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This article has been cited by other articles in PMC. Abstract Classically, phenotype is what is observed, and genotype is the genetic makeup. Introduction The terms genotype and phenotype have been in use at least since the turn of the last century.
Open in a separate window. Fig 1. Classical view of genotype—phenotype. Protein evolution in terms of biophysics The evolution of proteins in terms of their conformational ensembles has not been overlooked [ 31 , 32 ].
Statistical relationships between genotype and phenotype Monogenic traits are affected by single genes, polygenic traits are affected by multiple genes, and pleiotropy occurs when one gene influences multiple, seemingly unrelated phenotypic traits [ 73 — 75 ] Fig 1.
Fig 2. Network perturbations of genotype—phenotype. Fig 3. New paradigm of genotype—phenotype. Structural ensembles link genotype to phenotype Sickle cell anemia—a disorder that leads to atypical hemoglobin molecules that can distort red blood cells into a sickle, crescent shape resulting in anemia, repeated infections, and periodic episodes of pain—is one classical monogenic adaptive evolution example.
Single mutations Above, sets of nonsense mutations combinatorially decide the preferred conformational states that provoke the phenotype. Structural ensemble can link genotype to phenotype through phosphorylation Hepatitis C virus HCV requires only 10 proteins for evading the immune system.
The complexity of the genotype—Phenotype relationship The association between genotype and phenotype is hard to understand.
Fig 4. Cellular network. Prediction of phenotype from genotype Phenotypes may involve multiple traits that emerge from multiple nodes and edges, making it difficult to predict and relate specific nodes proteins or genes to specific traits [ 10 , 13 , ]; however, networks of pairs of nodes and edges that drive phenotypes can be identified [ , ].
Observed conformational changes To most clearly demonstrate our thesis, the examples should concomitantly 1 relate to signaling i. Fig 5. Phenotype switch of HIV-1 entry. Methods to associate ensembles and function The fact that a protein-coding gene encodes not a single conformation but an ensemble of conformations is indisputable, as is the fact that this must play a role in the making of the phenotype s.
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To model the information propagation using a minimum cost network flow, Yeger-Lotem et al. Differently expressed genes are linked to the sink of network flow. The cost of an edge is computed based on the probability that the two endpoints interact in a response pathway, which is estimated based on experimental evidences.
A constant negative cost is assigned to the links from the source. The capacity for a link from a target node to the sink is computed based on its transcript level while uniform capacity is assigned to all other links. Given the flow solution with minimum cost, a response network was predicted by ranking nodes in decreasing order of total incoming flows.
The ever-new discoveries of associations between genetic variations and complex traits such as common human diseases, posed a key question — how can we close the gap in genotype-phenotype relationships. To answer this challenging question, a number of computational network based approaches have been developed as surveyed in this review. Focusing on groups of related genes leads to increased statistical power and enhances interpretability of the results.
Through these method several new insights have been obtained including the involvement of macrophages in metabolic diseases Chen et al. One of the biggest challenges in understanding complex diseases relates to the fact that such diseases are highly heterogeneous. Therefore, in addition to being able to discover what individual disease cases have in common, we need to understand the differences between different disease subclasses. In this review, we have discussed several network based approaches for supervised disease classification.
Finally, to fully understand a disease, we need to grasp the precise molecular mechanism behind it. The understanding of the mechanistic processes is ultimately necessary for guiding a rational design of drug therapies.
While current network based approaches have certainly helped to understand the landscape of cellular level changes that accompany phenotypic changes, most of the results are of impressionist-type landscape, painted with the broad strokes of dysregulated pathways and groups of genes rather than with the precise and detailed molecular mechanisms.
While the approaches that rely on physical interactions, such as the current flow approach, may be, in theory, the closest to explanatory details, they are also limited by incompleteness and inaccuracy of physical interaction data. While some components like the dog or the lute are strong and clear despite some inaccuracy, others are less so.
This, in some sense, is also true for the interpretation of biological results obtained by computational network based approaches. They require some reference points such as GO categories, KEGG pathways, knowledge of a function of at least some genes, etc.
For example, such a network based method could identify perturbation of known biological pathways such as EGFR signaling. Figure 3. Such subnetworks provide somewhat distorted depiction of real relationships within the cell and while general components are distinguishable, much of the details are inaccurate.
For example, Kim et al. However, if we compare the topology of the retrieved pathway with the topology inferred using laborious small scale experiments, we usually find that the topology of inferred subnetwork is distorted relative to the real pathway. These issues notwithstanding, current computational techniques with no doubt have made significant progress toward pinpointing commonly dysregulated pathways, disease classification, and identification of disease associated genes.
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What role do genes play in development? How does your genotype contribute to your phenotype? Or more explicitly, how do genes work together to produce RNA that codes for proteins that make up your cells, tissues and organs, leading to your phenotype the physical expression of your genes?
This question intrigues Professor Peter Dearden , Director of Genetics Otago, who considers it one of the most important questions in biology. The genotype of an organism is defined as the sum of all its genes. The phenotype of an organism is the observable physical or biochemical characteristics of an organism, determined by both genetic make-up and environmental influences. The Human Genome Project has raised the profile of genome research — the genomes of over 1, organisms have now been sequenced.
This has provided a lot of information about genes and genomes and made it possible to investigate the relationship between genotype and phenotype. Researchers are finding that there are more similarities between genomes of different organisms than there are differences and that many of the phenotypic differences between organisms are due to differences in the way their genes are turned on and off, not due to what genes they have.
Evolution and development are particular themes for the research carried out by Peter and his colleagues. Current work focuses on investigating how the processes that occur during the development of an organism change over evolutionary time scales to give different forms of the same organism. To carry out this research, Peter and his colleagues work with a number of model organisms, including honey bees Apis mellifera and fruit flies Drosophila melanogaster.
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