Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

This paper is concerned with the modeling and evolving of Cell Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first provided, in which we describe the potential applications of modeling and evolving these biochemical networks in silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the ESIGNET project. Results obtained with these methods are summarized and discussed

Optimization Algorithms for Computational Systems Biology

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology

Vector Field Embryogeny

We present a novel approach toward evolving artificial embryogenies, which omits the graph representation of gene regulatory networks and directly shapes the dynamics of a system, i.e., its phase space. We show the feasibility of the approach by evolving cellular differentiation, a basic feature of both biological and artificial development. We demonstrate how a spatial hierarchy formulation can be integrated into the framework and investigate the evolution of a hierarchical system. Finally, we show how the framework allows the investigation of allometry, a biological phenomenon, and its role for evolution. We find that direct evolution of allometric change, i.e., the evolutionary adaptation of the speed of system states on transient trajectories in phase space, is advantageous for a cellular differentiation task

Evolutionary Synthesis of Stochastic Gene Network Models using Feature-based Search Spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series ’ sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic π-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour

From Cellular Components to Living Cells (and Back): Evolution of Function in Biological Networks

Network models pervade modern biology. From ecosystems down to molecular interactions in cells, they provide abstraction and explanation for biological processes. Thus, the relation between structure and function of networks is central to any comprehensive attempt for a theoretical understanding of life. Just as any living system, biological networks are shaped by evolutionary processes. In reverse, artificial evolution can be employed to reconstruct networks and to study their evolution. To this end, I have implemented an evolutionary algorithm specifically designed for the evolution of network models. With the developed evolutionary framework, a study of the evolution of information-processing networks was performed. It is shown that selection favours an organisational structure that is related to function, such that computations can be visualised as transitions between organisations. Furthermore, mathematical modelling is applied to extract reaction-kinetic constants from fluorescence microscopy data, and the model is presented and discussed in detail. Using this approach, a detailed quantitative model of exchange dynamics at PML nuclear bodies (NBs) is created, showing that PML NB components exhibit highly individual exchange kinetics. The FRAP data for PML NBs is additionally used as a test-case for automatic model inference using evolutionary methods, and a set of necessary and sufficient criteria for a good model fit is revealed. In the last part of this thesis, a stochastic analysis of the genetic regulatory system of DEF-like and GLO-like class B floral homeotic genes provides an explanation for their intricate regulatory wiring. The different potential regulatory architectures are investigated using Monte Carlo simulation, a simplified master-equation model, and fixedpoint analysis. It is shown that a positive autoregulatory loop via obligate heterodimerisation of transcription factor proteins reduces noise in cell-fate organ identity decisions.Netzwerkmodelle sind weit verbreitet in der modernen Biologie. In allen Teilgebieten - von der Ökologie bis hin zur Molekularbiologie - bieten sie die Möglichkeit, untersuchte Prozesse und Phänomene zu abstrahieren und damit auf theoretischer Ebene zugänglich zu machen. Es wird ein evolutionärer Algorithmus vorgestellt, der speziell für die Erzeugung von Netzwerkmodellen angepasst ist. Dafür wurde eine Genetische Programmierung der Netzwerkstruktur mit einer Evolutionsstrategie auf den kinetischen Parametern verknüpft. Mit dem neu entwickelten Evolutionären Algorithmus wurde dann eine Studie zur Evolution von informationsverarbeitenden Netzwerken durchgeführt. Selektion erzeugt eine funktionale Organisationsstruktur, in welcher eine Berechnung als Transition zwischen Organisationen abgebildet werden kann. Desweiteren wurden mathematische Modellierungsmethoden verwendet, um kinetische Reaktionskonstanten aus fluoreszenz-mikroskopischen Daten zu gewinnen. Die verwendete Methode wird im Detail vorgestellt und diskutiert. Auf diese Weise entstand ein detailliertes Modell des Proteinaustauschs an PML nuclear bodies (NBs), in welchem die Komponenten der PML NBs sehr differenzierte Austauschverhalten zeigen. Darüber hinaus werden die gewonnenen Daten genutzt, um die automatische Evolution von Netzwerkmodellen in einer realistischen Fallstudie zu testen. Zum Schluss wird eine stochastische Analyse des Zusammenspiels der DEF- und GLO-Gene in der Blütenentwicklung gezeigt, welche eine Erklärung für ihre überraschend komplexe Verschaltung liefert. Die verschiedenen möglichen Regulationsmechanismen werden mithilfe von Monte-Carlo-Simulation, einem Master-Equation-Ansatz und der Fixpunktanalyse verglichen. Es wird gezeigt, dass positive Autoregulation durch obligatorische Heterodimerisierung den Einfluss des Zufalls auf die Organidentität reduziert

Evolving Genetic Regulatory Networks for Systems Biology

Recently there has been significant interest in evolving genetic regulatory networks with a user-determined behaviour. It is unclear whether or not artificial evolution of biochemical networks can be of direct benefit for or biological relevance to systems biology. This article highlights some pitfalls when concluding from artificially evolved genetic regulatory networks to real networks. This article also gives a (brief) review of some previous attempts to evolve genetic regulatory networks with oscillatory behaviour; it also describes a new system to evolve networks and describes the networks that have been evolved. These networks seem to be very diverse sharing no apparent common motifs either with one another or with their real-life counterpart