Biology's Complexity Creates Problems We Can't Ignore
Science depends on reproducibility, yet getting consistent results in biological systems can often be challenging. The inherent complexity of these systems makes it difficult to observe reproducible results. But what are the contributing factors to this observed lack of reproducibility? In this blog, we explore why understanding contributors to variability is essential for improving research quality and translational value, covering:
Why episodic data collection risks missing fluctuations in dynamic biological systems
How inter-individual variability can obscure or distort experimental results
Why small cohort sizes make it difficult to achieve reliable statistical power
Understanding the Reproducibility Crisis
Biology is fundamentally complex, and contributors to variable results are frequently overlooked in preclinical research design. Reproducibility in preclinical animal studies is deemed to be especially poor, with more than half of published findings considered irreproducible, representing a cost of US$28 billion per year in the USA alone. Voelkl et al. (2020) in Nature Reviews Neuroscience argue that a major cause of this crisis is a persistent disregard for biological variation in study design. When studies fail to account for this variability, results may not reflect a generalizable biological truth, contributing to costly failures in clinical translation, where promising animal findings repeatedly fail to hold up in human trials.
Here are three key pitfalls of conventional methods that disregard contributors to variability and potentially undermine reproducibility and translational value:
Pitfall: Episodic Data Collection Misses Biology in Motion
Biology, especially as it occurs in an entire animal, is dynamic: gene expression, hormone levels, immune activity, and behavior all fluctuate over time, across the light/dark cycle, development, and disease progression. Biology comprises complex systems that cannot be explained or predicted through study of their individual parts alone, and should therefore be measured through multiple, relevant outputs. Data collected episodically or at discrete time points risks missing the full picture. Rosa et al. (2012) demonstrated this in the context of gene expression studies, showing that optimal time point sampling substantially improves the ability to detect meaningful biological signals. In behavioral research, this means that a single observation session may reflect where an animal happens to be in a biological cycle rather than a stable trait, detecting variability that is real but potentially misleading if not properly contextualized.
Pitfall: Inter-Individual Variability Obscures Consistency
Inter-individual variability between models, even individuals from an inbred strain, can exist in both the dimension and nature of their response. Without proper consideration of this variability, researchers cannot realistically account for it in experimental design or analysis. Ignoring it, or simply being unable to detect it, potentially makes it difficult to identify a clear signal, since variability is unlikely to be distributed evenly across experimental groups.
Van der Goot et al. (2021) demonstrated this empirically by first using a data-driven clustering approach to characterize mice into distinct behavioral response types, then comparing outcomes between a pool where experimental groups were matched on response type and a pool where this variability was not accounted for. The two designs yielded meaningfully different results: the pool matched to response type revealed a confounding experimental effect on avoidance behavior that was absent in the other pool, and uncontrolled individual variation appeared to augment treatment effects for activity behavior. Uncontrolled variability could therefore both mask effects and produce misleading ones, depending on how groups happened to be composed. This was only possible because the authors characterized animals prospectively before the experiment and clearly demonstrates the value of characterizing a key contributor to variability within a system.
Pitfall: Achieving Statistical Power is Difficult
Small cohort sizes may introduce sampling error, where results deviate from the true effect purely by chance. Increasing animal numbers can reduce this noise, but doing so raises ethical and regulatory concerns. As a result, studies with small groups may not accurately represent biological variability. Button and colleagues (2013) showed that small sample sizes in neuroscience studies produce unreliable results and false positives. If increasing cohort size is not an option, then collecting more data points or a variety of relevant measures may be a reasonable approach.
Addressing the Pitfalls
Given the complexity of biological systems, it’s no wonder that animal studies are often hard to interpret. Pre-characterizing your system to identify ideal time-points and contributors to inter-individual variability are approaches that hold promise to improve outcomes. Additionally, measuring a greater number and/or a variety of outputs will improve reproducibility, especially when paired with multidimensional relational analysis that can isolate variability within a system that is independent from the biological response of interest.
Technological progression has given researchers more options to scale data collection without scaling animal numbers. Methods that capture more biological outputs, such as digital monitoring technology, that continuously observe animals across multiple simultaneous endpoints, provide a means to both increase and optimize time-points. Methodically applying AI to the task of multidimensional relational analysis of this data shows great promise for the future of such platforms. The future is bright for researchers that apply these technologies with systematic rigor.
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