Friday, April 17th, 2015
160 Fifth Avenue at 21st Street
New York, NY 10010
On Friday, April 17th, 2015, the Simons Foundation held its second annual all-day Conference on Theory and Biology. The meeting was an occasion for scientists in the greater New York area to come together to meet, discuss and hear talks highlighting a few of the many areas where theoretical ideas are having an impact on topics in the life sciences.
Read more about the conference here and below.
Mark Churchland, Columbia University
How does your brain generate voluntary movement?
View Slides (PDF)
Although the spinal cord can produce simple reflexive movements, voluntary movement depends upon inputs from higher brain areas such as the primary motor cortex. A central goal of my laboratory is to characterize the signals that arrive in the motor cortex just before voluntary movement begins and to understand how those signals set in motion the neural machinery that produces movement. We observe two key classes of neural signals. The first signal primes the neural machinery regarding ‘what’ movement will be made, and the second signal sets that machinery in motion. We suggest that these two signals reflect the ability of your brain to first choose what to do and to subsequently commit to triggering that action. We found that this sequence unfolds slowly for contemplative movements and rapidly for reactive movements. We further found that a slight perturbation of the ‘what to do’ signal delays movement, presumably because your brain hesitates to execute a potentially incorrect movement. In contrast, a slight perturbation of the subsequent ‘when to do it’ signal hastens movement onset, as if the voluntary inclination to move were given a slight boost. These results illustrate how it is becoming possible for our field to characterize higher-level functions such as voluntary movement in mechanistic terms.
Surya Ganguli, Stanford University
The functional contribution of synaptic complexity to learning and memory
An incredible gulf separates theoretical models of synapses — often described solely by a single scalar value denoting the size of a postsynaptic potential — from the immense complexity of molecular signaling pathways underlying real synapses. To understand the functional contribution of such molecular complexity to learning and memory, we develop new mathematical theorems elucidating the relationship between the structural organization and memory properties of complex synapses that are themselves molecular networks. Moreover, in proving such theorems, we uncover a framework, based on first passage time theory, to impose an order on the internal states of complex synaptic models, thereby simplifying the relationship between synaptic structure and function. We also apply our theories to model the time course of learning visuomotor gain changes in the rodent vestibulo-ocular reflex, both in wild-type mice and genetically modified mice, in which cerebellar long-term depression (LTD) is enhanced; our results indicate that synaptic complexity is necessary to explain diverse behavioral learning curves arising from interactions of prior experience and enhanced LTD.
Thomas Gregor, Princeton University
How much complexity and tuning occurs at the level of the individual nodes that make up a genetic network?
The nodes of many genetic networks that are active during early development are transcription factors, i.e., proteins that cross-regulate each other via activating or repressive interactions. Hence, in order to understand generic properties of such transcription networks, obtaining quantitative access to the molecular players is key. In particular, in addition to proteins, quantitative handles to other molecular species such as RNA-polymerases and mRNA molecules are crucial to understand the transition from one network node to the next. I will report on our recent progress in developing methods to monitor transcriptional activity in vivo and at the single molecule level in the developing fly embryo. An overview is given on how we use our measurements in combination with simple theoretical models to gain mechanistic insights into the microscopic underpinnings of transcriptional regulation.
Pankaj Mehta, Boston University
Can we make predictive phenomenological models in biology?
Phenomenological models—models that relate empirical observations of phenomena to each other in a way that is consistent with fundamental theory but is not directly derived from theory—play a fundamental role in our understanding of physical systems. These models often lack mechanistic details but can make precise, quantitative predictions about the systems being studied. In this talk, I will argue that phenomenological models can, and should, play a fundamental role in furthering our understanding of biological systems. In this vein, I will discuss recent work by our group on: (1) using large-scale gene expression data to construct ‘epigenetic landscapes’ for cellular identity that can identify key transcription factors for reprogramming, explain the existence of partially-reprogrammed cells, and identify a universal reaction coordinate for reprogramming dynamics and (2) using the idea of universality to construct simple, predictive, quantitative models for the biochemical networks that control collective behavior in the social amoebae Dictyostelium discoideum.
Olivier Pourquié, Harvard University
Toward physical principles governing vertebrate morphogenesis
The body axis of a vertebrate embryo forms largely by a process of elongation taking place at the level of a terminal posterior growth zone. The tissue that will form embryonic segments, called the paraxial mesoderm, is a mesenchymal tissue that plays a key role in the generation of the elongation movements. We proposed that the establishment of a gradient of cellular diffusion in response to an effective temperature gradient controlling cell motility (FGF signaling) generates the forces responsible for these elongation movements. So far, quantitative approaches of the morphogenesis of embryonic structures have been mainly deployed for epithelia, and I will discuss our experimental and theoretical approaches aiming at understanding the morphogenetic properties of mesenchymal cells in the embryo.
Eric Siggia, The Rockefeller University
Differentiation of human embryonic stem cells in micropatterned colonies recapitulates early embryonic spatial patterning.
Stem cells are commonly grown on surfaces and when induced to differentiate show disorganized arrangements of fates. The simple process of spatial confinement leads to a reproducible arrangement of extraembryonic and germ layer fates as a function of colony radius that mimics the proximal distal axis in the mammalian embryo. Fate is defined by distance from the colony boundary, which can be hundreds of microns away. The stem cell colonies also form a radially localized primitive streak and exhibit gastrulation-like movements. This quantitative assay shows how in a context very different from the embryo, the same morphogens, both activators and secreted inhibitors, generate spatial patterns over large scales. A quantitative understanding of how cell communication robustly generates spatial structure will be key in exploiting stem cells for regenerative medicine.
Benjamin D. Simons, University of Cambridge
Dynamical stem cell heterogeneity
Lineage tracing studies based on inducible genetic labeling have emphasized a central role for stochasticity in the maintenance and regeneration of cycling adult tissues, with frequent stem cell loss through differentiation compensated by the duplication of neighbors, leading to the consolidation of clonal diversity. Through the combination of long-term genetic lineage tracing assays with short-term in vivo live-imaging, the cellular basis for stochastic stem cell loss and replacement has begun to be resolved. With a focus on mammalian spermatogenesis and intestinal maintenance, we review the role of dynamical heterogeneity in the regulation of adult stem cell self-renewal.
Aleksandra Walczak, École Normale Supérieure
Diversity generation and organization of immune receptor repertoires
Recognition of pathogens relies on the diversity of immune receptor proteins. Recent experiments that sequence the entire immune cell repertoires provide a new opportunity for quantitative insight into naturally occurring diversity and how it is generated. I will describe how we can use statistical inference to quantify the origins of diversity in these sequences and characterize selection in the somatic evolutionary process that leads to the observed receptor diversity. A well-adapted repertoire should be tuned to the pathogenic environment to reduce the cost of infections. I will finish by discussing the form of the optimal repertoire that minimizes the cost of infections contracted from a given distribution of pathogens.