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==Applications== ===Anatomy=== {{main|Computational anatomy}} Computational anatomy is the study of anatomical shape and form at the visible or [[Gross anatomy|gross anatomical]] <math> 50-100 \mu </math> scale of [[morphology (biology)|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.<ref name=":20">{{Cite journal|title = Computational Anatomy: An Emerging Discipline |journal = Q. Appl. Math. |volume=56 |issue=4 |pages=617β694 |date = 1998-12-01 |first1 = Ulf|last1 = Grenander|first2 = Michael I.|last2 = Miller|doi = 10.1090/qam/1668732|doi-access = free}}</ref> The [[diffeomorphism]] group is used to study different coordinate systems via [[change of basis|coordinate transformations]] as generated via the [[Lagrangian and Eulerian specification of the flow field|Lagrangian and Eulerian velocities of flow]] from one anatomical configuration in <math>{\mathbb R}^3</math> to another. It relates with [[shape statistics]] and [[morphometrics]], with the distinction that [[diffeomorphism]]s are used to map coordinate systems, whose study is known as diffeomorphometry. === Data and modeling === {{main|Bioinformatics}} Mathematical biology is the use of mathematical models of living organisms to examine the systems that govern structure, development, and behavior in [[biological system]]s. This entails a more theoretical approach to problems, rather than its more empirically-minded counterpart of [[experimental biology]].<ref>{{Cite web |title=Mathematical Biology {{!}} Faculty of Science |url=https://www.ualberta.ca/science/mathematical-biology.html |access-date=2022-04-18 |website=www.ualberta.ca}}</ref> Mathematical biology draws on [[discrete mathematics]], [[topology]] (also useful for computational modeling), [[Bayesian statistics]], [[linear algebra]] and [[Boolean algebra]].<ref name="nlcb.wordpress.com" /> These mathematical approaches have enabled the creation of [[database]]s and other methods for storing, retrieving, and analyzing biological data, a field known as [[bioinformatics]]. Usually, this process involves [[genetics]] and analyzing [[gene]]s. Gathering and analyzing large datasets have made room for growing research fields such as [[data mining]],<ref name="nlcb.wordpress.com">{{Cite web |date=2013-02-18 |title=The Sub-fields of Computational Biology |url=https://nlcb.wordpress.com/2013/02/17/the-sub-fields-of-computational-biology/ |access-date=2022-04-18 |website=Ninh Laboratory of Computational Biology |language=en}}{{self-published inline|date=August 2024}}</ref> and computational biomodeling, which refers to building [[computer model]]s and [[Augmented reality|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".<ref name="Kitano 2002 206β10">{{cite journal |last=Kitano |first=Hiroaki |date=14 November 2002 |title=Computational systems biology |journal=Nature |volume=420 |issue=6912 |pages=206β10 |bibcode=2002Natur.420..206K |doi=10.1038/nature01254 |pmid=12432404 |id={{ProQuest|204483859}} |s2cid=4401115}}</ref> 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]].<ref name="Kitano 2002 206β10" /> A useful modeling approach is to use [[Petri nets]] via tools such as [[esyN]].<ref name="Bean 2014">{{cite journal |last=Favrin |first=Bean |date=2 September 2014 |title=esyN: Network Building, Sharing and Publishing. |journal=PLOS ONE |volume=9 |issue=9 |pages=e106035 |bibcode=2014PLoSO...9j6035B |doi=10.1371/journal.pone.0106035 |pmc=4152123 |pmid=25181461 |doi-access=free}}</ref> Along similar lines, until recent decades [[theoretical ecology]] has largely dealt with [[Analytic function|analytic]] models that were detached from the [[statistical model]]s used by [[Empirical evidence|empirical]] ecologists. However, computational methods have aided in developing ecological theory via [[simulation]] of ecological systems, in addition to increasing application of methods from [[computational statistics]] in ecological analyses. === Systems biology === {{main|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 pathway]]s. Systems biology often uses computational techniques from biological modeling and [[graph theory]] to study these complex interactions at cellular levels.<ref name="nlcb.wordpress.com" /> ===Evolutionary biology=== {{main|Evolutionary biology}} Computational biology has assisted evolutionary biology by: * Using [[DNA]] data to reconstruct the tree of life with [[computational phylogenetics]] * Fitting [[population genetics]] models (either forward time<ref name=":2">{{cite journal|title=Simulation of Genes and Genomes Forward in Time|journal=Current Genomics|author = Antonio Carvajal-RodrΓguez|year = 2012|pmc=2851118|volume=11|issue=1|pages=58β61|doi=10.2174/138920210790218007|pmid=20808525}}</ref> or [[coalescent theory|backward time]]) to DNA data to make inferences about [[population growth|demographic]] or [[natural selection|selective]] history * Building [[population genetics]] models of [[evolutionary systems]] from first principles in order to predict what is likely to evolve === Genomics === {{main|Computational genomics}} {{See also|Earth BioGenome Project}} [[File:Genome viewer screenshot small.png|thumbnail|right|A partially sequenced genome]] Computational genomics is the study of the [[genome]]s of [[Cell (biology)|cells]] and [[organism]]s. 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]].<ref>{{cite magazine|title=Genome Sequencing to the Rest of Us|url=http://www.scientificamerican.com/article.cfm?id=personal-genome-sequencing|magazine=Scientific American}}</ref> 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.<ref name="Koonin 2001 155β158">{{cite journal|last=Koonin|first=Eugene|title=Computational Genomics|date=6 March 2001|volume=11|issue=5|pages=155β158|doi=10.1016/S0960-9822(01)00081-1|pmid=11267880|journal=Curr. Biol.|s2cid=17202180|doi-access=free|bibcode=2001CBio...11.R155K }}</ref> 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 [[Prokaryote|prokaryotic]] genomes can be identified this way.<ref name="Koonin 2001 155β158"/> [[Sequence alignment]] is another process for comparing and detecting similarities between biological sequences or genes. Sequence alignment is useful in a number of bioinformatics applications, such as computing the [[Longest common subsequence problem|longest common subsequence]] of two genes or comparing variants of certain [[disease]]s.{{fact|date=August 2024}} An untouched project in computational genomics is the analysis of intergenic regions, which comprise roughly 97% of the human genome.<ref name="Koonin 2001 155β158" /> Researchers are working to understand the functions of non-coding regions of the human genome through the development of computational and statistical methods and via large consortia projects such as [[ENCODE]] and the [[Epigenome#Roadmap epigenomics project|Roadmap Epigenomics Project]]. Understanding how individual [[gene]]s contribute to the [[biology]] of an organism at the [[Molecule|molecular]], [[Cell (biology)|cellular]], and organism levels is known as [[Gene Ontology|gene ontology]]. The [[Gene Ontology Consortium]]'s mission is to develop an up-to-date, comprehensive, computational model of [[biological system]]s, from the molecular level to larger pathways, cellular, and organism-level systems. The Gene Ontology resource provides a computational representation of current scientific knowledge about the functions of genes (or, more properly, the [[protein]] and non-coding [[RNA]] molecules produced by genes) from many different organisms, from humans to bacteria.<ref>{{Cite web |title=Gene Ontology Resource |url=http://geneontology.org/ |access-date=2022-04-18 |website=Gene Ontology Resource}}</ref> 3D genomics is a subsection in computational biology that focuses on the organization and interaction of genes within a [[Eukaryotic Cell|eukaryotic cell]]. One method used to gather 3D genomic data is through [[Genome architecture mapping|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 [[nucleotide]]s - the base unit of DNA. GAM captures a genome network of complex, multi enhancer chromatin contacts throughout a cell.<ref>{{Cite journal |last1=Beagrie |first1=Robert A. |last2=Scialdone |first2=Antonio |last3=Schueler |first3=Markus |last4=Kraemer |first4=Dorothee C. A. |last5=Chotalia |first5=Mita |last6=Xie |first6=Sheila Q. |last7=Barbieri |first7=Mariano |last8=de Santiago |first8=InΓͺs |last9=Lavitas |first9=Liron-Mark |last10=Branco |first10=Miguel R. |last11=Fraser |first11=James |date=March 2017 |title=Complex multi-enhancer contacts captured by genome architecture mapping |journal=Nature |language=en |volume=543 |issue=7646 |pages=519β524 |bibcode=2017Natur.543..519B |doi=10.1038/nature21411 |issn=1476-4687 |pmc=5366070 |pmid=28273065}}</ref> === Biomarker Discovery === Computational biology also plays a pivotal role in identifying [[Biomarker|biomarkers]] for diseases such as cardiovascular conditions. By integrating various '[[Omics|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]], thereby enhancing diagnostic precision.<ref>{{Cite journal |last1=Batta |first1=Irene |last2=Patial |first2=Ritika |last3=C Sobti |first3=Ranbir |last4=K Agrawal |first4=Devendra |date=2024 |title=Computational Biology in the Discovery of Biomarkers in the Diagnosis, Treatment and Management of Cardiovascular Diseases |url=https://doi.org/10.26502/fccm.92920400 |journal=Cardiology and Cardiovascular Medicine |volume=8 |issue=5 |doi=10.26502/fccm.92920400 |pmid=39328401 |issn=2572-9292|url-access=subscription }}</ref> ===Neuroscience=== {{main|Computational 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.<ref>{{Cite web |title=Computational Neuroscience | Neuroscience |url=http://www.bu.edu/neuro/academics/graduate/curriculum/computational-neuroscience/ |website=www.bu.edu}}</ref> 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.<ref name="Sejnowski 1988">{{cite journal|last=Sejnowski|first=Terrence |author2=Christof Koch |author3=Patricia S. Churchland|title=Computational Neuroscience|journal=Science |date=9 September 1988|volume=241|series=4871|issue=4871 |pages=1299β306 |doi=10.1126/science.3045969 |pmid=3045969 |bibcode=1988Sci...241.1299S }}</ref> * 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.<ref name="Sejnowski 1988"/> It is the work of computational neuroscientists to improve the [[algorithms]] and data structures currently used to increase the speed of such calculations. Computational [[neuropsychiatry]] is an emerging field that uses mathematical and computer-assisted modeling of brain mechanisms involved in [[mental disorder]]s. Several initiatives have demonstrated that computational modeling is an important contribution to understand neuronal circuits that could generate mental functions and dysfunctions.<ref>{{cite journal |last1=Dauvermann |first1=Maria R. |last2=Whalley |first2=Heather C. |last3=Schmidt |first3=AndrΓ© |last4=Lee |first4=Graham L. |last5=Romaniuk |first5=Liana |last6=Roberts |first6=Neil |last7=Johnstone |first7=Eve C. |last8=Lawrie |first8=Stephen M. |last9=Moorhead |first9=Thomas W. J. |title=Computational neuropsychiatry β schizophrenia as a cognitive brain network disorder |journal=Frontiers in Psychiatry |date=25 March 2014 |volume=5 |page=30 |doi=10.3389/fpsyt.2014.00030 |pmid=24723894 |pmc=3971172 |doi-access=free }}</ref><ref>{{cite journal |last1=Tretter |first1=F. |last2=Albus |first2=M. |date=December 2007 |title='Computational Neuropsychiatry' of Working Memory Disorders in Schizophrenia: The Network Connectivity in Prefrontal Cortex - Data and Models |journal=Pharmacopsychiatry |volume=40 |issue=S 1 |pages=S2βS16 |doi=10.1055/S-2007-993139 |s2cid=18574327}}</ref><ref>{{cite journal |last1=Marin-Sanguino |first1=A. |last2=Mendoza |first2=E. |year=2008 |title=Hybrid Modeling in Computational Neuropsychiatry |journal=Pharmacopsychiatry |volume=41 |pages=S85βS88 |doi=10.1055/s-2008-1081464 |pmid=18756425 |s2cid=22996341 }}</ref> ===Pharmacology=== {{main|Pharmacology}} Computational pharmacology is "the study of the effects of genomic data to find links between specific [[genotype]]s and diseases and then [[drug discovery|screening drug data]]".<ref>{{cite journal |last1=Price |first1=Michael |title=Computational Biologists: The Next Pharma Scientists? |journal=Science |date=13 April 2012 |doi=10.1126/science.caredit.a1200041 }}</ref> The [[pharmaceutical industry]] requires a shift in methods to analyze drug data. Pharmacologists were able to use [[Microsoft Excel]] to compare chemical and genomic data related to the effectiveness of drugs. However, the industry has reached what is referred to as the Excel barricade. This arises from the limited number of cells accessible on a [[spreadsheet]]. This development led to the need for computational pharmacology. Scientists and researchers develop computational methods to analyze these massive [[data set]]s. This allows for an efficient comparison between the notable data points and allows for more accurate drugs to be developed.<ref name="Walter">{{cite web|last=Jessen|first=Walter|title=Pharma's shifting strategy means more jobs for computational biologists|url=http://medcitynews.com/2012/04/pharmas-shifting-strategy-means-more-jobs-for-computational-biologists/|date=2012-04-15}}</ref> Analysts project that if major medications fail due to patents, that computational biology will be necessary to replace current drugs on the market. Doctoral students in computational biology are being encouraged to pursue careers in industry rather than take Post-Doctoral positions. This is a direct result of major pharmaceutical companies needing more qualified analysts of the large data sets required for producing new drugs.<ref name="Walter"/> === Oncology === {{Main|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.<ref name=":5">{{cite journal |last=Yakhini |first=Zohar |year=2011 |title=Cancer Computational Biology |journal=BMC Bioinformatics |volume=12 |pages=120 |doi=10.1186/1471-2105-12-120 |pmc=3111371 |pmid=21521513 |doi-access=free}}</ref> In cancer research, computational biology aids in the complex analysis of [[Neoplasm|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.<ref name=":5" /><ref>{{cite journal |last1=Barbolosi |first1=Dominique |last2=Ciccolini |first2=Joseph |last3=Lacarelle |first3=Bruno |last4=Barlesi |first4=Fabrice |last5=Andre |first5=Nicolas |year=2016 |title=Computational oncology--mathematical modelling of drug regimens for precision medicine |journal=Nature Reviews Clinical Oncology |volume=13 |issue=4 |pages=242β254 |doi=10.1038/nrclinonc.2015.204 |pmid=26598946 |s2cid=22492353}}</ref> === Toxicology === {{Main|Computational 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. === Drug Discovery === Computational biology has become instrumental in revolutionizing [[drug discovery]] processes. By integrating computational systems biology approaches, researchers can model complex biological systems, facilitating the identification of novel drug targets and the prediction of drug responses. These methodologies enable the simulation of [[intracellular]] and [[intercellular signaling]] events using data from genomic, proteomic, or metabolomic experiments, thereby streamlining the drug development pipeline and reducing associated costs.<ref>{{Cite journal |last1=Materi |first1=Wayne |last2=Wishart |first2=David S. |date=April 2007 |title=Computational systems biology in drug discovery and development: methods and applications |url=https://linkinghub.elsevier.com/retrieve/pii/S1359644607000943 |journal=Drug Discovery Today |language=en |volume=12 |issue=7β8 |pages=295β303 |doi=10.1016/j.drudis.2007.02.013|pmid=17395089 |url-access=subscription }}</ref> Moreover, the convergence of computational biology with artificial intelligence (AI) has further accelerated drug design. AI-driven models can analyze vast datasets to predict molecular behavior, optimize lead compounds, and anticipate potential side effects, thereby enhancing the efficiency and effectiveness of drug discovery.<ref>{{Cite journal |last1=Zhang |first1=Yue |last2=Luo |first2=Mengqi |last3=Wu |first3=Peng |last4=Wu |first4=Song |last5=Lee |first5=Tzong-Yi |last6=Bai |first6=Chen |date=November 2022 |title=Application of Computational Biology and Artificial Intelligence in Drug Design |journal=International Journal of Molecular Sciences |language=en |volume=23 |issue=21 |pages=13568 |doi=10.3390/ijms232113568 |doi-access=free |issn=1422-0067 |pmc=9658956 |pmid=36362355}}</ref>
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