Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Biological Information, Causality and Specificity - an Intimate Relationship

In this chapter we examine the relationship between biological information, the key biological concept of specificity, and recent philosophical work on causation. We begin by showing how talk of information in the molecular biosciences grew out of efforts to understand the sources of biological specificity. We then introduce the idea of ‘causal specificity’ from recent work on causation in philosophy, and our own, information theoretic measure of causal specificity. Biological specificity, we argue, is simple the causal specificity of certain biological processes. This, we suggest, means that causal relationships in biology are ‘informational’ relationships simply when they are highly specific relationships. Biological information can be identified with the storage, transmission and exercise of biological specificity. It has been argued that causal relationships should not be regarded as informational relationship unless they are ‘arbitrary’. We argue that, whilst arbitrariness is an important feature of many causal relationships in living systems, it should not be used in this way to delimit biological information. Finally, we argue that biological specificity, and hence biological information, is not confined to nucleic acids but distributed among a wide range of entities and processes

Complex Networks

Introduction to the Special Issue on Complex Networks, Artificial Life journal.Comment: 7 pages, in pres

The modern versus extended evolutionary synthesis : sketch of an intra-genomic gene's eye view for the evolutionary-genetic underpinning of epigenetic and developmental evolution

Studying the phenotypic evolution of organisms in terms of populations of genes and genotypes, the Modern Synthesis (MS) conceptualizes biological evolution in terms of 'inter-organismal' interactions among genes sitting in the different individual organisms that constitute a population. It 'black-boxes' the complex 'intra-organismic' molecular and developmental epigenetics mediating between genotypes and phenotypes. To conceptually integrate epigenetics and evo-devo into evolutionary theory, advocates of an Extended Evolutionary Synthesis (EES) argue that the MS's reductive gene-centrism should be abandoned in favor of a more inclusive organism-centered approach. To push the debate to a new level of understanding, we introduce the evolutionary biology of 'intra-genomic conflict' (IGC) to the controversy. This strategy is based on a twofold rationale. First, the field of IGC is both ‘gene-centered’ and 'intra-organismic' and, as such, could build a bridge between the gene-centered MS and the intra-organismic fields of epigenetics and evo-devo. And second, it is increasingly revealed that IGC plays a significant causal role in epigenetic and developmental evolution and even in speciation. Hence, to deal with the ‘discrepancy’ between the ‘gene-centered’ MS and the ‘intra-organismic’ fields of epigenetics and evo-devo, we sketch a conceptual solution in terms of ‘intra-genomic conflict and compromise’ – an ‘intra-genomic gene’s eye view’ that thinks in terms of intra-genomic ‘evolutionarily stable strategies’ (ESSs) among numerous and various DNA regions and elements – to evolutionary-genetically underwrite both epigenetic and developmental evolution, as such questioning the ‘gene-de-centered’ stance put forward by EES-advocates

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres

Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape