Computational models are increasingly being used to inform scientific studies, improve our understanding of complex systems, and develop new therapies and technologies. This article will discuss the application of computational modeling to the field of developmental biology.
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What is computational modeling?
Computational modeling is the use of mathematics, physics, and computer science to create complex, dynamic models of systems based on multiple data points. The growth of sophisticated data-gathering technology such as wearable sensors and internet-connected medical devices has in part been responsible for the recent growth of the field.
Computational modeling has been applied to several fields including weather forecasting, flight simulation, and earthquake simulations, as well as the biological sciences. Computational models in use today can provide data on a biological system at multiple levels. This type of modeling is known as multiscale modeling.
In a computational model, thousands of simulations can be run. By running these simulations, scientists identify the handful of lab experiments needed to solve the problem. This has several advantages, including significantly cutting down on the cost of studies and, in the case of biological research, reducing the use of animal models, a highly contentious ethical issue.
There are several ways computer science and biology interact. Computational Biology refers to the computer modeling and simulation of biological processes. Bioinformatics is used to develop systems that automatically manage and analyze biological data. Biological Computing studies how biological techniques can aid in solving computational problems.
Applying computational modeling to developmental biology
Developmental biology refers to the study of how organisms, specifically plants and animals, grow and develop. It encompasses several biological processes, including cell differentiation, regeneration, asexual reproduction, metamorphosis, and stem cell growth and differentiation.
Take as an example the multicellular organism itself: it is a dynamical system where both values of state variables as well as the set of state variables change over time. Multicellular systems are comprised of individual cell lines all constantly developing and regenerating over the lifetime of the organism. This is regulated by a complex process of precursor cell proliferation, differentiation, and movement. Each scale’s dynamic is determined by the collective activity of entities at the scale below.
When a mother cell divides, it creates two daughter cells. Once the mother cell divides, the whole topology of the system is adjusted. Connections between the mother cell and the rest of the organism are removed, connections are created between the daughter cells, as well as being inserted between the daughter cells and the rest of the organism. Thus, a large network of interconnected cells is gradually created. This occurs constantly over the lifetime of the organism.
There are many other biological processes that fall under the umbrella of developmental biology, with each one as complex as the rest. Modes of development within biological systems can be affected by a huge number of factors including genetic and environmental ones.
It is due to this complexity that computational modeling (also known as in silico studies) is increasingly being utilized by researchers. Whilst too numerous to mention in an article, these models are providing scientists with invaluable analysis of developmental systems in organisms.
Using computational biology to study embryonic stem-cell fate control
Pluripotency refers to an individual cell’s capacity to give rise to all cell lineages in a mature organism. Whilst transient and restricted to only a few epiblast cells in the mammalian embryo, it is maintained in vitro by embryonic stem cells (ESCs). Uncovering the molecular basis and understanding the process of pluripotency within these cells is of growing concern to modern medical science.
There are a few factors in pluripotency that are central topics in stem cell research: how it is maintained in ESCs, how it is lost during lineage specification, and how it is reintroduced to generate new pluripotent stem cells are some of them. A multitude of computational studies has been published in recent years alongside conventional in vitro and in vivo experiments.
Complementary application of computational methodologies is increasingly important for accurate comprehension and description of complex behaviors and interactions. Computational modeling approaches can define the dynamics and structure of gene regulatory networks, for example. However, interpretation of studies requires care, as different structures encode similar or the same biological functions. Analysis of the network is as important as structure identification.
This is just one application in developmental biology that computational modeling has been used in. There are many more examples of its use in the field.
Computational modeling has fundamentally changed the way in which scientists gather data for clinical trials and to provide knowledge of biological systems and processes. As data capture technology improves, the field of computational biology will become more sophisticated in the future and will likely remain on the cutting edge of developmental biology for decades to come.