φ-evo: A program to evolve phenotypic models of biological networks

Abstract

Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on , , and a interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.

Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise

“…Stochasticity is a constraint on what TRNs can achieve, but it can also be adaptively co-opted in evolution 26 ; either way, it might underlie the evolution of certain motifs. Most other computational models of TRN evolution that consider gene expression as the major phenotype do not simulate stochasticity in gene expression (but see three notable exceptions 27–29 ).…”

Evolution of cell size control is canalized towards adders or sizers by cell cycle structure and selective pressures

“…To evolve networks regulating cell size, we use the φ -evo software (Henry et al, 2018) with a modified numerical integrator accounting for volume dynamics and volume dependencies as described above (Fig. 1B).…”

“…All of this is easily made with our customized Python library encoding networks. For more details on technical aspects and implementations of the 𝝋evo software, we refer the reader to (Henry et al, 2018). Realistic evolutionary processes select for multiple phenotypes in parallel.…”

Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics

“…At the same time, theoretical understanding of such living dynamical systems has lagged behind, largely because, in the absence of symmetries, averaging, and small parameters to guide our intuition, building mathematical models of such complex biological processes has remained a very delicate art. Recent years have shown the emergence of automated modeling approaches, which use modern machine-learning methods to automatically infer the dynamical laws underlying a studied experimental system and predict its future dynamics (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). However, arguably, these methods have not yet been applied to any real experimental data with dynamics of a priori unknown structure to produce interpretable dynamical representations of the system.…”