and the change in these numbers by the binary either/or decisions of cells to divide or not, an approach we call Binary Cellular Analysis, provides new insights and new equations for quantifying the growth and development of the body and its parts. These equations include: 1) The Binary Cellular Universal Growth Equation, which captures growth from conception to adulthood, and provides a method for biologically-based, data-driven, Growth Curves; 2) The Binary Cellular Universal Mitotic Fraction Equation, which lies within the Binary Cellular Universal Growth Equation, and captures the mechanism of growth to adult size, control of cell division; 3) The Binary Cellular Allometric Growth Equation, which captures the creation and growth of the Tissues, Organs, and Anatomical Structures of the body, and how the embryo creates each Body Part from a Single Founder Cell; and, 4) Binary Cellular Estimated Fetal Weight Equations, derived from the Binary Cellular Allometric Growth Equation, which captures the relationship between Ultrasound Measurement and the Size of the body as a whole. These equations capture the developmental process of Cellular Selection, resulting from Differential Cellular Proliferation, which molds the formation of the body from a single fertilized ovum into a multicellular animal. The parameters of these equations both capture the differences in Size and Growth between animals across the taxonomic spectrum, and between human fetuses, as well as identify the individual mechanisms of mitosis that drive Growth and Development. From the Binary Cellular Universal Growth Equation, the Growth of human fetuses can be measured, thus providing a much-needed tool for understanding the biological forces that cause a fetus to grow to Small, Average, or Large Size at birth, for providing the basis of biologically-based, data-driven, Growth Curves, and for assisting the management of the obstetric care of the fetus and its mother.

developmental biology↗

How Our Cells Become Our Selves: The Cellular Phylodynamic Biology of Growth and Development

Our lives begin with 1 cell, then 2, then 4, then the trillion cell adult, comprised of cell lineages, tissues, organs. How does this occur? Examination in numbers of cells, N, Cellular Phylodynamics, revealed two previously unappreciated processes: UNI-GROWTH, the slowing of growth that occurs as we become larger, caused by fewer cells dividing, captured by the Universal Mitotic Fraction and Universal Growth Equations, with accuracy confirmed for 13 species, including nematodes, mollusks, and vertebrates; and ALLO-GROWTH, the creation of body parts from Founder Cells, captured by the Cellular Allometric Growth Equation, which describes mitotic expansion by Cell-Heritable change in the Cell Cycle Time. These equations can generate cell lineage approximations, bringing the power of coalescent theory to developmental biology.

developmental biology↗

Gene-level heritability analysis explains the polygenic architecture of cancer

Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with increased risk of cancer. However, the amount of heritable risk explained by these variants is limited, thus leaving most of cancer heritability unexplained. Recent studies have shown that genomic regions associated with specific biological functions explain a large proportion of the heritability of many traits. Since cancer is mostly triggered by aberrant genes function, we hypothesised that SNPs located in protein-coding genes could explain a significant proportion of cancer heritability. To perform this analysis, we developed a new method, called Bayesian Gene HERitability Analysis (BAGHERA), to estimate the heritability explained by all the genotyped SNPs and by those located in protein coding genes directly from GWAS summary statistics. By applying BAGHERA to the 38 cancers reported in the UK Biobank, we identified 1, 146 genes explaining a significant amount of cancer heritability. We found these genes to be tumour suppressors directly involved in the hallmark processes controlling the transformation from normal to cancer cell; moreover, these genes also harbour somatic driver mutation for many tumours, suggesting a two-hit model underpinning tumorigenesis. Our study provides new evidence for a functional role of SNPs in cancer and identifies new targets for risk assessment and patients stratification.

genetics↗