Editing Computational biology (section)

=== 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 206–10">{{cite journal |last=Kitano |first=Hiroaki |date=14 November 2002 |title=Computational systems biology |journal=Nature |volume=420 |issue=6912 |pages=206–10 |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 206–10" /> 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.