Bio papers — 2026-10-21

The Morning Preprint — this day as one edition: the day's papers, the five things worth knowing, and the episodes behind them.

Understanding biological systems by looking at dynamic responses instead of just static states is the focus today. This matters because many important biological outcomes, like whether a system adapts or overshoots a target, are determined by these transient behaviors that classical steady-state analyses miss.

Work has shown that the structure of these transient responses constrains the underlying network architecture, especially when contrasting monotone systems with those containing antagonistic pathways. For example, incoherent feedforward motifs provide a simple way to generate non-monotonic and adaptive responses across different levels of organization, from molecular signaling to immune regulation AKMPC. This contrasts with the sharp impossibility results that monotonicity imposes, which can be used to rule out entire model classes based on transient data alone.

Another area involves using periodic forcing and integral-type readouts, such as cumulative dose responses, as powerful experimental probes to reveal hidden structure AKMPC. This is complemented by kinetic modeling in molecular processes, where the kinetic equations for template-directed multistate copolymerization can be exactly solved for things like mean growth velocity and sequence probabilities Gaspard.

On an applied side, researchers are looking at how to use these dynamic concepts in prediction, such as evaluating pathway-level transcriptomic signatures under cross-cohort shift in sepsis mortality prediction Shukla. This work found that while gene-level representations offered the strongest discrimination, pathway-level representations showed more robust uncertainty behavior when moving data between different hospital settings.

Finally, there is work on establishing meaningful thresholds for digital health measures, specifically setting minimal clinically important differences for smartphone-derived gait metrics in multiple sclerosis Rinderknecht. This helps clinicians interpret real-world patient data to track disease progression objectively.

The most pressing issue right now is understanding how drug use relates to serious side effects like rhabdomyolysis because there are clear gaps between what drugs are known to cause and what is actually documented. Enpu Liang’s work, Drug-Associated Rhabdomyolysis in the FDA Adverse Event Reporting System (FAERS): A Disproportionality Analysis, systematically looked at real-world data from the FAERS database spanning twenty years to pinpoint risks for fifty-four different drugs. This study found that certain medications like gemfibrozil and various statins have potent risks of causing rhabdomyolysis, with adjusted odds ratios showing significant danger for these agents.

More concerning were the novel signals they uncovered, specifically flagging levetiracetam and donepezil as having strong risk signals even though they currently lack official warnings. This suggests a major labeling gap where drugs with known dangers are not properly cautioned on their labels. This finding is supported by Tony Lindeberg’s work, Do the receptive fields in the primary visual cortex span a variability over the degree of elongation of the receptive fields?, which suggests that biological receptive field shapes should span degrees of freedom in affine image transformations to align with observed orientation selectivity.

This concept of spanning variability is further explored by Lindeberg in Orientation selectivity properties for integrated affine quasi quadrature models of complex cells, where he shows that including variability over the degree of elongation leads to more uniform behavior in computational models compared to earlier versions. This theoretical framework connects to the visual system's structure, as Lindeberg’s Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields demonstrates that orientation selectivity becomes narrower when receptive fields are wider in certain directions.

In a different domain, Dhaker Kroumi’s research into evolutionary game theory shows how cooperation can be promoted by context-dependent migration in structured populations, where cooperators move more when surrounded by defectors. This behavioral plasticity is related to how individuals respond to local conditions, which mirrors the kind of contextual sensitivity seen in biological systems.

Similarly, Dhaker Kroumi’s work on stochastic viability in island models suggests that increasing variability in viability coefficients can enhance the fixation probability of a mutant type, indicating that environmental uncertainty plays a role similar to how drug risks might be assessed.

The most significant finding relates to how introducing randomness into payoffs affects cooperation in multi-player games, specifically in the context of selection and mutation dynamics. This work suggests that increasing the variance of a defection payoff or decreasing the variance of a cooperation payoff tends to boost the average abundance of cooperators. This is important because it provides a mechanism for how environmental uncertainty can favor cooperative behavior under certain conditions.

This insight is further supported by how covariance between payoffs influences outcomes, as shown in AKMPC, where the effect depends on the number of cooperators present in the group associated with those specific payoffs. This means that the interplay between different types of random fluctuations matters deeply for strategy stability.

More granular results come from examining specific social dilemmas like the stag hunt game and snowdrift game within AKMPC, where decreasing scaled variance or increasing scaled covariance makes weak selection favor cooperation in those scenarios. This contrasts with the public goods game, where the covariance of the benefit payoff has no effect on the average abundance of cooperators.

Another relevant piece is Aspiration can promote cooperation in well-mixed populations as in regular graphs, which proposes a dynamic where dissatisfied individuals imitate successful strategies rather than simply changing their strategy based on self-satisfaction. This mechanism helps cooperative members maintain their strategy, leading to selection favoring cooperation over defection in well-mixed settings structured like a regular graph.

Finally, the theoretical framework for temporal signal processing in A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time offers a way to model continuous phenomena across multiple scales without needing future information. This theory establishes a canonical kernel for real-time processing when standard Gaussian kernels are unsuitable due to their non-causal nature.

Today's papers

The papers

Important terms

Dynamic Responses
Focuses on how biological systems react over time rather than just looking at static states to understand adaptation and overshoot.
Monotone vs. Antagonistic Pathways
Contrasts systems where responses are always in one direction with those containing opposing pathways, which helps constrain network architecture.
Periodic Forcing and Integral-Type Readouts
Using timed inputs and cumulative measurements to reveal hidden structures in biological processes, like dose responses or growth rates.
Spanning Variability
The idea that biological features, like visual receptive fields, should vary over different degrees of freedom to match observed selectivity.
Covariance Between Payoffs
How the correlation between different types of random fluctuations in game payoffs deeply affects the stability and outcome of cooperative strategies.