Adaptive laboratory evolution of a genome-reduced Escherichia coli.

Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain's systems biology

Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types

Quantifying hidden order out of equilibrium

While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hindered the understanding of non-equilibrium phenomena in diverse settings, from glasses to driven systems to biology. The length of a losslessly compressed data file is a direct measure of its information content: The more ordered the data is, the lower its information content and the shorter the length of its encoding can be made. Here, we describe how data compression enables the quantification of order in non-equilibrium and equilibrium many-body systems, both discrete and continuous, even when the underlying form of order is unknown. We consider absorbing state models on and off-lattice, as well as a system of active Brownian particles undergoing motility-induced phase separation. The technique reliably identifies non-equilibrium phase transitions, determines their character, quantitatively predicts certain critical exponents without prior knowledge of the order parameters, and reveals previously unknown ordering phenomena. This technique should provide a quantitative measure of organization in condensed matter and other systems exhibiting collective phase transitions in and out of equilibrium

Dynamical Properties of a Two-gene Network with Hysteresis

A mathematical model for a two-gene regulatory network is derived and several of their properties analyzed. Due to the presence of mixed continuous/discrete dynamics and hysteresis, we employ a hybrid systems model to capture the dynamics of the system. The proposed model incorporates binary hysteresis with different thresholds capturing the interaction between the genes. We analyze properties of the solutions and asymptotic stability of equilibria in the system as a function of its parameters. Our analysis reveals the presence of limit cycles for a certain range of parameters, behavior that is associated with hysteresis. The set of points defining the limit cycle is characterized and its asymptotic stability properties are studied. Furthermore, the stability property of the limit cycle is robust to small perturbations. Numerical simulations are presented to illustrate the results.Comment: 55 pages, 31 figures.Expanded version of paper in Special Issue on Hybrid Systems and Biology, Elsevier Information and Computation, 201

Actionable models of control in complex systems for the biomedical domain

Modern artificial intelligence (AI) and machine learning (ML) techniques predict system behavior from previous observations. However, in biology, and in true complex systems in general, profound transformation in system behavior can occur from never before seen observations. Moreover, AI/ML techniques are often unable to provide an explanation for their predictions. I will argue that complex systems modeling, while using AI/ ML techniques to estimate parameters, needs to produce actionable, dynamical models that can predict and explain system behavior for rare or unseen control events. This is particularly true for understanding biochemical regulation under dynamical perturbations from environmental and evolutionary events. Towards that goal, I will present our recent work uncovering effective control pathways in the canalizing dynamics of biochemical regulation. Our methodology centers on removing redundancy from systems biology models of development, cell cycle, and cancer response, as well as in models of cortical networks cultured from mouse brains. Removing the large amounts of redundancy in these models, reveals preferred pathways for the spread of perturbations and the building blocks of dynamical response, leading to the prediction of actionable control interventions

THE SUM OF THE PARTS: HEURISTIC STRATEGIES IN SYSTEMS BIOLOGY

This thesis addresses philosophical issues regarding the young field of systems biology. Systems biologists commonly present their approach as a superior alternative to \u2018traditional\u2019 molecular biology that they describe as being overly \u2018reductionist.\u2019 However, the heterogeneity of systems approaches makes it difficult to understand what \u2018the\u2019 approach of systems biology exactly consists in. Here I propose a framework for the systematic comparison of different scientific approaches in biology. I argue that the relevant issues arise at the level of strategies of mechanistic discovery. These strategies are best understood as \u2018heuristic,\u2019 that is, as tools to reduce the complexity of a given research task. While having the virtue of making the search for mechanisms more efficient, heuristic strategies rely on particular assumptions about the system under study. This can introduce bias and lead biologists to underestimate the actual complexity of the system. Framing the analysis in terms of heuristic strategies pro- vides a precise way to distinguish between different approaches and to better understand the ongoing rhetoric battles. I discuss a number of case studies, both from molecular biology and from systems biology. I argue that the traditional approach of molecular biology relies on a relatively well-defined set of heuristics that corresponds to a particular idea of the organization and complexity of living systems. Approaches in systems biology relax some of the underlying assumptions of the traditional approach, notably by applying tools of mathematical modeling, but they have to make use of alternative heuristics in order to be efficient. As a result, they rely on different assumptions about organization and complexity. My detailed discussion of case studies reveals that there are a number of different systems approaches that can be distinguished by analyzing their heuristic character. The ambition of systems biologists to build formal models of biological mechanisms, however, has the virtue of making many of the underlying assumptions explicit which helps to recognize and reduce bias, and moreover facilitates the integration of different approaches. Some of the issues touched upon also have relevance for more general questions in the philosophy of biology. Assumptions about biological organization and complexity can heavily influence what we think of as a good scientific explanation. Since systems biology puts into question some of these assumptions, we might be forced to revise our ideas about mechanistic explanation. I argue that notably the concept of biological robustness has to be taken into account by philosophers who are thinking about mechanisms in biology

Teleosemantics and the free energy principle

The free energy principle is notoriously difficult to understand. In this paper, we relate the principle to a framework that philosophers of biology are familiar with: Ruth Millikan’s teleosemantics. We argue that: (i) systems that minimise free energy are systems with a proper function; and (ii) Karl Friston’s notion of implicit modelling can be understood in terms of Millikan’s notion of mapping relations. Our analysis reveals some surprising formal similarities between the two frameworks, and suggests interesting lines of future research. We hope this will aid further philosophical evaluation of the free energy principle

Maps of random walks on complex networks reveal community structure

To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of more than 6000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network -- including physics, chemistry, molecular biology, and medicine -- information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.Comment: 7 pages and 4 figures plus supporting material. For associated source code, see http://www.tp.umu.se/~rosvall

New Frontiers for Organismal Biology

Understanding how complex organisms function as integrated units that constantly interact with their environment is a long-standing challenge in biology. To address this challenge, organismal biology reveals general organizing principles of physiological systems and behavior—in particular, in complex multicellular animals. Organismal biology also focuses on the role of individual variability in the evolutionary maintenance of diversity. To broadly advance these frontiers, cross-compatibility of experimental designs, methodological approaches, and data interpretation pipelines represents a key prerequisite. It is now possible to rapidly and systematically analyze complete genomes to elucidate genetic variation associated with traits and conditions that define individuals, populations, and species. However, genetic variation alone does not explain the varied individual physiology and behavior of complex organisms. We propose that such emergent properties of complex organisms can best be explained through a renewed emphasis on the context and life-history dependence of individual phenotypes to complement genetic data.Organismic and Evolutionary Biolog

A statistical method for revealing form-function relations in biological networks

Over the past decade, a number of researchers in systems biology have sought to relate the function of biological systems to their network-level descriptions -- lists of the most important players and the pairwise interactions between them. Both for large networks (in which statistical analysis is often framed in terms of the abundance of repeated small subgraphs) and for small networks which can be analyzed in greater detail (or even synthesized in vivo and subjected to experiment), revealing the relationship between the topology of small subgraphs and their biological function has been a central goal. We here seek to pose this revelation as a statistical task, illustrated using a particular setup which has been constructed experimentally and for which parameterized models of transcriptional regulation have been studied extensively. The question "how does function follow form" is here mathematized by identifying which topological attributes correlate with the diverse possible information-processing tasks which a transcriptional regulatory network can realize. The resulting method reveals one form-function relationship which had earlier been predicted based on analytic results, and reveals a second for which we can provide an analytic interpretation. Resulting source code is distributed via http://formfunction.sourceforge.net.Comment: To appear in Proc. Natl. Acad. Sci. USA. 17 pages, 9 figures, 2 table