Richard Carthew
Northwestern University

 

Tweaking Mendel’s First Law of Inheritance with Growth

 

Expression of simple Mendelian traits rests upon the dominant-recessive features of gene alleles. However, it has long been known that limiting nutrition or lowering temperature can perturb the phenotypes of some dominant or recessive alleles. We study this phenomenon and find evidence for pervasive uncoupling of phenotype from genotype if animal growth is attenuated by either limiting carbohydrate metabolism or protein synthesis. We use a general model of developmental fate lineage restriction that is based on control theory and that simulates gene expression dynamics. The model predicts that crippling activators or repressors of gene expression (i.e., with mutations) has lesser effect on gene expression (and consequently on mutant phenotypes) when general protein translation or ATP turnover are limiting. These model predictions are experimentally validated in the Drosophila system.
 

Brent Doiron
University of Chicago

 

Heterogeneity and Dimension in Recurrent Neuronal Networks

 

We will discuss two distinct features of neuronal response. First, neuronal activity is very heterogeneous — in response to a specific stimulus or behavior some neurons emit many action potentials, and many others are relatively silent. Second, trial-to-trial fluctuations of neuronal response occupy a low dimensional space, owing to significant correlations between the joint activity of neurons within a population. We will link these two aspects of neural representation using a recurrent circuit model and derive the following relation: the more heterogeneous the distribution of trial-averaged responses, the lower the effective dimension of population trial-to-trial covariability. This surprising prediction is tested and validated using multiple population datasets from numerous brain areas in mice, non-human primates and in the motor cortex of human subjects. We present a simple theory whereby a more heterogeneous neuronal code leads to better fine discrimination performance through a lowering of the dimension of population covariability. In line with this result, we show that neural populations across the brain exhibit both more heterogeneous mean responses and lower-dimensional fluctuations when the brain is in more heightened states of information processing. In sum, we present a key organizational principle of neural population response that is widely observed across the nervous system and acts to synergistically improve population representation.
 

Daniel Fisher
Stanford University

 

Ecology and Perpetual Evolution in High Dimensions

 

In a simple, constant environment does evolution continue forever? Does extensive diversification via small genetic and ecological differences? What are general evolutionary consequences of organismic complexity? Hints from long term laboratory evolution experiments and from genomic data of within-species bacterial diversity motivate considering these questions. Several simple models of evolution with small ecological feedback will be introduced, with the high dimensionality of phenotype space enabling mathematical analysis.
 

Paul François
University of Montreal

 

Understanding Complex (Biological) Systems in Latent Space
 

Many phenomena in biology are considered too complicated or too contingent to be captured by predictive theories similar to what is done in physics. But complex systems theory has taught us that simple, higher-level laws with few effective parameters can emerge from the interaction of small-scale components. As biology is becoming more and more quantitative, one can use a combination of first-principle theoretical modeling with simple machine-learning techniques to build accurate and tractable theories of biological dynamics. Those dynamics can often be best understood in (abstract) latent spaces, giving “physics-like,” intuition. Paul Francois will illustrate the power of such approaches using examples from embryonic development, immunology and machine-learning.
 

Yogesh Goyal
Northwestern University

 

Chance, Necessity or Free Will: Decision Making in Single Cells

 

Single cell variations within a genetically homogeneous population of cells can lead to significant differences in cell fate in response to external stimuli. This is particularly relevant in cancer cells, where a small population of cells can evade therapies to develop resistance. In this talk, Yogesh Goyal will present our ongoing work on tracing the origins, nature and manifestations of single cell variations in response to a variety of cytotoxic chemotherapies and targeted therapies in various cancer models. Our experimental and computational designs promise to provide a foundation for controlling single-cell variabilities in cancer and other biological contexts, such as stem cell reprogramming and transdifferentiation.
 

Niall Mangan
Northwestern University

 

Data-Driven Model Discovery Meets Mechanistic Modeling for Biological Systems

 

Building models for biological, chemical and physical systems has traditionally relied on domain-specific intuition about which interactions and features most strongly influence a system. Alternatively, machine-learning methods are adept at finding novel patterns in large data sets and building predictive models but can be challenging to interpret in terms of or integrate with existing knowledge. Our group balances traditional modeling with data-driven methods and optimization to get the best of both worlds. Recently developed for and applied to dynamical systems, sparse optimization strategies can select a subset of terms from a library that best describes data, automatically interfering potential model structures from a broad but well-defined class. Niall Mangan will discuss their group’s application and development of data-driven methods for model selection to (1) recover chaotic systems models from data with hidden variables and (2) discover models for metabolic and temperature regulation in hibernating mammals. Mangan will briefly discuss current preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.
 

Arvind Murugan
University of Chicago

 

The Origin of Non-Equilibrium Order

 

Since at least Schrödinger, physicists have seen life as a non-equilibrium process that has successfully fought the 2nd law of thermodynamics by maintaining order for 4 billion years. While we understand how extant biological Maxwell Demons work, much less is known about how such Demons come into existence in the first place. Using theoretical and experimental work on the molecular machinery that copies DNA-based information, we suggest that surprisingly little might be needed — proofreading mechanisms that maintain non-equilibrium order can potentially arise due to selection for faster replication, even if the order itself is not beneficial in any way. We argue that such order-through-speed mechanisms might also be relevant for any process that creates a wide variance in the distribution of replication times. Our work suggests the intriguing possibility that non-equilibrium order can arise more easily than assumed, as a byproduct of fast self-replication, even before that order is directly functional.
 

Stephanie Palmer
University of Chicago

 

What Should Biological Systems Throw Away?

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Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living organisms. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints, but it treats all input features equally. Not all inputs are, however, of equal value to the organism. Our work quantifies whether and how the brain selectively encodes stimulus features, specifically predictive features, that are most useful for fast and effective movements. We have shown that efficient predictive computation starts at the earliest stages of the visual system, in the retina. We borrow techniques from statistical physics and information theory to assess how we get terrific, predictive vision from these imperfect component parts. In broader terms, we aim to build a more complete theory of efficient encoding in the brain, and along the way have found some intriguing connections between formal, mathematical notions of coarse graining in biology and physics.