Four Challenges That Decide How to Model a Biological System

Choosing a computational model for a biological system is less about picking a technique and more about diagnosing which problems will dominate your project. A review from the Computational Modeling of Biological Systems (SysMod) community, affiliated with the International Society for Computational Biology, identifies the recurring obstacles as integration, standardization, model complexity, and scalability. Which of these bites hardest depends on the scale of the question, the data in hand, and whether the goal is mechanistic insight or prediction of an observable phenotype.

Introduction to Computational Modeling in Biology

Over the past two decades, high-throughput technologies—gene microarrays, RNA sequencing, mass spectrometry, and metabolic profiling—have made it possible to measure biological systems at unprecedented depth, a shift that a review of mathematical and computational modeling in systems biology links directly to the rise of precision medicine. The field has evolved from simple models of small-scale molecular interactions toward representations that, as the SysMod review puts it, can now encompass nearly complete cellular functions and organism physiology.

Yet data volume is not understanding. As MIT News reported in 2024, large-scale experiments in genomics, transcriptomics, proteomics, and cytometry produce enormous quantities of data, but making sense of that information is difficult—especially for cascades of interactions like those triggered when the immune system meets a pathogen. Computational modeling exists to bridge that gap, and the four challenges below determine whether the bridge holds.

Challenge 1: Data Integration

The first hurdle is combining data types that were never designed to fit together. The SysMod review identifies integration of heterogeneous data—genomics, transcriptomics, proteomics, metabolomics, and epigenetics—as a key challenge, one that intensifies when data come from different studies or were collected at different times. Each omics layer observes a different aspect of the same system: sequencing reveals gene variants, transcript analysis shows which genes are expressed, and proteomics measures the proteins actually present.

The metabolomics research community has been blunt about the difficulty. Because of inherent differences between omics platforms, integrating multiple data types remains an ongoing challenge for many researchers. A review of multi-omics integration strategies notes that analyzing each omics dataset separately may not provide a comprehensive understanding of the subject under study—yet metabolites, as downstream products of interactions between genes, transcripts, and proteins, may offer a foothold for tying the layers together.

Integration is not merely a preprocessing chore; it defines what a model can claim. The landmark E-cell project led by Masanori Tomita in 1999 modeled the kinetic dynamics of metabolic pathways in Mycoplasma genitalium by combining experimental metabolomics data with genomics, transcriptomics, and proteomics data—an early demonstration that whole-cell understanding requires all the layers at once.

Challenge 2: Standardization

Even when data can be combined, combining it reproducibly is a separate problem. The SysMod review frames the field's challenges as spanning everything from data acquisition to model calibration and validation—stages that only connect smoothly when data are represented consistently. Without common formats and modeling protocols, models become difficult to share, reuse, and compare across studies or platforms.

The multi-omics review reinforces the point from the data side: transcriptomics, proteomics, and metabolomics each provide unique insights into different layers of a biological system, but their integration requires explicitly addressing the differences between them. Standardization is what turns a one-off model built in one lab into a resource another lab can extend. It is also what makes community progress cumulative rather than fragmented—which is precisely why the SysMod community invests in engagement through meetings, webinars, and conferences to drive the field forward.

Challenge 3: Model Complexity

Once data are integrated and standardized, the modeler faces a trade-off: how much biological detail to represent. The SysMod review notes that machine learning and simulation-based modeling are creating synergy toward more accurate and efficient exploration of complex biological system dynamics—but that this requires careful attention to data quality and calibration. More detail is not automatically better.

A perspective on computational modeling of biological systems, focused on cardiovascular applications, highlights a subtler cost of complexity: in silico research offers benefits such as the absence of inherent variability, but explicitly accounting for variability and quantifying uncertainty requires multiple simulation runs. Complexity multiplies that computational burden. An overly detailed model becomes expensive to run and hard to validate; an overly simple one may lack the predictive power that justified building it in the first place. The right level of detail is the level the question and the data can support.

Challenge 4: Scalability

Biological processes do not respect scale boundaries. Cancer development is a multiscale process in which genetic mutations at subcellular levels manifest at tissue and organism scales, and models must be adaptable enough to capture phenomena from individual cells to entire physiological systems. The systems biology review examined modeling approaches at different scales precisely because no single representation serves all of them.

Scalability also has a regulatory and clinical dimension. The latest version of the ICH guideline on QT/QTc interval prolongation and proarrhythmic potential explicitly encourages the use of in silico models to integrate experimental ion channel data for clinical and preclinical research—an example steered by international regulators, industry, and academics together. And the cardiovascular modeling perspective points toward individualized representations such as digital twins, which open new avenues for disease-specific models that account for patient-to-patient variability. Scaling from a generic model to an individualized one is among the field's most consequential frontiers.

Decision Framework: Matching Biological Questions to Modeling Challenges

The framework below categorizes a modeling problem by its key characteristics—biological scale, data availability, and predictive goal—and maps each combination to the primary challenges that must be addressed before a modeling strategy can be chosen. Read your own project's row to see where the effort should go.

Mapping biological research questions to the primary computational modeling challenges they entail
Modeling problem	Primary challenge	Impact on modeling	Illustrative example
Whole-cell or pathway model built from multiple measurement technologies	Data integration	Without effective integration, the model cannot capture system dynamics comprehensively; analyzing each omics layer separately is insufficient	The E-cell project combined metabolomics with genomics, transcriptomics, and proteomics data to model Mycoplasma genitalium
Model intended for sharing, reuse, or comparison across labs	Standardization	Inconsistent data representation makes models hard to share, reuse, and compare across studies and platforms	Community efforts such as SysMod, which drives progress through meetings, webinars, and conferences
High-detail system where variability and uncertainty matter	Model complexity	Overly complex models are computationally expensive and hard to validate; overly simple ones lack predictive power; uncertainty quantification requires multiple runs	Cardiovascular in silico models balancing physiological detail against feasibility
Question spanning subcellular to organism scales, or moving toward individualized prediction	Scalability	Models must adapt to capture phenomena from individual cells to whole physiological systems, including patient variability	Cancer models spanning subcellular mutations to tissue-level outcomes; digital twins for disease-specific models

The reasoning path is straightforward: identify your scale (molecular, cellular, organism), your data (high-throughput multi-omics or sparse measurements), and your goal (mechanistic insight or phenotype prediction). A whole-cell model with rich multi-omics data makes integration the dominant challenge; a model meant for regulatory use makes standardization and validation dominant; a patient-specific application makes scalability and complexity the binding constraints. Most real projects face several at once, but prioritizing the dominant one determines which class of model is even viable.

Navigating the Future of Biological System Modeling

Researchers should adopt a structured approach to model selection, prioritizing challenges like data integration, standardization, complexity, and scalability based on their specific biological question and available resources. The field's trajectory supports this: the cardiovascular modeling perspective observes that continued progress in machine learning and open science practices is propelling computational approaches toward increasingly complex questions with high precision, while the systems biology review ties integrated, holistic models to the identification of therapeutic targets.

The measurable indicator of success is concrete: successful model development and validation, indicated by the model's ability to accurately predict biological phenomena and its interoperability with other research efforts. A model that predicts what it claims to predict, and that other researchers can pick up and extend, has navigated all four challenges well. That dual test—predictive accuracy plus interoperability—is the standard against which any new modeling project, from a single pathway to a digital twin, should be judged.