Research

Life Science

Within Life Sciences, these are some of the areas we develop technology for:

  • Biology
  • Anatomy
  • Bacteriology
  • Biochemistry
  • Bioinformatics
  • Biolinguistics
  • Biological Oceanography
  • Biomechanics
  • Biophysics
  • Botany
  • Cell Biology / Cytology
  • Developmental Biology
  • Enzymology
  • Ethology
  • Evolutionary Biology
  • Evolutionary Developmental Biology
  • Genetics
  • Histology
  • Immunology
  • Microbiology
  • Molecular Biology
  • Mycology
  • Neuroscience
  • Paleontology
  • Pathology
  • Pharmacology
  • Phycology
  • Physiology
  • Population Biology
  • Quantum Biology
  • Structural Biology
  • Synthetic Biology
  • Systems Biology
  • Theoretical Biology
  • Toxicology
  • Virology
  • Zoology

Applied Life Sciences

These are some of the Applied Life Sciences fields that we develop technology for:

  • Agriculture
  • Biocomputers
  • Biocontrol
  • Bioengineering
  • Bioelectronics
  • Biomaterials
  • Biomedical Science
  • Biomonitoring
  • Biopolymer
  • Biotechnology
  • Conservation Biology
  • Environmental Health – Multidisciplinary field concerned with Environmental Epidemiology, Toxicology, and Exposure Science.
  • Fermentation Technology
  • Food Science
  • Genomics
  • Health Sciences
  • Immunotherapy
  • Kinesiology
  • Medical Device
  • Medical Imaging - Radiology, Radiomics, X-Ray, CT, Ultrasound, PET
  • Optogenetics
  • Pharmacogenomics
  • Pharmacology
  • Population Dynamics
  • Proteomics

Applied Sciences

Applied Science is the use of the scientific method and knowledge obtained via conclusions from the method to attain practical goals. It includes a broad range of disciplines such as Engineering and Medicine. Applied Science is often contrasted with Basic Science, which is focused on advancing scientific theories and laws that explain and predict events in the natural world.

Applied Science can also apply formal science, such as Statistics and Probability Theory, as in Epidemiology. Genetic Epidemiology is an Applied Science applying both Biological and Statistical methods.

Outline of Applied Science and Branches of Applied Science:

Engineering fields include Thermodynamics, Heat Transfer, Fluid Mechanics, Statics, Dynamics, Mechanics of Materials, Kinematics, Electromagnetism, Materials Science, Earth Sciences, Engineering Physics.

Medical Sciences, for instance Medical Microbiology and Clinical Virology, are Applied Sciences that apply Biology toward Medical knowledge and inventions, but not necessarily Medical Technology, whose development is more specifically Biomedicine or Biomedical Engineering.

Applied Research

Applied Research is the practical application of science. It accesses and uses accumulated theories, knowledge, methods, and techniques, for a specific, state-, business-, or client-driven purpose. Applied Research is contrasted with Pure Research / Basic Research in discussion about research ideals, methodologies, programs, and projects.

Applied Research usually has specific commercial objectives related to products, procedures, or services. The comparison of Pure Research and Applied Research provides a basic framework and direction for businesses to follow.

Applied Research deals with solving practical problems and generally employs empirical methodologies. Because Applied Research resides in the messy real world, strict research protocols may need to be relaxed. For example, it may be impossible to use a random sample. Thus, transparency in the methodology is crucial. Implications for interpretation of results brought about by relaxing an otherwise strict canon of methodology should also be considered.

Since Applied Research has a provisional close-to-the-problem and close-to-the-data orientation, it may also use a more provisional conceptual framework such as working hypotheses or pillar questions. The Organization for Economic Co-operation and Development - OECD's Frascati Manual describes Applied Research as one of the three forms of research, along with Basic Research and Experimental Development. Due to its practical focus, Applied Research information will be found in the literature associated with individual disciplines.

Omics Sciences Technologies

Omic Technologies are primarily aimed at the universal detection of genes / Genomics, mRNA / Transcriptomics), Proteins / Proteomics, and Metabolites / Metabolomics) in a specific biological sample. Omic Technologies have a broad range of applications.

  • Genomic and Transcriptomic Research has progressed due to advances in Microarray Technology.
  • Mass Spectrometry is the most common method used for detection of analyses in Proteomic and Metabolomic research.
  • Data Analysis is complex as a huge amount of data is generated and Statistician and Bioinformatician involvement in the process is essential.
  • Much of the Omic research in Obstetrics and Gynecology has concentrated on using the technology to develop screening tests for Gynecological Cancers (Oncology) and Obstetric complications.

Omics Sciences

The word Omics refers to a field of study in Biological Sciences that ends with -omics, such as Genomics, Transcriptomics, Proteomics, or Metabolomics. The ending -ome is used to address the objects of study of such fields, such as the Genome, Proteome, Transcriptome, or Metabolome, respectively. More specifically Genomics is the science that studies the structure, function, evolution, and mapping of Genomes and aims at characterization and quantification of Genes, which direct the production of Proteins with the assistance of Enzymes and Messenger Molecules.

Omics Sciences and Cognitive Genomics

Cognitive Genomics / Neurative Genomics is the sub-field of Genomics pertaining to Cognitive Function in which the genes and non-coding sequences of an organism's Genome related to the health and activity of the brain are studied.

By applying Comparative Genomics, the Genomes of multiple species are compared in order to identify Genetic and Phenotypical differences between species. Observed Phenotypical Characteristics related to the Neurological function include behavior, personality, Neuroanatomy, and Neuropathology. The theory behind Cognitive Genomics is based on elements of Genetics,

Evolutionary Biology, Molecular Biology, Cognitive Psychology, Behavioral Psychology, and Neurophysiology. Intelligence is the most extensively studied behavioral trait. In Humans, approximately 70% of all genes are expressed in the brain. Genetic variation accounts for 40% of Phenotypical Variation.

Approaches in Cognitive Genomics have been used to investigate the Genetic causes for many Mental and Neurodegenerative Disorders including Down syndrome, major depressive disorder, Autism, and Alzheimer's disease.

Omics Sciences and Comparative Genomics

Comparative Genomics is a field of Biological research in which the Genomic features of different organisms are compared. The Genomic features may include the DNA sequence, Genes, Gene Order, Regulatory Sequences, and other Genomic structural landmarks.

In this branch of Genomics, whole or large parts of Genomes resulting from Genome projects are compared to study basic Biological Similarities and differences as well as Evolutionary Relationships between organisms.

The major principle of Comparative Genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. Therefore, Comparative Genomic approaches start with making some form of alignment of Genome Sequences and looking for orthologous sequences (sequences that share a common ancestry) in the aligned Genomes and checking to what extent those sequences are conserved. Based on these, Genome and Molecular Evolution are inferred and this may in turn be put in the context of, for example, Phenotypic Evolution or Population Genetics.

Computational tools for analyzing sequences and complete Genomes are developing quickly due to the availability of large amount of Genomic Data. At the same time, comparative analysis tools are progressed and improved. In the challenges about these analyses, it is very important to visualize the comparative results.

Visualization of sequence conservation is a tough task of comparative sequence analysis. As we know, it is highly inefficient to examine the alignment of Long Genomic Regions manually. Internet-based Genome browsers provide many useful tools for investigating Genomic sequences due to integrating all sequence-based biological information on genomic regions. When we extract large amount of relevant Biological Data, they can be very easy to use and less time-consuming.

Medicine

The medical field also benefits from the study of Comparative Genomics. Vaccinology in particular has experienced useful advances in technology due to Genomic approaches to problems. In an approach known as Reverse Vaccinology, researchers can discover candidate antigens for Vaccine Development by analyzing the Genome of a Pathogen or a family of Pathogens.

Applying a Comparative Genomics approach by analyzing the Genomes of several related Pathogens can lead to the development of Vaccines that are multiprotective. A team of researchers employed such an approach to create a Universal Vaccine for Group B Streptococcus, a group of bacteria responsible for severe neonatal infection.

Comparative Genomics can also be used to generate specificity for Vaccines against Pathogens that are closely related to commensal microorganisms. For example, researchers used Comparative Genomic Analysis of commensal and pathogenic strains of E. coli to identify pathogen specific genes as a basis for finding antigens that result in immune response against pathogenic strains but not commensal ones. In May of 2019, using the Global Genome Set, a team in the UK and Australia sequenced thousands of globally-collected isolates of Group A Streptococcus, providing potential targets for developing a Vaccine against the pathogen, also known as S. pyogenes.

Research

Comparative Genomics also opens up new avenues in other areas of research. As DNA sequencing technology has become more accessible, the number of sequenced Genomes has grown. With the increasing reservoir of available Genomic Data, the potency of Comparative Genomic inference has grown as well.

A notable case of this increased potency is found in recent primate research. Comparative Genomic methods have allowed researchers to gather information about genetic variation, differential Gene expression, and Evolutionary Dynamics in primates that were indiscernible using previous data and methods.

Omics Sciences and Metagenomics

Metagenomics is the study of Genetic Material recovered directly from Environmental samples. The broad field may also be referred to as Environmental Genomics, Ecogenomics or Community Genomics.

While traditional Microbiology and Microbial Genome Sequencing and Genomics rely upon cultivated clonal cultures, early environmental gene sequencing cloned specific Genes (often the 16S rRNA Gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of microbial biodiversity had been missed by cultivation-based methods.

Because of its ability to reveal the previously hidden diversity of Microscopic Life, Metagenomics offers a powerful lens for viewing the microbial world that has the potential to revolutionize understanding of the entire living world.

As the price of DNA Sequencing continues to fall, Metagenomics now allows Microbial Ecology to be investigated at a much greater scale and detail than before. Recent studies use either "shotgun" or PCR directed sequencing to get largely unbiased samples of all genes from all the members of the sampled communities.

Bioinformatics and Metagenomics

The data generated by Metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. The sequencing of the cow rumen metagenome generated 279 gigabases, or 279 billion base pairs of nucleotide sequence data, while the human gut microbiome gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data. Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers.

Omics Sciences and Neurogenomics

Neurogenomics is the study of how the Genome of an organism influences the development and function of its nervous system. This field intends to unite Functional Genomics and Neurobiology in order to understand the Nervous System as a whole from a Genomic perspective.

The Nervous System in Vertebrates is made up of two major types of cells – Neurological cells and Neurons. Hundreds of different types of neurons exist in humans, with varying functions – some of them process external stimuli; others generate a response to stimuli; others organize in centralized structures (brain, spinal ganglia) that are responsible for cognition, perception, and regulation of motor functions.

Neurons in these centralized locations tend to organize in giant networks and communicate extensively with each other. Prior to the availability of expression arrays and DNA sequencing methodologies, researchers sought to understand the cellular behavior of neurons (including synapse formation and neuronal development and regionalization in the human nervous system) in terms of the underlying molecular biology and biochemistry, without any understanding of the influence of a neuron's genome on its development and behavior.

As our understanding of the Genome has expanded, the role of networks of Gene interactions in the maintenance of neuronal function and behavior has garnered interest in the Neuroscience research community.

Neurogenomics allows scientists to study the nervous system of organisms in the context of these underlying regulatory and transcriptional networks. This approach is distinct from neurogenetics, which emphasizes the role of single genes without a network-interaction context when studying the nervous system.

Omics Sciences and Pan_Genome / Pangenome or Supragenome

In the fields of molecular biology and genetics, a Pan-Genome (Pangenome or Supragenome) is the entire set of genes from all strains within a clade. More generally, it is the union of all the Genomes of a clade. The Pan-Genome can be broken down into a "core pangenome" that contains genes present in all individuals, a "shell pangenome" that contains genes present in two or more strains, and a "cloud pangenome" that contains genes only found in a single strain.

Some authors also refer to the cloud genome as "accessory genome" containing 'dispensable' genes present in a subset of the strains and strain-specific genes. Note that the use of the term 'dispensable' has been questioned, at least in plant genomes, as accessory genes play "an important role in genome evolution and in the complex interplay between the genome and the environment". The field of study of the pangenome is called Pangenomics.

The genetic repertoire of a bacterial species is much larger than the gene content of an individual strain. Some species have open (or extensive) pangenomes, while others have closed pangenomes. For species with a closed pan-genome, very few genes are added per sequenced genome (after sequencing many strains), and the size of the full pangenome can be theoretically predicted.

Species with an open pangenome have enough genes added per additional sequenced genome that predicting the size of the full pangenome is impossible. Population size and niche versatility have been suggested as the most influential factors in determining pan-genome size.

Pangenomes were originally constructed for species of bacteria and archaea, but more recently eukaryotic pan-genomes have been developed, particularly for plant species. Plant studies have shown that pan-genome dynamics are linked to transposable elements. The significance of the pan-genome arises in an evolutionary context, especially with relevance to metagenomics, but is also used in a broader genomics context. An open access book reviewing the pangenome concept and its implications,
edited by Tettelin and Medini, was published in the spring of 2020.

Data Structures

The number of sequenced Genomes is continuously growing "simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich Genomic Datasets". Pangenome graphs are emerging data structures designed to represent pangenomes and to efficiently map reads to them. They have been reviewed by Eizenga et al.

Software Tools

As interest in Pangenomes increased, there have been several software tools developed to help analyze this kind of data. To start a Pangenomic Analysis the first step is the homogenization of Genome annotation. The same software should be used to annotate all Genomes used, such as GeneMark or RAST.

In 2015, a group reviewed the different kinds of analyses and tools a researcher may have available. There are seven kinds of software developed to analyze Pangenomes: Those dedicated to Cluster Homologous Genes; identify SNPs; Plot Pangenomic Profiles; Build Phylogenetic Relationships of Orthologous genes/families of strains / isolates; function-based searching; annotation and/or curation; and visualization.

The two most cited software tools for Pangenomic Analysis at the end of 2014 were Panseq and the Pan-Genomes Analysis Pipeline (PGAP). Other options include BPGA – A Pan-Genome Analysis Pipeline for prokaryotic Genomes, GET_HOMOLOGUES, Roary. and PanDelos.

In 2015 a review focused on prokaryote pangenomes[60] and another for plant pan-genomes were published. Among the first software packages designed for plant Pangenomes were PanTools and GET_HOMOLOGUES-EST.

In 2018 panX was released, an interactive Web Tool that allows inspection of gene families evolutionary history. PanX can display an alignment of Genomes, a Phylogenetic Tree, mapping of mutations and inference about gain and loss of the family on the core-genome phylogeny.

In 2019 OrthoVenn 2.0 allowed comparative visualization of families of Homologous Genes in Venn diagrams up to 12 Genomes. In 2020 Anvi'o was available as a Multiomics platform that contains Pangenomic and Metapangenomic Analyses as well as Visualization Workflows. In Anvi'o, Genomes are displayed in concentrical circles and each radius represents a gene family, allowing for comparison of more than 100 Genomes in its interactive visualization.

In 2020, a Computational Comparison of tools for extracting Gene-based Pangenomic contents (such as GET_HOMOLOGUES, PanDelos, Roary, and others) has been released. Tools were compared from a methodological perspective, analyzing the causes that lead a given methodology to outperform other tools. The analysis was performed by taking into account different bacterial populations, which are synthetically generated by changing evolutionary parameters. Results show a differentiation of the performance of each tool that depends on the composition of the input Genomes.

Omics Sciences (continued)

Functional Genomics

Functional Genomics aims at identifying the functions of as many genes as possible of a given organism. It combines different -omics techniques such as transcriptomics and proteomics with saturated mutant collections.

Epigenomics

The Epigenome is the supporting structure of Genome, including protein and RNA binders, alternative DNA structures, and chemical modifications on DNA.

  • Epigenomics: Modern technologies include chromosome conformation by Hi-C, various ChIP-seq and other sequencing methods combined with proteomic fractionations, and sequencing methods that find chemical modification of cytosines, like bisulfite sequencing.
  • Nucleomics: Study of the complete set of genomic components which form "the cell nucleus as a complex, dynamic biological system, referred to as the nucleome". The 4D Nucleome Consortium officially joined the IHEC (International Human Epigenome Consortium) in 2017.

Lipidomics

The Lipidome is the entire complement of cellular lipids, including the modifications made to a particular set of lipids, produced by an organism or system. Lipidomics: Large-scale study of pathways and networks of lipids. Mass Spectrometry techniques are used.

Transcriptome

Transcriptome is the set of all messenger RNA molecules in one cell, tissue, or organism. It includes the amount or concentration of each RNA molecule in addition to the molecular identities.

Proteome

The term Proteome refers to the sum of all the proteins in a cell, tissue, or organism. Proteomics is the science that studies those proteins as related to their Biochemical properties and functional roles, and how their quantities, modifications, and structures change during growth and in response to internal and external stimuli.

Metabolome

The Metabolome represents the collection of all Metabolites in a Biological cell, tissue, organ, or organism, which are the end products of cellular processes. Metabolomics is the science that studies all chemical processes involving Metabolites. More specifically, Metabolomics is the study of chemical fingerprints that specific cellular processes establish during their activity; it is the study of all small-molecule Metabolite profiles.

Overall, the objective of Omics Sciences is to identify, characterize, and quantify all Biological Molecules that are involved in the structure, function, and dynamics of a cell, tissue, or organism.


NOTE:

Personal Genomics

Personal Genomics or Consumer Genetics is the branch of Genomics concerned with the sequencing, analysis and interpretation of the Genome of an individual. The Genotyping stage employs different techniques, including Single-Nucleotide Polymorphism (SNP) analysis chips (typically 0.02% of the Genome), or partial or full Genome Sequencing. Once the Genotypes are known, the individual's variations can be compared with the published literature to determine likelihood of trait expression, ancestry inference and disease risk. Automated high-throughput sequencers have increased the speed and reduced the cost of sequencing, making it possible to offer whole Genome Sequencing including interpretation to consumers since 2015 for less than $1,000. The emerging market of direct-to-consumer Genome Sequencing services has brought new questions about both the medical efficacy and the ethical dilemmas associated with widespread knowledge of individual Genetic information.