Living Matter Research

Question:   What distinguishes the living matter from the inanimate?

Hypothesis:   Distinctive hallmarks of living things originate from basic physical principles of this non-equilibrium state of matter.

Goal:   Construction of a unified framework or language to describe how living matter evolves and self-assembles into systems which are able to self-replicate, develop, process information, and learn.

Methods:   Physical and mathematical approaches, specifically non-equilibrium statistical mechanics, information theory, graph theory, topology, and soft active matter theory.

Physics of Living Matter

Living information channels rely on the ability of bio-molecules to specifically recognize each other among a vast variety of different, but structurally similar molecules. Molecular binding interactions are typically weak, not much above the thermal energy, and thermal fluctuations therefore play a significant role. We study the physical mechanisms that assist the organism to efficiently perform molecular recognition in this noisy environment.

What is the relation between conformational changes, especially induced fit, and specificity, that is the ability to discriminate between competing targets?   —   We use a statistical mechanics approach to understand the relations between flexibility and conformation and the quality of molecular recognition. We formulate molecular recognition as a signal detection problem and solve for the design of optimal bio-recognizer. We employed this framework to examine the process of homologous recombination, in the ribosome, and the rubisco enzyme, the most abundant protein on earth.

Molecular Information, Codes, and Biological Representation

In the living cell, information is carried by molecules. The outside environment and the biochemical circuitry of the cell churn out fluxes of molecular "words" that are read, processed and stored in memory by other molecules. The cell's information-processing networks need to translate or map a word written in one species of molecules into another word written in a different molecular language.

The genetic code the most basic example of representation or mapping in biology. The digital DNA triplets represent the information about the corresponding amino acids in the analogue space of chemical properties. Whenever information is translated from one type chemical or physiological language to another one, it is being represented in the new language. For example, the retina represents visual information as neural spikes, and signaling molecules represent information regarding the presence of neighboring bacteria in a colony.

Evolution poses the organism with a semantic challenge: its code-tables must assign symbols to meanings in a manner that minimizes the impact of the the error-load, while keeping down the cost of resources that the code-table necessitates. Our work tries to explain how these conflicting needs drive the emergence and evolution of molecular codes. It appears from our study that evolutionary optimization with respect to noise dominates the organization and evolution of molecular codes.

Non-equilibrium Physics

Our approach to living matter is related to the frameworks of statistical mechanics, dynamical systems and hydrodynamics. In these disciplines, we study non-equilibrium physics of microfluidic droplets (collaboration with R. Bar-Ziv). We found phonons, shocks and Burgers-like dynamics in microfluidic. We examined large-scale systems and observed long-range order of the velocity correlations, which is the first example for a dissipative system with long-range forces to be analytically calculated. The micron-scale viscous flow of complex fluids appears as an accessible system to study basic questions of non-equilibrium statistical mechanics, which are central for our understanding of living matter. Of particular interest is the effect of long-range interactions on the self-organization of particle-laden phases (for example the amino-acids in a protein).   —   Can living matter store a physical memory of the external forces, to be used in molecular learning and evolution?

Evolution and Language

Many organisms are known to cooperate, which may seem to contradict natural selection, but is in fact a consequence of selection at the gene level. We examined cooperation using evolutionary dynamics and game theory and found that cooperation is a broad characteristic that may include a diversity of phenomena that do not appear to be cooperative at first glance, for example, mutation and antibiotic resistance. We suggest that the tendency toward cooperation is stronger when the population is small and possibly subject to environmental pressures.

Dictionaries link a given word to a set of alternative words (the definition) which in turn point to further descendants. Iterating through definitions in this way, one typically finds that definitions loop back upon themselves. We demonstrated that such definitional loops are created in order to introduce new concepts into a language. In graphs of the dictionary, meaningful loops are quite short, although they are often linked to form larger, strongly connected components. These components are found to represent distinct semantic ideas. We used etymological data to show that elements of loops tend to be added to the English lexicon simultaneously.

Living Neural Networks

Neurons form complex webs of connections. Dendrites and axons extend, ramify, and form synaptic links with their neighbors. This complex wiring diagram has caught the attention of physicists and biologists for its resemblance to problems of percolation. Important questions are the critical distance that dendrites and axons have to travel in order to make the network percolate, i.e., to establish a path from one neuron of the network to any other, or the number of bonds (connections) or sites (cell bodies) that can be removed without critically damaging the functionality of the circuit.

In the brain, neural networks display such robust flexibility that circuits tolerate the destruction of many neurons or connections while keeping the same, though degraded, function. For example, it is currently believed that in Parkinson's disease, up to 70% of the functionality of the neurons in the affected areas can be lost before behavioral symptoms appear. At the core of the experiments and the model is a percolation approach. In our work, we consider a simplified model of a neural network in terms of bond-percolation on a graph.