Biological innovations and evolution

Project members:  Erik Hoel, Brennan Klein, Anshuman Swain, and Michael Levin

Status: Preprint out, In Review; bioRXiv: doi.org/10.1101/2020.05.03.074419 

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8,782,166 protein-protein interactions, at different scales. We demonstrate the emergence of higher order 'macroscales' in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared to nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of Eukaryota, as compared to Prokaryota. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being 'certain' at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

Project members: Brennan Klein, Ludvig Holmér, Keith Smith, Mackenzie M. Johnson, Anshuman Swain, Laura Stolp, Ashley I Teufel, and April S. Kleppe

Status: In Review, Preprint out: doi.org/10.1101/2020.07.02.184325; This project came out of a working group as a part of the Santa Fe Institute (SFI) CSSS 2019

In evolutionary biology, much attention has been given to how evolution alters traits by adaptive evolution. In a classic understanding of adaptive evolution, mutations will gradually alter existing features over long time spans. However, any alteration to a functioning system also carries the risk of ruining the system at hand. There is an evolutionary balance act- between maintaining functionality and simultaneously acquiring novelty- in order to adapt to constantly alternating environments. Much work has been dedicated to disentangling how evolutionary novelty may emerge from existing features, without wrecking the cellular environment already in place. However, while much research addresses wherefrom novel features emerge (e.g. gene duplication, de novo), little attention has been given to how novelty may become integrated as part of the cellular system. Whether or not a protein is able to engage with a given network, without ruining the network's biological function, should at least in part depend on the network's topological resilience. In other words, the evolutionary success of a novel protein should in part be reflected by the system-level proprieties of the existing network. 

We make use of network science in order to infer the protein interaction network's resilience, see attached figure. As shown by a computational study of Zitnik et al 2019, we predict that biological resilience enables tolerance to perturbations. We compute the change in the resilience of the networks in the presence of newly-added nodes, under three different node addition mechanisms. We show that adding nodes in a biologically-inspired manner (as opposed to random or degree-based attachment) preserves the original resilience of the network structure. Further, this holds in the three species regardless of i) the different distributions of gene expression values and ii) different network community organization. These findings introduce a network-general notion of prospective resilience, which highlights the key role that network structure can play in building our understanding of the evolvability of given phenotypic trait of a species.

Project members: Anshuman Swain*, Tyler Hoffman*, Kirtus Leyba, and William F Fagan

Status: In Review

Perception is central to an individual’s survival as it affects its ability to gather resources. Consequently, the costs associated with the process of perception are partially shaped by resource availability. Understanding the interplay of environmental factors (such as resource density and its distribution) with biological factors (such as growth rate, perceptual radius, and metabolic costs) allows the exploration of possible evolutionary trajectories of perception. Previous works have employed ordinary and partial differential equations to understand this phenomenon. Here we use a complex systems perspective by using an agent-based model in lieu of these deterministic approaches which take a top-down view of the system and present more challenges in implementation. In this model, we incorporate a context-dependent movement strategy for each agent where it switches between undirected (random walk) and directed (advective) movement based on its perception of resources. To incorporate an additional element of biological realism, we investigate evolution only through reproduction and impose limits on the amount of resources an individual can gather and store. To probe the average behavior of the system, we ran multiple simulations for each set of initial conditions and parameter values.

We observe a nonlinear, non-monotonic response of the distribution of perceptual radius as a function of resource density, such as a sharp peak followed by a gradual stabilization in the population’s mean perceptual radius and its quantile distribution. Resources also play a major role in determining the stability of endemic equilibria of the system. In addition, we see biologically tractable behavior of the system with varying metabolic and energetic costs, e.g., the costs associated with reproductive, perceptive, and regulatory processes.

Project members: Kunaal Joshi*, Anshuman Swain*, and Kazuya Horibe 

Status: Work in progress; This project came out of a working group as a part of the Santa Fe Institute (SFI) CSSS 2019

The mechanisms by which cells control their size, maintain size homeostasis and decide the time of division are important biological problems which are currently unsolved. In past literature, cell-size homeostasis and cellular division have been explored in through two dominant viewpoints: 'sizer', where cells track their size actively and trigger the cell cycle once they cross a certain critical size, and 'timer', where cells try to grow for a stipulated time before division. These ideas, along with 'growth law' and quantitative model of bacterial cell-cycle, inspired numerous theoretical models and experimental investigations, from predictions and exploration of growth to linking cell cycle and size control. However, experimental evidence involved difficult-to-verify assumptions or population-averaged data, which allowed different interpretations or limited conclusions. In particular, population-averaged data and correlations are inconclusive as the averaging process masks causal effects at the cellular level. Recent experiments have shown most cells follow neither of these models and their dependence on initial size is not trivial. This dependence is very important for maintaining cell size homeostasis, but these models cannot adequately explain the ecological and biological reasons behind cell division and what advantage these can bring to the cells. Moreover, different types of cells have different dependence on initial size for the decision to divide, and currently we do not know why these differences exist. In this work, we aim to find out why these different models for division arise within different cell types and whether cells obtain some evolutionary advantage from following these models in their natural environment. To achieve this objective, we simulated different scenarios corresponding to the natural environments of different cell types and used growth and metabolism laws collected from existing literature to simulate the growth of cells in these environments. We used a neural network decision making scheme that can mutate upon cell division to understand the process. In particular, we explore what will be the final size of the cell after division as a function of the initial size. We expect that after a long time, the surviving cells will be the fittest in the given environment and their division dynamics will help shed light on our problem.

Project members: Brennan Klein*, Anshuman Swain*, Jake Weissman, Harrison Hartle, Eric Haag, Sam Scarpino and William F Fagan

Status: Work in progress

This project tries to use network theory to explore the prokaryote-eukaryote divide – which is considered as the greatest (phenotypic) split, and one of the most fundamental developments in the history of life on earth. For this project we compiled and worked on a dataset of protein-protein interaction networks (PPIs). In these networks, which varied widely in both size (number of proteins) and density (average number of connections per protein), a node is a protein, and an edge exists between two proteins if both proteins are involved in the same cellular interaction. For each network, we calculated the metric known as effectiveness, a normalized version of the metric effective information to quantify the degree of uncertainty (i.e., noise) among the connections between nodes. Networks with high effectiveness generally consist of nodes that have relatively few connections, such that if one were to intervene in the network (i.e., introduce a certain type of protein, turn off a given gene, etc.), the effects of that intervention would be more predictable. In contrast, networks with less effectiveness are more likely to consist of nodes with many outputs or inputs such that intervening on any single node could bring about a large number of possible effects. 

Networks with low effectiveness can be algorithmically coarse-grained to create "macro-nodes,'' each of which groups together lower level nodes that contribute relatively little to the network's effectiveness. This coarse graining yields a compressed representation of the original network that has higher effectiveness than the original network. The magnitude of the increase in network effectiveness due to coarse graining is termed causal emergence, and this new metric offers promising avenues for approaching scientific questions from a non-reductionist point of view as well information about hierarchical structures in networks. We are also exploring on a theory of network mergers for this work.