2 research outputs found
Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-class Classification with a Convolutional Neural Network
Robustness and evolvability are essential properties to the evolution of
biological networks. To determine if a biological network is robust and/or
evolvable, it is required to compare its functions before and after mutations.
However, this sometimes takes a high computational cost as the network size
grows. Here we develop a predictive method to estimate the robustness and
evolvability of biological networks without an explicit comparison of
functions. We measure antifragility in Boolean network models of biological
systems and use this as the predictor. Antifragility occurs when a system
benefits from external perturbations. By means of the differences of
antifragility between the original and mutated biological networks, we train a
convolutional neural network (CNN) and test it to classify the properties of
robustness and evolvability. We found that our CNN model successfully
classified the properties. Thus, we conclude that our antifragility measure can
be used as a predictor of the robustness and evolvability of biological
networks.Comment: 22 pages, 10 figure
Emergence in artificial life
Even when concepts similar to emergence have been used since antiquity, we
lack an agreed definition. However, emergence has been identified as one of the
main features of complex systems. Most would agree on the statement ``life is
complex''. Thus, understanding emergence and complexity should benefit the
study of living systems.
It can be said that life emerges from the interactions of complex molecules.
But how useful is this to understand living systems? Artificial life (ALife)
has been developed in recent decades to study life using a synthetic approach:
build it to understand it. ALife systems are not so complex, be them soft
(simulations), hard (robots), or wet (protocells). Then, we can aim at first
understanding emergence in ALife, for then using this knowledge in biology.
I argue that to understand emergence and life, it becomes useful to use
information as a framework. In a general sense, I define emergence as
information that is not present at one scale but is present at another scale.
This perspective avoids problems of studying emergence from a materialist
framework, and can be also useful in the study of self-organization and
complexity.Comment: 28 pages, 1 figur