The second ExoMars mission will be launched in 2020 to target an ancient location interpreted to have strong potential for past habitability and for preserving physical and chemical biosignatures (as well as abiotic/prebiotic organics). The mission will deliver a lander with instruments for atmospheric and geophysical investigations and a rover tasked with searching for signs of extinct life. The ExoMars rover will be equipped with a drill to collect material from outcrops and at depth down to 2 m. This subsurface sampling capability will provide the best chance yet to gain access to chemical biosignatures. Using the powerful Pasteur payload instruments, the ExoMars science team will conduct a holistic search for traces of life and seek corroborating geological context information. Key Words: Biosignatures—ExoMars—Landing sites—Mars rover—Search for life. Astrobiology 17, 471–510.

Many origins-of-life scenarios depict a situation in which there are common and potentially scarce resources needed by molecules that compete for survival and reproduction. The dynamics of RNA assembly in a complex mixture of sequences is a frequencydependent process and mimics such scenarios. By synthesizing Azoarcus ribozyme genotypes that differ in their single-nucleotide interactions with other genotypes, we can create molecules that interact among each other to reproduce. Pairwise interplays between RNAs involve both cooperation and selfishness, quantifiable in a 2 × 2 payoff matrix. We show that a simple model of differential equations based on chemical kinetics accurately predicts the outcomes of these molecular competitions using simple rate inputs into these matrices. In some cases, we find that mixtures of different RNAs reproduce much better than each RNA type alone, reflecting a molecular form of reciprocal cooperation. We also demonstrate that three RNA genotypes can stably coexist in a rock-paper-scissors analog. Our experiments suggest a new type of evolutionary game dynamics, called prelife game dynamics or chemical game dynamics. These operate without templatedirected replication, illustrating how small networks of RNAs could have developed and evolved in an RNA world.RNA | prebiotic chemistry | origins of life | game theory | ribozyme A plausible description of a sequence of events that could have led to the origins of life on the Earth from a purely chemical milieu has long been desirable, yet remains elusive. The RNA world hypothesis has helped sharpen our focus on what could have taken place 4 Gya, in that RNA serves as a powerful model for a self-sustaining chemical system capable of evolutionary change (1-6). Although this hypothesis has engendered much debate, both in its general applicability and in the details of its implementation (7-9), there are some clear emerging trends. Among the recent advances in prebiotic RNA studies is the concept of an evolving "network" of RNAs being required to kick-start life, rather than a single selfish entity. This idea dates back to the formative studies of Eigen and Schuster in the 1970s (10, 11). However, it can be sharply seen in the 20+-y effort aimed at developing a generalized RNA replicase ribozyme in the laboratory: new successes have taken advantage of a fragmentation of the best such artificial ribozyme and invoke a network of reactions to provide for its assembly (12). Our own laboratories have focused on a variety of "prelife" (13, 14) and cooperative network (15, 16) approaches to understand how evolving RNA systems could have arisen from abiotic sources of nucleotides and short oligomers. Many others have also stressed the need for distributed functionality at the onset of life, both chemically (17) and in space and time (18,19).To advance a network approach to the "single biomolecule problem" in the RNA world, what is needed now is an understanding of how prebiotic networks could have evolved. Auspiciously, the mechanisms of network evol...

Inclusive fitness and reciprocal altruism are widely thought to be distinct explanations for how altruism evolves. Here we show that they rely on the same underlying mechanism. We demonstrate this commonality by applying Hamilton's rule, normally associated with inclusive fitness, to two simple models of reciprocal altruism: one, an iterated prisoner's dilemma model with conditional behavior; the other, a mutualistic symbiosis model where two interacting species differ in conditional behaviors, fitness benefits, and costs. We employ Queller's generalization of Hamilton's rule because the traditional version of this rule does not apply when genotype and phenotype frequencies differ or when fitness effects are nonadditive, both of which are true in classic models of reciprocal altruism. Queller's equation is more general in that it applies to all situations covered by earlier versions of Hamilton's rule but also handles nonadditivity, conditional behavior, and lack of genetic similarity between altruists and recipients. Our results suggest changes to standard interpretations of Hamilton's rule that focus on kinship and indirect fitness. Despite being more than 20 years old, Queller's generalization of Hamilton's rule is not sufficiently appreciated, especially its implications for the unification of the theories of inclusive fitness and reciprocal altruism.

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This paper is an overview of reconstructability analysis (RA), an approach to discrete multivariate modeling developed in the systems community. RA includes settheoretic modeling of relations and infonnation-theoretic modeling of frequency and probability distributions. It thus encompasses both statistical and non-statistical problems. It overlaps with logic design and machine learning in engineering and with log-linear modeling in the social. sciences. Its generality gives it considerable potential for knowledge representation and data mining. I. INTRODUCTION This paper is an overview of reconstructability analysis (RA), a discrete multivariate modeling methodology developed in the systems literature; an earlier version of this tutorial is (Zwick 2001). RA derives from Ashby (1964), and was developed by Broekstra, Cavallo, Cellier, Conant, Jones, Klir, Krippendorff, and others (Klir 1986, 1996). RA resembles and partially overlaps log-linear (LL) statistical methods used in the social sciences (Bishop et al 1978; Knoke & Burke 1980). RA also resembles and overlaps methods used in logic design and machine learning (LDL) in electrical and computer engineering (e.g., Perkowski 1997). Applications of RA, like those of LL and LDL modeling, are diverse, including time-series analysis, classification, decomposition, compression, pattern recognition, prediction, control, and decision analysis. RA involves the set-theoretic modeling ofrelations and mappings and the information-theoretic modeling of probability/frequency distributions. Its different uses can be categorized using the dimensions of variable, system, data, problem, and method-types shown in Table I. These will ' now be briefly discussed. Section II explains RA in more detail. Section JlJ gives examples, Section IV discusses software, and Section V offers a concluding discussion. 2. System-type: directed vs. neutral To relate RA to a familiar LDL problem, consider the task of decomposing a logic function Z=g(A, B, C), where variables are either binary or multivalued. In RA tenninology this is a directed system, since inputs and outputs ("independent variables" and "dependent variables")