A biological circuit to anticipate trend.

Organisms gain by anticipating future changes in the environment. Those environmental changes often follow stochastic trends. The steeper the slope of the trend, the more likely the trend's momentum carries the future trend in the same direction.

The origin of novel traits in cancer.

The traditional view of cancer emphasizes a genes-first process. Novel cancer traits arise by genetic mutations that spread to drive phenotypic change. However, recent data support a phenotypes-first process in which nonheritable cellular variability creates novel traits that later become heritably stabilized by genetic and epigenetic changes.

Robustness and complexity.

When a system robustly corrects component-level errors, the direct pressure on component performance declines. Components become less reliable, maintain more genetic variability, or drift neutrally, creating new forms of complexity. Examples include the hourglass pattern of biological development and the hourglass architecture for robustly complex systems in engineering.

Disease from opposing forces in regulatory control.

Danger requires a strong rapid response. Speedy triggers are prone to false signals. False alarms can be costly, requiring strong negative regulators to oppose the initial triggers.

Precise Traits from Sloppy Components: Perception and the Origin of Phenotypic Response.

Organisms perceive their environment and respond. The origin of perception-response traits presents a puzzle. Perception provides no value without response.

Evolutionary explanations for heterogeneous behavior in clonal bacterial populations.

Cellular heterogeneity in clonal bacterial populations is widespread. Division of labor and bet hedging are common adaptive explanations for the function of such heterogeneity. We suggest group-level phenotypes via shareable molecules and variation in cellular vigor as two alternative evolutionary explanations for bacterial cellular heterogeneity.

Optimizing differential equations to fit data and predict outcomes.

Many scientific problems focus on observed patterns of change or on how to design a system to achieve particular dynamics. Those problems often require fitting differential equation models to target trajectories. Fitting such models can be difficult because each evaluation of the fit must calculate the distance between the model and target patterns at numerous points along a trajectory.

The number of neutral mutants in an expanding Luria-Delbrück population is approximately Fréchet.

A growing population of cells accumulates mutations. A single mutation early in the growth process carries forward to all descendant cells, causing the final population to have a lot of mutant cells. When the first mutation happens later in growth, the final population typically has fewer mutants.

Robustness increases heritability: implications for familial disease.

Robustness protects organisms in two ways. Homeostatic buffering lowers the variation of traits caused by internal or external perturbations. Tolerance reduces the consequences of bad situations, such as extreme phenotypes or infections.

Optimization of Transcription Factor Genetic Circuits.

Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks.

The Fundamental Equations of Change in Statistical Ensembles and Biological Populations.

A recent article in unified key results from thermodynamics, statistics, and information theory. The unification arose from a general equation for the rate of change in the information content of a system. The general equation describes the change in the moments of an observable quantity over a probability distribution.

Developmental Mutators and Early Onset Cancer.

A new hypothesis suggests that somatic genome remodeling during normal development can cause mutations that explain many early onset cancers in children and adults.

Simple unity among the fundamental equations of science.

The Price equation describes the change in populations. Change concerns some value, such as biological fitness, information or physical work. The Price equation reveals universal aspects for the nature of change, independently of the meaning ascribed to values.

Invariance in ecological pattern.

The abundance of different species in a community often follows the log series distribution. Other ecological patterns also have simple forms. Why does the complexity and variability of ecological systems reduce to such simplicity? Common answers include maximum entropy, neutrality, and convergent outcome from different underlying biological processes.

Sexual antagonism leads to a mosaic of X-autosome conflict.

Males and females have different optimal values for some traits, such as body size. When the same genes control these traits in both sexes, selection pushes in opposite directions in males and females. Alleles at autosomal loci spend equal amounts of time in males and females, suggesting that the sexually antagonistic selective forces may approximately balance between the opposing optima.

The common patterns of abundance: the log series and Zipf's law.

In a language corpus, the probability that a word occurs times is often proportional to 1/ . Assigning rank, , to words according to their abundance, log vs log typically has a slope of minus one. That simple Zipf's law pattern also arises in the population sizes of cities, the sizes of corporations, and other patterns of abundance.

How to Understand Behavioral Patterns in Big Data: The Case of Human Collective Memory.

Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one.

Evolutionary design of regulatory control. II. Robust error-correcting feedback increases genetic and phenotypic variability.

As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the system's components, leading to component decay. The paradoxical coupling of robustness and decay predicts that robust systems evolve cheaper, lower performing components, which accumulate greater mutational genetic variability and which have greater phenotypic stochasticity in trait expression.

Evolutionary design of regulatory control. I. A robust control theory analysis of tradeoffs.

The evolutionary design of regulatory control balances various tradeoffs in performance. Fast reaction to environmental change tends to favor plastic responsiveness at the expense of greater sensitivity to perturbations that degrade homeostatic control. Greater homeostatic stability against unpredictable disturbances tends to reduce performance in tracking environmental change.

The Price Equation Program: Simple Invariances Unify Population Dynamics, Thermodynamics, Probability, Information and Inference.

The fundamental equations of various disciplines often seem to share the same basic structure. Natural selection increases information in the same way that Bayesian updating increases information. Thermodynamics and the forms of common probability distributions express maximum increase in entropy, which appears mathematically as loss of information.

Measurement invariance explains the universal law of generalization for psychological perception.

The universal law of generalization describes how animals discriminate between alternative sensory stimuli. On an appropriate perceptual scale, the probability that an organism perceives two stimuli as similar typically declines exponentially with the difference on the perceptual scale. Exceptions often follow a Gaussian probability pattern rather than an exponential pattern.

A biochemical logarithmic sensor with broad dynamic range.

Sensory perception often scales logarithmically with the input level. Similarly, the output response of biochemical systems sometimes scales logarithmically with the input signal that drives the system. How biochemical systems achieve logarithmic sensing remains an open puzzle.