The geometry of the Pareto front in biological phenotype space

When organisms perform a single task, selection leads to phenotypes that maximize performance at that task. When organisms need to perform multiple tasks, a trade‐off arises because no phenotype can optimize all tasks. Recent work addressed this question, and assumed that the performance at each task decays with distance in trait space from the best phenotype at that task. Under this assumption, the best‐fitness solutions (termed the Pareto front) lie on simple low‐dimensional shapes in trait space: line segments, triangles and other polygons. The vertices of these polygons are specialists at a single task. Here, we generalize this finding, by considering performance functions of general form, not necessarily functions that decay monotonically with distance from their peak. We find that, except for performance functions with highly eccentric contours, simple shapes in phenotype space are still found, but with mildly curving edges instead of straight ones. In a wide range of systems, complex data on multiple quantitative traits, which might be expected to fill a high‐dimensional phenotype space, is predicted instead to collapse onto low‐dimensional shapes; phenotypes near the vertices of these shapes are predicted to be specialists, and can thus suggest which tasks may be at play.     

Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network

Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.     

The Emergence of Synaesthesia in a Neuronal Network Model via Changes in Perceptual Sensitivity and Plasticity

Synaesthesia is an unusual perceptual experience in which an inducer stimulus triggers a percept in a different domain in addition to its own. To explore the conditions under which synaesthesia evolves, we studied a neuronal network model that represents two recurrently connected neural systems. The interactions in the network evolve according to learning rules that optimize sensory sensitivity. We demonstrate several scenarios, such as sensory deprivation or heightened plasticity, under which synaesthesia can evolve even though the inputs to the two systems are statistically independent and the initial cross-talk interactions are zero. Sensory deprivation is the known causal mechanism for acquired synaesthesia and increased plasticity is implicated in developmental synaesthesia. The model unifies different causes of synaesthesia within a single theoretical framework and repositions synaesthesia not as some quirk of aberrant connectivity, but rather as a functional brain state that can emerge as a consequence of optimising sensory information processing.     

Anatomical changes with needle length are correlated with leaf structural and physiological traits across five Pinus species

The genus Pinus has wide geographical range and includes species that are the most economically valued among forest trees worldwide. Pine needle length varies greatly among species, but the effects of needle length on anatomy, function, and coordination and trade‐offs among traits are poorly understood. We examined variation in leaf morphological, anatomical, mechanical, chemical, and physiological characteristics among five southern pine species: Pinus echinata, Pinus elliottii, Pinus palustris, Pinus taeda, and Pinus virginiana. We found that increasing needle length contributed to a trade‐off between the relative fractions of support versus photosynthetic tissue (mesophyll) across species. From the shortest (7 cm) to the longest (36 cm) needles, mechanical tissue fraction increased by 50%, whereas needle dry density decreased by 21%, revealing multiple adjustments to a greater need for mechanical support in longer needles. We also found a fourfold increase in leaf hydraulic conductance over the range of needle length across species, associated with weaker upward trends in stomatal conductance and photosynthetic capacity. Our results suggest that the leaf size strongly influences their anatomical traits, which, in turn, are reflected in leaf mechanical support and physiological capacity.     

2D-NMR and ATR-FTIR study of the structure of a cell-selective diastereomer of melittin and its orientation in phospholipids

Melittin, a 26 residue, non-cell-selective cytolytic peptide, is the major component of the venom of the honey bee Apis mellifera. In a previous study, a diastereomer ([D]-V(5,8),I(17),K(21)-melittin, D-amino acids at positions V(5,8),I(17),K(21)) of melittin was synthesized and its function was investigated [Oren, Z., and Shai, Y. (1997) Biochemistry 36, 1826-1835]. [D]-V(5,8),I(17),K(21)-melittin lost its cytotoxic effects on mammalian cells; however, it retained antibacterial activity. Furthermore, [D]-V(5,8),I(17),K(21)-melittin binds strongly and destabilizes only negatively charged phospholipid vesicles, in contrast to native melittin, which binds strongly also zwitterionic phospholipids. To understand the differences in the properties of melittin and its diastereomer, 2D-NMR experiments were carried out with [D]-V(5,8),I(17),K(21)-melittin, and polarized attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy experiments were done with both melittin and [D]-V(5,8), I(17),K(21)-melittin. The structure of the diastereomer was characterized by NMR in water, as well as in three different membrane-mimicking environment, 40% 2,2,2-trifluoroethanol (TFE)/water, methanol, and dodecylphosphocholine/phosphatidylglycerol (DPC/DMPG) micelles. The NMR data revealed an amphipathic alpha-helix only in the C-terminal region of the diastereomer in TFE/water and methanol solutions and in DPC/DMPG micelles. ATR-FTIR experiments revealed that melittin and [D]-V(5,8),I(17),K(21)-melittin are oriented parallel to the membrane surface. This study indicates the role of secondary structure formation in selective cytolytic activity of [D]-V(5,8), I(17),K(21)-melittin. While the N-terminal helical structure is not required for the cytolytic activity toward negatively charged membranes and bacterial cells, it appears to be a crucial structural element for binding and insertion into zwitterionic membranes and for hemolytic activity.