Anatomy Computational anatomy is the study of anatomical shape and form at the visible or
gross anatomical 50-100 \mu scale of
morphology. It involves the development of computational mathematical and data-analytical methods for modeling and simulating biological structures. It focuses on the anatomical structures being imaged, rather than the medical imaging devices. Due to the availability of dense 3D measurements via technologies such as
magnetic resonance imaging, computational anatomy has emerged as a subfield of
medical imaging and
bioengineering for extracting anatomical coordinate systems at the morpheme scale in 3D. The original formulation of computational anatomy is as a generative model of shape and form from exemplars acted upon via transformations. The
diffeomorphism group is used to study different coordinate systems via
coordinate transformations as generated via the
Lagrangian and Eulerian velocities of flow from one anatomical configuration in {\mathbb R}^3 to another. It relates with
shape statistics and
morphometrics, with the distinction that
diffeomorphisms are used to map coordinate systems, whose study is known as diffeomorphometry.
Data and modeling Mathematical biology is the use of mathematical models of living organisms to examine the systems that govern structure, development, and behavior in
biological systems. This entails a more theoretical approach to problems, rather than its more empirically minded counterpart of
experimental biology. Mathematical biology draws on
discrete mathematics,
topology (also useful for computational modeling),
Bayesian statistics,
linear algebra and
Boolean algebra. and computational biomodeling, which refers to building
computer models and
visual simulations of biological systems. This allows researchers to predict how such systems will react to different environments, which is useful for determining if a system can "maintain their state and functions against external and internal perturbations". While current techniques focus on small biological systems, researchers are working on approaches that will allow for larger networks to be analyzed and modeled. A majority of researchers believe this will be essential in developing modern medical approaches to creating new drugs and gene
therapy. Until recent decades
theoretical ecology has largely dealt with
analytic models that were detached from the
statistical models used by
empirical ecologists. More recently, computational methods have aided in developing theories via
simulation of ecological systems, in addition to increasing application of methods from
computational statistics in ecological analyses.
Systems biology Systems biology consists of computing the interactions between various biological systems ranging from the cellular level to entire populations with the goal of discovering emergent properties. This process usually involves networking
cell signaling and
metabolic pathways. Systems biology often uses computational techniques from biological modeling and
graph theory to study these complex interactions at cellular levels. or
backward time) to DNA data to make inferences about
demographic or
selective history • Building
population genetics models of
evolutionary systems from first principles in order to predict what is likely to evolve
Genomics Computational genomics is the study of the
genomes of
cells and
organisms. The
Human Genome Project is one example of computational genomics. This project looks to sequence the entire human genome into a set of data. Once fully implemented, this could allow for doctors to analyze the genome of an individual
patient. This opens the possibility of personalized medicine, prescribing treatments based on an individual's pre-existing genetic patterns. Researchers are looking to sequence the genomes of animals, plants,
bacteria, and all other types of life. One of the main ways that genomes are compared is by
sequence homology. Homology is the study of biological structures and nucleotide sequences in different organisms that come from a common
ancestor. Research suggests that between 80 and 90% of genes in newly sequenced
prokaryotic genomes can be identified this way. 3D genomics is a subsection in computational biology that focuses on the organization and interaction of genes within a
eukaryotic cell. One method used to gather 3D genomic data is through
Genome Architecture Mapping (GAM). GAM measures 3D distances of
chromatin and DNA in the genome by combining
cryosectioning, the process of cutting a strip from the nucleus to examine the DNA, with laser microdissection. A nuclear profile is simply this strip or slice that is taken from the nucleus. Each nuclear profile contains genomic windows, which are certain sequences of
nucleotides - the base unit of DNA. GAM captures a genome network of complex, multi enhancer chromatin contacts throughout a cell.
Biomarker discovery Computational biology also plays a role in identifying
biomarkers for diseases such as cardiovascular conditions, with the integration of various '
Omic' data - such as
genomics,
proteomics, and
metabolomics - researchers can uncover potential biomarkers that aid in disease diagnosis, prognosis, and treatment strategies. For instance, metabolomic analyses have identified specific metabolites capable of distinguishing between
coronary artery disease and
myocardial infarction.
Neuroscience Computational
neuroscience is the study of brain function in terms of the information processing properties of the
nervous system. A subset of neuroscience, it looks to model the brain to examine specific aspects of the neurological system. Models of the brain include: • Realistic Brain Models: These models look to represent every aspect of the brain, including as much detail at the cellular level as possible. Realistic models provide the most information about the brain, but also have the largest margin for
error. More variables in a brain model create the possibility for more error to occur. These models do not account for parts of the cellular structure that scientists do not know about. Realistic brain models are the most computationally heavy and the most expensive to implement. • Simplifying Brain Models: These models look to limit the scope of a model in order to assess a specific
physical property of the neurological system. This allows for the intensive computational problems to be solved, and reduces the amount of potential error from a realistic brain model.
Oncology Computational biology plays a crucial role in discovering signs of new, previously unknown living creatures and in
cancer research. This field involves large-scale measurements of cellular processes, including
RNA,
DNA, and proteins, which pose significant computational challenges. To overcome these, biologists rely on computational tools to accurately measure and analyze biological data. In cancer research, computational biology aids in the complex analysis of
tumor samples, helping researchers develop new ways to characterize tumors and understand various cellular properties. The use of high-throughput measurements, involving millions of data points from DNA, RNA, and other biological structures, helps in diagnosing cancer at early stages and in understanding the key factors that contribute to cancer development. Areas of focus include analyzing molecules that are deterministic in causing cancer and understanding how the human genome relates to tumor causation.
Toxicology Computational toxicology is a multidisciplinary area of study, which is employed in the early stages of drug discovery and development to predict the safety and potential toxicity of drug candidates.
Pharmacology Computational pharmacology is "the study of the effects of genomic data to find links between specific
genotypes and diseases and then
screening drug data".
Drug discovery A growing application of computational biology is
drug discovery. For example, simulations of
intracellular and
intercellular signaling events, using data from proteomic or metabolomic experiments, may reduce dependence on experimentation in elucidating
pharmacokinetics and
pharmacodynamics of drug candidates in living organisms. Increasingly, artificial intelligence plays a central role in the drug discovery process. Using chemical structures of known pharmaceutical agents as inputs, AI models can suggest structures of lead compounds or predict novel modes of drug-protein binding. AI is also used for
virtual screening of candidate molecules, avoiding the need to synthesize large numbers of molecules for screening. == Techniques ==