Modeling the Unimaginable Complexity of Gene Regulatory Networks
In the realm of biological research, the complexity of gene regulatory networks (GRNs) presents an enormous challenge. The intricacies of these networks far exceed what can be experimentally validated in a wet lab. The human genome, with its approximately 3.2 billion base pairs with each position having 4 possible values of A T C or G (4³²⁰⁰⁰⁰⁰⁰⁰⁰), is just the beginning.
Add to this the vast diversity of chemical compounds in treatment regimes (10⁶⁰), the multitude of potential contractable diseases, the complexity of system biology states (including various phenotypes and nutrient conditions), the influence of epigenetic modifications, the variability in gene expression profiles, the impact of microbiome interactions, and environmental factors, and we are faced with a combinatorial explosion of potential outcomes — potentially in multiple trillions.
The Necessity of Mathematical and Physical Modeling
Given the sheer scale of this complexity, it becomes clear that experimental biology alone cannot provide all the answers. This is where the expertise of mathematicians and physicists, who are adept at modeling complex systems such as the universe, becomes invaluable. To understand and predict the behavior of a single cell, we need to apply the same rigorous mathematical modeling techniques used in physics and other fields.
Divergent Approaches: Biologists vs. Mathematicians
When a mathematician encounters a gene regulatory network (GRN), their approach is fundamentally different from that of a biologist. A biologist tends to focus on specific empirical questions, such as, “What are the interactions between particular genes, and how do these interactions affect gene expression under various conditions? What experimental data can we collect to validate these interactions and understand the underlying biological mechanisms?”
In contrast, a mathematician asks broader questions, such as, “What is the most fundamental governing function that explains the behavior of a GRN at any point in time, and how can we model such a network?” This difference in approach highlights the complementary nature of these disciplines.
The Collaborative Path Forward
The path forward involves a synergistic collaboration between biologists and mathematicians. Initially, biologists must dissect the network and capture a few sample interactions through meticulous experimentation. These experimentally validated interactions serve as the foundation upon which mathematicians can build.
Luckily, biologists have dissected and labeled a good portion of gene functions. Majority of them available in public datasets!
Mathematicians can then take these representative samples and generalize a model that accounts for all possible future states, including unforeseen ones. The emphasis is on building predictive models that can navigate the vast space of potential outcomes. By leveraging mathematical models, we can make sense of the complex and dynamic behavior of gene regulatory networks.
Building Predictive Models for the Future
The goal is to construct models that can predict the behavior of GRNs across the trillions of possible state transitions. This includes considering the size of the genome, the diversity of chemical compounds in treatment regimes, the potential diseases, and the diverse system biology states (phenotype and nutrients). It also involves factoring in the influence of epigenetic modifications, variability in gene expression profiles, microbiome interactions, and environmental conditions.
Cognit AI is one such company which is taking a mathematics-first approach to biology, building complex predictive models to encapsulate the behavior of a GRN at any time and state. By integrating rigorous mathematical frameworks with empirical biological data, Cognit aims to revolutionize our understanding of gene regulatory networks and advance precision medicine.
Discussion
Let’s continue this discussion and explore how we can further integrate mathematical and biological approaches to tackle the complexities of gene regulatory networks. How can we refine our models and what other interdisciplinary collaborations can enhance our understanding? Your insights and thoughts are valuable as we navigate this challenging yet exciting frontier.
