Computational Biology

You want an article on Computational biology? Fine. Don't expect me to hold your hand. This isn't some fluffy introduction to daisies; it's about the cold, hard logic of machines dissecting life. And frankly, the original text is a bit… pedestrian. Let’s inject some actual perspective.

Branch of biology

Computational biology is not merely a subset of biology; it’s the relentless interrogation of biological systems through the unforgiving lens of computation. It’s where the elegant, messy dance of life meets the brutal, beautiful precision of the digital realm. Forget quaint notions of petri dishes and microscopes; this is about leveraging the power of computer science, the rigor of data analysis, the abstract beauty of mathematical modeling, and the sheer, unadulterated force of computational simulations to unravel the deepest secrets of biological systems and their intricate relationships. It is, in essence, a triumvirate of computer science, biology, and data science, built upon the bedrock of applied mathematics, molecular biology, cell biology, chemistry, and genetics.

History

The genesis of bioinformatics – the analysis of informatics processes within biological systems – can be traced back to the early 1970s. This era saw researchers in artificial intelligence exploring network models of the human brain, an endeavor that, surprisingly, spurred biological researchers to consider computers for evaluating and comparing their own burgeoning, complex datasets. The initial rudimentary sharing of information via punch cards by 1982 was a testament to the nascent stage of this field. But the exponential growth of biological data by the late 1980s rendered such methods obsolete, demanding the urgent development of new computational approaches for swift, meaningful interpretation.

The Human Genome Project, officially launched in 1990, stands as perhaps the most celebrated, and certainly the most ambitious, undertaking in computational biology. By 2003, it had achieved its initial goal of mapping approximately 85% of the human genome. However, the pursuit of completeness is a relentless mistress. The project’s work continued, pushing towards a "complete genome" by 2021, with only a minuscule fraction remaining problematic. The final piece, the elusive Y chromosome, was finally integrated in January 2022.

Since the turn of the millennium, computational biology has ascended from a niche pursuit to an indispensable pillar of biological research, spawning a multitude of specialized subfields. The International Society for Computational Biology now acknowledges 21 distinct "Communities of Special Interest," each representing a vital facet of this expansive domain. Beyond sequencing the human genome, computational biology has been instrumental in constructing sophisticated models of the human brain, meticulously mapping the 3D structure of genomes, and simulating the behavior of complex biological systems. While early advancements were largely concentrated in the United States and Western Europe, driven by their robust computational infrastructures, the past few decades have witnessed a remarkable surge in contributions from less affluent nations. Colombia, for instance, has been engaged in significant international computational biology efforts since 1998, focusing on the genomics and diseases affecting crucial national crops like coffee and potatoes. Similarly, Poland has emerged as a leader in biomolecular simulations and the analysis of macromolecular sequences.

Applications

Anatomy

Computational anatomy

Computational anatomy delves into the shape and form of anatomical structures, operating at the scale of gross anatomy – the anatomy visible to the naked eye. It is concerned with the creation and application of computational mathematical and data-analytical methods for the modeling and simulation of biological structures. Its focus lies squarely on the anatomical structures themselves, rather than the medical imaging devices used to capture them. The advent of high-resolution 3D imaging technologies, such as magnetic resonance imaging, has propelled computational anatomy into a prominent subfield of medical imaging and bioengineering, enabling the extraction of anatomical coordinate systems at the morpheme scale within three-dimensional space.

The foundational concept of computational anatomy posits a generative model of shape and form, derived from exemplars and acted upon by transformations. The diffeomorphism group is a critical tool in this domain, employed to analyze different coordinate systems through coordinate transformations, akin to tracking the Lagrangian and Eulerian velocities of flow from one anatomical configuration in ℝ³ to another. This approach is deeply intertwined with shape statistics and morphometrics, with the distinguishing characteristic being the use of diffeomorphisms to map coordinate systems—a study known as diffeomorphometry.

Data and modeling

Mathematical biology

Mathematical biology employs mathematical models of living organisms to scrutinize the systems that dictate structure, development, and behavior within biological systems. This discipline adopts a more theoretical stance, in stark contrast to the empirically driven approach of experimental biology. It draws heavily from discrete mathematics, topology (which also proves invaluable for computational modeling), Bayesian statistics, linear algebra, and Boolean algebra.

These sophisticated mathematical frameworks have paved the way for the development of databases and other advanced methods for the storage, retrieval, and analysis of biological data—a field collectively known as bioinformatics. Typically, this process centers on genetics and the analysis of genes.

The capacity to gather and analyze vast datasets has catalyzed the growth of research areas like data mining. Furthermore, computational biomodeling, which involves constructing computer models and visual simulations of biological systems, allows researchers to predict their responses to diverse environmental conditions. This predictive power is crucial for determining a system's resilience and its ability to "maintain their state and functions against external and internal perturbations." While current techniques are often limited to smaller biological systems, ongoing research is focused on developing approaches capable of analyzing and modeling far larger networks. The consensus among many researchers is that this capability will be indispensable for the advancement of modern medical strategies, particularly in the development of new drugs and gene therapy. A particularly effective modeling approach involves the use of Petri nets, often implemented through specialized tools like esyN.

In a similar vein, theoretical ecology, until relatively recently, primarily relied on analytic models that were somewhat divorced from the statistical models employed by empirical ecologists. However, computational methods have significantly bolstered the development of ecological theory through the simulation of ecological systems, alongside an increased application of computational statistics in ecological analyses.

Systems biology

Systems biology is dedicated to computing the complex interactions between diverse biological systems, spanning from the cellular level to entire populations, with the ultimate objective of uncovering emergent properties. This process typically involves mapping cell signaling and metabolic pathways. Systems biology frequently employs computational techniques derived from biological modeling and graph theory to dissect these intricate interactions at the cellular level.

Evolutionary biology

Computational biology has provided substantial assistance to evolutionary biology through several key avenues:

Reconstructing the tree of life by analyzing DNA data using computational phylogenetics.
Fitting population genetics models (both forward-time population genetics and backward time models) to DNA data to infer historical demographic trends or patterns of natural selection.
Constructing population genetics models of evolutionary systems from first principles to predict likely evolutionary trajectories.

Genomics

Computational genomics

Computational genomics is the study of the genomes of cells and organisms. The aforementioned Human Genome Project serves as a prime illustration of computational genomics. This monumental project aimed to sequence the entirety of the human genome, transforming it into a comprehensive dataset. The ultimate aspiration is to empower physicians to analyze an individual patient's genome, thereby unlocking the potential for personalized medicine, where treatments are tailored to an individual's unique genetic makeup. The scope of this endeavor extends beyond humans, with researchers actively working to sequence the genomes of animals, plants, bacteria, and all other forms of life.

A fundamental technique for comparing genomes involves sequence homology, which examines biological structures and nucleotide sequences across different organisms to identify common ancestor origins. Research indicates that approximately 80 to 90% of genes in newly sequenced prokaryotic genomes can be identified through this method.

Sequence alignment is another critical process for comparing and detecting similarities between biological sequences or genes. This technique is vital for numerous bioinformatics applications, including the computation of the longest common subsequence between genes or the comparison of variants associated with specific diseases.

A significant, yet largely unexplored, frontier in computational genomics lies in the analysis of intergenic regions, which constitute roughly 97% of the human genome. Researchers are actively developing computational and statistical methodologies, alongside large collaborative projects like ENCODE and the Roadmap Epigenomics Project, to elucidate the functions of these non-coding regions.

Understanding how individual genes contribute to the overall biology of an organism at the molecular, cellular, and organismal levels is the domain of gene ontology. The Gene Ontology Consortium is dedicated to creating and maintaining a comprehensive, up-to-date computational model of biological systems, encompassing everything from molecular pathways to cellular and organism-level processes. The Gene Ontology resource provides a computational representation of current scientific knowledge regarding the functions of genes (or, more precisely, the protein and non-coding RNA molecules they produce) across a vast array of organisms, from humans to bacteria.

3D genomics is a specialized area within computational biology focused on the intricate organization and interactions of genes within a eukaryotic cell. Genome Architecture Mapping (GAM) is one technique employed to acquire 3D genomic data. GAM quantifies the three-dimensional distances between chromatin and DNA within the genome by integrating cryosectioning—the process of cutting a thin slice from the nucleus for DNA examination—with laser microdissection. This resulting "nuclear profile" is essentially a slice of the nucleus containing specific genomic windows, which are particular sequences of nucleotides, the fundamental building blocks of DNA. GAM effectively maps a complex network of multi-enhancer chromatin contacts throughout a cell.

Biomarker Discovery

Computational biology is also a crucial player in identifying biomarkers for various diseases, including cardiovascular conditions. By integrating diverse 'Omic' data—such as genomics, proteomics, and metabolomics—researchers can uncover potential biomarkers that aid in disease diagnosis, prognosis, and the development of effective treatment strategies. For instance, metabolomic analyses have successfully identified specific metabolites capable of differentiating between coronary artery disease and myocardial infarction, thereby enhancing diagnostic accuracy.

Neuroscience

Computational neuroscience

[Computational neuroscience](/Computational_neuroscience) is dedicated to understanding brain function through the lens of information processing within the nervous system. As a subfield of neuroscience, it aims to model the brain to investigate specific aspects of the neurological system. The models employed in computational neuroscience include:

Realistic Brain Models: These models strive for comprehensive representation, aiming to capture as much cellular-level detail as possible. While offering the potential for the most extensive information, they also carry a significant margin for error, as the sheer number of variables increases the possibility of inaccuracies. Furthermore, these models cannot account for aspects of cellular structure that remain unknown to scientists. Realistic brain models are also the most computationally intensive and expensive to implement.
Simplifying Brain Models: These models deliberately limit the scope to focus on specific physical property of the neurological system. This constraint allows for the resolution of computationally demanding problems and reduces the potential for error inherent in more realistic models.

The ongoing work of computational neuroscientists is focused on refining the algorithms and data structures used to accelerate these complex calculations.

[Computational neuropsychiatry](/Computational_neuropsychiatry) is an emerging field that applies mathematical and computer-assisted modeling to investigate the brain mechanisms underlying mental disorders. Several research initiatives have demonstrated the significant contribution of computational modeling to understanding the neuronal circuits responsible for both normal mental functions and their dysfunctions.

Pharmacology

Computational pharmacology is defined as "the study of the effects of genomic data to find links between specific genotypes and diseases and then screening drug data." The pharmaceutical industry is undergoing a necessary transformation in its methods for analyzing drug data. While Microsoft Excel was once sufficient for comparing chemical and genomic data related to drug efficacy, the industry has encountered what is termed the "Excel barricade"—a limitation imposed by the finite capacity of spreadsheets. This bottleneck has necessitated the emergence of computational pharmacology, where scientists and researchers develop computational methods to analyze these massive data sets, enabling efficient comparison of significant data points and leading to the development of more effective drugs.

Projections suggest that as major medications lose patent protection, computational biology will become essential for developing replacements. Consequently, doctoral students in computational biology are increasingly being encouraged to pursue careers in industry rather than traditional postdoctoral positions, driven by the high demand for skilled analysts of the large datasets required for new drug development.

Oncology

Computational biology plays a critical role in the ongoing fight against cancer, aiding in the discovery of new therapeutic targets and the understanding of tumor development. This field involves the large-scale measurement of cellular processes, including RNA, DNA, and proteins, which presents significant computational challenges. Biologists rely heavily on computational tools to accurately measure and analyze this complex biological data. In cancer research, computational biology is instrumental in the intricate analysis of tumor samples, assisting researchers in developing novel methods for characterizing tumors and understanding their diverse cellular properties. The application of high-throughput measurements, generating millions of data points from DNA, RNA, and other biological structures, is crucial for early cancer diagnosis and for identifying the key factors driving cancer development. Current research focuses on analyzing the molecules that deterministically cause cancer and elucidating the intricate relationship between the human genome and tumor etiology.

Toxicology

Computational toxicology

Computational toxicology is a multidisciplinary field dedicated to predicting the safety and potential toxicity of drug candidates during the early stages of drug discovery and development.

Drug discovery

A rapidly expanding application of computational biology is in the realm of drug discovery. For example, simulations of intracellular and intercellular signaling events, informed by proteomic or metabolomic experimental data, can reduce the reliance on extensive laboratory experimentation for elucidating the pharmacokinetics and pharmacodynamics of drug candidates within living organisms.

Artificial intelligence (AI) is increasingly central to the drug discovery process. By inputting the chemical structures of known pharmaceutical agents, AI models can propose structures for lead compounds or predict novel mechanisms of drug-protein binding. AI is also employed for virtual screening of candidate molecules, thereby eliminating the need for the laborious synthesis and screening of vast numbers of compounds.

Techniques

Computational biologists employ a diverse array of software and algorithms to conduct their research.

Unsupervised Learning

Unsupervised learning encompasses algorithms designed to identify patterns within unlabeled data. A prime example is k-means clustering, which partitions a set of n data points into k clusters, assigning each point to the cluster whose mean is nearest. A variation, the k-medoids algorithm, selects cluster centers (medoids) directly from the data points themselves, rather than relying on an average.

The algorithm typically proceeds as follows:

Initialization: Randomly select k distinct data points to serve as the initial cluster centers.
Assignment: Calculate the distance between each data point and each of the k cluster centers. Assign each data point to the nearest cluster.
Update: Recalculate the medoid (the most central point) for each cluster.
Iteration: Repeat the assignment and update steps until the cluster assignments no longer change.
Evaluation: Assess the quality of the clustering by summing the variance within each cluster.
Optimization: Repeat the entire process with different values of k.
Selection: Choose the optimal value of k by identifying the "elbow" point in a plot of k values against their corresponding variances, indicating the point of diminishing returns.

A biological application of this technique can be found in the 3D mapping of a genome. Data concerning the HIST1 region of chromosome 13 in mice, obtained from Gene Expression Omnibus, can be analyzed. This data includes information on which nuclear profiles appear in specific genomic regions. Using this, the Jaccard distance can be computed to determine a normalized distance between all loci.

Graph Analytics

Graph analytics, also known as network analysis, focuses on the study of graphs that represent relationships between different entities. These graphs can model a wide range of biological networks, including protein-protein interaction networks, regulatory networks, and metabolic and biochemical networks. Various methods exist for analyzing these networks, one of which involves assessing centrality. Graph centrality measures assign rankings to nodes based on their popularity or importance within the network. This is particularly useful for identifying the most critical nodes. For instance, analyzing gene activity data over time using degree centrality can reveal which genes are most active or interact most frequently within the network, thereby contributing to an understanding of their roles.

Numerous methods exist for calculating centrality in graphs, each providing distinct insights. Centrality analysis in biology finds application in diverse contexts, including gene regulatory, protein interaction, and metabolic networks.

Supervised Learning

Supervised learning involves algorithms trained on labeled data, enabling them to assign labels to new, unlabeled data. In biology, supervised learning is invaluable when dealing with data that can be categorized, and the goal is to apply these categories to additional data.

A common supervised learning algorithm is the random forest, which employs multiple decision trees to train a model for classifying datasets. Each decision tree, forming the basis of the random forest, is a structure designed to classify or label data based on known features. A practical biological example involves using an individual's genetic data to predict their predisposition to a specific disease or cancer. At each internal node of the tree, the algorithm examines a specific feature (e.g., a particular gene) and branches left or right based on the outcome. The leaf nodes then assign a class label to the dataset. In essence, the algorithm traverses a root-to-leaf path determined by the input dataset, resulting in its classification. Decision trees typically predict discrete target variables (e.g., yes/no), in which case they are termed classification trees. If the target variable is continuous, they are referred to as regression trees. Constructing a decision tree requires training it on a dataset to identify the features that best predict the target variable.

Open source software

Open source software provides a collaborative platform for computational biology, ensuring broad access to and benefit from research-developed software. PLOS identifies four primary advantages of using open source software:

Reproducibility: It allows researchers to utilize the exact methods employed in calculations, ensuring the reliability of findings regarding relationships within biological data.
Faster Development: Developers and researchers can leverage existing code for common tasks, rather than reinventing it, thus accelerating the development and implementation of larger projects.
Increased Quality: Input from multiple researchers studying the same topic provides a safeguard against errors in the code.
Long-term Availability: Open source programs are independent of specific companies or patents, allowing them to be hosted on multiple web pages and ensuring their continued accessibility.

Research

Several prominent conferences are dedicated to computational biology, including Intelligent Systems for Molecular Biology, the European Conference on Computational Biology, and Research in Computational Molecular Biology.

Numerous journals also focus on this field. Notable examples include the Journal of Computational Biology and PLOS Computational Biology, an open-access journal featuring significant research projects. These journals often include reviews of software, tutorials for open source programs, and information on upcoming conferences. Other relevant journals include Bioinformatics, Computers in Biology and Medicine, BMC Bioinformatics, Nature Methods, Nature Communications, Scientific Reports, and PLOS One.

Related fields

Computational biology, bioinformatics, and mathematical biology are all interdisciplinary fields that apply quantitative disciplines like mathematics and information science to the life sciences. The NIH defines computational/mathematical biology as the application of computational/mathematical approaches to address theoretical and experimental questions in biology, while defining bioinformatics as the application of information science to interpret complex life sciences data.

Specifically, the NIH provides these definitions:

Computational biology: The development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological, behavioral, and social systems.
Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral, or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.

While distinct, these fields often exhibit significant overlap, leading to the interchangeable use of the terms "bioinformatics" and "computational biology" by many.

The terms "computational biology" and evolutionary computation bear a superficial resemblance but are not synonymous. Evolutionary computation is a branch of computer science that draws inspiration from biological evolution. Algorithms developed within evolutionary computation can be, and often are, applied to problems in computational biology.